Do All Hospitals Need Cesarean Delivery Capability?

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Do All Hospitals Need Cesarean Delivery Capability?

ABSTRACT

OBJECTIVES: We analyzed perinatal outcomes at a rural hospital without cesarean delivery capability.

STUDY DESIGN: This was a historical cohort outcomes study.

POPULATION: The study population included all pregnant women at 20 weeks or greater of gestational age (n = 1132) over a 5-year period in a predominantly Native American region of northwestern New Mexico.

OUTCOMES MEASURED: The outcomes studied included perinatal mortality, neonatal morbidity, obstetric emergencies, intrapartum and antepartum transfers, and cesarean delivery rate. We did a detailed case review of all obstetric emergencies and low-Apgar-score births at Zuni-Ramah Hospital and all cesarean deliveries for fetal distress at referral hospitals.

RESULTS: Of the 1132 women in the study population, 64.7% (n = 735) were able to give birth at the hospital without operative facilities; 25.6% (n = 290) were transferred before labor; and 9.5% (n = 107) were transferred during labor. The perinatal mortality rate of 11.4 per 1000 (95% confidence interval, 5.1-17.8) was similar to the nationwide rate of 12.8 per 1000 even though Zuni-Ramah has a high-risk obstetric population. No instances of major neonatal or maternal morbidity caused by lack of surgical facilities occurred. The cesarean delivery rate of 7.3% was significantly lower than the nationwide rate of 20.7% (P < .001). The incidence of neonates with low Apgar scores (0.54%) was significantly lower than the nationwide rate (1.4%). The incidence of neonates requiring resuscitation (3.4%) was comparable to the nationwide rate (2.9%).

CONCLUSIONS: The presence of a rural maternity care unit without surgical facilities can safely allow a high proportion of women to give birth closer to their communities. This study demonstrated a low level of perinatal risk. Most transfers were made for induction or augmentation of labor. Rural hospitals that do not have cesarean delivery capability but are part of an integrated perinatal system can safely offer obstetric services by using appropriate antepartum and intrapartum screening criteria for obstetric risk.

KEY POINTS FOR CLINICIANS

  • Rural hospitals without cesarean delivery capability can safely offer obstetric care to selected patients as part of an integrated perinatal network.
  • Measures of maternal and neonatal morbidity and mortality were at or below national averages despite a higher-risk population.
  • Antepartum (25.6%) or intrapartum (9.5%) transfer to hospitals with surgical or tertiary-care facilities was required for 35% of pregnant women.
  • The use of oxytocin induction or augmentation, if determined safe, may significantly lower the transfer rate from rural hospitals that lack cesarean delivery capability.

The availability of perinatal care in rural communities produces better pregnancy outcomes than do perinatal systems that require rural women to seek maternity care in distant urban areas.1-3 Unfortunately, rural maternity care has been affected by the loss of physicians who offer these services and by the closing of many rural hospitals’ maternity care units. Maintaining 24-hour operative obstetric capabilities is difficult in rural areas because they have an insufficient population base to support a physician trained in operative obstetrics. Another barrier is the lack of anesthesia services and operating room personnel.

The Guidelines for Perinatal Care developed by the American College of Obstetricians and Gynecologists (ACOG) and the American Academy of Pediatrics (AAP) state, “All hospitals offering labor and delivery services should be equipped to perform emergency cesarean delivery.” 4 Nevertheless, not all rural obstetric units can offer cesarean delivery and must transfer patients to a referral hospital for operative needs. Advisory panels in the United States and Canada have recommended similar models of rural perinatal care.5-8 A Canadian panel estimated that 125 Canadian hospitals offer obstetric care without surgical facilities.

Studies of rural hospitals in Canada, Australia, and the United Kingdom that lack continuous on-site cesarean capability are limited by the small number of deliveries.9-12 Most such studies are hospital based rather than population based and lack data on women who are transferred to outlying hospitals. The only population-based study that we identified found no evidence of adverse events caused by the lack of cesarean facilities; the sample size, however, was limited to 286 births.9

We studied all pregnancies occurring in a predominantly Native American region of New Mexico over a 5-year period to ascertain the safety of rural perinatal care based in a hospital without cesarean capability. Population-based and hospital-based outcomes are presented. This is the first study from a US community using this model of care.

Methods

We conducted an outcomes study using a historical cohort study design of all pregnancies beyond 20 weeks of gestation in the Zuni Pueblo and Ramah Navajo communities of northwestern New Mexico from 1992 to 1996. The perinatal services based at the Zuni-Ramah Indian Health Service (IHS) Hospital are the focus of this study. This 37-bed community hospital, staffed by family physicians and a part-time nurse-midwife, is part of an integrated perinatal system. The birthing unit has access to obstetrician-gynecologist (OBG) consultants at the Gallup Indian Medical Center (GIMC), 33 miles to the north, and perinatology and neonatology care in Albuquerque, 147 miles to the east. GIMC, the primary referral hospital and closest surgical facility, has an obstetric unit staffed by OBGs, family physicians, and nurse-mid-wives. Transportation time is 40 minutes by ground ambulance to GIMC or by fixed wing aircraft to Albuquerque.

 

 

The Zuni-Ramah Hospital limits intrapartum care to women designated as at low or moderate risk by criteria established by Zuni-Ramah family physicians and reviewed by GIMC OBGs. Criteria mandating transfer included prior cesarean, malpresentation, multiple gestation, intrauterine growth restriction, severe preeclampsia, placenta previa, significant vaginal bleeding, major fetal anomalies, anticipated preterm delivery (< 36 weeks), nonreassuring fetal heart tones (NRFHTs), and need for labor induction or augmentation with oxytocin. Women with gestational or type 2 diabetes who were well controlled could give birth at Zuni-Ramah unless they had end-organ damage or the fetus had known macrosomia. Physicians successfully completed the Advanced Life Support in Obstetrics (ALSO, ®American Academy of Family Physicians, 4th ed., 2000) course, attended weekly high-risk obstetric rounds, and performed quarterly reviews of obstetric complications. The family physicians performed vacuum-assisted deliveries, utilized amnioinfusion, and used continuous or intermittent fetal monitoring.

A review of the delivery and transfer records of the Zuni-Ramah Hospital and GIMC obstetric services revealed that there had been 1132 births of 1137 infants during the study period. The authors used a data collection form to review prenatal and newborn records from every birth. We reviewed intrapartum records for all births at the Zuni-Ramah and GIMC hospitals. We obtained discharge summaries from tertiary-care sites. We interviewed perinatal coordinators, public health nurses, and pediatric care providers to obtain information about patients who had received perinatal care outside of the IHS system.

The outcomes measured included perinatal mortality, neonatal morbidity, obstetric emergencies, intrapartum and antepartum transfers, and cesarean delivery rate. All obstetric emergencies originating at Zuni-Ramah Hospital were reviewed to determine whether the lack of surgical facilities had resulted in adverse outcomes. The physician’s notes were used to differentiate a NRFHT pattern requiring observation at a hospital with operative facilities from a truly worrisome pattern that required urgent intervention for fetal distress.

Births were defined as deliveries of infants at 20 weeks or more of estimated gestational age. We analyzed each birth in a multiple gestation individually. The population-based perinatal mortality rate was calculated from 20 weeks’ estimated gestational age to the 28th neonatal day. The Zuni-Ramah Hospital perinatal mortality rate was calculated by inclusion of all women delivered at Zuni-Ramah Hospital or transferred during labor. Approval for the study was obtained from the IHS Institutional Review Board and the Zuni Tribal Council.

Results

Study population

We identified 1137 births to 945 women between 1992 and 1996. Zuni and Navajo births were 66.9% and 30.8%, respectively; 30% of women were primiparous and 70%, multiparous. We found that 10.4% of births had occurred in women older than 35 years and 7.8% in women younger than 18 years. Regarding prenatal care, 3.9% of women had received none; 43.0% had established prenatal care in the first trimester; 40.4%, in the second trimester; and 12.8%, in the third trimester.

Delivery sites and maternal transfers

The majority of women (64.4%, n = 732) gave birth at the Zuni-Ramah Hospital (Figure) or at GIMC (29.6%, n = 337). A small number (2.2%, n = 25) gave birth at a private hospital with surgical facilities in Gallup. Albuquerque tertiary-care hospitals were the sites of 3.2% (n = 36) of deliveries. Primary indications for tertiary care were prematurity and fetal anomalies. Seven (0.6%) deliveries occurred at other sites, including home and ambulance.

The antepartum transfers (Table 1) were required primarily for pregnancy complications requiring labor induction. Preeclampsia, diabetes, nonreassuring antepartum testing, and post dates patients accounted for 56.8% of the 290 transfers. The 107 intrapartum transfers were made predominantly for labor induction or augmentation (64.5%, n = 69), a concerning fetal heart tracing (15.9%, n = 17), or fetal malpresentation diagnosed during labor (8.4%, n = 9).

FIGURE
PREGNANCIES AT ZUNI-RAMAH HOSPITAL

TABLE 1
ANTEPARTUM AND INTRAPARTUM TRANSFERS FROM ZUNI-RAMAH HOSPITAL

IndicationNumber of Transfers (%)
Antepartum Transfers*
  Preeclampsia83 (28.6)
  Prior cesarean delivery55 (19.0)
  Nonreassuring testing39 (13.4)
  Preterm (includes PPROM)24 (8.3)
  Diabetes22 (7.6)
  Postdates21 (7.2)
  Other18 (6.2)
  Malpresentation16 (5.5)
  Chronic HTN8 (2.8)
  Macrosomia7 (2.4)
  IUFD6 (2.1)
  IUGR5 (1.7)
  Anomalies4 (1.4)
Total290 (25.6% of population)
Intrapartum Transfers
  First-stage arrest of labor37 (34.6)
  PROM without active labor32 (29.9)
  Malpresentation9 (8.4)
  Fetal distress5 (4.7)
  Nonreassuring tracing12 (11.2)
  Other12 (11.2)
Total107 (9.5% of population)
*Greater than 290 because of 18 patients with 2 reasons for antepartum transfer.
HTN denotes hypertension; IUFD, intrauterine fetal demise; IUGR, intrauterine growth restriction; PROM, premature rupture of membranes; PPROM, preterm premature rupture of membranes.

Obstetric interventions

The total cesarean delivery rate (7.3%) was approximately one third the nationwide rate of 20.7% in 1996. The primary cesarean delivery rate (number of cesareans in women without prior cesarean divided by the number of women who have never had a cesarean) of 5.3% compares with a nationwide primary rate of 14.6%. The cesarean rate was 22.1% for antepartum transfers and 17.8% for intrapartum transfers. Operative vaginal delivery occurred in 5.4% of births, well below the nationwide rate of 9.4%. The induction rate of 13.8% is lower than the nationwide rate of 16.9%. The oxytocin augmentation rate of 7.7% is well below the nationwide rate of 16.9% in 1996.13 Parenteral narcotics were available at Zuni-Ramah; however, 81.4% of women elected to receive no labor analgesia. Epidural anesthesia was not available at Zuni-Ramah Hospital.

 

 

Perinatal mortality

The perinatal mortality rate for the population was 11.4 per 1000 births (95% CI, 5.1-17.8 by Poisson distribution), comparable to the 1991 nationwide peri-natal mortality rate of 12.8/1000.14 Nine of the 13 neonatal deaths were caused by intrauterine fetal demise before labor (Table 2). The Zuni-Ramah Hospital–based perinatal mortality rate of 1.2/1000 was comparable with the 1.3/1000 perinatal mortality rate for women in the National Birth Center study even though Zuni-Ramah Hospital accepts higher-risk patients.15

TABLE 2
PERINATAL MORTALITY IN ZUNI-RAMAH POPULATION

Age (wk)Weight (g)SiteCause
Intrauterine Fetal Death
311410GIMCUnexplained
393130GIMCUnexplained
351540GIMCUnexplained; IUGR
351690GIMCUnexplained; IUGR
21330GIMCPPROM
21560GIMCPPROM
413040GIMCOligohydramnios and post dates. Two days prior, refused induction with amniotic fluid volume index of 3.8
281290AlbAbruption
32UnknownZuniNecrotizing/calcifying encephalopathy (probable CMV) with severe IUGR
Early Neonatal Death (< 7 days)
382805AlbOsteogenesis imperfecta
31UnknownAlbPotter’s syndrome
Late Neonatal Death (7 to 27 days)
351590GIMCPulmonary interstitial emphysema caused by respiratory failure of unknown etiology/IUGR
413220GIMCSepsis at 12 days; had been discharged home as healthy infant
Alb denotes Albuquerque tertiary-care hospital; CMV, cytomegalovirus; GIMC, Gallup Indian Medical Center; IUGR, intrauterine growth restriction; PPROM, preterm premature rupture of membranes.

Neonatal morbidity

Measures of neonatal morbidity are summarized in Table 3. The frequency of 5-minute Apgar scores below 7, low birthweight, and prematurity compares favorably with 1996 US rates.13 The rate of assisted ventilation (intubation or bag-mask) for the entire population (4.6%, n = 52) is greater than the 1996 nationwide rate (2.9%), although the difference is of questionable clinical significance, since international studies have demonstrated a range for assisted ventilation of 1% to 10% of hospital births.16 Neonatal Intensive Care Unit (NICU) transfer occurred in 27 (2.4%) of deliveries from non-tertiary-care sites. Thirteen (1.8%) babies born at Zuni-Ramah were transferred to Albuquerque for NICU care because of respiratory distress (n = 10) or neonatal anomalies (n = 3). The 3 cases of low Apgar scores at Zuni-Ramah were attributed to pneumothorax, respiratory distress syndrome of prematurity, and sepsis with meconium aspiration.

TABLE 3
NEONATAL MORBIDITY IN ZUNI-RAMAH POPULATION, BASED ON LIVE BIRTHS

 Zuni-Ramah Hospital (N=732)Zuni-Ramah Population (n = 1128)1996 US Population
5-minute Apgar score < 73 (0.41%), P = .0236 (0.54%), P = .0141.4%
Assisted ventilation19 (2.6%), P = 0.6252 (4.6%), P < .0012.9%
Birthweight < 2500 g14 (1.9%), P < .00161 (5.4%), P < .00111%
Preterm (37 weeks)22 (3.0%), P < .00175 (6.7%), P = .367.4%
P values are based on comparison with the US population. US population figures for 1996 were extracted from Ventura SJ, Martin JA, Curtin SC, Mathews TJ. Report of Final Natality Statistics, 1996. Monthly vital statistics report; vol 46, no 11, supp. Hyattsville, Md: National Center for Health Statistics, 1998.

Obstetric risk factors

The study population had a greater incidence of pregnancy-induced hypertension (14.5% vs 2.6% by 1996 ACOG criteria17), chronic hypertension (2.7% vs 0.7%15), and diabetes (9.2% vs 2.6%15) than the average US obstetric population. Gestational diabetes was diagnosed according to National Diabetes Data Group criteria:18 7.1% had gestational diabetes (class A1 and A2 ) and 2.1% had type 2 antepartum diabetes (classes B and C).

Outcomes of obstetric emergencies at zuni-ramah hospital

We reviewed all cases of placental abruption, uterine inversion, umbilical cord prolapse, and fetal distress at Zuni-Ramah Hospital to identify potentially preventable adverse outcomes caused by lack of operative facilities (Table W1). Umbilical cord prolapse and uterine inversion each occurred once and were appropriately managed, with excellent outcomes. In 3 of the 4 cases of placental abruption, there were clearly no adverse outcomes caused by lack of on-site operative facilities, as patients were expectantly managed upon arrival to the referral hospital (cases 3 and 4) or presented to Zuni-Ramah Hospital as an intrauterine demise (case 5).

The fourth patient with placental abruption (case 6) presented at Zuni-Ramah with vaginal bleeding, severe variable decelerations, and a 10-point drop from baseline hematocrit. She was scheduled to labor at GIMC because of a history of prior cesarean but presented to the Zuni-Ramah emergency room with vaginal bleeding. She was transferred to GIMC for an anticipated cesarean delivery; however, on arrival the patient rapidly progressed and gave birth to an infant vaginally with Apgar scores of 3 and 9. Her infant had a neonatal seizure and magnetic resonance imaging evidence of sagittal sinus thrombosis. The infant had a normal neurologic evaluation, developmental assessment, and electroencephalogram at 15 months.

We reviewed 5 cases of urgent transfer for fetal distress. These were differentiated from the 8 intrapartum transfers for NRFHTs based on the severity of fetal heart monitor tracings. Four of the 5 women who had been transferred for fetal distress gave birth to healthy infants vaginally more than 2 hours after arrival at the referral institution. One patient who was urgently transferred for repetitive late decelerations is discussed below.

 

 

Cesarean deliveries for fetal distress at referral hospitals

We reviewed all cases of cesarean deliveries for fetal distress (n = 10) at referral institutions to determine whether outcomes for any of the patients could potentially have been improved by having their cesarean deliveries earlier if operative facilities had been available at Zuni-Ramah Hospital or by being transferred earlier (Table W1). Seven of the 10 patients were transferred for preeclampsia, NRFHTs, or failure to progress. All had their cesareans for fetal distress many hours after arrival at the referral institution.

Two cases of cesarean delivery for fetal distress after transfer because of abruption were previously described. A patient presented in early labor with repetitive late decelerations and was urgently transferred to GIMC, where she underwent an immediate cesarean delivery. Her infant had Apgar scores of 1 and 7, an unremarkable neonatal course, and a normal 15-month developmental screen.

Discussion

Our outcomes demonstrate that with the use of appropriate screening criteria, childbirth can safely occur in institutions that lack surgical suites. The population-based perinatal mortality rate was similar to the nationwide rate. A review of obstetric emergencies and low Apgar scores among the 839 women laboring at Zuni-Ramah Hospital failed to identify adverse outcomes that might have been prevented if the hospital had had operative facilities. Cesarean rates were approximately one third the nationwide rate even though Zuni-Ramah patients had a higher prevalence of such risk factors as diabetes and preeclampsia.

Although they represented a high-risk obstetric population, 65% of women were able to give birth at Zuni-Ramah Hospital through use of the perinatal screening criteria. The 35% rate of transfer was caused largely by the need for oxytocin augmentation or induction. Only 21.6% of the women who were transferred for dysfunctional labor or premature rupture of membranes ultimately had a cesarean delivery. Oxytocin has not been permitted at Zuni-Ramah Hospital because of the ACOG guideline permitting oxytocin use only if “a physician capable of performing a cesarean delivery is readily available.”19 There are no studies addressing the safety of labor induction or augmentation without on-site cesarean capability.

Canadian guidelines for rural maternity care do not prohibit the use of prostaglandins or oxytocin at hospitals without operative facilities. A Consensus Conference on Obstetric Services in Rural or Remote Communities addressed the issue of labor induction or augmentation in hospitals without cesarean capability by stating, “If caring for a woman in labour is appropriate in the community, then caring for her during an augmented/induced labour is equally appropriate when there is support by trained local staff and resources.”20 We concur that use of oxytocin in rural hospital units without operative facilities should be considered under well-defined clinical guidelines or research protocols.

Limitations

Our study’s limitations include lack of long-term neonatal outcomes, small size of the Zuni-Ramah population, an almost exclusively Native American population, and lack of examiner blinding during record review. Transfer rates may be increased in populations with higher rates of cesarean delivery or epidural anesthesia use. Alternatively, the high incidence of preeclampsia, chronic hypertension, and diabetes in these communities may have resulted in a higher proportion of induction. Umbilical cord pro-lapse and significant placental abruption are routinely treated by urgent cesarean delivery; therefore, obstetric literature on outcomes without immediate operative intervention is limited.21,22 A larger study would be required to determine the potential increased neonatal morbidity or mortality resulting from delayed intervention.

Conclusions

The ACOG/AAP guideline requiring on-site surgical facilities and the ability to initiate a cesarean in 30 minutes is not based on evidence. Four small retrospective studies of emergency cesarean deliveries delayed for more than 30 minutes did not demonstrate adverse neonatal outcomes.23-26 In our study population, no adverse outcomes (none in 839 births) were determined to have been caused by a lack of surgical facilities. Despite these excellent outcomes, the possibility always exists for outcomes that can be prevented by doing a rapid emergent cesarean delivery. Women deciding to give birth in facilities without operative capabilities should receive information regarding the risks and benefits of delivering there and should have access to other facilities. Provider discretion and patient choice must be respected to ensure community support of these birthing units. Practitioners at the rural units must have assurance that any patients who require an urgent transfer will be readily accepted.

Rural communities, medical providers, and health care facilities need to consider the overall effect of maintaining local maternity care units, as the loss of rural maternity care can increase the risk of adverse perinatal outcomes.1-3 We concur with the Canadian panel that although maintenance of rural surgical and anesthesia capabilities is desirable, “good outcomes can be sustained within an integrated risk management system without local access to operative delivery.”8 Guidelines should be developed to permit rural hospitals without cesarean capability to provide maternity care as part of integrated perinatal systems with well-developed transport protocols and supportive referral institutions. Women living in rural areas should have the option to give birth near their homes in such units if they so desire.

 

 

Acknowledgments

The authors thank Robert Rhyne, MD, for editorial assistance in manuscript preparation and Betty Skipper, PhD, for statistical assistance. Current Zuni-Ramah Obstetric Guidelines are available at http://hsc.unm.edu/fcm/research/zuni.

References

1. Allen DT, Kamradt MS. Relationship of infant mortality to the availability of obstetric care in Indiana. J Fam Pract 1991;33:609-13.

2. Larimore WL, Davis A. Relation of infant mortality to the availability of maternity care in rural Florida. J Am Board Fam Pract 1995;8:392-9.

3. Nesbitt TS, Connell FA, Hart LG, Rosenblatt RA. Access to obstetric care in rural areas: effect on birth outcomes Am J Public Health. 1990;80:814-8.

4. American Academy of Pediatrics and American College of Obstetricians and Gynecologists. Guidelines for Perinatal Care. 4th ed. Washington, DC: ACOG, 1997.

5. New York State Department of Health, Office of Rural Health. Report on the provision of birthing services in rural health networks. Albany, NY; 1994.

6. Nesbitt TS. Rural maternity care: new models of access. Birth. 1996;23:161-5.

7. Rosenthal TC, Holden DM, Woodward W. Primary care obstetrics in rural Western New York: a multi-center case review. NY State J Med 1990;90:537-40.

8. Iglesias S, Grzybowski S, Klein M, Gagne GP, Lalonde A. Joint position paper on rural maternity care. Society of Rural Physicians, Society of Obstetricians and Gynecologists of Canada, College of Family Physicians of Canada, 1998.

9. Grzybowski SCW, Cadesky AS, Hogg WE. Rural obstetrics: a 5-year prospective study of the outcomes of all pregnancies in a remote northern community. Can Med Assoc J 1991;144:987-94.

10. Black DB, Fyfe IM. The safety of obstetrics services in small communities in northern Ontario. Can Med Assoc J 1984;130:571-6.

11. Woollard LA, Hays RB. Rural obstetrics in NSW. Aust N Z J Obstet Gynaecol 1993;33:240-2.

12. McIlwain R, Smith S. Obstetrics in a small isolated community: the cesarean section dilemma. Can J Rural Med 2000;5:221-3.

13. Ventura SJ, Martin JA, Curtin SC, Mathews TJ. Report of Final Natality Statistics, 1996. Monthly vital statistics report; Vol 46 no 11, suppl. Hyattsville, Md: National Center for Health Statistics, 1998.

14. Hoyert DL. Perinatal mortality in the United States, 1985-91. National Center for Health Statistics. Vital Health Stat 20(26), 1995.

15. Rooks JP, Weatherby NL, Ernst EKM, Stapleton S, Rosen D, Rosenfield A. Outcomes of care in birth centers: the National Birth Center Study. N Engl J Med 1989;321:1804-11.

16. International guidelines for neonatal resuscitation: An excerpt from the guidelines 2000 for cardiopulmonary resuscitation and emergency cardiovascular care. Pediatrics 2000;106(3):Available from: http://www.pediatrics.org/cgi/content/full/106/3/e29.

17. American College of Obstetricians and Gynecologists. Hypertension in pregnancy. ACOG Technical Bulletin 219. Washington, DC: ACOG, 1996.

18. National Diabetes Data Group. Classification and diagnosis of diabetes mellitus and other categories of glucose intolerance. Diabetes 1979;28:1039-57.

19. American College of Obstetricians and Gynecologists. Induction of labor. ACOG Practice Bulletin 10. Washington, DC: ACOG; 1999.

20. Torr E, ed, for the British Columbia Reproductive Care Program. Report on the findings of the Consensus Conference on Obstetric Services in Rural or Remote Communities. Can J Rural Med 2000;5:211-7.

21. Barrett J. Funic reduction for the management of umbilical cord prolapse. Am J Obstet Gynecol 1991;165:654-7.

22. Knab DR. Abruptio placentae: an assessment of the time and method of delivery. Obstet Gynecol 1978;52:625-9.

23. Chauhan SP, Roach H, Naef RW, Magann EF, Morrison JC, Martin JN. Cesarean section for suspected fetal distress: Does the decision-incision time make a difference? J Reprod Med 1997;42:347-52.

24. MacKenzie IZ, Cooke I. Prospective 12 month study of 30 minute decision to delivery intervals for “emergency” caesarean section. BMJ 2001;322:1334-5.

25. Schauberger CW, Rooney BL, Beguin EA, Schaper AM, Spindler J. Evaluating the thirty minute interval in emergency cesarean sections. J Am Coll Surg 1994;179:151-5.

26. Tuffnell DJ, Wilkinson K, Beresford N. Interval between decision and delivery by caesarean section: Are current standards achievable? BMJ 2001;322:1330-3.

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LAWRENCE LEEMAN, MD, MPH
REBECCA LEEMAN, CNM, MSN
Albuquerque, New Mexico
Submitted, revised, August 26, 2001.
From the Department of Family and Community Medicine and the Department of Obstetrics and Gynecology, University of New Mexico School of Medicine, Albuquerque, New Mexico (L.L.), and Women’s Specialists of New Mexico, Albuquerque, New Mexico (R.L.). The authors report no competing interest. Reprint requests should be addressed to Lawrence Leeman, MD, MPH, UNM Family Practice, 2400 Tucker NE, Albuquerque, NM 87131. E-mail: [email protected].

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LAWRENCE LEEMAN, MD, MPH
REBECCA LEEMAN, CNM, MSN
Albuquerque, New Mexico
Submitted, revised, August 26, 2001.
From the Department of Family and Community Medicine and the Department of Obstetrics and Gynecology, University of New Mexico School of Medicine, Albuquerque, New Mexico (L.L.), and Women’s Specialists of New Mexico, Albuquerque, New Mexico (R.L.). The authors report no competing interest. Reprint requests should be addressed to Lawrence Leeman, MD, MPH, UNM Family Practice, 2400 Tucker NE, Albuquerque, NM 87131. E-mail: [email protected].

Author and Disclosure Information

LAWRENCE LEEMAN, MD, MPH
REBECCA LEEMAN, CNM, MSN
Albuquerque, New Mexico
Submitted, revised, August 26, 2001.
From the Department of Family and Community Medicine and the Department of Obstetrics and Gynecology, University of New Mexico School of Medicine, Albuquerque, New Mexico (L.L.), and Women’s Specialists of New Mexico, Albuquerque, New Mexico (R.L.). The authors report no competing interest. Reprint requests should be addressed to Lawrence Leeman, MD, MPH, UNM Family Practice, 2400 Tucker NE, Albuquerque, NM 87131. E-mail: [email protected].

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ABSTRACT

OBJECTIVES: We analyzed perinatal outcomes at a rural hospital without cesarean delivery capability.

STUDY DESIGN: This was a historical cohort outcomes study.

POPULATION: The study population included all pregnant women at 20 weeks or greater of gestational age (n = 1132) over a 5-year period in a predominantly Native American region of northwestern New Mexico.

OUTCOMES MEASURED: The outcomes studied included perinatal mortality, neonatal morbidity, obstetric emergencies, intrapartum and antepartum transfers, and cesarean delivery rate. We did a detailed case review of all obstetric emergencies and low-Apgar-score births at Zuni-Ramah Hospital and all cesarean deliveries for fetal distress at referral hospitals.

RESULTS: Of the 1132 women in the study population, 64.7% (n = 735) were able to give birth at the hospital without operative facilities; 25.6% (n = 290) were transferred before labor; and 9.5% (n = 107) were transferred during labor. The perinatal mortality rate of 11.4 per 1000 (95% confidence interval, 5.1-17.8) was similar to the nationwide rate of 12.8 per 1000 even though Zuni-Ramah has a high-risk obstetric population. No instances of major neonatal or maternal morbidity caused by lack of surgical facilities occurred. The cesarean delivery rate of 7.3% was significantly lower than the nationwide rate of 20.7% (P < .001). The incidence of neonates with low Apgar scores (0.54%) was significantly lower than the nationwide rate (1.4%). The incidence of neonates requiring resuscitation (3.4%) was comparable to the nationwide rate (2.9%).

CONCLUSIONS: The presence of a rural maternity care unit without surgical facilities can safely allow a high proportion of women to give birth closer to their communities. This study demonstrated a low level of perinatal risk. Most transfers were made for induction or augmentation of labor. Rural hospitals that do not have cesarean delivery capability but are part of an integrated perinatal system can safely offer obstetric services by using appropriate antepartum and intrapartum screening criteria for obstetric risk.

KEY POINTS FOR CLINICIANS

  • Rural hospitals without cesarean delivery capability can safely offer obstetric care to selected patients as part of an integrated perinatal network.
  • Measures of maternal and neonatal morbidity and mortality were at or below national averages despite a higher-risk population.
  • Antepartum (25.6%) or intrapartum (9.5%) transfer to hospitals with surgical or tertiary-care facilities was required for 35% of pregnant women.
  • The use of oxytocin induction or augmentation, if determined safe, may significantly lower the transfer rate from rural hospitals that lack cesarean delivery capability.

The availability of perinatal care in rural communities produces better pregnancy outcomes than do perinatal systems that require rural women to seek maternity care in distant urban areas.1-3 Unfortunately, rural maternity care has been affected by the loss of physicians who offer these services and by the closing of many rural hospitals’ maternity care units. Maintaining 24-hour operative obstetric capabilities is difficult in rural areas because they have an insufficient population base to support a physician trained in operative obstetrics. Another barrier is the lack of anesthesia services and operating room personnel.

The Guidelines for Perinatal Care developed by the American College of Obstetricians and Gynecologists (ACOG) and the American Academy of Pediatrics (AAP) state, “All hospitals offering labor and delivery services should be equipped to perform emergency cesarean delivery.” 4 Nevertheless, not all rural obstetric units can offer cesarean delivery and must transfer patients to a referral hospital for operative needs. Advisory panels in the United States and Canada have recommended similar models of rural perinatal care.5-8 A Canadian panel estimated that 125 Canadian hospitals offer obstetric care without surgical facilities.

Studies of rural hospitals in Canada, Australia, and the United Kingdom that lack continuous on-site cesarean capability are limited by the small number of deliveries.9-12 Most such studies are hospital based rather than population based and lack data on women who are transferred to outlying hospitals. The only population-based study that we identified found no evidence of adverse events caused by the lack of cesarean facilities; the sample size, however, was limited to 286 births.9

We studied all pregnancies occurring in a predominantly Native American region of New Mexico over a 5-year period to ascertain the safety of rural perinatal care based in a hospital without cesarean capability. Population-based and hospital-based outcomes are presented. This is the first study from a US community using this model of care.

Methods

We conducted an outcomes study using a historical cohort study design of all pregnancies beyond 20 weeks of gestation in the Zuni Pueblo and Ramah Navajo communities of northwestern New Mexico from 1992 to 1996. The perinatal services based at the Zuni-Ramah Indian Health Service (IHS) Hospital are the focus of this study. This 37-bed community hospital, staffed by family physicians and a part-time nurse-midwife, is part of an integrated perinatal system. The birthing unit has access to obstetrician-gynecologist (OBG) consultants at the Gallup Indian Medical Center (GIMC), 33 miles to the north, and perinatology and neonatology care in Albuquerque, 147 miles to the east. GIMC, the primary referral hospital and closest surgical facility, has an obstetric unit staffed by OBGs, family physicians, and nurse-mid-wives. Transportation time is 40 minutes by ground ambulance to GIMC or by fixed wing aircraft to Albuquerque.

 

 

The Zuni-Ramah Hospital limits intrapartum care to women designated as at low or moderate risk by criteria established by Zuni-Ramah family physicians and reviewed by GIMC OBGs. Criteria mandating transfer included prior cesarean, malpresentation, multiple gestation, intrauterine growth restriction, severe preeclampsia, placenta previa, significant vaginal bleeding, major fetal anomalies, anticipated preterm delivery (< 36 weeks), nonreassuring fetal heart tones (NRFHTs), and need for labor induction or augmentation with oxytocin. Women with gestational or type 2 diabetes who were well controlled could give birth at Zuni-Ramah unless they had end-organ damage or the fetus had known macrosomia. Physicians successfully completed the Advanced Life Support in Obstetrics (ALSO, ®American Academy of Family Physicians, 4th ed., 2000) course, attended weekly high-risk obstetric rounds, and performed quarterly reviews of obstetric complications. The family physicians performed vacuum-assisted deliveries, utilized amnioinfusion, and used continuous or intermittent fetal monitoring.

A review of the delivery and transfer records of the Zuni-Ramah Hospital and GIMC obstetric services revealed that there had been 1132 births of 1137 infants during the study period. The authors used a data collection form to review prenatal and newborn records from every birth. We reviewed intrapartum records for all births at the Zuni-Ramah and GIMC hospitals. We obtained discharge summaries from tertiary-care sites. We interviewed perinatal coordinators, public health nurses, and pediatric care providers to obtain information about patients who had received perinatal care outside of the IHS system.

The outcomes measured included perinatal mortality, neonatal morbidity, obstetric emergencies, intrapartum and antepartum transfers, and cesarean delivery rate. All obstetric emergencies originating at Zuni-Ramah Hospital were reviewed to determine whether the lack of surgical facilities had resulted in adverse outcomes. The physician’s notes were used to differentiate a NRFHT pattern requiring observation at a hospital with operative facilities from a truly worrisome pattern that required urgent intervention for fetal distress.

Births were defined as deliveries of infants at 20 weeks or more of estimated gestational age. We analyzed each birth in a multiple gestation individually. The population-based perinatal mortality rate was calculated from 20 weeks’ estimated gestational age to the 28th neonatal day. The Zuni-Ramah Hospital perinatal mortality rate was calculated by inclusion of all women delivered at Zuni-Ramah Hospital or transferred during labor. Approval for the study was obtained from the IHS Institutional Review Board and the Zuni Tribal Council.

Results

Study population

We identified 1137 births to 945 women between 1992 and 1996. Zuni and Navajo births were 66.9% and 30.8%, respectively; 30% of women were primiparous and 70%, multiparous. We found that 10.4% of births had occurred in women older than 35 years and 7.8% in women younger than 18 years. Regarding prenatal care, 3.9% of women had received none; 43.0% had established prenatal care in the first trimester; 40.4%, in the second trimester; and 12.8%, in the third trimester.

Delivery sites and maternal transfers

The majority of women (64.4%, n = 732) gave birth at the Zuni-Ramah Hospital (Figure) or at GIMC (29.6%, n = 337). A small number (2.2%, n = 25) gave birth at a private hospital with surgical facilities in Gallup. Albuquerque tertiary-care hospitals were the sites of 3.2% (n = 36) of deliveries. Primary indications for tertiary care were prematurity and fetal anomalies. Seven (0.6%) deliveries occurred at other sites, including home and ambulance.

The antepartum transfers (Table 1) were required primarily for pregnancy complications requiring labor induction. Preeclampsia, diabetes, nonreassuring antepartum testing, and post dates patients accounted for 56.8% of the 290 transfers. The 107 intrapartum transfers were made predominantly for labor induction or augmentation (64.5%, n = 69), a concerning fetal heart tracing (15.9%, n = 17), or fetal malpresentation diagnosed during labor (8.4%, n = 9).

FIGURE
PREGNANCIES AT ZUNI-RAMAH HOSPITAL

TABLE 1
ANTEPARTUM AND INTRAPARTUM TRANSFERS FROM ZUNI-RAMAH HOSPITAL

IndicationNumber of Transfers (%)
Antepartum Transfers*
  Preeclampsia83 (28.6)
  Prior cesarean delivery55 (19.0)
  Nonreassuring testing39 (13.4)
  Preterm (includes PPROM)24 (8.3)
  Diabetes22 (7.6)
  Postdates21 (7.2)
  Other18 (6.2)
  Malpresentation16 (5.5)
  Chronic HTN8 (2.8)
  Macrosomia7 (2.4)
  IUFD6 (2.1)
  IUGR5 (1.7)
  Anomalies4 (1.4)
Total290 (25.6% of population)
Intrapartum Transfers
  First-stage arrest of labor37 (34.6)
  PROM without active labor32 (29.9)
  Malpresentation9 (8.4)
  Fetal distress5 (4.7)
  Nonreassuring tracing12 (11.2)
  Other12 (11.2)
Total107 (9.5% of population)
*Greater than 290 because of 18 patients with 2 reasons for antepartum transfer.
HTN denotes hypertension; IUFD, intrauterine fetal demise; IUGR, intrauterine growth restriction; PROM, premature rupture of membranes; PPROM, preterm premature rupture of membranes.

Obstetric interventions

The total cesarean delivery rate (7.3%) was approximately one third the nationwide rate of 20.7% in 1996. The primary cesarean delivery rate (number of cesareans in women without prior cesarean divided by the number of women who have never had a cesarean) of 5.3% compares with a nationwide primary rate of 14.6%. The cesarean rate was 22.1% for antepartum transfers and 17.8% for intrapartum transfers. Operative vaginal delivery occurred in 5.4% of births, well below the nationwide rate of 9.4%. The induction rate of 13.8% is lower than the nationwide rate of 16.9%. The oxytocin augmentation rate of 7.7% is well below the nationwide rate of 16.9% in 1996.13 Parenteral narcotics were available at Zuni-Ramah; however, 81.4% of women elected to receive no labor analgesia. Epidural anesthesia was not available at Zuni-Ramah Hospital.

 

 

Perinatal mortality

The perinatal mortality rate for the population was 11.4 per 1000 births (95% CI, 5.1-17.8 by Poisson distribution), comparable to the 1991 nationwide peri-natal mortality rate of 12.8/1000.14 Nine of the 13 neonatal deaths were caused by intrauterine fetal demise before labor (Table 2). The Zuni-Ramah Hospital–based perinatal mortality rate of 1.2/1000 was comparable with the 1.3/1000 perinatal mortality rate for women in the National Birth Center study even though Zuni-Ramah Hospital accepts higher-risk patients.15

TABLE 2
PERINATAL MORTALITY IN ZUNI-RAMAH POPULATION

Age (wk)Weight (g)SiteCause
Intrauterine Fetal Death
311410GIMCUnexplained
393130GIMCUnexplained
351540GIMCUnexplained; IUGR
351690GIMCUnexplained; IUGR
21330GIMCPPROM
21560GIMCPPROM
413040GIMCOligohydramnios and post dates. Two days prior, refused induction with amniotic fluid volume index of 3.8
281290AlbAbruption
32UnknownZuniNecrotizing/calcifying encephalopathy (probable CMV) with severe IUGR
Early Neonatal Death (< 7 days)
382805AlbOsteogenesis imperfecta
31UnknownAlbPotter’s syndrome
Late Neonatal Death (7 to 27 days)
351590GIMCPulmonary interstitial emphysema caused by respiratory failure of unknown etiology/IUGR
413220GIMCSepsis at 12 days; had been discharged home as healthy infant
Alb denotes Albuquerque tertiary-care hospital; CMV, cytomegalovirus; GIMC, Gallup Indian Medical Center; IUGR, intrauterine growth restriction; PPROM, preterm premature rupture of membranes.

Neonatal morbidity

Measures of neonatal morbidity are summarized in Table 3. The frequency of 5-minute Apgar scores below 7, low birthweight, and prematurity compares favorably with 1996 US rates.13 The rate of assisted ventilation (intubation or bag-mask) for the entire population (4.6%, n = 52) is greater than the 1996 nationwide rate (2.9%), although the difference is of questionable clinical significance, since international studies have demonstrated a range for assisted ventilation of 1% to 10% of hospital births.16 Neonatal Intensive Care Unit (NICU) transfer occurred in 27 (2.4%) of deliveries from non-tertiary-care sites. Thirteen (1.8%) babies born at Zuni-Ramah were transferred to Albuquerque for NICU care because of respiratory distress (n = 10) or neonatal anomalies (n = 3). The 3 cases of low Apgar scores at Zuni-Ramah were attributed to pneumothorax, respiratory distress syndrome of prematurity, and sepsis with meconium aspiration.

TABLE 3
NEONATAL MORBIDITY IN ZUNI-RAMAH POPULATION, BASED ON LIVE BIRTHS

 Zuni-Ramah Hospital (N=732)Zuni-Ramah Population (n = 1128)1996 US Population
5-minute Apgar score < 73 (0.41%), P = .0236 (0.54%), P = .0141.4%
Assisted ventilation19 (2.6%), P = 0.6252 (4.6%), P < .0012.9%
Birthweight < 2500 g14 (1.9%), P < .00161 (5.4%), P < .00111%
Preterm (37 weeks)22 (3.0%), P < .00175 (6.7%), P = .367.4%
P values are based on comparison with the US population. US population figures for 1996 were extracted from Ventura SJ, Martin JA, Curtin SC, Mathews TJ. Report of Final Natality Statistics, 1996. Monthly vital statistics report; vol 46, no 11, supp. Hyattsville, Md: National Center for Health Statistics, 1998.

Obstetric risk factors

The study population had a greater incidence of pregnancy-induced hypertension (14.5% vs 2.6% by 1996 ACOG criteria17), chronic hypertension (2.7% vs 0.7%15), and diabetes (9.2% vs 2.6%15) than the average US obstetric population. Gestational diabetes was diagnosed according to National Diabetes Data Group criteria:18 7.1% had gestational diabetes (class A1 and A2 ) and 2.1% had type 2 antepartum diabetes (classes B and C).

Outcomes of obstetric emergencies at zuni-ramah hospital

We reviewed all cases of placental abruption, uterine inversion, umbilical cord prolapse, and fetal distress at Zuni-Ramah Hospital to identify potentially preventable adverse outcomes caused by lack of operative facilities (Table W1). Umbilical cord prolapse and uterine inversion each occurred once and were appropriately managed, with excellent outcomes. In 3 of the 4 cases of placental abruption, there were clearly no adverse outcomes caused by lack of on-site operative facilities, as patients were expectantly managed upon arrival to the referral hospital (cases 3 and 4) or presented to Zuni-Ramah Hospital as an intrauterine demise (case 5).

The fourth patient with placental abruption (case 6) presented at Zuni-Ramah with vaginal bleeding, severe variable decelerations, and a 10-point drop from baseline hematocrit. She was scheduled to labor at GIMC because of a history of prior cesarean but presented to the Zuni-Ramah emergency room with vaginal bleeding. She was transferred to GIMC for an anticipated cesarean delivery; however, on arrival the patient rapidly progressed and gave birth to an infant vaginally with Apgar scores of 3 and 9. Her infant had a neonatal seizure and magnetic resonance imaging evidence of sagittal sinus thrombosis. The infant had a normal neurologic evaluation, developmental assessment, and electroencephalogram at 15 months.

We reviewed 5 cases of urgent transfer for fetal distress. These were differentiated from the 8 intrapartum transfers for NRFHTs based on the severity of fetal heart monitor tracings. Four of the 5 women who had been transferred for fetal distress gave birth to healthy infants vaginally more than 2 hours after arrival at the referral institution. One patient who was urgently transferred for repetitive late decelerations is discussed below.

 

 

Cesarean deliveries for fetal distress at referral hospitals

We reviewed all cases of cesarean deliveries for fetal distress (n = 10) at referral institutions to determine whether outcomes for any of the patients could potentially have been improved by having their cesarean deliveries earlier if operative facilities had been available at Zuni-Ramah Hospital or by being transferred earlier (Table W1). Seven of the 10 patients were transferred for preeclampsia, NRFHTs, or failure to progress. All had their cesareans for fetal distress many hours after arrival at the referral institution.

Two cases of cesarean delivery for fetal distress after transfer because of abruption were previously described. A patient presented in early labor with repetitive late decelerations and was urgently transferred to GIMC, where she underwent an immediate cesarean delivery. Her infant had Apgar scores of 1 and 7, an unremarkable neonatal course, and a normal 15-month developmental screen.

Discussion

Our outcomes demonstrate that with the use of appropriate screening criteria, childbirth can safely occur in institutions that lack surgical suites. The population-based perinatal mortality rate was similar to the nationwide rate. A review of obstetric emergencies and low Apgar scores among the 839 women laboring at Zuni-Ramah Hospital failed to identify adverse outcomes that might have been prevented if the hospital had had operative facilities. Cesarean rates were approximately one third the nationwide rate even though Zuni-Ramah patients had a higher prevalence of such risk factors as diabetes and preeclampsia.

Although they represented a high-risk obstetric population, 65% of women were able to give birth at Zuni-Ramah Hospital through use of the perinatal screening criteria. The 35% rate of transfer was caused largely by the need for oxytocin augmentation or induction. Only 21.6% of the women who were transferred for dysfunctional labor or premature rupture of membranes ultimately had a cesarean delivery. Oxytocin has not been permitted at Zuni-Ramah Hospital because of the ACOG guideline permitting oxytocin use only if “a physician capable of performing a cesarean delivery is readily available.”19 There are no studies addressing the safety of labor induction or augmentation without on-site cesarean capability.

Canadian guidelines for rural maternity care do not prohibit the use of prostaglandins or oxytocin at hospitals without operative facilities. A Consensus Conference on Obstetric Services in Rural or Remote Communities addressed the issue of labor induction or augmentation in hospitals without cesarean capability by stating, “If caring for a woman in labour is appropriate in the community, then caring for her during an augmented/induced labour is equally appropriate when there is support by trained local staff and resources.”20 We concur that use of oxytocin in rural hospital units without operative facilities should be considered under well-defined clinical guidelines or research protocols.

Limitations

Our study’s limitations include lack of long-term neonatal outcomes, small size of the Zuni-Ramah population, an almost exclusively Native American population, and lack of examiner blinding during record review. Transfer rates may be increased in populations with higher rates of cesarean delivery or epidural anesthesia use. Alternatively, the high incidence of preeclampsia, chronic hypertension, and diabetes in these communities may have resulted in a higher proportion of induction. Umbilical cord pro-lapse and significant placental abruption are routinely treated by urgent cesarean delivery; therefore, obstetric literature on outcomes without immediate operative intervention is limited.21,22 A larger study would be required to determine the potential increased neonatal morbidity or mortality resulting from delayed intervention.

Conclusions

The ACOG/AAP guideline requiring on-site surgical facilities and the ability to initiate a cesarean in 30 minutes is not based on evidence. Four small retrospective studies of emergency cesarean deliveries delayed for more than 30 minutes did not demonstrate adverse neonatal outcomes.23-26 In our study population, no adverse outcomes (none in 839 births) were determined to have been caused by a lack of surgical facilities. Despite these excellent outcomes, the possibility always exists for outcomes that can be prevented by doing a rapid emergent cesarean delivery. Women deciding to give birth in facilities without operative capabilities should receive information regarding the risks and benefits of delivering there and should have access to other facilities. Provider discretion and patient choice must be respected to ensure community support of these birthing units. Practitioners at the rural units must have assurance that any patients who require an urgent transfer will be readily accepted.

Rural communities, medical providers, and health care facilities need to consider the overall effect of maintaining local maternity care units, as the loss of rural maternity care can increase the risk of adverse perinatal outcomes.1-3 We concur with the Canadian panel that although maintenance of rural surgical and anesthesia capabilities is desirable, “good outcomes can be sustained within an integrated risk management system without local access to operative delivery.”8 Guidelines should be developed to permit rural hospitals without cesarean capability to provide maternity care as part of integrated perinatal systems with well-developed transport protocols and supportive referral institutions. Women living in rural areas should have the option to give birth near their homes in such units if they so desire.

 

 

Acknowledgments

The authors thank Robert Rhyne, MD, for editorial assistance in manuscript preparation and Betty Skipper, PhD, for statistical assistance. Current Zuni-Ramah Obstetric Guidelines are available at http://hsc.unm.edu/fcm/research/zuni.

ABSTRACT

OBJECTIVES: We analyzed perinatal outcomes at a rural hospital without cesarean delivery capability.

STUDY DESIGN: This was a historical cohort outcomes study.

POPULATION: The study population included all pregnant women at 20 weeks or greater of gestational age (n = 1132) over a 5-year period in a predominantly Native American region of northwestern New Mexico.

OUTCOMES MEASURED: The outcomes studied included perinatal mortality, neonatal morbidity, obstetric emergencies, intrapartum and antepartum transfers, and cesarean delivery rate. We did a detailed case review of all obstetric emergencies and low-Apgar-score births at Zuni-Ramah Hospital and all cesarean deliveries for fetal distress at referral hospitals.

RESULTS: Of the 1132 women in the study population, 64.7% (n = 735) were able to give birth at the hospital without operative facilities; 25.6% (n = 290) were transferred before labor; and 9.5% (n = 107) were transferred during labor. The perinatal mortality rate of 11.4 per 1000 (95% confidence interval, 5.1-17.8) was similar to the nationwide rate of 12.8 per 1000 even though Zuni-Ramah has a high-risk obstetric population. No instances of major neonatal or maternal morbidity caused by lack of surgical facilities occurred. The cesarean delivery rate of 7.3% was significantly lower than the nationwide rate of 20.7% (P < .001). The incidence of neonates with low Apgar scores (0.54%) was significantly lower than the nationwide rate (1.4%). The incidence of neonates requiring resuscitation (3.4%) was comparable to the nationwide rate (2.9%).

CONCLUSIONS: The presence of a rural maternity care unit without surgical facilities can safely allow a high proportion of women to give birth closer to their communities. This study demonstrated a low level of perinatal risk. Most transfers were made for induction or augmentation of labor. Rural hospitals that do not have cesarean delivery capability but are part of an integrated perinatal system can safely offer obstetric services by using appropriate antepartum and intrapartum screening criteria for obstetric risk.

KEY POINTS FOR CLINICIANS

  • Rural hospitals without cesarean delivery capability can safely offer obstetric care to selected patients as part of an integrated perinatal network.
  • Measures of maternal and neonatal morbidity and mortality were at or below national averages despite a higher-risk population.
  • Antepartum (25.6%) or intrapartum (9.5%) transfer to hospitals with surgical or tertiary-care facilities was required for 35% of pregnant women.
  • The use of oxytocin induction or augmentation, if determined safe, may significantly lower the transfer rate from rural hospitals that lack cesarean delivery capability.

The availability of perinatal care in rural communities produces better pregnancy outcomes than do perinatal systems that require rural women to seek maternity care in distant urban areas.1-3 Unfortunately, rural maternity care has been affected by the loss of physicians who offer these services and by the closing of many rural hospitals’ maternity care units. Maintaining 24-hour operative obstetric capabilities is difficult in rural areas because they have an insufficient population base to support a physician trained in operative obstetrics. Another barrier is the lack of anesthesia services and operating room personnel.

The Guidelines for Perinatal Care developed by the American College of Obstetricians and Gynecologists (ACOG) and the American Academy of Pediatrics (AAP) state, “All hospitals offering labor and delivery services should be equipped to perform emergency cesarean delivery.” 4 Nevertheless, not all rural obstetric units can offer cesarean delivery and must transfer patients to a referral hospital for operative needs. Advisory panels in the United States and Canada have recommended similar models of rural perinatal care.5-8 A Canadian panel estimated that 125 Canadian hospitals offer obstetric care without surgical facilities.

Studies of rural hospitals in Canada, Australia, and the United Kingdom that lack continuous on-site cesarean capability are limited by the small number of deliveries.9-12 Most such studies are hospital based rather than population based and lack data on women who are transferred to outlying hospitals. The only population-based study that we identified found no evidence of adverse events caused by the lack of cesarean facilities; the sample size, however, was limited to 286 births.9

We studied all pregnancies occurring in a predominantly Native American region of New Mexico over a 5-year period to ascertain the safety of rural perinatal care based in a hospital without cesarean capability. Population-based and hospital-based outcomes are presented. This is the first study from a US community using this model of care.

Methods

We conducted an outcomes study using a historical cohort study design of all pregnancies beyond 20 weeks of gestation in the Zuni Pueblo and Ramah Navajo communities of northwestern New Mexico from 1992 to 1996. The perinatal services based at the Zuni-Ramah Indian Health Service (IHS) Hospital are the focus of this study. This 37-bed community hospital, staffed by family physicians and a part-time nurse-midwife, is part of an integrated perinatal system. The birthing unit has access to obstetrician-gynecologist (OBG) consultants at the Gallup Indian Medical Center (GIMC), 33 miles to the north, and perinatology and neonatology care in Albuquerque, 147 miles to the east. GIMC, the primary referral hospital and closest surgical facility, has an obstetric unit staffed by OBGs, family physicians, and nurse-mid-wives. Transportation time is 40 minutes by ground ambulance to GIMC or by fixed wing aircraft to Albuquerque.

 

 

The Zuni-Ramah Hospital limits intrapartum care to women designated as at low or moderate risk by criteria established by Zuni-Ramah family physicians and reviewed by GIMC OBGs. Criteria mandating transfer included prior cesarean, malpresentation, multiple gestation, intrauterine growth restriction, severe preeclampsia, placenta previa, significant vaginal bleeding, major fetal anomalies, anticipated preterm delivery (< 36 weeks), nonreassuring fetal heart tones (NRFHTs), and need for labor induction or augmentation with oxytocin. Women with gestational or type 2 diabetes who were well controlled could give birth at Zuni-Ramah unless they had end-organ damage or the fetus had known macrosomia. Physicians successfully completed the Advanced Life Support in Obstetrics (ALSO, ®American Academy of Family Physicians, 4th ed., 2000) course, attended weekly high-risk obstetric rounds, and performed quarterly reviews of obstetric complications. The family physicians performed vacuum-assisted deliveries, utilized amnioinfusion, and used continuous or intermittent fetal monitoring.

A review of the delivery and transfer records of the Zuni-Ramah Hospital and GIMC obstetric services revealed that there had been 1132 births of 1137 infants during the study period. The authors used a data collection form to review prenatal and newborn records from every birth. We reviewed intrapartum records for all births at the Zuni-Ramah and GIMC hospitals. We obtained discharge summaries from tertiary-care sites. We interviewed perinatal coordinators, public health nurses, and pediatric care providers to obtain information about patients who had received perinatal care outside of the IHS system.

The outcomes measured included perinatal mortality, neonatal morbidity, obstetric emergencies, intrapartum and antepartum transfers, and cesarean delivery rate. All obstetric emergencies originating at Zuni-Ramah Hospital were reviewed to determine whether the lack of surgical facilities had resulted in adverse outcomes. The physician’s notes were used to differentiate a NRFHT pattern requiring observation at a hospital with operative facilities from a truly worrisome pattern that required urgent intervention for fetal distress.

Births were defined as deliveries of infants at 20 weeks or more of estimated gestational age. We analyzed each birth in a multiple gestation individually. The population-based perinatal mortality rate was calculated from 20 weeks’ estimated gestational age to the 28th neonatal day. The Zuni-Ramah Hospital perinatal mortality rate was calculated by inclusion of all women delivered at Zuni-Ramah Hospital or transferred during labor. Approval for the study was obtained from the IHS Institutional Review Board and the Zuni Tribal Council.

Results

Study population

We identified 1137 births to 945 women between 1992 and 1996. Zuni and Navajo births were 66.9% and 30.8%, respectively; 30% of women were primiparous and 70%, multiparous. We found that 10.4% of births had occurred in women older than 35 years and 7.8% in women younger than 18 years. Regarding prenatal care, 3.9% of women had received none; 43.0% had established prenatal care in the first trimester; 40.4%, in the second trimester; and 12.8%, in the third trimester.

Delivery sites and maternal transfers

The majority of women (64.4%, n = 732) gave birth at the Zuni-Ramah Hospital (Figure) or at GIMC (29.6%, n = 337). A small number (2.2%, n = 25) gave birth at a private hospital with surgical facilities in Gallup. Albuquerque tertiary-care hospitals were the sites of 3.2% (n = 36) of deliveries. Primary indications for tertiary care were prematurity and fetal anomalies. Seven (0.6%) deliveries occurred at other sites, including home and ambulance.

The antepartum transfers (Table 1) were required primarily for pregnancy complications requiring labor induction. Preeclampsia, diabetes, nonreassuring antepartum testing, and post dates patients accounted for 56.8% of the 290 transfers. The 107 intrapartum transfers were made predominantly for labor induction or augmentation (64.5%, n = 69), a concerning fetal heart tracing (15.9%, n = 17), or fetal malpresentation diagnosed during labor (8.4%, n = 9).

FIGURE
PREGNANCIES AT ZUNI-RAMAH HOSPITAL

TABLE 1
ANTEPARTUM AND INTRAPARTUM TRANSFERS FROM ZUNI-RAMAH HOSPITAL

IndicationNumber of Transfers (%)
Antepartum Transfers*
  Preeclampsia83 (28.6)
  Prior cesarean delivery55 (19.0)
  Nonreassuring testing39 (13.4)
  Preterm (includes PPROM)24 (8.3)
  Diabetes22 (7.6)
  Postdates21 (7.2)
  Other18 (6.2)
  Malpresentation16 (5.5)
  Chronic HTN8 (2.8)
  Macrosomia7 (2.4)
  IUFD6 (2.1)
  IUGR5 (1.7)
  Anomalies4 (1.4)
Total290 (25.6% of population)
Intrapartum Transfers
  First-stage arrest of labor37 (34.6)
  PROM without active labor32 (29.9)
  Malpresentation9 (8.4)
  Fetal distress5 (4.7)
  Nonreassuring tracing12 (11.2)
  Other12 (11.2)
Total107 (9.5% of population)
*Greater than 290 because of 18 patients with 2 reasons for antepartum transfer.
HTN denotes hypertension; IUFD, intrauterine fetal demise; IUGR, intrauterine growth restriction; PROM, premature rupture of membranes; PPROM, preterm premature rupture of membranes.

Obstetric interventions

The total cesarean delivery rate (7.3%) was approximately one third the nationwide rate of 20.7% in 1996. The primary cesarean delivery rate (number of cesareans in women without prior cesarean divided by the number of women who have never had a cesarean) of 5.3% compares with a nationwide primary rate of 14.6%. The cesarean rate was 22.1% for antepartum transfers and 17.8% for intrapartum transfers. Operative vaginal delivery occurred in 5.4% of births, well below the nationwide rate of 9.4%. The induction rate of 13.8% is lower than the nationwide rate of 16.9%. The oxytocin augmentation rate of 7.7% is well below the nationwide rate of 16.9% in 1996.13 Parenteral narcotics were available at Zuni-Ramah; however, 81.4% of women elected to receive no labor analgesia. Epidural anesthesia was not available at Zuni-Ramah Hospital.

 

 

Perinatal mortality

The perinatal mortality rate for the population was 11.4 per 1000 births (95% CI, 5.1-17.8 by Poisson distribution), comparable to the 1991 nationwide peri-natal mortality rate of 12.8/1000.14 Nine of the 13 neonatal deaths were caused by intrauterine fetal demise before labor (Table 2). The Zuni-Ramah Hospital–based perinatal mortality rate of 1.2/1000 was comparable with the 1.3/1000 perinatal mortality rate for women in the National Birth Center study even though Zuni-Ramah Hospital accepts higher-risk patients.15

TABLE 2
PERINATAL MORTALITY IN ZUNI-RAMAH POPULATION

Age (wk)Weight (g)SiteCause
Intrauterine Fetal Death
311410GIMCUnexplained
393130GIMCUnexplained
351540GIMCUnexplained; IUGR
351690GIMCUnexplained; IUGR
21330GIMCPPROM
21560GIMCPPROM
413040GIMCOligohydramnios and post dates. Two days prior, refused induction with amniotic fluid volume index of 3.8
281290AlbAbruption
32UnknownZuniNecrotizing/calcifying encephalopathy (probable CMV) with severe IUGR
Early Neonatal Death (< 7 days)
382805AlbOsteogenesis imperfecta
31UnknownAlbPotter’s syndrome
Late Neonatal Death (7 to 27 days)
351590GIMCPulmonary interstitial emphysema caused by respiratory failure of unknown etiology/IUGR
413220GIMCSepsis at 12 days; had been discharged home as healthy infant
Alb denotes Albuquerque tertiary-care hospital; CMV, cytomegalovirus; GIMC, Gallup Indian Medical Center; IUGR, intrauterine growth restriction; PPROM, preterm premature rupture of membranes.

Neonatal morbidity

Measures of neonatal morbidity are summarized in Table 3. The frequency of 5-minute Apgar scores below 7, low birthweight, and prematurity compares favorably with 1996 US rates.13 The rate of assisted ventilation (intubation or bag-mask) for the entire population (4.6%, n = 52) is greater than the 1996 nationwide rate (2.9%), although the difference is of questionable clinical significance, since international studies have demonstrated a range for assisted ventilation of 1% to 10% of hospital births.16 Neonatal Intensive Care Unit (NICU) transfer occurred in 27 (2.4%) of deliveries from non-tertiary-care sites. Thirteen (1.8%) babies born at Zuni-Ramah were transferred to Albuquerque for NICU care because of respiratory distress (n = 10) or neonatal anomalies (n = 3). The 3 cases of low Apgar scores at Zuni-Ramah were attributed to pneumothorax, respiratory distress syndrome of prematurity, and sepsis with meconium aspiration.

TABLE 3
NEONATAL MORBIDITY IN ZUNI-RAMAH POPULATION, BASED ON LIVE BIRTHS

 Zuni-Ramah Hospital (N=732)Zuni-Ramah Population (n = 1128)1996 US Population
5-minute Apgar score < 73 (0.41%), P = .0236 (0.54%), P = .0141.4%
Assisted ventilation19 (2.6%), P = 0.6252 (4.6%), P < .0012.9%
Birthweight < 2500 g14 (1.9%), P < .00161 (5.4%), P < .00111%
Preterm (37 weeks)22 (3.0%), P < .00175 (6.7%), P = .367.4%
P values are based on comparison with the US population. US population figures for 1996 were extracted from Ventura SJ, Martin JA, Curtin SC, Mathews TJ. Report of Final Natality Statistics, 1996. Monthly vital statistics report; vol 46, no 11, supp. Hyattsville, Md: National Center for Health Statistics, 1998.

Obstetric risk factors

The study population had a greater incidence of pregnancy-induced hypertension (14.5% vs 2.6% by 1996 ACOG criteria17), chronic hypertension (2.7% vs 0.7%15), and diabetes (9.2% vs 2.6%15) than the average US obstetric population. Gestational diabetes was diagnosed according to National Diabetes Data Group criteria:18 7.1% had gestational diabetes (class A1 and A2 ) and 2.1% had type 2 antepartum diabetes (classes B and C).

Outcomes of obstetric emergencies at zuni-ramah hospital

We reviewed all cases of placental abruption, uterine inversion, umbilical cord prolapse, and fetal distress at Zuni-Ramah Hospital to identify potentially preventable adverse outcomes caused by lack of operative facilities (Table W1). Umbilical cord prolapse and uterine inversion each occurred once and were appropriately managed, with excellent outcomes. In 3 of the 4 cases of placental abruption, there were clearly no adverse outcomes caused by lack of on-site operative facilities, as patients were expectantly managed upon arrival to the referral hospital (cases 3 and 4) or presented to Zuni-Ramah Hospital as an intrauterine demise (case 5).

The fourth patient with placental abruption (case 6) presented at Zuni-Ramah with vaginal bleeding, severe variable decelerations, and a 10-point drop from baseline hematocrit. She was scheduled to labor at GIMC because of a history of prior cesarean but presented to the Zuni-Ramah emergency room with vaginal bleeding. She was transferred to GIMC for an anticipated cesarean delivery; however, on arrival the patient rapidly progressed and gave birth to an infant vaginally with Apgar scores of 3 and 9. Her infant had a neonatal seizure and magnetic resonance imaging evidence of sagittal sinus thrombosis. The infant had a normal neurologic evaluation, developmental assessment, and electroencephalogram at 15 months.

We reviewed 5 cases of urgent transfer for fetal distress. These were differentiated from the 8 intrapartum transfers for NRFHTs based on the severity of fetal heart monitor tracings. Four of the 5 women who had been transferred for fetal distress gave birth to healthy infants vaginally more than 2 hours after arrival at the referral institution. One patient who was urgently transferred for repetitive late decelerations is discussed below.

 

 

Cesarean deliveries for fetal distress at referral hospitals

We reviewed all cases of cesarean deliveries for fetal distress (n = 10) at referral institutions to determine whether outcomes for any of the patients could potentially have been improved by having their cesarean deliveries earlier if operative facilities had been available at Zuni-Ramah Hospital or by being transferred earlier (Table W1). Seven of the 10 patients were transferred for preeclampsia, NRFHTs, or failure to progress. All had their cesareans for fetal distress many hours after arrival at the referral institution.

Two cases of cesarean delivery for fetal distress after transfer because of abruption were previously described. A patient presented in early labor with repetitive late decelerations and was urgently transferred to GIMC, where she underwent an immediate cesarean delivery. Her infant had Apgar scores of 1 and 7, an unremarkable neonatal course, and a normal 15-month developmental screen.

Discussion

Our outcomes demonstrate that with the use of appropriate screening criteria, childbirth can safely occur in institutions that lack surgical suites. The population-based perinatal mortality rate was similar to the nationwide rate. A review of obstetric emergencies and low Apgar scores among the 839 women laboring at Zuni-Ramah Hospital failed to identify adverse outcomes that might have been prevented if the hospital had had operative facilities. Cesarean rates were approximately one third the nationwide rate even though Zuni-Ramah patients had a higher prevalence of such risk factors as diabetes and preeclampsia.

Although they represented a high-risk obstetric population, 65% of women were able to give birth at Zuni-Ramah Hospital through use of the perinatal screening criteria. The 35% rate of transfer was caused largely by the need for oxytocin augmentation or induction. Only 21.6% of the women who were transferred for dysfunctional labor or premature rupture of membranes ultimately had a cesarean delivery. Oxytocin has not been permitted at Zuni-Ramah Hospital because of the ACOG guideline permitting oxytocin use only if “a physician capable of performing a cesarean delivery is readily available.”19 There are no studies addressing the safety of labor induction or augmentation without on-site cesarean capability.

Canadian guidelines for rural maternity care do not prohibit the use of prostaglandins or oxytocin at hospitals without operative facilities. A Consensus Conference on Obstetric Services in Rural or Remote Communities addressed the issue of labor induction or augmentation in hospitals without cesarean capability by stating, “If caring for a woman in labour is appropriate in the community, then caring for her during an augmented/induced labour is equally appropriate when there is support by trained local staff and resources.”20 We concur that use of oxytocin in rural hospital units without operative facilities should be considered under well-defined clinical guidelines or research protocols.

Limitations

Our study’s limitations include lack of long-term neonatal outcomes, small size of the Zuni-Ramah population, an almost exclusively Native American population, and lack of examiner blinding during record review. Transfer rates may be increased in populations with higher rates of cesarean delivery or epidural anesthesia use. Alternatively, the high incidence of preeclampsia, chronic hypertension, and diabetes in these communities may have resulted in a higher proportion of induction. Umbilical cord pro-lapse and significant placental abruption are routinely treated by urgent cesarean delivery; therefore, obstetric literature on outcomes without immediate operative intervention is limited.21,22 A larger study would be required to determine the potential increased neonatal morbidity or mortality resulting from delayed intervention.

Conclusions

The ACOG/AAP guideline requiring on-site surgical facilities and the ability to initiate a cesarean in 30 minutes is not based on evidence. Four small retrospective studies of emergency cesarean deliveries delayed for more than 30 minutes did not demonstrate adverse neonatal outcomes.23-26 In our study population, no adverse outcomes (none in 839 births) were determined to have been caused by a lack of surgical facilities. Despite these excellent outcomes, the possibility always exists for outcomes that can be prevented by doing a rapid emergent cesarean delivery. Women deciding to give birth in facilities without operative capabilities should receive information regarding the risks and benefits of delivering there and should have access to other facilities. Provider discretion and patient choice must be respected to ensure community support of these birthing units. Practitioners at the rural units must have assurance that any patients who require an urgent transfer will be readily accepted.

Rural communities, medical providers, and health care facilities need to consider the overall effect of maintaining local maternity care units, as the loss of rural maternity care can increase the risk of adverse perinatal outcomes.1-3 We concur with the Canadian panel that although maintenance of rural surgical and anesthesia capabilities is desirable, “good outcomes can be sustained within an integrated risk management system without local access to operative delivery.”8 Guidelines should be developed to permit rural hospitals without cesarean capability to provide maternity care as part of integrated perinatal systems with well-developed transport protocols and supportive referral institutions. Women living in rural areas should have the option to give birth near their homes in such units if they so desire.

 

 

Acknowledgments

The authors thank Robert Rhyne, MD, for editorial assistance in manuscript preparation and Betty Skipper, PhD, for statistical assistance. Current Zuni-Ramah Obstetric Guidelines are available at http://hsc.unm.edu/fcm/research/zuni.

References

1. Allen DT, Kamradt MS. Relationship of infant mortality to the availability of obstetric care in Indiana. J Fam Pract 1991;33:609-13.

2. Larimore WL, Davis A. Relation of infant mortality to the availability of maternity care in rural Florida. J Am Board Fam Pract 1995;8:392-9.

3. Nesbitt TS, Connell FA, Hart LG, Rosenblatt RA. Access to obstetric care in rural areas: effect on birth outcomes Am J Public Health. 1990;80:814-8.

4. American Academy of Pediatrics and American College of Obstetricians and Gynecologists. Guidelines for Perinatal Care. 4th ed. Washington, DC: ACOG, 1997.

5. New York State Department of Health, Office of Rural Health. Report on the provision of birthing services in rural health networks. Albany, NY; 1994.

6. Nesbitt TS. Rural maternity care: new models of access. Birth. 1996;23:161-5.

7. Rosenthal TC, Holden DM, Woodward W. Primary care obstetrics in rural Western New York: a multi-center case review. NY State J Med 1990;90:537-40.

8. Iglesias S, Grzybowski S, Klein M, Gagne GP, Lalonde A. Joint position paper on rural maternity care. Society of Rural Physicians, Society of Obstetricians and Gynecologists of Canada, College of Family Physicians of Canada, 1998.

9. Grzybowski SCW, Cadesky AS, Hogg WE. Rural obstetrics: a 5-year prospective study of the outcomes of all pregnancies in a remote northern community. Can Med Assoc J 1991;144:987-94.

10. Black DB, Fyfe IM. The safety of obstetrics services in small communities in northern Ontario. Can Med Assoc J 1984;130:571-6.

11. Woollard LA, Hays RB. Rural obstetrics in NSW. Aust N Z J Obstet Gynaecol 1993;33:240-2.

12. McIlwain R, Smith S. Obstetrics in a small isolated community: the cesarean section dilemma. Can J Rural Med 2000;5:221-3.

13. Ventura SJ, Martin JA, Curtin SC, Mathews TJ. Report of Final Natality Statistics, 1996. Monthly vital statistics report; Vol 46 no 11, suppl. Hyattsville, Md: National Center for Health Statistics, 1998.

14. Hoyert DL. Perinatal mortality in the United States, 1985-91. National Center for Health Statistics. Vital Health Stat 20(26), 1995.

15. Rooks JP, Weatherby NL, Ernst EKM, Stapleton S, Rosen D, Rosenfield A. Outcomes of care in birth centers: the National Birth Center Study. N Engl J Med 1989;321:1804-11.

16. International guidelines for neonatal resuscitation: An excerpt from the guidelines 2000 for cardiopulmonary resuscitation and emergency cardiovascular care. Pediatrics 2000;106(3):Available from: http://www.pediatrics.org/cgi/content/full/106/3/e29.

17. American College of Obstetricians and Gynecologists. Hypertension in pregnancy. ACOG Technical Bulletin 219. Washington, DC: ACOG, 1996.

18. National Diabetes Data Group. Classification and diagnosis of diabetes mellitus and other categories of glucose intolerance. Diabetes 1979;28:1039-57.

19. American College of Obstetricians and Gynecologists. Induction of labor. ACOG Practice Bulletin 10. Washington, DC: ACOG; 1999.

20. Torr E, ed, for the British Columbia Reproductive Care Program. Report on the findings of the Consensus Conference on Obstetric Services in Rural or Remote Communities. Can J Rural Med 2000;5:211-7.

21. Barrett J. Funic reduction for the management of umbilical cord prolapse. Am J Obstet Gynecol 1991;165:654-7.

22. Knab DR. Abruptio placentae: an assessment of the time and method of delivery. Obstet Gynecol 1978;52:625-9.

23. Chauhan SP, Roach H, Naef RW, Magann EF, Morrison JC, Martin JN. Cesarean section for suspected fetal distress: Does the decision-incision time make a difference? J Reprod Med 1997;42:347-52.

24. MacKenzie IZ, Cooke I. Prospective 12 month study of 30 minute decision to delivery intervals for “emergency” caesarean section. BMJ 2001;322:1334-5.

25. Schauberger CW, Rooney BL, Beguin EA, Schaper AM, Spindler J. Evaluating the thirty minute interval in emergency cesarean sections. J Am Coll Surg 1994;179:151-5.

26. Tuffnell DJ, Wilkinson K, Beresford N. Interval between decision and delivery by caesarean section: Are current standards achievable? BMJ 2001;322:1330-3.

References

1. Allen DT, Kamradt MS. Relationship of infant mortality to the availability of obstetric care in Indiana. J Fam Pract 1991;33:609-13.

2. Larimore WL, Davis A. Relation of infant mortality to the availability of maternity care in rural Florida. J Am Board Fam Pract 1995;8:392-9.

3. Nesbitt TS, Connell FA, Hart LG, Rosenblatt RA. Access to obstetric care in rural areas: effect on birth outcomes Am J Public Health. 1990;80:814-8.

4. American Academy of Pediatrics and American College of Obstetricians and Gynecologists. Guidelines for Perinatal Care. 4th ed. Washington, DC: ACOG, 1997.

5. New York State Department of Health, Office of Rural Health. Report on the provision of birthing services in rural health networks. Albany, NY; 1994.

6. Nesbitt TS. Rural maternity care: new models of access. Birth. 1996;23:161-5.

7. Rosenthal TC, Holden DM, Woodward W. Primary care obstetrics in rural Western New York: a multi-center case review. NY State J Med 1990;90:537-40.

8. Iglesias S, Grzybowski S, Klein M, Gagne GP, Lalonde A. Joint position paper on rural maternity care. Society of Rural Physicians, Society of Obstetricians and Gynecologists of Canada, College of Family Physicians of Canada, 1998.

9. Grzybowski SCW, Cadesky AS, Hogg WE. Rural obstetrics: a 5-year prospective study of the outcomes of all pregnancies in a remote northern community. Can Med Assoc J 1991;144:987-94.

10. Black DB, Fyfe IM. The safety of obstetrics services in small communities in northern Ontario. Can Med Assoc J 1984;130:571-6.

11. Woollard LA, Hays RB. Rural obstetrics in NSW. Aust N Z J Obstet Gynaecol 1993;33:240-2.

12. McIlwain R, Smith S. Obstetrics in a small isolated community: the cesarean section dilemma. Can J Rural Med 2000;5:221-3.

13. Ventura SJ, Martin JA, Curtin SC, Mathews TJ. Report of Final Natality Statistics, 1996. Monthly vital statistics report; Vol 46 no 11, suppl. Hyattsville, Md: National Center for Health Statistics, 1998.

14. Hoyert DL. Perinatal mortality in the United States, 1985-91. National Center for Health Statistics. Vital Health Stat 20(26), 1995.

15. Rooks JP, Weatherby NL, Ernst EKM, Stapleton S, Rosen D, Rosenfield A. Outcomes of care in birth centers: the National Birth Center Study. N Engl J Med 1989;321:1804-11.

16. International guidelines for neonatal resuscitation: An excerpt from the guidelines 2000 for cardiopulmonary resuscitation and emergency cardiovascular care. Pediatrics 2000;106(3):Available from: http://www.pediatrics.org/cgi/content/full/106/3/e29.

17. American College of Obstetricians and Gynecologists. Hypertension in pregnancy. ACOG Technical Bulletin 219. Washington, DC: ACOG, 1996.

18. National Diabetes Data Group. Classification and diagnosis of diabetes mellitus and other categories of glucose intolerance. Diabetes 1979;28:1039-57.

19. American College of Obstetricians and Gynecologists. Induction of labor. ACOG Practice Bulletin 10. Washington, DC: ACOG; 1999.

20. Torr E, ed, for the British Columbia Reproductive Care Program. Report on the findings of the Consensus Conference on Obstetric Services in Rural or Remote Communities. Can J Rural Med 2000;5:211-7.

21. Barrett J. Funic reduction for the management of umbilical cord prolapse. Am J Obstet Gynecol 1991;165:654-7.

22. Knab DR. Abruptio placentae: an assessment of the time and method of delivery. Obstet Gynecol 1978;52:625-9.

23. Chauhan SP, Roach H, Naef RW, Magann EF, Morrison JC, Martin JN. Cesarean section for suspected fetal distress: Does the decision-incision time make a difference? J Reprod Med 1997;42:347-52.

24. MacKenzie IZ, Cooke I. Prospective 12 month study of 30 minute decision to delivery intervals for “emergency” caesarean section. BMJ 2001;322:1334-5.

25. Schauberger CW, Rooney BL, Beguin EA, Schaper AM, Spindler J. Evaluating the thirty minute interval in emergency cesarean sections. J Am Coll Surg 1994;179:151-5.

26. Tuffnell DJ, Wilkinson K, Beresford N. Interval between decision and delivery by caesarean section: Are current standards achievable? BMJ 2001;322:1330-3.

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Treatment of Postherpetic Neuralgia

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Treatment of Postherpetic Neuralgia

ABSTRACT

OBJECTIVES: We wanted to determine whether any treatment had been shown to reduce pain or disability from postherpetic neuralgia (PHN), a common sequela of herpes zoster in elderly patients.

STUDY DESIGN: We undertook a systematic review of English-language randomized controlled trials (RCTs) of treatments of PHN with evaluation periods longer than 24 hours.

DATA SOURCES: We systematically searched MEDLINE, Current Contents, and the Cochrane Library. We also searched reference lists of identified trials and reviews and contacted content experts.

OUTCOMES MEASURED: Two reviewers independently evaluated RCTs for methodologic quality and data extraction. Outcomes of primary focus were pain and quality of life.

RESULTS: Twenty-seven RCTs met inclusion criteria and were reviewed. Six trials of tricyclic antidepressants found evidence for clinically meaningful effects over 6 weeks. All other treatments were evaluated in no more than 2 trials meeting our inclusion criteria. Topical capsaicin 0.075%, gabapentin, and controlled-release oxycodone were shown to be effective, but the clinically meaningful benefit is difficult to quantify. Intrathecal methylprednisolone and possibly bupivacaine sympathetic blocks are helpful in refractory cases. Other treatments evaluated, including topical lidocaine, had no evidence or inconsistent evidence of benefit.

CONCLUSIONS: No single best treatment for PHN is known. Tricyclic antidepressants, topical capsaicin, gabapentin, and oxycodone are effective for alleviating PHN; however, long-term, clinically meaningful benefits are uncertain and side effects are common. Patients with PHN refractory to these therapies may benefit from intrathecal methylprednisolone. Little evidence is available regarding treatment of PHN of less than 6 months’ duration.

KEY POINTS FOR CLINICIANS

  • Spontaneous resolution is common in patients whose postherpetic neuralgia (PHN) has lasted for less than 6 months; treatment decisions are largely empiric and not evidence based.
  • For PHN of longer duration, treatments shown to be more effective than placebo include tricyclic antidepressants, topical capsaicin 0.075%, gabapentin, and controlled-release oxycodone. Benefits should be weighed against adverse effects and costs.
  • Patients with PHN refractory to currently available and studied topical and oral agents should be considered for intrathecal steroid therapy.

Postherpetic neuralgia (PHN), the most common complication of herpes zoster, is much more prevalent among adults older than 50 years than in younger people.1,2 The largest English-language prospective study of patients presenting with zoster suggests that the average family physician can expect to see 4 cases of zoster per year and 1 case of PHN lasting more than 3 months every 3 years.3 Among placebo cohorts from randomized controlled trials (RCTs) of acute zoster treatment, the incidence of pain at 3 months has been reported as 17% to 60%; at 6 months, 5% to 39%.4 There is limited evidence that therapies for acute zoster have an impact on PHN.4

This review addresses therapies to reduce pain or improve quality of life in patients with PHN. The condition has been variously defined in terms of timing (either following resolution of acute zoster [rash healing] or a defined time after onset of zoster), duration (any time after zoster or a minimum of 6 months after zoster), and type of pain (such as lancinating pain or allodynia [pain caused by a stimulus that does not nor mally provoke pain]).5,6 PHN may include a spectrum of presentations, from brief intermittent mild pain that resolves spontaneously to chronic persistent disabling pain recalcitrant to multiple therapies. To avoid missing potentially relevant findings, we defined PHN broadly as any pain after cutaneous healing of zoster.

Methods

Search strategy

Medline (1966 to present) was searched on October 19, 2000. The search combined the terms “post-herpetic or postherpetic” and “neuralgia or neuropathy or pain” and publication type “clinical trial (including phases I to IV) or controlled clinical trial or randomized controlled trial.” The Cochrane Controlled Trials Registry 2000, Issue 3, was searched with the same terms. Current Contents was searched to identify more recent references. We also identified trials through article reference lists (from included trials and 40 reviews), contact of authors and content experts, and the Food and Drug Administration (FDA) Web site.

Selection criteria

Inclusion criteria for this review were RCTs that enrolled patients with PHN (history of zoster, pain in the dermatomal distribution of the zoster rash, and pain persisting or occurring after resolution of the zoster rash), addressed relevant end points (pain resolution, pain severity, effect on quality of life), and had full reports available in English. Since responses to initial therapy may change over time, we included only trials with evaluation periods lasting more than 24 hours.

The authors independently evaluated trials meeting these inclusion criteria for quality and independently extracted data. Quality was evaluated using the Jadad scale,7 which addresses selected criteria (randomization technique, allocation concealment, blinding, accounting of dropouts) and rates methodologic quality on a 5-point scale, with 5 representing the highest score. Differences were resolved through discussion. Trials scoring only 1 point were excluded except for 2 instances noted in our discussion.

 

 

Results

Searching identified 186 potential trials, of which 92 were excluded as irrelevant on the basis of titles and abstracts alone. Of the 94 citations reviewed in greater detail, 64 were excluded for the following reasons: not describing a trial (10), not describing a trial of treatment for PHN (10), describing an uncontrolled trial (7), no randomization (7), evaluation period limited to 24 hours or less (13), duplicate publication (4), language other than English (7), not providing results specifically for patients with PHN (3), and available only in abstract form (3). One unpublished trial of mexiletine was identified through a review by Hanania and Brietstein; Dr Hanania informed us, however, that this trial had been stopped early because the treatment group had experienced serious side effects. One controlled trial was excluded because correspondence with the investigator did not confirm that it had been randomized. One trial was excluded because it included only 6 patients with PHN. (A list of excluded studies is available in Table W1.)

Of the remaining 27 trials reviewed for methodologic quality, most (16) received a Jadad score of 4. The 2 authors had substantial agreement on quality ratings ( = 0.75). Table W2 provides details of treatment regimens, quality ratings, ages of subjects, and duration of PHN. One trial with a Jadad score of 1 was excluded.8 Most subjects were elderly and had had PHN for longer than 6 months.

Topical therapies

Topical therapies evaluated were lidocaine, capsaicin, and benzydamine (Table 1). Lidocaine patch therapy is the only agent with a specific FDA indication for PHN. We found few trials supporting the FDA approval. The only published RCT of relatively unselected patients with PHN (n = 35) showed significant benefit versus placebo but was excluded because evaluation sessions had been limited to 12 hours.9 We reviewed a report of an unpublished RCT comparing lidocaine patch with vehicle placebo used in the application for FDA approval.10 This trial found a large, statistically significant reduction in pain scores with placebo throughout the 3- to 4-week trial. This trial found a similar statistically significant reduction in pain scores with lidocaine patch and no significant difference comparing lidocaine patch with placebo.

Three findings in the unpublished trial were used to support arguments for efficacy: (1) a statistically significant difference in the pain relief score at the final visit; (2) differences in allodynia (based on investigators’ sensory skin testing, described as stroking the maximally painful area with a foam brush and recording the pain scale rating) at the beginning of the trial; and (3) a greater increase in pain scores among lidocaine subjects upon trial conclusion (ie, after stopping study medication). The clinical relevance of these 3 findings is unclear.

The FDA declined to approve lidocaine patch therapy on the basis of these 2 studies and required an additional trial to demonstrate benefit. An “enriched enrollment study” involved subjects who had used lidocaine patch for at least 1 month and received at least moderate relief but had pain without the patch.11 Lidocaine patch was clearly effective in this highly selected cohort.

Capsaicin 0.075% cream was effective in 2 trials of patients with severe refractory PHN.12,13 The benefit appeared modest in the larger trial (pain was eliminated or nearly eliminated in less than 20% of capsaicin patients) and greater in the smaller trial. Blinding had limited efficacy because of the stinging effect of capsaicin.

Benzydamine cream, an antiprostaglandin, was not effective in a 2-week crossover trial.14 The cream showed a nonsignificant trend for pain reduction in an earlier 2-week crossover trial.15

TABLE 1

RESULTS
Treatment vs ControlTreatment DurationEfficacy ResultsAdverse Effects
TOPICALTHERAPIES
Lidocaine patch vs placebo103-4 weeksNo significant difference between groups in pain VAS improvement.Not reported clearly, but no significant differences.
Lidocaine > placebo in 0-5 pain relief scale (2.6 vs 2.1, P = .023).1% vs 0 dropouts (because of s kin irritation).
Lidocaine patch vs placebo112-14 daysLidocaine > placebo in median time to withdrawal because of pain (> 14 days vs 3.8 days, P >.001).28% vs 34% (all skin reactions).
More preferred lidocaine (78% vs 10%, P < .001).0 vs 6% dropouts.
Capsaicin 0.075% cream vs placebo126 weeksCapsaicin > placebo on pain relief VAS (20.9% vs 5.8%).61% vs 33% skin reactions (P < .05, NNH = 3).
Capsaicin > placebo in improvement in functional capacity (NS).24% vs 3% dropouts (NNH = 4) (mostly skin reactions).
Capsaicin 0.075% cream vs placebo136 weeksCapsaicin > placebo in mean change in pain VAS (30% decrease vs 1% increase, P < .05). Capsaicin > placebo for 40% or greater pain relief (54% vs 6%, P < .02, NNT 2).31% vs 13% skin reactions (NNH = 5).
No dropouts (but 3 lost to follow-up).
Benzydamine cream vs placebo142 weeksNo differences between groups in pain measures or sleep scores.17% vs 4% skin reactions (NNH = 7).
Patients favored vehicle more often than benzydamine.4% vs 0 dropouts (NNH = 25) (rash).
Benzydamine cream vs placebo152 weeksBenzydamine > placebo for proportion reporting pain reduction (52% vs 38%, NS).Not reported.
4% vs 2% dropouts (NNH = 50) (skin irritation).
ORALTHERAPIES
Amitriptyline vs lorazepam vs placebo166 weeksAmitriptyline > lorazepam or placebo for pain relief "complete" or "a lot" (39% vs 8% vs 8%, P < .001, NNT = 3). Lorazepam >placebo for 1-2 weeks but effect not maintained.88% vs 98% vs 72% (NNH = 6 for amitriptyline).
Amitriptyline adverse effect rate decreased to 62% in final 2 weeks.
5 vs 6 vs 3 dropouts.
Amitriptyline vs placebo173 weeksAmitriptyline > placebo for good or excellent results (67% vs 4%, P < .001, NNT = 2). Amitriptyline > placebo in sleep score improvement (P <.001).67% vs 54% (NNH = 8) (dry mouth, drowsiness, constipation). 4% vs 21% dropouts (appears related to amitriptyline withdrawal symptoms).
Amitriptyline vs Fluphenazine vs combination vs control188 weeksMean decrease in VAS score 29.3 for amitriptyline (P < .001), 12.2 for combination (P = .04), 11.5 for fluphenazine (P = .08), and 5.39 for control (NS).Incidence not reported. Dry mouth more common with amitriptyline. Sleepiness more common with fluphenazine.
One amitriptyline withdrawal because of sedation.
Amitriptyline vs nortriptyline195 weeksBoth drugs effective.97% vs 97% (dry mouth, constipation, dizziness).
No significant differences in efficacy (including sleep and disability measures).30% vs 15% intolerable side effects (P = .05, NNH = 7).
Amitriptyline vs maprotiline205 weeksAmitriptyline > maprotiline in VAS scales (P < .01). No significant differences in categorical scale of pain relief, sleep or disability ratings.63% vs 88% (dry mouth, constipation, sedation, dizziness).
34% vs 47% dropouts.
Desipramine vs benztropine216 weeksDesipramine > benztropine for pain relief "complete" or "a lot" (42% vs 5%, NNT = 3).100% vs 79% (NNH = 4) (dry mouth, dizziness, constipation). 89% vs 42% side effects in final 2 weeks. 5 vs 3 dropouts.
Desipramine > benztropine for pain relief rated moderate or better (63% vs 11%, NNT = 2).
Gabapentin vs placebo228 weeksAll measures, including quality of life and sleep, favored gabapentin. Gabapentin > placebo for pain much or moderately improved (43.2% vs 12.1%, NNT = 3.2). Gabapentin > placebo for "no pain" at final week (16% vs 8.8%, NNT = 13.9).55% vs 28% (NNH = 3) (somnolence, dizziness, ataxia). 19% vs 12% (NNH = 15) or 13% vs 10% (NNH = 26) dropouts (reporting inconsistent).
Oxycodone controlled-release vs placebo234 weeksOxycodone > placebo in 1-5 pain relief scale (2.9 vs 1.9) and lower disability scores.76% vs 49% (NNH = 3) (constipation, nausea, sedation). 5 vs 3 dropouts.
No significant differences between groups in pain intensity. More preferred oxycodone (67% vs 11%, P = .001, NNT = 2).
Tramadol vs clomipramine with or without levomepromazine246 weeksNo significant differences between groups in pain intensity. Tramadol > control for pain relief satisfactory or better (90% vs 55%).77% vs 83% (dry mouth and constipation with tramadol). 41% vs 39% dropouts.
Tramadol > control for pain relief good or excellent (60% vs 45%).
Dextromethorphan vs placebo256 weeksNo significant differences between groups.100% vs 3% (NNH = 1) (sedation, dizziness, lightheadedness). 22% vs 0 dropouts (NNH = 4).
Memantine vs placebo265 weeks (7 weeks)No significant differences between groups.83% vs 67% (NS) (dizziness, headache, nausea).
25% vs 8% dropouts (NNH = 6).
Acyclovir vs placebo2712 weeks (6 months)Acyclovir associated with higher pain rating than placebo at some time points. No difference in proportion with clinical improvement (40% vs 40%).Not reported.
OTHER THERAPIES
Vincristine iontophoresis vs saline iontophoresis284 weeks (90 days)Vincristine < saline for pain relief at 4 weeks (36% vs 56%, NS).Not explicitly reported.
Vincristine < saline for pain relief at 90 days (27% vs 33%, NS)."Most patients complained of a burning sensation at the negative electrode."
Vincristine in DMSO iontophoresis vs saline iontophoresis294 weeks (6 weeks)Vincristine > saline for "improved" at 4 weeks (90% vs 10%, NNT = 1) and 6 weeks (60% vs 0, NNT = 2). Most treated patients had "improvement" but none were "cured.""Most patients were prescribed a mild steroid cream to reduce irritation." "Several burns were seen" but "they were painless."One vincristine death in patient with heart disease.
Acupuncture vs mock TENS316 weeks (14 weeks)7 patients in each group were "better" after treatment. No significant differences between treatments.Not reported.
"Auricular acupuncture is a painful and unpleasant experience." 43% vs 9% dropouts.
Acupuncture vs TENS326 weeks (6 months)Acupuncture > TENS for pain improvement during treatment (50% vs 7.7%).All but 1 acupuncture patient dropped out after treatment because of inadequate pain relief.
Intrathecal methylprednisolone plus lidocaine vs intrathecal lidocaine alone vs no treatment334 weeks (2 years)92% vs 7% vs 3% had pain relief > 50% at 2 years (P < .001, NNT = 1).Not reported formally, but "no clinical complications were observed."
Similar results in analgesic use.
Intrathecal methylprednisolone vs epidural methylprednisolone344 weeks (24 weeks)Intrathecal > epidural for global pain relief > 50% at 4 weeks (92% vs 25%, P < .01, NNT = 2) and 24 weeks (92% vs 17%, P < .01, NNT = 2).Not reported formally, but "no clinical complications were observed."
Mixture of gangliosides (Cronassial) vs placebo SQ358 weeksMean pain scores and mean sleep scores improved with Cronassial but not placebo.50% vs 0 (NNH = 2) (injection site pain).
25% vs 0 dropouts (NNH = 4).
Bupivacaine sympathetic blocks vs lidocaine IV362-3 weeks (1 year)Bupivacaine had lower pain VAS than lidocaine at 3 months (24 vs 57, P < .0001) and 1 year (16 vs 44, P < .003). Similar results in global score, including pain, sleep, analgesic use, and incapacity."No complications," but side effects not reported.
*Follow-up duration listed in parentheses if separate from treatment duration.
DMSO denotes dimethylsulfoxide; IV, intravenous; NNH, number needed to harm; NNT, number needed to treat; NS, not significant, used for P values > .1; SQ, subcuta neous; TENS, transcutaneous electrical nerve stimulation; VAS, visual analog scale reported as 100-mm scale.
 

 

Oral therapies

Oral therapies evaluated were tricyclic antidepressants, gabapentin, oxycodone, tramadol, dextromethorphan, memantine, acyclovir, lorazepam, and fluphenazine (Table 1).

Tricyclic antidepressants have been shown to be effective in multiple small short-term crossover trials. Amitriptyline was highly effective in 2 placebo-controlled trials.16,17 In 1 of these trials, amitriptyline was more effective than lorazepam.16 In another trial, amitriptyline was more effective than fluphenazine (a phenothiazine) and glycopyrrolate placebo.18 Nortriptyline was as effective as amitriptyline in a comparison trial,19 while maprotiline was not.20 Desipramine was highly effective in a trial using benztropine as an “active placebo” in that the anticholinergic properties of benztropine were used to match the side effects of desipramine.21 In all these studies, the analgesic effects of tricyclic antidepressants appeared independent of antidepressant effects. No randomized trial data were collected to assess the use of antidepressants for longer than 8 weeks.

Gabapentin, an anticonvulsant, was effective in a single large placebo-controlled trial.22 The number needed to treat (NNT) was 3.2 for the outcome of moderate or better pain relief and 13.9 for the outcome of no pain during the eighth week of treatment. The proportion of patients whose pain was much improved or who had no pain was not reported.

Controlled-release oxycodone was effective in a crossover trial in which 45% of patients had previously used opioids.23 Tramadol may be effective but was not compared against placebo.24 High-dose dextromethorphan, an N-methyl-D-aspartate (NMDA) receptor antagonist, was not shown to be effective for PHN in a small crossover trial.25 Memantine, another NMDA antagonist, was also ineffective.26 Acyclovir did not show any greater efficacy than placebo in a small trial.27 Lorazepam and fluphenazine did not show statistically significant benefit in comparison with placebo in the amitriptyline trials.16,18

Other therapies

Other therapies evaluated were vincristine iontophoresis, acupuncture, intrathecal methylprednisolone, and subcutaneous administration of a mixture of gangliosides (Table 1).

Iontophoresis is a process whereby topical medications are applied via electricity. Vincristine iontophoresis was no more effective than saline iontophoresis in one small trial.28 Vincristine and dimethylsulfoxide iontophoresis was effective at reducing but not eliminating pain in another small trial.29 Dimethylsulfoxide may have an independent analgesic effect.30

Acupuncture was no more effective than mock transcutaneous electrical nerve stimulation (TENS) in 1 trial,31 while a smaller trial suggested a short-term effect.32

Intrathecal methylprednisolone acetate plus lidocaine was highly effective for achieving good or excellent results (pain relief > 50%) in patients with longstanding PHN refractory to multiple conventional therapies.33 All patients whose response to methylprednisolone was poor (8%) had had PHN for more than 5 years. The intrathecal route appears more effective than the epidural route of administration.34

A mixture of gangliosides given by subcutaneous injection was more effective than placebo, but poor tolerability and derivation from bovine brain tissue severely limit its acceptability.35

Sympathetic blocks using bupivacaine were more effective than intravenous lidocaine infusions in 1 trial,36 but results were not reported in a fashion that conveys the proportion of patients who improved significantly.

Discussion

Effective therapies

The therapy for which evidence for efficacy is best is tricyclic antidepressants. Three placebo-controlled RCTs demonstrated that only 2 to 3 patients with PHN need to be treated to achieve 1 good or excellent result (NNT = 2-3). Since none of these studies lasted longer than 8 weeks, the long-term efficacy of antidepressants for the treatment of PHN is unknown. Follow-up of 10 patients who did well in 1 antidepressant trial found that only 2 patients were still doing well at 2 years.19

Other therapies shown to be effective in 1 or 2 trials are topical capsaicin 0.075%, gabapentin, and controlled-release oxycodone. For these studies, it is difficult to determine the number needed to treat for “meaningful” clinical benefit, although gabapentin demonstrated superiority to placebo in numerous quality-of-life measures.

For patients with severe PHN refractory to other treatments, 2 trials support benefit from intrathecal methylprednisolone and 1 trial suggests benefit from bupivacaine sympathetic blocks. Cost data for selected therapies are presented in Table 2.

TABLE 2
COSTS OF SELECTED DRUG THERAPIES

MedicationTypical DosingAmount for 30 daysAWP* (Generic)Local Pharmacy Charge† (Generic)
Amitriptyline (Elavil)75 mg nightly17Ninety 25-mg tablets$42.35 ($9.86 to $31.95)$35.72 ($11.62)
Thirty 75-mg tablets$34.38 ($7.41 to $26.00)$42.78 ($10.98)
Nortriptyline (Pamelor)75 mg nightly19Ninety 25-mg tablets$125.99 ($30.86)$133.46 ($26.78)
Thirty 75-mg tablets$120.64 ($64.94)$128.72 ($18.84)
Capsaicin 0.075% (Zostrix)Apply 4 times dailyTwo 60-g tubes$32.40 ($23.98)$27.46 ($11.94)
Gabapentin (Neurontin)1,200 mg 3 times daily#180 600-mg tablets$381.83$369.62
Oxycodone (OxyContin)20 mg every 12 hoursSixty 20-mg tablets$142.67$150.78
Lidocaine patch (Lidoderm)Up to three 700-mg patches for up to 12 hours per dayNinety 700-mg patches$394.14$388.72
*AWP denotes average wholesale price, AmeriSource ECHO Retail Price Program, version 3.1q, (c) 1991-2000, March 5, 2001.
† Includes pharmacy dispensing fee from local (Columbia, Mo.) branch of national pharmacy chain for 30 days, March 5, 2001.
 

 

Therapies not proved effective

Therapies of uncertain benefit that have not been adequately studied in randomized controlled trials include lidocaine patch, benzydamine cream, tramadol, and vincristine (and/or dimethylsulfoxide) iontophoresis.

Therapies unlikely to be beneficial based on single trials include lorazepam, fluphenazine, dextromethorphan, memantine, acyclovir, and acupuncture. Most of the negative trials did not report power; therefore, potential benefits of these treatments cannot be excluded.

Safety and tolerability

The rates of adverse effects are high in all effective oral and topical therapies (Table 1). This situation is of special concern in elderly patients who have comorbid conditions and are taking multiple medications. Two tricyclic antidepressant trials16,21 report a decreased incidence of side effects over time. The researchers emphasize the importance of starting at the lowest available dose with oral therapies and titrating slowly as indicated and tolerated.

No clinical complications were observed in the intrathecal steroid trials; specific side effects were not reported.

Lidocaine patch therapy has been promoted as causing clinically insignificant serum levels, no systemic side effects, and no drug–drug interactions.11 However, the largest lidocaine patch RCT was too small to rule out significant but uncommon risks such as ventricular arrhythmia.10 One death that could potentially be attributed to lidocaine absorption occurred in a patient with diffuse vascular disease who was on chronic hemodialysis for renal failure. Blood lidocaine levels were not obtained because venous access was poor.

Limitations

Variations in outcomes limit comparative conclusions. Sindrup and Jensen37 reviewed treatments for neuropathic pain and presented data based on a successful outcome defined as 50% reduction in pain scores, 50% pain relief, or categorical ratings of excellent, good, or moderate pain relief. Most studies used visual analog scales, but it is not clear that a 50% reduction in these measures is equivalent to clinically meaningful benefits at all levels of pain. We attempted to determine NNT data based on clearly meaningful outcomes such as “excellent or good,” “complete,” or “marked” pain relief as distinct from “moderate” or “some,” but found these data unavailable in most reports. Quality-of-life measures, such as sleep and disability ratings, may be more important than measures of pain, but were reported in only one third of the trials.

We may have failed to include relevant trials. We identified 7 potentially relevant non–English language studies. Five had no abstracts available.38-42 One single-blind trial reported reduced pain scores with topical prostaglandin E1 dissolved in Vaseline.43 One trial compared iontophoresis with lidocaine and iontophoresis with 3 different calcium channel blockers in 10 patients. The authors found that all 4 treatments reduced pain, but had not included a placebo control group.44 We also contacted 15 content experts to identify reports of unpublished trials; none of the 6 responses received identified such reports.

The evidence base for treatment of PHN is limited. Among the RCTs reviewed, 78% enrolled 50 or fewer patients. Because most of the subjects had PHN lasting longer than 1 year, our conclusions may not apply to patients with PHN of shorter duration. The latter group represents the majority of subjects with PHN presenting to primary care physicians.

We used the Jadad scale7 as an attempt to quantitatively assess the methodologic quality of the studies we reviewed. In general, explicit validity checklists with summary scores have not consistently been shown to provide more reliable assessments of validity than qualitative assessments.45-4 The Jadad scale is the first validity checklist that has some rigorous evidence supporting its use,7,48 although its inter-rater reliability has been questioned.49 We found the Jadad scale had an inherent bias against therapies that could not be adequately double-blinded. Thus 2 trials with a score of 1 were included.29,36 In 1 trial of vincristine iontophoresis, the authors described the trial as single-blinded and provided ample explanation of why double-blinding was not achieved.29 In 1 trial of sympathetic blocks, the treatment studied included an invasive procedure; therefore, there was no apparent way for the procedure itself to be double-blinded.36

The Jadad scale does not account for some threats to validity of included studies. We encountered numerous methodologic flaws, such as lack of intention to treat analysis in parallel trials (11) and lack of washout periods in crossover trials (4). Further limitations to interpretation of selected study results included potentially significant baseline differences between groups (8), small numbers, and short study durations. A list of the studies with these specific methodologic concerns is available in Table W3.

Conclusions

For patients with PHN lasting less than 6 months, spontaneous resolution is common and treatment decisions are largely empiric and not evidence based. For PHN of longer duration, treatments shown to be more effective than placebo include tricyclic antidepressants, topical capsaicin 0.075%, gabapentin, and controlled-release oxycodone. These treatments all have adverse effects or costs that need to be considered on an individual basis. Lidocaine patch therapy may be safer for most patients but may be no more effective than placebo and is not suitable for patients with trigeminal PHN. Patients with PHN refractory to the currently available and studied topical and oral agents should be considered for intrathecal steroid therapy.

 

 

Acknowledgments

The authors wish to thank Susan Meadows, MLS, Susan Elliott, MLS, Stacey Rautzhan, and Steve Calloway, RPh, for their assistance and Sigurdur Helgason, MD, Carin Reust, MD, Steven Zweig, MD, MSPH, and Alan Adelman, MD, MS, for editorial review.

References

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10. Lidoderm (Lidocaine) Patch. Center for Drug Evaluation and Research application number: NDA 20-612. Medical reviews. Washington, DC: US Food and Drug Administration, Center for Drug Evaluation and Research. Last updated November 30, 1999. Accessed October 31, 2000, at: http://www.fda.gov/cder/foi/nda/99/20612.htm/.

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12. Watson CP, Tyler KL, Bickers DR, Millikan LE, Smith S, Coleman E. A randomized vehicle-controlled trial of topical capsaicin in the treatment of postherpetic neuralgia. Clin Ther 1993;15:510-26.

13. Bernstein JE, Korman NJ, Bickers DR, Dahl MV, Millikan LE. Topical capsaicin treatment of chronic postherpetic neuralgia. J Am Acad Dermatol 1989;21(2 pt 1):265-70.

14. McQuay HJ, Carroll D, Moxon A, Glynn CJ, Moore RA. Benzydamine cream for the treatment of post-herpetic neuralgia: minimum duration of treatment periods in a cross-over trial. Pain 1990;40:131-5.

15. Coniam SW, Huntan J. A study of benzydamine cream in post-herpetic neuralgia. Res Clin Forums 1988;10:65-8.

16. Max MB, Schafer SC, Culnane M, Smoller B, Dubner R, Gracely RH. Amitriptyline, but not lorazepam, relieves postherpetic neuralgia. Neurology 1988;38:1427-32.

17. Watson CP, Evans RJ, Reed K, Merskey H, Goldsmith L, Warsh J. Amitriptyline versus placebo in postherpetic neuralgia. Neurology 1982;32:671-3.

18. Graff-Radford SB, Shaw LR, Naliboff BN. Amitriptyline and fluphenazine in the treatment of postherpetic neuralgia. Clin J Pain 2000;16:188-92.

19. Watson CP, Vernich L, Chipman M, Reed K. Nortriptyline versus amitriptyline in postherpetic neuralgia: a randomized trial. Neurology 1998;51:1166-71.

20. Watson CP, Chipman M, Reed K, Evans RJ, Birkett N. Amitriptyline versus maprotiline in postherpetic neuralgia: a randomized, double-blind, crossover trial. Pain 1992;48:29-36.

21. Kishore-Kumar R, Max MB, Schafer SC, et al. Desipramine relieves postherpetic neuralgia. Clin Pharmacol Ther 1990;47:305-12.

22. Rowbotham M, Harden N, Stacey B, Bernstein P, Magnus-Miller L. Gabapentin for the treatment of postherpetic neuralgia: a randomized controlled trial. JAMA 1998;280:1837-42.

23. Watson CP, Babul N. Efficacy of oxycodone in neuropathic pain: a randomized trial in postherpetic neuralgia. Neurology 1998;50:1837-41.

24. Gobel H, Stadler T. Treatment of pain due to postherpetic neuralgia with tramadol-results of an open, parallel pilot study vs clomipramine with or without levomepromazine. Clin Drug Invest 1995;10:208-14.

25. Nelson KA, Park KM, Robinovitz E, Tsigos C, Max MB. High-dose oral dextromethorphan versus placebo in painful diabetic neuropathy and postherpetic neuralgia. Neurology 1997;48:1212-8.

26. Eisenberg E, Kleiser A, Dortort A, Haim T, Yarnitsky D. The NMDA (N-methyl-D-aspartate) receptor antagonist memantine in the treatment of postherpetic neuralgia: a double-blind, placebo-controlled study. Eur J Pain 1998;2:321-27.

27. Surman OS, Flynn T, Schooley RT, et al. A double-blind, placebo-controlled study of oral acyclovir in postherpetic neuralgia. Psychosomatics 1990;31:287-92.

28. Dowd NP, Day F, Timon D, Cunningham AJ, Brown L. Iontophoretic vincristine in the treatment of postherpetic neuralgia: a double-blind, randomized, controlled trial. J Pain Symptom Manage 1999;17:175-80.

29. Layman PR, Argyras E, Glynn CJ. Iontophoresis of vincristine versus saline in post-herpetic neuralgia. A controlled trial. Pain 1986;25:165-70.

30. Zuurmond WW, Langendijk PN, Bezemer PD, Brink HE, de Lange JJ, van loenen AC. Treatment of acute reflex sympathetic dystrophy with DMSO 50% in a fatty cream. Acta Anaesthesiol Scand 1996;40:364-7.

31. Lewith GT, Field J, Machin D. Acupuncture compared with placebo in post-herpetic pain. Pain 1983;17:361-8.

32. Rutgers MJ, Van Romunde LKJ, Osman PO. A small randomized comparative trial of acupuncture versus transcutaneous electrical neurostimulation in postherpetic neuralgia. Pain Clin 1988;2:87-9.

33. Kotani N, Kushikata T, Hashimoto H, et al. Intrathecal methylprednisolone for intractable postherpetic neuralgia. N Engl J Med 2000;343:1514-9.

34. Kikuchi A, Kotani N, Sato T, Takamura K, Sakai I, Matsuki A. Comparative therapeutic evaluation of intrathecal versus epidural methylprednisolone for long-term analgesia in patients with intractable postherpetic neuralgia. Reg Anesth Pain Med 1999;24:287-93.

35. Staughton RC, Good J. Double-blind, placebo-controlled clinical trial of a mixture of gangliosides (“Cronassial”) in post-herpetic neuralgia. Curr Med Res Opin 1990;12:169-76.

36. Catala E, Ferrandiz M, Aliaga L, Serra R, Castro MA, Villar LJM. Intravenous lidocaine compared with sympathetic blocks as treatment for post-herpetic neuralgia. A 1-year survey. Pain Clin 1994;7:205-10.

37. Sindrup SH, Jensen TS. Efficacy of pharmacological treatments of neuropathic pain: an update and effect related to mechanism of drug action. Pain 1999;83:389-400.

38. Dekonenko EP, Shishov AS, Kupriianova LV, Rudometov I, Bagrov FI. Postherpetic neuralgia in herpes zoster: its treatment with Zovirax. Zhurnal Nevrologii i Psikhiatrii Imeni S S Korsakova 1999;99(6):56-8.

39. Sigwald J, Bouttier D, Caille F. The treatment of zona and of its associated pains. Study of the results obtained with levomepromazine. Therapie 1959;14:818-24.

40. Hirschmann J. Zoster-neuralgia. Dtsch Med Wochenschr 1971;96:924-5.

41. Mertens HG, Lutzenkirchen H. Neuropsychotropic drugs in the treatment of so-called pain syndromes. Arzneimittelforschung 1970;20:928-30.

42. Lutzenkirchen H, Mertens HG. Treatment of chronic pain syndromes. Analgesic effect of a neuroleptic. Arzneimittelforschung 1970;20:930-1.

43. Tamakawa S, Tsujimoto J, Iharada A, Ogawa H. Treatment of postherpetic neuralgia by topical application of prostaglandin E1-vaseline mixture-a single blind controlled clinical trial. Masui 1999;48:292-4.

44. Ikebe H, Miyagawa A, Mizutani A, Miyamoto M, Taniguchi K, Honda N. The effect of iontophoresis with several Ca channel blockers for PHN patients. Masui 1995;44:428-33.

45. Emerson JD, Burdick E, Hoaglin DC, Mosteller F, Chalmers TC. An empirical study of the possible relation of treatment differences to quality scores in controlled randomized clinical trials. Control Clin Trials 1990;11:339-52.

46. “Quality” scales and checklists. Cochrane Collaboration Handbook [updated September 1997]; Section 6.7.2. In: Mulrow CD, Oxman AD, eds. Cochrane Library [database on disk and CD-ROM]. The Cochrane Collaboration. Oxford, UK: Update Software; 1997.

47. Juni P, Altman DG, Egger M. Systematic reviews in health care: assessing the quality of controlled clinical trials. BMJ 2001;323:42-6.

48. Moher D, Pham B, Jones A, et al. Does quality of reports of randomised trials affect estimates of intervention efficacy reported in meta-analyses? Lancet 1998;352:609-13.

49. Clark HD, Wells GA, Huet C, et al. Assessing the quality of randomized trials: reliability of the Jadad scale. Control Clin Trials 1999;20:448-52.

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Author and Disclosure Information

BRIAN S. ALPER, MD, MSPH
PETER R. LEWIS, MD
Columbia, Missouri, and Hershey, Pennsylvania
Submitted, revised, September 10, 2001.
From the Department of Family and Community Medicine, University of Missouri–Columbia School of Medicine (B.S.A.), and the Department of Family and Community Medicine, Penn State College of Medicine, The Milton S. Hershey Medical Center, Hershey, Pennsylvania (P.R.L.). The authors report no competing interest. This study was supported in part through a training grant from the Bureau of Health Professions Awards (DHHS 1-D14-HP-00029-01) from the Health Resources and Services Administration to the Department of Family and Community Medicine, University of Missouri–Columbia. Requests for reprints should be addressed to Brian S. Alper, MD, MSPH, Department of Family and Community Medicine, University of Missouri–Columbia School of Medicine, Columbia, MO 65212. E-mail: [email protected].

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BRIAN S. ALPER, MD, MSPH
PETER R. LEWIS, MD
Columbia, Missouri, and Hershey, Pennsylvania
Submitted, revised, September 10, 2001.
From the Department of Family and Community Medicine, University of Missouri–Columbia School of Medicine (B.S.A.), and the Department of Family and Community Medicine, Penn State College of Medicine, The Milton S. Hershey Medical Center, Hershey, Pennsylvania (P.R.L.). The authors report no competing interest. This study was supported in part through a training grant from the Bureau of Health Professions Awards (DHHS 1-D14-HP-00029-01) from the Health Resources and Services Administration to the Department of Family and Community Medicine, University of Missouri–Columbia. Requests for reprints should be addressed to Brian S. Alper, MD, MSPH, Department of Family and Community Medicine, University of Missouri–Columbia School of Medicine, Columbia, MO 65212. E-mail: [email protected].

Author and Disclosure Information

BRIAN S. ALPER, MD, MSPH
PETER R. LEWIS, MD
Columbia, Missouri, and Hershey, Pennsylvania
Submitted, revised, September 10, 2001.
From the Department of Family and Community Medicine, University of Missouri–Columbia School of Medicine (B.S.A.), and the Department of Family and Community Medicine, Penn State College of Medicine, The Milton S. Hershey Medical Center, Hershey, Pennsylvania (P.R.L.). The authors report no competing interest. This study was supported in part through a training grant from the Bureau of Health Professions Awards (DHHS 1-D14-HP-00029-01) from the Health Resources and Services Administration to the Department of Family and Community Medicine, University of Missouri–Columbia. Requests for reprints should be addressed to Brian S. Alper, MD, MSPH, Department of Family and Community Medicine, University of Missouri–Columbia School of Medicine, Columbia, MO 65212. E-mail: [email protected].

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ABSTRACT

OBJECTIVES: We wanted to determine whether any treatment had been shown to reduce pain or disability from postherpetic neuralgia (PHN), a common sequela of herpes zoster in elderly patients.

STUDY DESIGN: We undertook a systematic review of English-language randomized controlled trials (RCTs) of treatments of PHN with evaluation periods longer than 24 hours.

DATA SOURCES: We systematically searched MEDLINE, Current Contents, and the Cochrane Library. We also searched reference lists of identified trials and reviews and contacted content experts.

OUTCOMES MEASURED: Two reviewers independently evaluated RCTs for methodologic quality and data extraction. Outcomes of primary focus were pain and quality of life.

RESULTS: Twenty-seven RCTs met inclusion criteria and were reviewed. Six trials of tricyclic antidepressants found evidence for clinically meaningful effects over 6 weeks. All other treatments were evaluated in no more than 2 trials meeting our inclusion criteria. Topical capsaicin 0.075%, gabapentin, and controlled-release oxycodone were shown to be effective, but the clinically meaningful benefit is difficult to quantify. Intrathecal methylprednisolone and possibly bupivacaine sympathetic blocks are helpful in refractory cases. Other treatments evaluated, including topical lidocaine, had no evidence or inconsistent evidence of benefit.

CONCLUSIONS: No single best treatment for PHN is known. Tricyclic antidepressants, topical capsaicin, gabapentin, and oxycodone are effective for alleviating PHN; however, long-term, clinically meaningful benefits are uncertain and side effects are common. Patients with PHN refractory to these therapies may benefit from intrathecal methylprednisolone. Little evidence is available regarding treatment of PHN of less than 6 months’ duration.

KEY POINTS FOR CLINICIANS

  • Spontaneous resolution is common in patients whose postherpetic neuralgia (PHN) has lasted for less than 6 months; treatment decisions are largely empiric and not evidence based.
  • For PHN of longer duration, treatments shown to be more effective than placebo include tricyclic antidepressants, topical capsaicin 0.075%, gabapentin, and controlled-release oxycodone. Benefits should be weighed against adverse effects and costs.
  • Patients with PHN refractory to currently available and studied topical and oral agents should be considered for intrathecal steroid therapy.

Postherpetic neuralgia (PHN), the most common complication of herpes zoster, is much more prevalent among adults older than 50 years than in younger people.1,2 The largest English-language prospective study of patients presenting with zoster suggests that the average family physician can expect to see 4 cases of zoster per year and 1 case of PHN lasting more than 3 months every 3 years.3 Among placebo cohorts from randomized controlled trials (RCTs) of acute zoster treatment, the incidence of pain at 3 months has been reported as 17% to 60%; at 6 months, 5% to 39%.4 There is limited evidence that therapies for acute zoster have an impact on PHN.4

This review addresses therapies to reduce pain or improve quality of life in patients with PHN. The condition has been variously defined in terms of timing (either following resolution of acute zoster [rash healing] or a defined time after onset of zoster), duration (any time after zoster or a minimum of 6 months after zoster), and type of pain (such as lancinating pain or allodynia [pain caused by a stimulus that does not nor mally provoke pain]).5,6 PHN may include a spectrum of presentations, from brief intermittent mild pain that resolves spontaneously to chronic persistent disabling pain recalcitrant to multiple therapies. To avoid missing potentially relevant findings, we defined PHN broadly as any pain after cutaneous healing of zoster.

Methods

Search strategy

Medline (1966 to present) was searched on October 19, 2000. The search combined the terms “post-herpetic or postherpetic” and “neuralgia or neuropathy or pain” and publication type “clinical trial (including phases I to IV) or controlled clinical trial or randomized controlled trial.” The Cochrane Controlled Trials Registry 2000, Issue 3, was searched with the same terms. Current Contents was searched to identify more recent references. We also identified trials through article reference lists (from included trials and 40 reviews), contact of authors and content experts, and the Food and Drug Administration (FDA) Web site.

Selection criteria

Inclusion criteria for this review were RCTs that enrolled patients with PHN (history of zoster, pain in the dermatomal distribution of the zoster rash, and pain persisting or occurring after resolution of the zoster rash), addressed relevant end points (pain resolution, pain severity, effect on quality of life), and had full reports available in English. Since responses to initial therapy may change over time, we included only trials with evaluation periods lasting more than 24 hours.

The authors independently evaluated trials meeting these inclusion criteria for quality and independently extracted data. Quality was evaluated using the Jadad scale,7 which addresses selected criteria (randomization technique, allocation concealment, blinding, accounting of dropouts) and rates methodologic quality on a 5-point scale, with 5 representing the highest score. Differences were resolved through discussion. Trials scoring only 1 point were excluded except for 2 instances noted in our discussion.

 

 

Results

Searching identified 186 potential trials, of which 92 were excluded as irrelevant on the basis of titles and abstracts alone. Of the 94 citations reviewed in greater detail, 64 were excluded for the following reasons: not describing a trial (10), not describing a trial of treatment for PHN (10), describing an uncontrolled trial (7), no randomization (7), evaluation period limited to 24 hours or less (13), duplicate publication (4), language other than English (7), not providing results specifically for patients with PHN (3), and available only in abstract form (3). One unpublished trial of mexiletine was identified through a review by Hanania and Brietstein; Dr Hanania informed us, however, that this trial had been stopped early because the treatment group had experienced serious side effects. One controlled trial was excluded because correspondence with the investigator did not confirm that it had been randomized. One trial was excluded because it included only 6 patients with PHN. (A list of excluded studies is available in Table W1.)

Of the remaining 27 trials reviewed for methodologic quality, most (16) received a Jadad score of 4. The 2 authors had substantial agreement on quality ratings ( = 0.75). Table W2 provides details of treatment regimens, quality ratings, ages of subjects, and duration of PHN. One trial with a Jadad score of 1 was excluded.8 Most subjects were elderly and had had PHN for longer than 6 months.

Topical therapies

Topical therapies evaluated were lidocaine, capsaicin, and benzydamine (Table 1). Lidocaine patch therapy is the only agent with a specific FDA indication for PHN. We found few trials supporting the FDA approval. The only published RCT of relatively unselected patients with PHN (n = 35) showed significant benefit versus placebo but was excluded because evaluation sessions had been limited to 12 hours.9 We reviewed a report of an unpublished RCT comparing lidocaine patch with vehicle placebo used in the application for FDA approval.10 This trial found a large, statistically significant reduction in pain scores with placebo throughout the 3- to 4-week trial. This trial found a similar statistically significant reduction in pain scores with lidocaine patch and no significant difference comparing lidocaine patch with placebo.

Three findings in the unpublished trial were used to support arguments for efficacy: (1) a statistically significant difference in the pain relief score at the final visit; (2) differences in allodynia (based on investigators’ sensory skin testing, described as stroking the maximally painful area with a foam brush and recording the pain scale rating) at the beginning of the trial; and (3) a greater increase in pain scores among lidocaine subjects upon trial conclusion (ie, after stopping study medication). The clinical relevance of these 3 findings is unclear.

The FDA declined to approve lidocaine patch therapy on the basis of these 2 studies and required an additional trial to demonstrate benefit. An “enriched enrollment study” involved subjects who had used lidocaine patch for at least 1 month and received at least moderate relief but had pain without the patch.11 Lidocaine patch was clearly effective in this highly selected cohort.

Capsaicin 0.075% cream was effective in 2 trials of patients with severe refractory PHN.12,13 The benefit appeared modest in the larger trial (pain was eliminated or nearly eliminated in less than 20% of capsaicin patients) and greater in the smaller trial. Blinding had limited efficacy because of the stinging effect of capsaicin.

Benzydamine cream, an antiprostaglandin, was not effective in a 2-week crossover trial.14 The cream showed a nonsignificant trend for pain reduction in an earlier 2-week crossover trial.15

TABLE 1

RESULTS
Treatment vs ControlTreatment DurationEfficacy ResultsAdverse Effects
TOPICALTHERAPIES
Lidocaine patch vs placebo103-4 weeksNo significant difference between groups in pain VAS improvement.Not reported clearly, but no significant differences.
Lidocaine > placebo in 0-5 pain relief scale (2.6 vs 2.1, P = .023).1% vs 0 dropouts (because of s kin irritation).
Lidocaine patch vs placebo112-14 daysLidocaine > placebo in median time to withdrawal because of pain (> 14 days vs 3.8 days, P >.001).28% vs 34% (all skin reactions).
More preferred lidocaine (78% vs 10%, P < .001).0 vs 6% dropouts.
Capsaicin 0.075% cream vs placebo126 weeksCapsaicin > placebo on pain relief VAS (20.9% vs 5.8%).61% vs 33% skin reactions (P < .05, NNH = 3).
Capsaicin > placebo in improvement in functional capacity (NS).24% vs 3% dropouts (NNH = 4) (mostly skin reactions).
Capsaicin 0.075% cream vs placebo136 weeksCapsaicin > placebo in mean change in pain VAS (30% decrease vs 1% increase, P < .05). Capsaicin > placebo for 40% or greater pain relief (54% vs 6%, P < .02, NNT 2).31% vs 13% skin reactions (NNH = 5).
No dropouts (but 3 lost to follow-up).
Benzydamine cream vs placebo142 weeksNo differences between groups in pain measures or sleep scores.17% vs 4% skin reactions (NNH = 7).
Patients favored vehicle more often than benzydamine.4% vs 0 dropouts (NNH = 25) (rash).
Benzydamine cream vs placebo152 weeksBenzydamine > placebo for proportion reporting pain reduction (52% vs 38%, NS).Not reported.
4% vs 2% dropouts (NNH = 50) (skin irritation).
ORALTHERAPIES
Amitriptyline vs lorazepam vs placebo166 weeksAmitriptyline > lorazepam or placebo for pain relief "complete" or "a lot" (39% vs 8% vs 8%, P < .001, NNT = 3). Lorazepam >placebo for 1-2 weeks but effect not maintained.88% vs 98% vs 72% (NNH = 6 for amitriptyline).
Amitriptyline adverse effect rate decreased to 62% in final 2 weeks.
5 vs 6 vs 3 dropouts.
Amitriptyline vs placebo173 weeksAmitriptyline > placebo for good or excellent results (67% vs 4%, P < .001, NNT = 2). Amitriptyline > placebo in sleep score improvement (P <.001).67% vs 54% (NNH = 8) (dry mouth, drowsiness, constipation). 4% vs 21% dropouts (appears related to amitriptyline withdrawal symptoms).
Amitriptyline vs Fluphenazine vs combination vs control188 weeksMean decrease in VAS score 29.3 for amitriptyline (P < .001), 12.2 for combination (P = .04), 11.5 for fluphenazine (P = .08), and 5.39 for control (NS).Incidence not reported. Dry mouth more common with amitriptyline. Sleepiness more common with fluphenazine.
One amitriptyline withdrawal because of sedation.
Amitriptyline vs nortriptyline195 weeksBoth drugs effective.97% vs 97% (dry mouth, constipation, dizziness).
No significant differences in efficacy (including sleep and disability measures).30% vs 15% intolerable side effects (P = .05, NNH = 7).
Amitriptyline vs maprotiline205 weeksAmitriptyline > maprotiline in VAS scales (P < .01). No significant differences in categorical scale of pain relief, sleep or disability ratings.63% vs 88% (dry mouth, constipation, sedation, dizziness).
34% vs 47% dropouts.
Desipramine vs benztropine216 weeksDesipramine > benztropine for pain relief "complete" or "a lot" (42% vs 5%, NNT = 3).100% vs 79% (NNH = 4) (dry mouth, dizziness, constipation). 89% vs 42% side effects in final 2 weeks. 5 vs 3 dropouts.
Desipramine > benztropine for pain relief rated moderate or better (63% vs 11%, NNT = 2).
Gabapentin vs placebo228 weeksAll measures, including quality of life and sleep, favored gabapentin. Gabapentin > placebo for pain much or moderately improved (43.2% vs 12.1%, NNT = 3.2). Gabapentin > placebo for "no pain" at final week (16% vs 8.8%, NNT = 13.9).55% vs 28% (NNH = 3) (somnolence, dizziness, ataxia). 19% vs 12% (NNH = 15) or 13% vs 10% (NNH = 26) dropouts (reporting inconsistent).
Oxycodone controlled-release vs placebo234 weeksOxycodone > placebo in 1-5 pain relief scale (2.9 vs 1.9) and lower disability scores.76% vs 49% (NNH = 3) (constipation, nausea, sedation). 5 vs 3 dropouts.
No significant differences between groups in pain intensity. More preferred oxycodone (67% vs 11%, P = .001, NNT = 2).
Tramadol vs clomipramine with or without levomepromazine246 weeksNo significant differences between groups in pain intensity. Tramadol > control for pain relief satisfactory or better (90% vs 55%).77% vs 83% (dry mouth and constipation with tramadol). 41% vs 39% dropouts.
Tramadol > control for pain relief good or excellent (60% vs 45%).
Dextromethorphan vs placebo256 weeksNo significant differences between groups.100% vs 3% (NNH = 1) (sedation, dizziness, lightheadedness). 22% vs 0 dropouts (NNH = 4).
Memantine vs placebo265 weeks (7 weeks)No significant differences between groups.83% vs 67% (NS) (dizziness, headache, nausea).
25% vs 8% dropouts (NNH = 6).
Acyclovir vs placebo2712 weeks (6 months)Acyclovir associated with higher pain rating than placebo at some time points. No difference in proportion with clinical improvement (40% vs 40%).Not reported.
OTHER THERAPIES
Vincristine iontophoresis vs saline iontophoresis284 weeks (90 days)Vincristine < saline for pain relief at 4 weeks (36% vs 56%, NS).Not explicitly reported.
Vincristine < saline for pain relief at 90 days (27% vs 33%, NS)."Most patients complained of a burning sensation at the negative electrode."
Vincristine in DMSO iontophoresis vs saline iontophoresis294 weeks (6 weeks)Vincristine > saline for "improved" at 4 weeks (90% vs 10%, NNT = 1) and 6 weeks (60% vs 0, NNT = 2). Most treated patients had "improvement" but none were "cured.""Most patients were prescribed a mild steroid cream to reduce irritation." "Several burns were seen" but "they were painless."One vincristine death in patient with heart disease.
Acupuncture vs mock TENS316 weeks (14 weeks)7 patients in each group were "better" after treatment. No significant differences between treatments.Not reported.
"Auricular acupuncture is a painful and unpleasant experience." 43% vs 9% dropouts.
Acupuncture vs TENS326 weeks (6 months)Acupuncture > TENS for pain improvement during treatment (50% vs 7.7%).All but 1 acupuncture patient dropped out after treatment because of inadequate pain relief.
Intrathecal methylprednisolone plus lidocaine vs intrathecal lidocaine alone vs no treatment334 weeks (2 years)92% vs 7% vs 3% had pain relief > 50% at 2 years (P < .001, NNT = 1).Not reported formally, but "no clinical complications were observed."
Similar results in analgesic use.
Intrathecal methylprednisolone vs epidural methylprednisolone344 weeks (24 weeks)Intrathecal > epidural for global pain relief > 50% at 4 weeks (92% vs 25%, P < .01, NNT = 2) and 24 weeks (92% vs 17%, P < .01, NNT = 2).Not reported formally, but "no clinical complications were observed."
Mixture of gangliosides (Cronassial) vs placebo SQ358 weeksMean pain scores and mean sleep scores improved with Cronassial but not placebo.50% vs 0 (NNH = 2) (injection site pain).
25% vs 0 dropouts (NNH = 4).
Bupivacaine sympathetic blocks vs lidocaine IV362-3 weeks (1 year)Bupivacaine had lower pain VAS than lidocaine at 3 months (24 vs 57, P < .0001) and 1 year (16 vs 44, P < .003). Similar results in global score, including pain, sleep, analgesic use, and incapacity."No complications," but side effects not reported.
*Follow-up duration listed in parentheses if separate from treatment duration.
DMSO denotes dimethylsulfoxide; IV, intravenous; NNH, number needed to harm; NNT, number needed to treat; NS, not significant, used for P values > .1; SQ, subcuta neous; TENS, transcutaneous electrical nerve stimulation; VAS, visual analog scale reported as 100-mm scale.
 

 

Oral therapies

Oral therapies evaluated were tricyclic antidepressants, gabapentin, oxycodone, tramadol, dextromethorphan, memantine, acyclovir, lorazepam, and fluphenazine (Table 1).

Tricyclic antidepressants have been shown to be effective in multiple small short-term crossover trials. Amitriptyline was highly effective in 2 placebo-controlled trials.16,17 In 1 of these trials, amitriptyline was more effective than lorazepam.16 In another trial, amitriptyline was more effective than fluphenazine (a phenothiazine) and glycopyrrolate placebo.18 Nortriptyline was as effective as amitriptyline in a comparison trial,19 while maprotiline was not.20 Desipramine was highly effective in a trial using benztropine as an “active placebo” in that the anticholinergic properties of benztropine were used to match the side effects of desipramine.21 In all these studies, the analgesic effects of tricyclic antidepressants appeared independent of antidepressant effects. No randomized trial data were collected to assess the use of antidepressants for longer than 8 weeks.

Gabapentin, an anticonvulsant, was effective in a single large placebo-controlled trial.22 The number needed to treat (NNT) was 3.2 for the outcome of moderate or better pain relief and 13.9 for the outcome of no pain during the eighth week of treatment. The proportion of patients whose pain was much improved or who had no pain was not reported.

Controlled-release oxycodone was effective in a crossover trial in which 45% of patients had previously used opioids.23 Tramadol may be effective but was not compared against placebo.24 High-dose dextromethorphan, an N-methyl-D-aspartate (NMDA) receptor antagonist, was not shown to be effective for PHN in a small crossover trial.25 Memantine, another NMDA antagonist, was also ineffective.26 Acyclovir did not show any greater efficacy than placebo in a small trial.27 Lorazepam and fluphenazine did not show statistically significant benefit in comparison with placebo in the amitriptyline trials.16,18

Other therapies

Other therapies evaluated were vincristine iontophoresis, acupuncture, intrathecal methylprednisolone, and subcutaneous administration of a mixture of gangliosides (Table 1).

Iontophoresis is a process whereby topical medications are applied via electricity. Vincristine iontophoresis was no more effective than saline iontophoresis in one small trial.28 Vincristine and dimethylsulfoxide iontophoresis was effective at reducing but not eliminating pain in another small trial.29 Dimethylsulfoxide may have an independent analgesic effect.30

Acupuncture was no more effective than mock transcutaneous electrical nerve stimulation (TENS) in 1 trial,31 while a smaller trial suggested a short-term effect.32

Intrathecal methylprednisolone acetate plus lidocaine was highly effective for achieving good or excellent results (pain relief > 50%) in patients with longstanding PHN refractory to multiple conventional therapies.33 All patients whose response to methylprednisolone was poor (8%) had had PHN for more than 5 years. The intrathecal route appears more effective than the epidural route of administration.34

A mixture of gangliosides given by subcutaneous injection was more effective than placebo, but poor tolerability and derivation from bovine brain tissue severely limit its acceptability.35

Sympathetic blocks using bupivacaine were more effective than intravenous lidocaine infusions in 1 trial,36 but results were not reported in a fashion that conveys the proportion of patients who improved significantly.

Discussion

Effective therapies

The therapy for which evidence for efficacy is best is tricyclic antidepressants. Three placebo-controlled RCTs demonstrated that only 2 to 3 patients with PHN need to be treated to achieve 1 good or excellent result (NNT = 2-3). Since none of these studies lasted longer than 8 weeks, the long-term efficacy of antidepressants for the treatment of PHN is unknown. Follow-up of 10 patients who did well in 1 antidepressant trial found that only 2 patients were still doing well at 2 years.19

Other therapies shown to be effective in 1 or 2 trials are topical capsaicin 0.075%, gabapentin, and controlled-release oxycodone. For these studies, it is difficult to determine the number needed to treat for “meaningful” clinical benefit, although gabapentin demonstrated superiority to placebo in numerous quality-of-life measures.

For patients with severe PHN refractory to other treatments, 2 trials support benefit from intrathecal methylprednisolone and 1 trial suggests benefit from bupivacaine sympathetic blocks. Cost data for selected therapies are presented in Table 2.

TABLE 2
COSTS OF SELECTED DRUG THERAPIES

MedicationTypical DosingAmount for 30 daysAWP* (Generic)Local Pharmacy Charge† (Generic)
Amitriptyline (Elavil)75 mg nightly17Ninety 25-mg tablets$42.35 ($9.86 to $31.95)$35.72 ($11.62)
Thirty 75-mg tablets$34.38 ($7.41 to $26.00)$42.78 ($10.98)
Nortriptyline (Pamelor)75 mg nightly19Ninety 25-mg tablets$125.99 ($30.86)$133.46 ($26.78)
Thirty 75-mg tablets$120.64 ($64.94)$128.72 ($18.84)
Capsaicin 0.075% (Zostrix)Apply 4 times dailyTwo 60-g tubes$32.40 ($23.98)$27.46 ($11.94)
Gabapentin (Neurontin)1,200 mg 3 times daily#180 600-mg tablets$381.83$369.62
Oxycodone (OxyContin)20 mg every 12 hoursSixty 20-mg tablets$142.67$150.78
Lidocaine patch (Lidoderm)Up to three 700-mg patches for up to 12 hours per dayNinety 700-mg patches$394.14$388.72
*AWP denotes average wholesale price, AmeriSource ECHO Retail Price Program, version 3.1q, (c) 1991-2000, March 5, 2001.
† Includes pharmacy dispensing fee from local (Columbia, Mo.) branch of national pharmacy chain for 30 days, March 5, 2001.
 

 

Therapies not proved effective

Therapies of uncertain benefit that have not been adequately studied in randomized controlled trials include lidocaine patch, benzydamine cream, tramadol, and vincristine (and/or dimethylsulfoxide) iontophoresis.

Therapies unlikely to be beneficial based on single trials include lorazepam, fluphenazine, dextromethorphan, memantine, acyclovir, and acupuncture. Most of the negative trials did not report power; therefore, potential benefits of these treatments cannot be excluded.

Safety and tolerability

The rates of adverse effects are high in all effective oral and topical therapies (Table 1). This situation is of special concern in elderly patients who have comorbid conditions and are taking multiple medications. Two tricyclic antidepressant trials16,21 report a decreased incidence of side effects over time. The researchers emphasize the importance of starting at the lowest available dose with oral therapies and titrating slowly as indicated and tolerated.

No clinical complications were observed in the intrathecal steroid trials; specific side effects were not reported.

Lidocaine patch therapy has been promoted as causing clinically insignificant serum levels, no systemic side effects, and no drug–drug interactions.11 However, the largest lidocaine patch RCT was too small to rule out significant but uncommon risks such as ventricular arrhythmia.10 One death that could potentially be attributed to lidocaine absorption occurred in a patient with diffuse vascular disease who was on chronic hemodialysis for renal failure. Blood lidocaine levels were not obtained because venous access was poor.

Limitations

Variations in outcomes limit comparative conclusions. Sindrup and Jensen37 reviewed treatments for neuropathic pain and presented data based on a successful outcome defined as 50% reduction in pain scores, 50% pain relief, or categorical ratings of excellent, good, or moderate pain relief. Most studies used visual analog scales, but it is not clear that a 50% reduction in these measures is equivalent to clinically meaningful benefits at all levels of pain. We attempted to determine NNT data based on clearly meaningful outcomes such as “excellent or good,” “complete,” or “marked” pain relief as distinct from “moderate” or “some,” but found these data unavailable in most reports. Quality-of-life measures, such as sleep and disability ratings, may be more important than measures of pain, but were reported in only one third of the trials.

We may have failed to include relevant trials. We identified 7 potentially relevant non–English language studies. Five had no abstracts available.38-42 One single-blind trial reported reduced pain scores with topical prostaglandin E1 dissolved in Vaseline.43 One trial compared iontophoresis with lidocaine and iontophoresis with 3 different calcium channel blockers in 10 patients. The authors found that all 4 treatments reduced pain, but had not included a placebo control group.44 We also contacted 15 content experts to identify reports of unpublished trials; none of the 6 responses received identified such reports.

The evidence base for treatment of PHN is limited. Among the RCTs reviewed, 78% enrolled 50 or fewer patients. Because most of the subjects had PHN lasting longer than 1 year, our conclusions may not apply to patients with PHN of shorter duration. The latter group represents the majority of subjects with PHN presenting to primary care physicians.

We used the Jadad scale7 as an attempt to quantitatively assess the methodologic quality of the studies we reviewed. In general, explicit validity checklists with summary scores have not consistently been shown to provide more reliable assessments of validity than qualitative assessments.45-4 The Jadad scale is the first validity checklist that has some rigorous evidence supporting its use,7,48 although its inter-rater reliability has been questioned.49 We found the Jadad scale had an inherent bias against therapies that could not be adequately double-blinded. Thus 2 trials with a score of 1 were included.29,36 In 1 trial of vincristine iontophoresis, the authors described the trial as single-blinded and provided ample explanation of why double-blinding was not achieved.29 In 1 trial of sympathetic blocks, the treatment studied included an invasive procedure; therefore, there was no apparent way for the procedure itself to be double-blinded.36

The Jadad scale does not account for some threats to validity of included studies. We encountered numerous methodologic flaws, such as lack of intention to treat analysis in parallel trials (11) and lack of washout periods in crossover trials (4). Further limitations to interpretation of selected study results included potentially significant baseline differences between groups (8), small numbers, and short study durations. A list of the studies with these specific methodologic concerns is available in Table W3.

Conclusions

For patients with PHN lasting less than 6 months, spontaneous resolution is common and treatment decisions are largely empiric and not evidence based. For PHN of longer duration, treatments shown to be more effective than placebo include tricyclic antidepressants, topical capsaicin 0.075%, gabapentin, and controlled-release oxycodone. These treatments all have adverse effects or costs that need to be considered on an individual basis. Lidocaine patch therapy may be safer for most patients but may be no more effective than placebo and is not suitable for patients with trigeminal PHN. Patients with PHN refractory to the currently available and studied topical and oral agents should be considered for intrathecal steroid therapy.

 

 

Acknowledgments

The authors wish to thank Susan Meadows, MLS, Susan Elliott, MLS, Stacey Rautzhan, and Steve Calloway, RPh, for their assistance and Sigurdur Helgason, MD, Carin Reust, MD, Steven Zweig, MD, MSPH, and Alan Adelman, MD, MS, for editorial review.

ABSTRACT

OBJECTIVES: We wanted to determine whether any treatment had been shown to reduce pain or disability from postherpetic neuralgia (PHN), a common sequela of herpes zoster in elderly patients.

STUDY DESIGN: We undertook a systematic review of English-language randomized controlled trials (RCTs) of treatments of PHN with evaluation periods longer than 24 hours.

DATA SOURCES: We systematically searched MEDLINE, Current Contents, and the Cochrane Library. We also searched reference lists of identified trials and reviews and contacted content experts.

OUTCOMES MEASURED: Two reviewers independently evaluated RCTs for methodologic quality and data extraction. Outcomes of primary focus were pain and quality of life.

RESULTS: Twenty-seven RCTs met inclusion criteria and were reviewed. Six trials of tricyclic antidepressants found evidence for clinically meaningful effects over 6 weeks. All other treatments were evaluated in no more than 2 trials meeting our inclusion criteria. Topical capsaicin 0.075%, gabapentin, and controlled-release oxycodone were shown to be effective, but the clinically meaningful benefit is difficult to quantify. Intrathecal methylprednisolone and possibly bupivacaine sympathetic blocks are helpful in refractory cases. Other treatments evaluated, including topical lidocaine, had no evidence or inconsistent evidence of benefit.

CONCLUSIONS: No single best treatment for PHN is known. Tricyclic antidepressants, topical capsaicin, gabapentin, and oxycodone are effective for alleviating PHN; however, long-term, clinically meaningful benefits are uncertain and side effects are common. Patients with PHN refractory to these therapies may benefit from intrathecal methylprednisolone. Little evidence is available regarding treatment of PHN of less than 6 months’ duration.

KEY POINTS FOR CLINICIANS

  • Spontaneous resolution is common in patients whose postherpetic neuralgia (PHN) has lasted for less than 6 months; treatment decisions are largely empiric and not evidence based.
  • For PHN of longer duration, treatments shown to be more effective than placebo include tricyclic antidepressants, topical capsaicin 0.075%, gabapentin, and controlled-release oxycodone. Benefits should be weighed against adverse effects and costs.
  • Patients with PHN refractory to currently available and studied topical and oral agents should be considered for intrathecal steroid therapy.

Postherpetic neuralgia (PHN), the most common complication of herpes zoster, is much more prevalent among adults older than 50 years than in younger people.1,2 The largest English-language prospective study of patients presenting with zoster suggests that the average family physician can expect to see 4 cases of zoster per year and 1 case of PHN lasting more than 3 months every 3 years.3 Among placebo cohorts from randomized controlled trials (RCTs) of acute zoster treatment, the incidence of pain at 3 months has been reported as 17% to 60%; at 6 months, 5% to 39%.4 There is limited evidence that therapies for acute zoster have an impact on PHN.4

This review addresses therapies to reduce pain or improve quality of life in patients with PHN. The condition has been variously defined in terms of timing (either following resolution of acute zoster [rash healing] or a defined time after onset of zoster), duration (any time after zoster or a minimum of 6 months after zoster), and type of pain (such as lancinating pain or allodynia [pain caused by a stimulus that does not nor mally provoke pain]).5,6 PHN may include a spectrum of presentations, from brief intermittent mild pain that resolves spontaneously to chronic persistent disabling pain recalcitrant to multiple therapies. To avoid missing potentially relevant findings, we defined PHN broadly as any pain after cutaneous healing of zoster.

Methods

Search strategy

Medline (1966 to present) was searched on October 19, 2000. The search combined the terms “post-herpetic or postherpetic” and “neuralgia or neuropathy or pain” and publication type “clinical trial (including phases I to IV) or controlled clinical trial or randomized controlled trial.” The Cochrane Controlled Trials Registry 2000, Issue 3, was searched with the same terms. Current Contents was searched to identify more recent references. We also identified trials through article reference lists (from included trials and 40 reviews), contact of authors and content experts, and the Food and Drug Administration (FDA) Web site.

Selection criteria

Inclusion criteria for this review were RCTs that enrolled patients with PHN (history of zoster, pain in the dermatomal distribution of the zoster rash, and pain persisting or occurring after resolution of the zoster rash), addressed relevant end points (pain resolution, pain severity, effect on quality of life), and had full reports available in English. Since responses to initial therapy may change over time, we included only trials with evaluation periods lasting more than 24 hours.

The authors independently evaluated trials meeting these inclusion criteria for quality and independently extracted data. Quality was evaluated using the Jadad scale,7 which addresses selected criteria (randomization technique, allocation concealment, blinding, accounting of dropouts) and rates methodologic quality on a 5-point scale, with 5 representing the highest score. Differences were resolved through discussion. Trials scoring only 1 point were excluded except for 2 instances noted in our discussion.

 

 

Results

Searching identified 186 potential trials, of which 92 were excluded as irrelevant on the basis of titles and abstracts alone. Of the 94 citations reviewed in greater detail, 64 were excluded for the following reasons: not describing a trial (10), not describing a trial of treatment for PHN (10), describing an uncontrolled trial (7), no randomization (7), evaluation period limited to 24 hours or less (13), duplicate publication (4), language other than English (7), not providing results specifically for patients with PHN (3), and available only in abstract form (3). One unpublished trial of mexiletine was identified through a review by Hanania and Brietstein; Dr Hanania informed us, however, that this trial had been stopped early because the treatment group had experienced serious side effects. One controlled trial was excluded because correspondence with the investigator did not confirm that it had been randomized. One trial was excluded because it included only 6 patients with PHN. (A list of excluded studies is available in Table W1.)

Of the remaining 27 trials reviewed for methodologic quality, most (16) received a Jadad score of 4. The 2 authors had substantial agreement on quality ratings ( = 0.75). Table W2 provides details of treatment regimens, quality ratings, ages of subjects, and duration of PHN. One trial with a Jadad score of 1 was excluded.8 Most subjects were elderly and had had PHN for longer than 6 months.

Topical therapies

Topical therapies evaluated were lidocaine, capsaicin, and benzydamine (Table 1). Lidocaine patch therapy is the only agent with a specific FDA indication for PHN. We found few trials supporting the FDA approval. The only published RCT of relatively unselected patients with PHN (n = 35) showed significant benefit versus placebo but was excluded because evaluation sessions had been limited to 12 hours.9 We reviewed a report of an unpublished RCT comparing lidocaine patch with vehicle placebo used in the application for FDA approval.10 This trial found a large, statistically significant reduction in pain scores with placebo throughout the 3- to 4-week trial. This trial found a similar statistically significant reduction in pain scores with lidocaine patch and no significant difference comparing lidocaine patch with placebo.

Three findings in the unpublished trial were used to support arguments for efficacy: (1) a statistically significant difference in the pain relief score at the final visit; (2) differences in allodynia (based on investigators’ sensory skin testing, described as stroking the maximally painful area with a foam brush and recording the pain scale rating) at the beginning of the trial; and (3) a greater increase in pain scores among lidocaine subjects upon trial conclusion (ie, after stopping study medication). The clinical relevance of these 3 findings is unclear.

The FDA declined to approve lidocaine patch therapy on the basis of these 2 studies and required an additional trial to demonstrate benefit. An “enriched enrollment study” involved subjects who had used lidocaine patch for at least 1 month and received at least moderate relief but had pain without the patch.11 Lidocaine patch was clearly effective in this highly selected cohort.

Capsaicin 0.075% cream was effective in 2 trials of patients with severe refractory PHN.12,13 The benefit appeared modest in the larger trial (pain was eliminated or nearly eliminated in less than 20% of capsaicin patients) and greater in the smaller trial. Blinding had limited efficacy because of the stinging effect of capsaicin.

Benzydamine cream, an antiprostaglandin, was not effective in a 2-week crossover trial.14 The cream showed a nonsignificant trend for pain reduction in an earlier 2-week crossover trial.15

TABLE 1

RESULTS
Treatment vs ControlTreatment DurationEfficacy ResultsAdverse Effects
TOPICALTHERAPIES
Lidocaine patch vs placebo103-4 weeksNo significant difference between groups in pain VAS improvement.Not reported clearly, but no significant differences.
Lidocaine > placebo in 0-5 pain relief scale (2.6 vs 2.1, P = .023).1% vs 0 dropouts (because of s kin irritation).
Lidocaine patch vs placebo112-14 daysLidocaine > placebo in median time to withdrawal because of pain (> 14 days vs 3.8 days, P >.001).28% vs 34% (all skin reactions).
More preferred lidocaine (78% vs 10%, P < .001).0 vs 6% dropouts.
Capsaicin 0.075% cream vs placebo126 weeksCapsaicin > placebo on pain relief VAS (20.9% vs 5.8%).61% vs 33% skin reactions (P < .05, NNH = 3).
Capsaicin > placebo in improvement in functional capacity (NS).24% vs 3% dropouts (NNH = 4) (mostly skin reactions).
Capsaicin 0.075% cream vs placebo136 weeksCapsaicin > placebo in mean change in pain VAS (30% decrease vs 1% increase, P < .05). Capsaicin > placebo for 40% or greater pain relief (54% vs 6%, P < .02, NNT 2).31% vs 13% skin reactions (NNH = 5).
No dropouts (but 3 lost to follow-up).
Benzydamine cream vs placebo142 weeksNo differences between groups in pain measures or sleep scores.17% vs 4% skin reactions (NNH = 7).
Patients favored vehicle more often than benzydamine.4% vs 0 dropouts (NNH = 25) (rash).
Benzydamine cream vs placebo152 weeksBenzydamine > placebo for proportion reporting pain reduction (52% vs 38%, NS).Not reported.
4% vs 2% dropouts (NNH = 50) (skin irritation).
ORALTHERAPIES
Amitriptyline vs lorazepam vs placebo166 weeksAmitriptyline > lorazepam or placebo for pain relief "complete" or "a lot" (39% vs 8% vs 8%, P < .001, NNT = 3). Lorazepam >placebo for 1-2 weeks but effect not maintained.88% vs 98% vs 72% (NNH = 6 for amitriptyline).
Amitriptyline adverse effect rate decreased to 62% in final 2 weeks.
5 vs 6 vs 3 dropouts.
Amitriptyline vs placebo173 weeksAmitriptyline > placebo for good or excellent results (67% vs 4%, P < .001, NNT = 2). Amitriptyline > placebo in sleep score improvement (P <.001).67% vs 54% (NNH = 8) (dry mouth, drowsiness, constipation). 4% vs 21% dropouts (appears related to amitriptyline withdrawal symptoms).
Amitriptyline vs Fluphenazine vs combination vs control188 weeksMean decrease in VAS score 29.3 for amitriptyline (P < .001), 12.2 for combination (P = .04), 11.5 for fluphenazine (P = .08), and 5.39 for control (NS).Incidence not reported. Dry mouth more common with amitriptyline. Sleepiness more common with fluphenazine.
One amitriptyline withdrawal because of sedation.
Amitriptyline vs nortriptyline195 weeksBoth drugs effective.97% vs 97% (dry mouth, constipation, dizziness).
No significant differences in efficacy (including sleep and disability measures).30% vs 15% intolerable side effects (P = .05, NNH = 7).
Amitriptyline vs maprotiline205 weeksAmitriptyline > maprotiline in VAS scales (P < .01). No significant differences in categorical scale of pain relief, sleep or disability ratings.63% vs 88% (dry mouth, constipation, sedation, dizziness).
34% vs 47% dropouts.
Desipramine vs benztropine216 weeksDesipramine > benztropine for pain relief "complete" or "a lot" (42% vs 5%, NNT = 3).100% vs 79% (NNH = 4) (dry mouth, dizziness, constipation). 89% vs 42% side effects in final 2 weeks. 5 vs 3 dropouts.
Desipramine > benztropine for pain relief rated moderate or better (63% vs 11%, NNT = 2).
Gabapentin vs placebo228 weeksAll measures, including quality of life and sleep, favored gabapentin. Gabapentin > placebo for pain much or moderately improved (43.2% vs 12.1%, NNT = 3.2). Gabapentin > placebo for "no pain" at final week (16% vs 8.8%, NNT = 13.9).55% vs 28% (NNH = 3) (somnolence, dizziness, ataxia). 19% vs 12% (NNH = 15) or 13% vs 10% (NNH = 26) dropouts (reporting inconsistent).
Oxycodone controlled-release vs placebo234 weeksOxycodone > placebo in 1-5 pain relief scale (2.9 vs 1.9) and lower disability scores.76% vs 49% (NNH = 3) (constipation, nausea, sedation). 5 vs 3 dropouts.
No significant differences between groups in pain intensity. More preferred oxycodone (67% vs 11%, P = .001, NNT = 2).
Tramadol vs clomipramine with or without levomepromazine246 weeksNo significant differences between groups in pain intensity. Tramadol > control for pain relief satisfactory or better (90% vs 55%).77% vs 83% (dry mouth and constipation with tramadol). 41% vs 39% dropouts.
Tramadol > control for pain relief good or excellent (60% vs 45%).
Dextromethorphan vs placebo256 weeksNo significant differences between groups.100% vs 3% (NNH = 1) (sedation, dizziness, lightheadedness). 22% vs 0 dropouts (NNH = 4).
Memantine vs placebo265 weeks (7 weeks)No significant differences between groups.83% vs 67% (NS) (dizziness, headache, nausea).
25% vs 8% dropouts (NNH = 6).
Acyclovir vs placebo2712 weeks (6 months)Acyclovir associated with higher pain rating than placebo at some time points. No difference in proportion with clinical improvement (40% vs 40%).Not reported.
OTHER THERAPIES
Vincristine iontophoresis vs saline iontophoresis284 weeks (90 days)Vincristine < saline for pain relief at 4 weeks (36% vs 56%, NS).Not explicitly reported.
Vincristine < saline for pain relief at 90 days (27% vs 33%, NS)."Most patients complained of a burning sensation at the negative electrode."
Vincristine in DMSO iontophoresis vs saline iontophoresis294 weeks (6 weeks)Vincristine > saline for "improved" at 4 weeks (90% vs 10%, NNT = 1) and 6 weeks (60% vs 0, NNT = 2). Most treated patients had "improvement" but none were "cured.""Most patients were prescribed a mild steroid cream to reduce irritation." "Several burns were seen" but "they were painless."One vincristine death in patient with heart disease.
Acupuncture vs mock TENS316 weeks (14 weeks)7 patients in each group were "better" after treatment. No significant differences between treatments.Not reported.
"Auricular acupuncture is a painful and unpleasant experience." 43% vs 9% dropouts.
Acupuncture vs TENS326 weeks (6 months)Acupuncture > TENS for pain improvement during treatment (50% vs 7.7%).All but 1 acupuncture patient dropped out after treatment because of inadequate pain relief.
Intrathecal methylprednisolone plus lidocaine vs intrathecal lidocaine alone vs no treatment334 weeks (2 years)92% vs 7% vs 3% had pain relief > 50% at 2 years (P < .001, NNT = 1).Not reported formally, but "no clinical complications were observed."
Similar results in analgesic use.
Intrathecal methylprednisolone vs epidural methylprednisolone344 weeks (24 weeks)Intrathecal > epidural for global pain relief > 50% at 4 weeks (92% vs 25%, P < .01, NNT = 2) and 24 weeks (92% vs 17%, P < .01, NNT = 2).Not reported formally, but "no clinical complications were observed."
Mixture of gangliosides (Cronassial) vs placebo SQ358 weeksMean pain scores and mean sleep scores improved with Cronassial but not placebo.50% vs 0 (NNH = 2) (injection site pain).
25% vs 0 dropouts (NNH = 4).
Bupivacaine sympathetic blocks vs lidocaine IV362-3 weeks (1 year)Bupivacaine had lower pain VAS than lidocaine at 3 months (24 vs 57, P < .0001) and 1 year (16 vs 44, P < .003). Similar results in global score, including pain, sleep, analgesic use, and incapacity."No complications," but side effects not reported.
*Follow-up duration listed in parentheses if separate from treatment duration.
DMSO denotes dimethylsulfoxide; IV, intravenous; NNH, number needed to harm; NNT, number needed to treat; NS, not significant, used for P values > .1; SQ, subcuta neous; TENS, transcutaneous electrical nerve stimulation; VAS, visual analog scale reported as 100-mm scale.
 

 

Oral therapies

Oral therapies evaluated were tricyclic antidepressants, gabapentin, oxycodone, tramadol, dextromethorphan, memantine, acyclovir, lorazepam, and fluphenazine (Table 1).

Tricyclic antidepressants have been shown to be effective in multiple small short-term crossover trials. Amitriptyline was highly effective in 2 placebo-controlled trials.16,17 In 1 of these trials, amitriptyline was more effective than lorazepam.16 In another trial, amitriptyline was more effective than fluphenazine (a phenothiazine) and glycopyrrolate placebo.18 Nortriptyline was as effective as amitriptyline in a comparison trial,19 while maprotiline was not.20 Desipramine was highly effective in a trial using benztropine as an “active placebo” in that the anticholinergic properties of benztropine were used to match the side effects of desipramine.21 In all these studies, the analgesic effects of tricyclic antidepressants appeared independent of antidepressant effects. No randomized trial data were collected to assess the use of antidepressants for longer than 8 weeks.

Gabapentin, an anticonvulsant, was effective in a single large placebo-controlled trial.22 The number needed to treat (NNT) was 3.2 for the outcome of moderate or better pain relief and 13.9 for the outcome of no pain during the eighth week of treatment. The proportion of patients whose pain was much improved or who had no pain was not reported.

Controlled-release oxycodone was effective in a crossover trial in which 45% of patients had previously used opioids.23 Tramadol may be effective but was not compared against placebo.24 High-dose dextromethorphan, an N-methyl-D-aspartate (NMDA) receptor antagonist, was not shown to be effective for PHN in a small crossover trial.25 Memantine, another NMDA antagonist, was also ineffective.26 Acyclovir did not show any greater efficacy than placebo in a small trial.27 Lorazepam and fluphenazine did not show statistically significant benefit in comparison with placebo in the amitriptyline trials.16,18

Other therapies

Other therapies evaluated were vincristine iontophoresis, acupuncture, intrathecal methylprednisolone, and subcutaneous administration of a mixture of gangliosides (Table 1).

Iontophoresis is a process whereby topical medications are applied via electricity. Vincristine iontophoresis was no more effective than saline iontophoresis in one small trial.28 Vincristine and dimethylsulfoxide iontophoresis was effective at reducing but not eliminating pain in another small trial.29 Dimethylsulfoxide may have an independent analgesic effect.30

Acupuncture was no more effective than mock transcutaneous electrical nerve stimulation (TENS) in 1 trial,31 while a smaller trial suggested a short-term effect.32

Intrathecal methylprednisolone acetate plus lidocaine was highly effective for achieving good or excellent results (pain relief > 50%) in patients with longstanding PHN refractory to multiple conventional therapies.33 All patients whose response to methylprednisolone was poor (8%) had had PHN for more than 5 years. The intrathecal route appears more effective than the epidural route of administration.34

A mixture of gangliosides given by subcutaneous injection was more effective than placebo, but poor tolerability and derivation from bovine brain tissue severely limit its acceptability.35

Sympathetic blocks using bupivacaine were more effective than intravenous lidocaine infusions in 1 trial,36 but results were not reported in a fashion that conveys the proportion of patients who improved significantly.

Discussion

Effective therapies

The therapy for which evidence for efficacy is best is tricyclic antidepressants. Three placebo-controlled RCTs demonstrated that only 2 to 3 patients with PHN need to be treated to achieve 1 good or excellent result (NNT = 2-3). Since none of these studies lasted longer than 8 weeks, the long-term efficacy of antidepressants for the treatment of PHN is unknown. Follow-up of 10 patients who did well in 1 antidepressant trial found that only 2 patients were still doing well at 2 years.19

Other therapies shown to be effective in 1 or 2 trials are topical capsaicin 0.075%, gabapentin, and controlled-release oxycodone. For these studies, it is difficult to determine the number needed to treat for “meaningful” clinical benefit, although gabapentin demonstrated superiority to placebo in numerous quality-of-life measures.

For patients with severe PHN refractory to other treatments, 2 trials support benefit from intrathecal methylprednisolone and 1 trial suggests benefit from bupivacaine sympathetic blocks. Cost data for selected therapies are presented in Table 2.

TABLE 2
COSTS OF SELECTED DRUG THERAPIES

MedicationTypical DosingAmount for 30 daysAWP* (Generic)Local Pharmacy Charge† (Generic)
Amitriptyline (Elavil)75 mg nightly17Ninety 25-mg tablets$42.35 ($9.86 to $31.95)$35.72 ($11.62)
Thirty 75-mg tablets$34.38 ($7.41 to $26.00)$42.78 ($10.98)
Nortriptyline (Pamelor)75 mg nightly19Ninety 25-mg tablets$125.99 ($30.86)$133.46 ($26.78)
Thirty 75-mg tablets$120.64 ($64.94)$128.72 ($18.84)
Capsaicin 0.075% (Zostrix)Apply 4 times dailyTwo 60-g tubes$32.40 ($23.98)$27.46 ($11.94)
Gabapentin (Neurontin)1,200 mg 3 times daily#180 600-mg tablets$381.83$369.62
Oxycodone (OxyContin)20 mg every 12 hoursSixty 20-mg tablets$142.67$150.78
Lidocaine patch (Lidoderm)Up to three 700-mg patches for up to 12 hours per dayNinety 700-mg patches$394.14$388.72
*AWP denotes average wholesale price, AmeriSource ECHO Retail Price Program, version 3.1q, (c) 1991-2000, March 5, 2001.
† Includes pharmacy dispensing fee from local (Columbia, Mo.) branch of national pharmacy chain for 30 days, March 5, 2001.
 

 

Therapies not proved effective

Therapies of uncertain benefit that have not been adequately studied in randomized controlled trials include lidocaine patch, benzydamine cream, tramadol, and vincristine (and/or dimethylsulfoxide) iontophoresis.

Therapies unlikely to be beneficial based on single trials include lorazepam, fluphenazine, dextromethorphan, memantine, acyclovir, and acupuncture. Most of the negative trials did not report power; therefore, potential benefits of these treatments cannot be excluded.

Safety and tolerability

The rates of adverse effects are high in all effective oral and topical therapies (Table 1). This situation is of special concern in elderly patients who have comorbid conditions and are taking multiple medications. Two tricyclic antidepressant trials16,21 report a decreased incidence of side effects over time. The researchers emphasize the importance of starting at the lowest available dose with oral therapies and titrating slowly as indicated and tolerated.

No clinical complications were observed in the intrathecal steroid trials; specific side effects were not reported.

Lidocaine patch therapy has been promoted as causing clinically insignificant serum levels, no systemic side effects, and no drug–drug interactions.11 However, the largest lidocaine patch RCT was too small to rule out significant but uncommon risks such as ventricular arrhythmia.10 One death that could potentially be attributed to lidocaine absorption occurred in a patient with diffuse vascular disease who was on chronic hemodialysis for renal failure. Blood lidocaine levels were not obtained because venous access was poor.

Limitations

Variations in outcomes limit comparative conclusions. Sindrup and Jensen37 reviewed treatments for neuropathic pain and presented data based on a successful outcome defined as 50% reduction in pain scores, 50% pain relief, or categorical ratings of excellent, good, or moderate pain relief. Most studies used visual analog scales, but it is not clear that a 50% reduction in these measures is equivalent to clinically meaningful benefits at all levels of pain. We attempted to determine NNT data based on clearly meaningful outcomes such as “excellent or good,” “complete,” or “marked” pain relief as distinct from “moderate” or “some,” but found these data unavailable in most reports. Quality-of-life measures, such as sleep and disability ratings, may be more important than measures of pain, but were reported in only one third of the trials.

We may have failed to include relevant trials. We identified 7 potentially relevant non–English language studies. Five had no abstracts available.38-42 One single-blind trial reported reduced pain scores with topical prostaglandin E1 dissolved in Vaseline.43 One trial compared iontophoresis with lidocaine and iontophoresis with 3 different calcium channel blockers in 10 patients. The authors found that all 4 treatments reduced pain, but had not included a placebo control group.44 We also contacted 15 content experts to identify reports of unpublished trials; none of the 6 responses received identified such reports.

The evidence base for treatment of PHN is limited. Among the RCTs reviewed, 78% enrolled 50 or fewer patients. Because most of the subjects had PHN lasting longer than 1 year, our conclusions may not apply to patients with PHN of shorter duration. The latter group represents the majority of subjects with PHN presenting to primary care physicians.

We used the Jadad scale7 as an attempt to quantitatively assess the methodologic quality of the studies we reviewed. In general, explicit validity checklists with summary scores have not consistently been shown to provide more reliable assessments of validity than qualitative assessments.45-4 The Jadad scale is the first validity checklist that has some rigorous evidence supporting its use,7,48 although its inter-rater reliability has been questioned.49 We found the Jadad scale had an inherent bias against therapies that could not be adequately double-blinded. Thus 2 trials with a score of 1 were included.29,36 In 1 trial of vincristine iontophoresis, the authors described the trial as single-blinded and provided ample explanation of why double-blinding was not achieved.29 In 1 trial of sympathetic blocks, the treatment studied included an invasive procedure; therefore, there was no apparent way for the procedure itself to be double-blinded.36

The Jadad scale does not account for some threats to validity of included studies. We encountered numerous methodologic flaws, such as lack of intention to treat analysis in parallel trials (11) and lack of washout periods in crossover trials (4). Further limitations to interpretation of selected study results included potentially significant baseline differences between groups (8), small numbers, and short study durations. A list of the studies with these specific methodologic concerns is available in Table W3.

Conclusions

For patients with PHN lasting less than 6 months, spontaneous resolution is common and treatment decisions are largely empiric and not evidence based. For PHN of longer duration, treatments shown to be more effective than placebo include tricyclic antidepressants, topical capsaicin 0.075%, gabapentin, and controlled-release oxycodone. These treatments all have adverse effects or costs that need to be considered on an individual basis. Lidocaine patch therapy may be safer for most patients but may be no more effective than placebo and is not suitable for patients with trigeminal PHN. Patients with PHN refractory to the currently available and studied topical and oral agents should be considered for intrathecal steroid therapy.

 

 

Acknowledgments

The authors wish to thank Susan Meadows, MLS, Susan Elliott, MLS, Stacey Rautzhan, and Steve Calloway, RPh, for their assistance and Sigurdur Helgason, MD, Carin Reust, MD, Steven Zweig, MD, MSPH, and Alan Adelman, MD, MS, for editorial review.

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2. Burgoon CF, Burgoon JS, Baldridge GD. The natural history of herpes zoster. JAMA 1957;164:265-9.

3. Helgason S, Sigurdsson JA, Gudmundsson S. The clinical course of herpes zoster: a prospective study in primary care. Eur J Gen Pract 1996;2:12-6.

4. Alper BS, Lewis PR. Does treatment of acute herpes zoster prevent or shorten postherpetic neuralgia? J Fam Pract 2000;49:255-64.

5. Dwyer DE. Management issues in herpes zoster. Austral Fam Physician 1996;25:299-307.

6. Wood MJ. How should we measure pain in herpes zoster? Neurology 1995;45(12 suppl 8):S61-2.

7. Jadad AR, Moore RA, Carroll D, et al. Assessing the quality of reports of randomized clinical trials: Is blinding necessary? Control Clin Trials 1996;17:1-12.

8. Gerson GR, Jones RB, Luscombe DK. Studies on the concomitant use of carbamazepine and clomipramine for the relief of post-herpetic neuralgia. Postgrad Med J 1977;53(suppl):104-9.

9. Rowbotham MC, Davies PS, Verkempinck C, Galer BS. Lidocaine patch: double-blind controlled study of a new treatment method for post-herpetic neuralgia. Pain 1996;65:39-44.

10. Lidoderm (Lidocaine) Patch. Center for Drug Evaluation and Research application number: NDA 20-612. Medical reviews. Washington, DC: US Food and Drug Administration, Center for Drug Evaluation and Research. Last updated November 30, 1999. Accessed October 31, 2000, at: http://www.fda.gov/cder/foi/nda/99/20612.htm/.

11. Galer BS, Rowbotham MC, Perander J, Friedman E. Topical lidocaine patch relieves postherpetic neuralgia more effectively than a vehicle topical patch: results of an enriched enrollment study. Pain 1999;80:533-8.

12. Watson CP, Tyler KL, Bickers DR, Millikan LE, Smith S, Coleman E. A randomized vehicle-controlled trial of topical capsaicin in the treatment of postherpetic neuralgia. Clin Ther 1993;15:510-26.

13. Bernstein JE, Korman NJ, Bickers DR, Dahl MV, Millikan LE. Topical capsaicin treatment of chronic postherpetic neuralgia. J Am Acad Dermatol 1989;21(2 pt 1):265-70.

14. McQuay HJ, Carroll D, Moxon A, Glynn CJ, Moore RA. Benzydamine cream for the treatment of post-herpetic neuralgia: minimum duration of treatment periods in a cross-over trial. Pain 1990;40:131-5.

15. Coniam SW, Huntan J. A study of benzydamine cream in post-herpetic neuralgia. Res Clin Forums 1988;10:65-8.

16. Max MB, Schafer SC, Culnane M, Smoller B, Dubner R, Gracely RH. Amitriptyline, but not lorazepam, relieves postherpetic neuralgia. Neurology 1988;38:1427-32.

17. Watson CP, Evans RJ, Reed K, Merskey H, Goldsmith L, Warsh J. Amitriptyline versus placebo in postherpetic neuralgia. Neurology 1982;32:671-3.

18. Graff-Radford SB, Shaw LR, Naliboff BN. Amitriptyline and fluphenazine in the treatment of postherpetic neuralgia. Clin J Pain 2000;16:188-92.

19. Watson CP, Vernich L, Chipman M, Reed K. Nortriptyline versus amitriptyline in postherpetic neuralgia: a randomized trial. Neurology 1998;51:1166-71.

20. Watson CP, Chipman M, Reed K, Evans RJ, Birkett N. Amitriptyline versus maprotiline in postherpetic neuralgia: a randomized, double-blind, crossover trial. Pain 1992;48:29-36.

21. Kishore-Kumar R, Max MB, Schafer SC, et al. Desipramine relieves postherpetic neuralgia. Clin Pharmacol Ther 1990;47:305-12.

22. Rowbotham M, Harden N, Stacey B, Bernstein P, Magnus-Miller L. Gabapentin for the treatment of postherpetic neuralgia: a randomized controlled trial. JAMA 1998;280:1837-42.

23. Watson CP, Babul N. Efficacy of oxycodone in neuropathic pain: a randomized trial in postherpetic neuralgia. Neurology 1998;50:1837-41.

24. Gobel H, Stadler T. Treatment of pain due to postherpetic neuralgia with tramadol-results of an open, parallel pilot study vs clomipramine with or without levomepromazine. Clin Drug Invest 1995;10:208-14.

25. Nelson KA, Park KM, Robinovitz E, Tsigos C, Max MB. High-dose oral dextromethorphan versus placebo in painful diabetic neuropathy and postherpetic neuralgia. Neurology 1997;48:1212-8.

26. Eisenberg E, Kleiser A, Dortort A, Haim T, Yarnitsky D. The NMDA (N-methyl-D-aspartate) receptor antagonist memantine in the treatment of postherpetic neuralgia: a double-blind, placebo-controlled study. Eur J Pain 1998;2:321-27.

27. Surman OS, Flynn T, Schooley RT, et al. A double-blind, placebo-controlled study of oral acyclovir in postherpetic neuralgia. Psychosomatics 1990;31:287-92.

28. Dowd NP, Day F, Timon D, Cunningham AJ, Brown L. Iontophoretic vincristine in the treatment of postherpetic neuralgia: a double-blind, randomized, controlled trial. J Pain Symptom Manage 1999;17:175-80.

29. Layman PR, Argyras E, Glynn CJ. Iontophoresis of vincristine versus saline in post-herpetic neuralgia. A controlled trial. Pain 1986;25:165-70.

30. Zuurmond WW, Langendijk PN, Bezemer PD, Brink HE, de Lange JJ, van loenen AC. Treatment of acute reflex sympathetic dystrophy with DMSO 50% in a fatty cream. Acta Anaesthesiol Scand 1996;40:364-7.

31. Lewith GT, Field J, Machin D. Acupuncture compared with placebo in post-herpetic pain. Pain 1983;17:361-8.

32. Rutgers MJ, Van Romunde LKJ, Osman PO. A small randomized comparative trial of acupuncture versus transcutaneous electrical neurostimulation in postherpetic neuralgia. Pain Clin 1988;2:87-9.

33. Kotani N, Kushikata T, Hashimoto H, et al. Intrathecal methylprednisolone for intractable postherpetic neuralgia. N Engl J Med 2000;343:1514-9.

34. Kikuchi A, Kotani N, Sato T, Takamura K, Sakai I, Matsuki A. Comparative therapeutic evaluation of intrathecal versus epidural methylprednisolone for long-term analgesia in patients with intractable postherpetic neuralgia. Reg Anesth Pain Med 1999;24:287-93.

35. Staughton RC, Good J. Double-blind, placebo-controlled clinical trial of a mixture of gangliosides (“Cronassial”) in post-herpetic neuralgia. Curr Med Res Opin 1990;12:169-76.

36. Catala E, Ferrandiz M, Aliaga L, Serra R, Castro MA, Villar LJM. Intravenous lidocaine compared with sympathetic blocks as treatment for post-herpetic neuralgia. A 1-year survey. Pain Clin 1994;7:205-10.

37. Sindrup SH, Jensen TS. Efficacy of pharmacological treatments of neuropathic pain: an update and effect related to mechanism of drug action. Pain 1999;83:389-400.

38. Dekonenko EP, Shishov AS, Kupriianova LV, Rudometov I, Bagrov FI. Postherpetic neuralgia in herpes zoster: its treatment with Zovirax. Zhurnal Nevrologii i Psikhiatrii Imeni S S Korsakova 1999;99(6):56-8.

39. Sigwald J, Bouttier D, Caille F. The treatment of zona and of its associated pains. Study of the results obtained with levomepromazine. Therapie 1959;14:818-24.

40. Hirschmann J. Zoster-neuralgia. Dtsch Med Wochenschr 1971;96:924-5.

41. Mertens HG, Lutzenkirchen H. Neuropsychotropic drugs in the treatment of so-called pain syndromes. Arzneimittelforschung 1970;20:928-30.

42. Lutzenkirchen H, Mertens HG. Treatment of chronic pain syndromes. Analgesic effect of a neuroleptic. Arzneimittelforschung 1970;20:930-1.

43. Tamakawa S, Tsujimoto J, Iharada A, Ogawa H. Treatment of postherpetic neuralgia by topical application of prostaglandin E1-vaseline mixture-a single blind controlled clinical trial. Masui 1999;48:292-4.

44. Ikebe H, Miyagawa A, Mizutani A, Miyamoto M, Taniguchi K, Honda N. The effect of iontophoresis with several Ca channel blockers for PHN patients. Masui 1995;44:428-33.

45. Emerson JD, Burdick E, Hoaglin DC, Mosteller F, Chalmers TC. An empirical study of the possible relation of treatment differences to quality scores in controlled randomized clinical trials. Control Clin Trials 1990;11:339-52.

46. “Quality” scales and checklists. Cochrane Collaboration Handbook [updated September 1997]; Section 6.7.2. In: Mulrow CD, Oxman AD, eds. Cochrane Library [database on disk and CD-ROM]. The Cochrane Collaboration. Oxford, UK: Update Software; 1997.

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48. Moher D, Pham B, Jones A, et al. Does quality of reports of randomised trials affect estimates of intervention efficacy reported in meta-analyses? Lancet 1998;352:609-13.

49. Clark HD, Wells GA, Huet C, et al. Assessing the quality of randomized trials: reliability of the Jadad scale. Control Clin Trials 1999;20:448-52.

References

1. Helgason S, Petursson G, Gudmundsson S, Sigurdsson JA. Prevalence of postherpetic neuralgia after a first episode of herpes zoster: prospective study with long-term follow up. BMJ 2000;321:794-6.

2. Burgoon CF, Burgoon JS, Baldridge GD. The natural history of herpes zoster. JAMA 1957;164:265-9.

3. Helgason S, Sigurdsson JA, Gudmundsson S. The clinical course of herpes zoster: a prospective study in primary care. Eur J Gen Pract 1996;2:12-6.

4. Alper BS, Lewis PR. Does treatment of acute herpes zoster prevent or shorten postherpetic neuralgia? J Fam Pract 2000;49:255-64.

5. Dwyer DE. Management issues in herpes zoster. Austral Fam Physician 1996;25:299-307.

6. Wood MJ. How should we measure pain in herpes zoster? Neurology 1995;45(12 suppl 8):S61-2.

7. Jadad AR, Moore RA, Carroll D, et al. Assessing the quality of reports of randomized clinical trials: Is blinding necessary? Control Clin Trials 1996;17:1-12.

8. Gerson GR, Jones RB, Luscombe DK. Studies on the concomitant use of carbamazepine and clomipramine for the relief of post-herpetic neuralgia. Postgrad Med J 1977;53(suppl):104-9.

9. Rowbotham MC, Davies PS, Verkempinck C, Galer BS. Lidocaine patch: double-blind controlled study of a new treatment method for post-herpetic neuralgia. Pain 1996;65:39-44.

10. Lidoderm (Lidocaine) Patch. Center for Drug Evaluation and Research application number: NDA 20-612. Medical reviews. Washington, DC: US Food and Drug Administration, Center for Drug Evaluation and Research. Last updated November 30, 1999. Accessed October 31, 2000, at: http://www.fda.gov/cder/foi/nda/99/20612.htm/.

11. Galer BS, Rowbotham MC, Perander J, Friedman E. Topical lidocaine patch relieves postherpetic neuralgia more effectively than a vehicle topical patch: results of an enriched enrollment study. Pain 1999;80:533-8.

12. Watson CP, Tyler KL, Bickers DR, Millikan LE, Smith S, Coleman E. A randomized vehicle-controlled trial of topical capsaicin in the treatment of postherpetic neuralgia. Clin Ther 1993;15:510-26.

13. Bernstein JE, Korman NJ, Bickers DR, Dahl MV, Millikan LE. Topical capsaicin treatment of chronic postherpetic neuralgia. J Am Acad Dermatol 1989;21(2 pt 1):265-70.

14. McQuay HJ, Carroll D, Moxon A, Glynn CJ, Moore RA. Benzydamine cream for the treatment of post-herpetic neuralgia: minimum duration of treatment periods in a cross-over trial. Pain 1990;40:131-5.

15. Coniam SW, Huntan J. A study of benzydamine cream in post-herpetic neuralgia. Res Clin Forums 1988;10:65-8.

16. Max MB, Schafer SC, Culnane M, Smoller B, Dubner R, Gracely RH. Amitriptyline, but not lorazepam, relieves postherpetic neuralgia. Neurology 1988;38:1427-32.

17. Watson CP, Evans RJ, Reed K, Merskey H, Goldsmith L, Warsh J. Amitriptyline versus placebo in postherpetic neuralgia. Neurology 1982;32:671-3.

18. Graff-Radford SB, Shaw LR, Naliboff BN. Amitriptyline and fluphenazine in the treatment of postherpetic neuralgia. Clin J Pain 2000;16:188-92.

19. Watson CP, Vernich L, Chipman M, Reed K. Nortriptyline versus amitriptyline in postherpetic neuralgia: a randomized trial. Neurology 1998;51:1166-71.

20. Watson CP, Chipman M, Reed K, Evans RJ, Birkett N. Amitriptyline versus maprotiline in postherpetic neuralgia: a randomized, double-blind, crossover trial. Pain 1992;48:29-36.

21. Kishore-Kumar R, Max MB, Schafer SC, et al. Desipramine relieves postherpetic neuralgia. Clin Pharmacol Ther 1990;47:305-12.

22. Rowbotham M, Harden N, Stacey B, Bernstein P, Magnus-Miller L. Gabapentin for the treatment of postherpetic neuralgia: a randomized controlled trial. JAMA 1998;280:1837-42.

23. Watson CP, Babul N. Efficacy of oxycodone in neuropathic pain: a randomized trial in postherpetic neuralgia. Neurology 1998;50:1837-41.

24. Gobel H, Stadler T. Treatment of pain due to postherpetic neuralgia with tramadol-results of an open, parallel pilot study vs clomipramine with or without levomepromazine. Clin Drug Invest 1995;10:208-14.

25. Nelson KA, Park KM, Robinovitz E, Tsigos C, Max MB. High-dose oral dextromethorphan versus placebo in painful diabetic neuropathy and postherpetic neuralgia. Neurology 1997;48:1212-8.

26. Eisenberg E, Kleiser A, Dortort A, Haim T, Yarnitsky D. The NMDA (N-methyl-D-aspartate) receptor antagonist memantine in the treatment of postherpetic neuralgia: a double-blind, placebo-controlled study. Eur J Pain 1998;2:321-27.

27. Surman OS, Flynn T, Schooley RT, et al. A double-blind, placebo-controlled study of oral acyclovir in postherpetic neuralgia. Psychosomatics 1990;31:287-92.

28. Dowd NP, Day F, Timon D, Cunningham AJ, Brown L. Iontophoretic vincristine in the treatment of postherpetic neuralgia: a double-blind, randomized, controlled trial. J Pain Symptom Manage 1999;17:175-80.

29. Layman PR, Argyras E, Glynn CJ. Iontophoresis of vincristine versus saline in post-herpetic neuralgia. A controlled trial. Pain 1986;25:165-70.

30. Zuurmond WW, Langendijk PN, Bezemer PD, Brink HE, de Lange JJ, van loenen AC. Treatment of acute reflex sympathetic dystrophy with DMSO 50% in a fatty cream. Acta Anaesthesiol Scand 1996;40:364-7.

31. Lewith GT, Field J, Machin D. Acupuncture compared with placebo in post-herpetic pain. Pain 1983;17:361-8.

32. Rutgers MJ, Van Romunde LKJ, Osman PO. A small randomized comparative trial of acupuncture versus transcutaneous electrical neurostimulation in postherpetic neuralgia. Pain Clin 1988;2:87-9.

33. Kotani N, Kushikata T, Hashimoto H, et al. Intrathecal methylprednisolone for intractable postherpetic neuralgia. N Engl J Med 2000;343:1514-9.

34. Kikuchi A, Kotani N, Sato T, Takamura K, Sakai I, Matsuki A. Comparative therapeutic evaluation of intrathecal versus epidural methylprednisolone for long-term analgesia in patients with intractable postherpetic neuralgia. Reg Anesth Pain Med 1999;24:287-93.

35. Staughton RC, Good J. Double-blind, placebo-controlled clinical trial of a mixture of gangliosides (“Cronassial”) in post-herpetic neuralgia. Curr Med Res Opin 1990;12:169-76.

36. Catala E, Ferrandiz M, Aliaga L, Serra R, Castro MA, Villar LJM. Intravenous lidocaine compared with sympathetic blocks as treatment for post-herpetic neuralgia. A 1-year survey. Pain Clin 1994;7:205-10.

37. Sindrup SH, Jensen TS. Efficacy of pharmacological treatments of neuropathic pain: an update and effect related to mechanism of drug action. Pain 1999;83:389-400.

38. Dekonenko EP, Shishov AS, Kupriianova LV, Rudometov I, Bagrov FI. Postherpetic neuralgia in herpes zoster: its treatment with Zovirax. Zhurnal Nevrologii i Psikhiatrii Imeni S S Korsakova 1999;99(6):56-8.

39. Sigwald J, Bouttier D, Caille F. The treatment of zona and of its associated pains. Study of the results obtained with levomepromazine. Therapie 1959;14:818-24.

40. Hirschmann J. Zoster-neuralgia. Dtsch Med Wochenschr 1971;96:924-5.

41. Mertens HG, Lutzenkirchen H. Neuropsychotropic drugs in the treatment of so-called pain syndromes. Arzneimittelforschung 1970;20:928-30.

42. Lutzenkirchen H, Mertens HG. Treatment of chronic pain syndromes. Analgesic effect of a neuroleptic. Arzneimittelforschung 1970;20:930-1.

43. Tamakawa S, Tsujimoto J, Iharada A, Ogawa H. Treatment of postherpetic neuralgia by topical application of prostaglandin E1-vaseline mixture-a single blind controlled clinical trial. Masui 1999;48:292-4.

44. Ikebe H, Miyagawa A, Mizutani A, Miyamoto M, Taniguchi K, Honda N. The effect of iontophoresis with several Ca channel blockers for PHN patients. Masui 1995;44:428-33.

45. Emerson JD, Burdick E, Hoaglin DC, Mosteller F, Chalmers TC. An empirical study of the possible relation of treatment differences to quality scores in controlled randomized clinical trials. Control Clin Trials 1990;11:339-52.

46. “Quality” scales and checklists. Cochrane Collaboration Handbook [updated September 1997]; Section 6.7.2. In: Mulrow CD, Oxman AD, eds. Cochrane Library [database on disk and CD-ROM]. The Cochrane Collaboration. Oxford, UK: Update Software; 1997.

47. Juni P, Altman DG, Egger M. Systematic reviews in health care: assessing the quality of controlled clinical trials. BMJ 2001;323:42-6.

48. Moher D, Pham B, Jones A, et al. Does quality of reports of randomised trials affect estimates of intervention efficacy reported in meta-analyses? Lancet 1998;352:609-13.

49. Clark HD, Wells GA, Huet C, et al. Assessing the quality of randomized trials: reliability of the Jadad scale. Control Clin Trials 1999;20:448-52.

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Tobacco Cessation Counseling Among Underserved Patients: A Report from CaReNet

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Tobacco Cessation Counseling Among Underserved Patients: A Report from CaReNet

ABSTRACT

OBJECTIVES: The purpose of our study was to determine the frequency of smoking cessation counseling in relation to insurance status in a practice-based research network.

STUDY DESIGN: We administered a modified National Ambulatory Medical Care Survey (NAMCS), with an additional payment category to identify uninsured patients, quarterly to 100 random patients at each practice site for 1 year.

POPULATION: The study population included the patients at the 7 practices within the Colorado Research Network (CaReNet), associated with the Department of Family Medicine, University of Colorado Health Science Center.

OUTCOMES MEASURED: We measured the prevalence of smoking and the frequency of cessation counseling.

RESULTS: Of 2773 visits analyzed, 1443 were made by adults who were either was uninsured (39%), had Medicaid (22%), or had private or a health maintenance organization insurance (private/HMO; 40%). Smoking prevalence was significantly greater in uninsured patients (30%) and Medicaid patients (31%), compared with private/HMO patients (22%) (P =.008). However, those smokers with private/HMO insurance were more likely to receive tobacco counseling (50%) than Medicaid (41%) and uninsured (25%) patients (P <.001). After controlling for potential confounders, this difference remained significant.

CONCLUSIONS: Although smoking is more common among Medicaid and uninsured patients, these smokers are less likely to receive counseling. Possible explanations for this disparity include lack of access to cessation interventions or lower quality of care for underserved patients. This finding may have implications for achieving national public health goals on smoking cessation.

KEY POINTS FOR CLINICIANS

  • Prevalence of smoking is greater in patients who are uninsured or who have Medicaid insurance.
  • Advice on smoking cessation is given less frequently to these same patients.
  • Not providing cessation counseling is a missed opportunity in underserved patients.

Among underserved populations, the burden of tobacco is substantial. There is a clear association between poverty and high rates of tobacco use,1-3 and smoking is more prevalent among the uninsured (39%) than those with insurance (23%).4 Smoking cessation interventions can be successful among low-income and minority patients, especially when tailored to these populations.5-8 Tobacco counseling, including simple advice to quit, has been shown effective in primary care.9-11 Since disadvantaged patients, including 63% of the uninsured,12 are commonly seen in primary care settings, primary care providers are in a unique position to impact tobacco use in underserved patients.

Previous research on cessation counseling rates in low-income patients has yielded conflicting results. Taira and colleagues11 demonstrated that cessation advice by primary care providers was given more frequently to low-income groups. However, this study’s results were based on a written patient questionnaire, and recall may have been a significant limitation. Another study examined physician-reported rates of tobacco cessation counseling, and found that cessation was addressed more frequently with health maintenance organization (HMO)–insured patients (30%) than Medicaid patients (24%).13 However, this analysis did not differentiate between primary care providers and specialists, and neither of these studies identified low-income uninsured patients.

It thus remains unclear whether this effective intervention is routinely provided to underserved patients, including the uninsured, in primary care settings. Using a provider survey instrument that clearly identified medically indigent patients, this study examined the frequency with which primary care providers address tobacco use with their Medicaid-insured and uninsured patients compared with those with private or HMO insurance.

Methods

This study was conducted in the 7 primary care practices in the Colorado Research Network (CaReNet) in 1998 and 1999. CaReNet is a state-wide primary care, practice-based research network founded in 1997 with a particular focus on disadvantaged populations, including rural people, minorities, and the urban poor. The practices in CaReNet are affiliated with the University of Colorado Department of Family Medicine. Of the 7 practices, 4 are family medicine residency sites, 2 are federally-funded community health centers, and 1 is a clinic for the medically indigent. The provider mix in CaReNet includes 56% residents (residents average approximately 3 half-day clinics weekly), 21% full-time clinical faculty, 7% private physicians, and 15% other providers (nurse practitioners, physician assistants, and so forth). At the time of our study, none of the practices had a comprehensive tobacco cessation program on site. Colorado Medicaid recipients were eligible for a limited amount of smoking cessation products (this benefit required prior authorization), but Medicaid did not cover comprehensive programs.

A modified version of the 1994 National Ambulatory Medical Care Survey (NAMCS) was administered in each CaReNet practice. The NAMCS instrument is a physician survey that collects information about an ambulatory visit; it has been used by the National Center for Health Statistics since 1973 to analyze trends of ambulatory care. In the context of our study, the key modification was the addition of “uninsured” in the Expected Source of Payment category. This category included patients who were in 1 of several programs that discount charges on the basis of income, thus covering some of the costs of care. All providers received detailed instructions on completing this modified NAMCS form.

 

 

Each CaReNet practice collected data on a total of 400 patient visits in 1-week cycles (100 patients per cycle), quarterly, for 1 year. We used the typical NAMCS protocol of collecting data on every second patient presenting for medical care during the study period.14 The anonymous visit survey forms were coded using standard NAMCS nomenclature. Only patients aged between 13 years and 65 years were included in this analysis because there are almost no uninsured people older than 65 years. To identify patients with private insurance, the options “Private/commercial” and “HMO/other prepaid” were combined (hereafter referred to as “Private/HMO”).

For the present study, we examined the impact of patient insurance on 2 primary outcomes: (1) patient smoking status, and (2) whether smokers received smoking cessation counseling. Each provider coded smoking status as “Yes,” “No,” or “Unknown.” Only patients with a known smoking status (90% of sample) were included in the present analysis. For those patients coded as smokers, we determined whether providers checked the “Smoking Cessation” box.

Analysis

To examine the association between insurance group and study outcomes, we used chi-square tests to determine whether insurance group and other patient demographics (sex, age, ethnicity, and race) were reliably associated with smoking status and cessation counseling. Next, for each primary outcome, we conducted multivariate analyses to examine the effect of patient insurance, while controlling for other important demographic factors (ie, those with P values 0.20 in univariate analyses,15 as well as additional factors that may account for variability in this relationship. These factors included duration of visit, whether the patient had been seen before in the practice, and whether the patient had at least 1 of the chronic conditions listed on the NAMCS form (hypertension, depression, obesity, or hypercholesterolemia). Because initial random effects analyses revealed no significant practice site effects on the frequency of tobacco use and cessation counseling, all analyses include patient-level data.

The Colorado Multiple Institutional Review Board approved our study design.

Results

Description of sample

CaReNet providers completed NAMCS forms on 2773 patient encounters of 2800 eligible visits (99% completion rate). For this study, of the 2773 encounters, 1443 remained after excluding patients younger than 13 or older than 65 years, and those with sources of payment other than Medicaid, Uninsured, or Private/HMO. As shown in (Table 1), CaReNet patients in the present study were demographically diverse, with a high percentage who were Hispanic (26%), female (74%), or low-income (39% uninsured, 22% Medicaid).

TABLE 1
DEMOGRAPHIC CHARACTERISTICS OF CARENET STUDY SAMPLE

CharacteristicsN%*
Sex
  Female106374
  Male38026
Age
  13-17755
  18-4488661
  45-6448233
Ethnicity
  Hispanic36926
  Non-Hispanic106874
Race ‡†
  Asian-Pacific Islander10< 1
  Black1047
  Indian-Eskimo-Aleut322
  White128289
Insurance Status
  Uninsured56039
  Medicaid31122
  Private/HMO57240
*Percentages may not add to 100 because of rounding.
† Ethnic background is missing for 6 patients.
‡ Race is missing for 15 patients.
§ For all remaining analyses, we have re-coded race into “white” or “non-white.”

Univariate and multivariate analysis of smoking

A total of 351 patients in the study sample (24%) were identified as smokers. As expected, smoking was significantly more prevalent in the Medicaid and uninsured groups (Table W1*).

(Table 2) presents multivariate logistic regression results showing the significant relationship between insurance and smoking status after controlling for other important demographic and practice variables. Uninsured patients had similar rates of smoking as those with Medicaid; however, smoking among Private/HMO–insured patients was approximately half as frequent as among the uninsured.

In addition to patient insurance, ethnicity and clinical factors predicted whether patients smoked. Non-Hispanic patients were more than twice as likely to be identified as smokers compared with Hispanic patients (P <.001). Also, patients who were new to the practice or who had at least one chronic condition were significantly more likely to be identified as smokers (P = .011 and P = .001, respectively).

Table 2
LOGISTIC REGRESSION RESULTS: RELATIONSHIP OF PATIENT FACTORS WITH LIKELIHOOD OF SMOKING

Patient FactorOdds Ratio for Smoking (95% CI)P
Insurance
  Uninsured*1.00. 
  Medicaid1.01 (0.73 – 1.4).937
  Private/HMO0.55 (0.41 – 0.73)< .001
Sex
  Female*1.00 
  Male1.22 (0.92 – 1.6).164
Ethnicity
  Hispanic*1.00 
  Non-Hispanic2.1 (1.5 – 3.0)< .001
Patient Seen Before
  Yes*1.00 
  No1.6 (1.1 – 2.3).011
Duration of Visit1.00.990
Chronic Disease
  None*1.00 
  One or more1.6 (1.2 – 2.0).001
CI denotes confidence interval.
*Reference group.

Univeriate and multivariate analysis of cessation advice or counseling

The second primary analysis examined whether insurance is associated with how often smokers are counseled during visits. Out of 351 smokers, 129 (37%) received tobacco counseling during the medical encounter. Private/HMO insurance and duration of visit were the only factors univariately associated with whether a smoker received counseling (Table W2*).

Multivariate results indicate that patient insurance remained the only significant variable after controlling for other factors that might explain whether smokers received counseling. Smokers with Medicaid were more than twice as likely, and Private/HMO–insured smokers were more than 3 times as likely as uninsured patients (P <.001) to receive smoking cessation counseling (Table 3).

 

 

TABLE 3
LOGISTIC REGRESSION RESULTS: PATIENT FACTORS ASSOCIATED WITH LIKELIHOOD OF RECEIVING SMOKING CESSATION COUNSELING

Patient FactorOdds Ratio of Receiving Counseling (95% CI)P
Insurance
  Uninsured*1.00 
  Medicaid2.1 (1.2 – 3.7).011
  Private/HMO3.0 (1.8 – 5.3)< .001
Seen Patient Before
  Yes*1.00 
  No1.1 (0.6 – 2.1).707
Duration of Visit1.02 (0.99 – 1.0).158
Chronic Disease
  None*1.00 
  One or more1.1 (0.66 – 1.7).811
*Reference group.

Discussion

These findings demonstrate that although smoking is more common in CaReNet’s Medicaid and uninsured patients, providers gave cessation advice less often to these patients. The actual prevalence of tobacco use may be even greater than we think because providers may underreport it, but our results are similar to national trends.4 The decreased rate of tobacco counseling in underserved patients is in contrast to the findings in a study that were based on patient recall,11 rather than the provider-report methodology of NAMCS. However, our counseling results are consistent with a national NAMCS analysis, which found that tobacco use was addressed more frequently with HMO-insured patients than Medicaid patients.13 In that study, the overall primary care counseling rate (33%) was similar to that of CaReNet providers (37%). To the best of our knowledge, our finding of a lower rate of tobacco counseling in uninsured patients has not been previously reported.

Our study does not address why providers are less likely to advise Medicaid or uninsured patients to quit smoking. It is possible that tobacco interventions, such as pharmacologic aids and comprehensive cessation programs, may not be available to these groups because of cost. Providers may simply be reflecting this situation by not addressing cessation. Even so, cost and access barriers do not explain why providers would be less likely to give simple cessation advice to disadvantaged smokers. One possibility is that these findings may indicate a lower quality of care for these patients. Other preventive care measures have been shown to be performed less often in uninsured patients,16 and several studies have documented a lower quality of care for Medicaid and uninsured patients with chronic diseases.17-19

Limitations

A major limitation of our study is that the uninsured or Medicaid groups may have included sicker or more complex patients at the surveyed visits, thus there may have been less time to devote to tobacco cessation advice during that clinic visit. Unfortunately, the NAMCS instrument does not readily measure disease severity or case mix. In our analysis, we controlled for the presence of 1 or more chronic diseases (limited in NAMCS to 4 specific conditions), but this is only a crude measure of patient complexity. If patients in one of the payment groups were sicker, they might have had more frequent clinic visits, and tobacco cessation may have been addressed at higher rates over time than were found in this cross-sectional study. However, even in the presence of major morbidities, the uninsured often lack continuity because of their tenuous access to care.

If the payer mix of residents and faculty was significantly different, and residents addressed tobacco use at a different rate than faculty, this could explain some of the counseling differences. Unfortunately, this NAMCS instrument is anonymous and cannot identify the type of provider. Similarly, it is possible that the type of visit (acute care, chronic care, or prevention) may account for some of the findings. However, NAMCS also does not specify type of visit and there may be considerable overlap at any given encounter.

Our study administered NAMCS to the practices that make up CaReNet, and the results are not necessarily generalizable to other populations. There is substantial regional variation in health care access programs for the uninsured20; therefore the uninsured patients in CaReNet may not be representative of uninsured in primary care elsewhere. Also, the demographics of CaReNet include higher percentages of Hispanics and Medicaid recipients compared with a national analysis of primary care trends.21 CaReNet more closely resembles community health centers,22 except CaReNet has a greater number of Hispanic patients and fewer black patients, reflecting the particular demographics of Colorado. However, the smoking prevalence rates we found in the privately insured, Medicaid, and uninsured groups were similar to national patterns.

Conclusions

Our study argues for the inclusion of a separate payment category that clearly identifies the uninsured in NAMCS and other data collection instruments. Future studies on tobacco counseling rates should be designed to differentiate factors associated with the lower rate of counseling in disadvantaged populations, such as patient complexity, competing demands, lack of access to cessation resources, or lower standards of care. Identification of these factors may be valuable in implementing interventions to improve the rate of counseling for these patients.

 

 

If national tobacco goals are to be realized, then socioeconomic disparities in counseling need to be addressed. Our results show that primary care providers can substantially improve the tobacco counseling rate among disadvantaged smokers. As this occurs, the rate of smoking in these patients can be expected to decrease.

*Tables W1 and W2 are available on the JFP Web site, www.jfponline.com.

Acknowledgments

We appreciate the financial support of CaReNet by the University of Colorado School of Medicine Academic Enrichment Fund. We would also like to thank the faculty, residents, and staff at the following CaReNet sites for their assistance with this study: CU Care, Denver, Colorado; St. Mary’s Family Practice, Grand Junction, Colorado; Brighton Salud Family Health Center, Brighton, Colorado; Rose Family Medicine Center, Denver, Colorado; Swedish Family Medicine Center, Littleton, Colorado; AF Williams Family Medicine Center, Denver, Colorado; and La CasaQuigg Newton Health Center, Denver, Colorado. We are also grateful to Elizabeth Staton and Michael Huiras, MD, for their comments on the manuscript. The authors deny any conflict of interest.

References

1. Fiscella K. Is lower income associated with greater biopsychosocial morbidity? J Fam Pract 1999;48:372-7.

2. Flint AF, Novotny TE. Poverty status and cigarette smoking prevalence and cessation in the United States, 1983-1999. Tob Control 1997;6:5-6.

3. Hyman DJ, Simons-Morton DG, Dunn JK, Ho K. Smoking, smoking cessation, and understanding of the role of multiple cardiac risk factors among the urban poor. Prev Med 1996;25:653-9.

4. Anonymous. Self-assessed health status and selected behavioral risk factors among persons with and without health-care coverage— United States, 1994-1995. Morb Mortal Wkly Rep 1998;47:16-8.

5. Albrecht SA, Rosella JD, Patrick T. Smoking among low-income, pregnant women: prevalence rates, cessation interventions, and clinical implications. Birth 1994;21:155-62.

6. O’Loughlin J, Paradis G, Renaud L, Meshefedjian G, Barnett T. The “Yes, I Quit” smoking cessation course: does it help women in a low income community quit? J Community Health 1997;22:451-68.

7. Wadland WC, Stoffelmayr B, East KI. Enhancing smoking cessation of low-income smokers in managed care. J Fam Pract 2001;50:138-44.

8. Friedman DB, Williams AN, Levine BD. Compliance and efficacy of cardiac rehabilitation and risk factor modification in the medically indigent. Am J Card 1997;79:281-5.

9. Russell MA, Wilson C, Taylor C, Baker CD. Effect of general practitioners’ advice against smoking. Br Med J 1979;2:231-35.

10. Kreuter MW, Chheda SG, Bull FC. How does physician advice influence patient behavior? Evidence for a priming effect. Arch Fam Med 2000;9:426-33.

11. Taira DA, Safran DG, Seto TB, Rogers WH, Tarlov AR. The relationship between patient income and physician discussion of health risk behaviors. JAMA 1997;278:1412-7.

12. The Kaiser Commission on Medicaid and the Uninsured. Uninsured in Amerca: a chart book. June 1998; p.51.

13. Thorndike AN, Rigotti NA, Stafford RS, Singer DE. National patterns in the treatment of smokers by physicians. JAMA 1998;279:604-8.

14. Schappert SM, Nelson C. National Ambulatory Medical Care Survey, 1995-96 Summary. National Center for Health Statistics. Vital Health Stat [serial online] 13(142). 1999. [cited 2000 Dec 18] Available at www.cdc.gov/nchs/data/sr13_142.pdf.

15. Hosmer DW, Lemeshow S. Applied logistic regression. New York, NY: John Wiley and Sons. 1989.

16. Anonymous. Health insurance coverage and receipt of preventive health services—United States, 1993. MMWR Morb Mortal Wkly Rep 1995;44:219-25.

17. Ayanian JZ, Kohler BA, Abe T, Epstein AM. The relation between health insurance coverage and clinical outcomes among women with breast cancer. N Engl J Med 1993;329:326-31.

18. Carlisle DM, Leape LL, Bickel S, et al. Underuse and overuse of diagnostic testing for coronary artery disease in patients presenting with new-onset chest pain. Am J Med 1999;106:391-8.

19. Sada MJ, French WJ, Carlisle DM, Chandra NC, Gore JM, Rogers WJ. Influence of payor on use of invasive cardiac procedures and patient outcome after myocardial infarction in the United States. Participants in the National Registry of Myocardial Infarction. J Am Coll Cardiol 1998;31:1474-80.

20. Cunningham PJ, Kemper P. Ability to obtain medical care for the uninsured: How much does it vary across communities? JAMA 1998;280:921-7.

21. Stafford RS, Saglam D, Causino N, et al. Trends in adult visits to primary care physicians in the United States. Arch Fam Med 1999;8:26-32.

22. Forrest CB, Whelan EM. A comparison of community health centers, hospital outpatient departments, and physicians’ offices. JAMA 2000;284:2077-83

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BENNETT PARNES, MD
DEBORAH S. MAIN, PHD
SHERRY HOLCOMB
WILSON PACE, MD
Denver, Colorado
Submitted, May 24, 2001.
This work was presented at The North American Primary Care Research Group annual meeting; November 1999; San Diego, California. From CaReNet, Colorado Research Network, the Department of Family Medicine, University of Colorado Health Sciences Center, Denver. All reprint requests should be addressed to Bennett Parnes, MD, University of Colorado School of Medicine, 1180 Clermont, Denver, CO 80220. E-mail: [email protected].

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BENNETT PARNES, MD
DEBORAH S. MAIN, PHD
SHERRY HOLCOMB
WILSON PACE, MD
Denver, Colorado
Submitted, May 24, 2001.
This work was presented at The North American Primary Care Research Group annual meeting; November 1999; San Diego, California. From CaReNet, Colorado Research Network, the Department of Family Medicine, University of Colorado Health Sciences Center, Denver. All reprint requests should be addressed to Bennett Parnes, MD, University of Colorado School of Medicine, 1180 Clermont, Denver, CO 80220. E-mail: [email protected].

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BENNETT PARNES, MD
DEBORAH S. MAIN, PHD
SHERRY HOLCOMB
WILSON PACE, MD
Denver, Colorado
Submitted, May 24, 2001.
This work was presented at The North American Primary Care Research Group annual meeting; November 1999; San Diego, California. From CaReNet, Colorado Research Network, the Department of Family Medicine, University of Colorado Health Sciences Center, Denver. All reprint requests should be addressed to Bennett Parnes, MD, University of Colorado School of Medicine, 1180 Clermont, Denver, CO 80220. E-mail: [email protected].

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ABSTRACT

OBJECTIVES: The purpose of our study was to determine the frequency of smoking cessation counseling in relation to insurance status in a practice-based research network.

STUDY DESIGN: We administered a modified National Ambulatory Medical Care Survey (NAMCS), with an additional payment category to identify uninsured patients, quarterly to 100 random patients at each practice site for 1 year.

POPULATION: The study population included the patients at the 7 practices within the Colorado Research Network (CaReNet), associated with the Department of Family Medicine, University of Colorado Health Science Center.

OUTCOMES MEASURED: We measured the prevalence of smoking and the frequency of cessation counseling.

RESULTS: Of 2773 visits analyzed, 1443 were made by adults who were either was uninsured (39%), had Medicaid (22%), or had private or a health maintenance organization insurance (private/HMO; 40%). Smoking prevalence was significantly greater in uninsured patients (30%) and Medicaid patients (31%), compared with private/HMO patients (22%) (P =.008). However, those smokers with private/HMO insurance were more likely to receive tobacco counseling (50%) than Medicaid (41%) and uninsured (25%) patients (P <.001). After controlling for potential confounders, this difference remained significant.

CONCLUSIONS: Although smoking is more common among Medicaid and uninsured patients, these smokers are less likely to receive counseling. Possible explanations for this disparity include lack of access to cessation interventions or lower quality of care for underserved patients. This finding may have implications for achieving national public health goals on smoking cessation.

KEY POINTS FOR CLINICIANS

  • Prevalence of smoking is greater in patients who are uninsured or who have Medicaid insurance.
  • Advice on smoking cessation is given less frequently to these same patients.
  • Not providing cessation counseling is a missed opportunity in underserved patients.

Among underserved populations, the burden of tobacco is substantial. There is a clear association between poverty and high rates of tobacco use,1-3 and smoking is more prevalent among the uninsured (39%) than those with insurance (23%).4 Smoking cessation interventions can be successful among low-income and minority patients, especially when tailored to these populations.5-8 Tobacco counseling, including simple advice to quit, has been shown effective in primary care.9-11 Since disadvantaged patients, including 63% of the uninsured,12 are commonly seen in primary care settings, primary care providers are in a unique position to impact tobacco use in underserved patients.

Previous research on cessation counseling rates in low-income patients has yielded conflicting results. Taira and colleagues11 demonstrated that cessation advice by primary care providers was given more frequently to low-income groups. However, this study’s results were based on a written patient questionnaire, and recall may have been a significant limitation. Another study examined physician-reported rates of tobacco cessation counseling, and found that cessation was addressed more frequently with health maintenance organization (HMO)–insured patients (30%) than Medicaid patients (24%).13 However, this analysis did not differentiate between primary care providers and specialists, and neither of these studies identified low-income uninsured patients.

It thus remains unclear whether this effective intervention is routinely provided to underserved patients, including the uninsured, in primary care settings. Using a provider survey instrument that clearly identified medically indigent patients, this study examined the frequency with which primary care providers address tobacco use with their Medicaid-insured and uninsured patients compared with those with private or HMO insurance.

Methods

This study was conducted in the 7 primary care practices in the Colorado Research Network (CaReNet) in 1998 and 1999. CaReNet is a state-wide primary care, practice-based research network founded in 1997 with a particular focus on disadvantaged populations, including rural people, minorities, and the urban poor. The practices in CaReNet are affiliated with the University of Colorado Department of Family Medicine. Of the 7 practices, 4 are family medicine residency sites, 2 are federally-funded community health centers, and 1 is a clinic for the medically indigent. The provider mix in CaReNet includes 56% residents (residents average approximately 3 half-day clinics weekly), 21% full-time clinical faculty, 7% private physicians, and 15% other providers (nurse practitioners, physician assistants, and so forth). At the time of our study, none of the practices had a comprehensive tobacco cessation program on site. Colorado Medicaid recipients were eligible for a limited amount of smoking cessation products (this benefit required prior authorization), but Medicaid did not cover comprehensive programs.

A modified version of the 1994 National Ambulatory Medical Care Survey (NAMCS) was administered in each CaReNet practice. The NAMCS instrument is a physician survey that collects information about an ambulatory visit; it has been used by the National Center for Health Statistics since 1973 to analyze trends of ambulatory care. In the context of our study, the key modification was the addition of “uninsured” in the Expected Source of Payment category. This category included patients who were in 1 of several programs that discount charges on the basis of income, thus covering some of the costs of care. All providers received detailed instructions on completing this modified NAMCS form.

 

 

Each CaReNet practice collected data on a total of 400 patient visits in 1-week cycles (100 patients per cycle), quarterly, for 1 year. We used the typical NAMCS protocol of collecting data on every second patient presenting for medical care during the study period.14 The anonymous visit survey forms were coded using standard NAMCS nomenclature. Only patients aged between 13 years and 65 years were included in this analysis because there are almost no uninsured people older than 65 years. To identify patients with private insurance, the options “Private/commercial” and “HMO/other prepaid” were combined (hereafter referred to as “Private/HMO”).

For the present study, we examined the impact of patient insurance on 2 primary outcomes: (1) patient smoking status, and (2) whether smokers received smoking cessation counseling. Each provider coded smoking status as “Yes,” “No,” or “Unknown.” Only patients with a known smoking status (90% of sample) were included in the present analysis. For those patients coded as smokers, we determined whether providers checked the “Smoking Cessation” box.

Analysis

To examine the association between insurance group and study outcomes, we used chi-square tests to determine whether insurance group and other patient demographics (sex, age, ethnicity, and race) were reliably associated with smoking status and cessation counseling. Next, for each primary outcome, we conducted multivariate analyses to examine the effect of patient insurance, while controlling for other important demographic factors (ie, those with P values 0.20 in univariate analyses,15 as well as additional factors that may account for variability in this relationship. These factors included duration of visit, whether the patient had been seen before in the practice, and whether the patient had at least 1 of the chronic conditions listed on the NAMCS form (hypertension, depression, obesity, or hypercholesterolemia). Because initial random effects analyses revealed no significant practice site effects on the frequency of tobacco use and cessation counseling, all analyses include patient-level data.

The Colorado Multiple Institutional Review Board approved our study design.

Results

Description of sample

CaReNet providers completed NAMCS forms on 2773 patient encounters of 2800 eligible visits (99% completion rate). For this study, of the 2773 encounters, 1443 remained after excluding patients younger than 13 or older than 65 years, and those with sources of payment other than Medicaid, Uninsured, or Private/HMO. As shown in (Table 1), CaReNet patients in the present study were demographically diverse, with a high percentage who were Hispanic (26%), female (74%), or low-income (39% uninsured, 22% Medicaid).

TABLE 1
DEMOGRAPHIC CHARACTERISTICS OF CARENET STUDY SAMPLE

CharacteristicsN%*
Sex
  Female106374
  Male38026
Age
  13-17755
  18-4488661
  45-6448233
Ethnicity
  Hispanic36926
  Non-Hispanic106874
Race ‡†
  Asian-Pacific Islander10< 1
  Black1047
  Indian-Eskimo-Aleut322
  White128289
Insurance Status
  Uninsured56039
  Medicaid31122
  Private/HMO57240
*Percentages may not add to 100 because of rounding.
† Ethnic background is missing for 6 patients.
‡ Race is missing for 15 patients.
§ For all remaining analyses, we have re-coded race into “white” or “non-white.”

Univariate and multivariate analysis of smoking

A total of 351 patients in the study sample (24%) were identified as smokers. As expected, smoking was significantly more prevalent in the Medicaid and uninsured groups (Table W1*).

(Table 2) presents multivariate logistic regression results showing the significant relationship between insurance and smoking status after controlling for other important demographic and practice variables. Uninsured patients had similar rates of smoking as those with Medicaid; however, smoking among Private/HMO–insured patients was approximately half as frequent as among the uninsured.

In addition to patient insurance, ethnicity and clinical factors predicted whether patients smoked. Non-Hispanic patients were more than twice as likely to be identified as smokers compared with Hispanic patients (P <.001). Also, patients who were new to the practice or who had at least one chronic condition were significantly more likely to be identified as smokers (P = .011 and P = .001, respectively).

Table 2
LOGISTIC REGRESSION RESULTS: RELATIONSHIP OF PATIENT FACTORS WITH LIKELIHOOD OF SMOKING

Patient FactorOdds Ratio for Smoking (95% CI)P
Insurance
  Uninsured*1.00. 
  Medicaid1.01 (0.73 – 1.4).937
  Private/HMO0.55 (0.41 – 0.73)< .001
Sex
  Female*1.00 
  Male1.22 (0.92 – 1.6).164
Ethnicity
  Hispanic*1.00 
  Non-Hispanic2.1 (1.5 – 3.0)< .001
Patient Seen Before
  Yes*1.00 
  No1.6 (1.1 – 2.3).011
Duration of Visit1.00.990
Chronic Disease
  None*1.00 
  One or more1.6 (1.2 – 2.0).001
CI denotes confidence interval.
*Reference group.

Univeriate and multivariate analysis of cessation advice or counseling

The second primary analysis examined whether insurance is associated with how often smokers are counseled during visits. Out of 351 smokers, 129 (37%) received tobacco counseling during the medical encounter. Private/HMO insurance and duration of visit were the only factors univariately associated with whether a smoker received counseling (Table W2*).

Multivariate results indicate that patient insurance remained the only significant variable after controlling for other factors that might explain whether smokers received counseling. Smokers with Medicaid were more than twice as likely, and Private/HMO–insured smokers were more than 3 times as likely as uninsured patients (P <.001) to receive smoking cessation counseling (Table 3).

 

 

TABLE 3
LOGISTIC REGRESSION RESULTS: PATIENT FACTORS ASSOCIATED WITH LIKELIHOOD OF RECEIVING SMOKING CESSATION COUNSELING

Patient FactorOdds Ratio of Receiving Counseling (95% CI)P
Insurance
  Uninsured*1.00 
  Medicaid2.1 (1.2 – 3.7).011
  Private/HMO3.0 (1.8 – 5.3)< .001
Seen Patient Before
  Yes*1.00 
  No1.1 (0.6 – 2.1).707
Duration of Visit1.02 (0.99 – 1.0).158
Chronic Disease
  None*1.00 
  One or more1.1 (0.66 – 1.7).811
*Reference group.

Discussion

These findings demonstrate that although smoking is more common in CaReNet’s Medicaid and uninsured patients, providers gave cessation advice less often to these patients. The actual prevalence of tobacco use may be even greater than we think because providers may underreport it, but our results are similar to national trends.4 The decreased rate of tobacco counseling in underserved patients is in contrast to the findings in a study that were based on patient recall,11 rather than the provider-report methodology of NAMCS. However, our counseling results are consistent with a national NAMCS analysis, which found that tobacco use was addressed more frequently with HMO-insured patients than Medicaid patients.13 In that study, the overall primary care counseling rate (33%) was similar to that of CaReNet providers (37%). To the best of our knowledge, our finding of a lower rate of tobacco counseling in uninsured patients has not been previously reported.

Our study does not address why providers are less likely to advise Medicaid or uninsured patients to quit smoking. It is possible that tobacco interventions, such as pharmacologic aids and comprehensive cessation programs, may not be available to these groups because of cost. Providers may simply be reflecting this situation by not addressing cessation. Even so, cost and access barriers do not explain why providers would be less likely to give simple cessation advice to disadvantaged smokers. One possibility is that these findings may indicate a lower quality of care for these patients. Other preventive care measures have been shown to be performed less often in uninsured patients,16 and several studies have documented a lower quality of care for Medicaid and uninsured patients with chronic diseases.17-19

Limitations

A major limitation of our study is that the uninsured or Medicaid groups may have included sicker or more complex patients at the surveyed visits, thus there may have been less time to devote to tobacco cessation advice during that clinic visit. Unfortunately, the NAMCS instrument does not readily measure disease severity or case mix. In our analysis, we controlled for the presence of 1 or more chronic diseases (limited in NAMCS to 4 specific conditions), but this is only a crude measure of patient complexity. If patients in one of the payment groups were sicker, they might have had more frequent clinic visits, and tobacco cessation may have been addressed at higher rates over time than were found in this cross-sectional study. However, even in the presence of major morbidities, the uninsured often lack continuity because of their tenuous access to care.

If the payer mix of residents and faculty was significantly different, and residents addressed tobacco use at a different rate than faculty, this could explain some of the counseling differences. Unfortunately, this NAMCS instrument is anonymous and cannot identify the type of provider. Similarly, it is possible that the type of visit (acute care, chronic care, or prevention) may account for some of the findings. However, NAMCS also does not specify type of visit and there may be considerable overlap at any given encounter.

Our study administered NAMCS to the practices that make up CaReNet, and the results are not necessarily generalizable to other populations. There is substantial regional variation in health care access programs for the uninsured20; therefore the uninsured patients in CaReNet may not be representative of uninsured in primary care elsewhere. Also, the demographics of CaReNet include higher percentages of Hispanics and Medicaid recipients compared with a national analysis of primary care trends.21 CaReNet more closely resembles community health centers,22 except CaReNet has a greater number of Hispanic patients and fewer black patients, reflecting the particular demographics of Colorado. However, the smoking prevalence rates we found in the privately insured, Medicaid, and uninsured groups were similar to national patterns.

Conclusions

Our study argues for the inclusion of a separate payment category that clearly identifies the uninsured in NAMCS and other data collection instruments. Future studies on tobacco counseling rates should be designed to differentiate factors associated with the lower rate of counseling in disadvantaged populations, such as patient complexity, competing demands, lack of access to cessation resources, or lower standards of care. Identification of these factors may be valuable in implementing interventions to improve the rate of counseling for these patients.

 

 

If national tobacco goals are to be realized, then socioeconomic disparities in counseling need to be addressed. Our results show that primary care providers can substantially improve the tobacco counseling rate among disadvantaged smokers. As this occurs, the rate of smoking in these patients can be expected to decrease.

*Tables W1 and W2 are available on the JFP Web site, www.jfponline.com.

Acknowledgments

We appreciate the financial support of CaReNet by the University of Colorado School of Medicine Academic Enrichment Fund. We would also like to thank the faculty, residents, and staff at the following CaReNet sites for their assistance with this study: CU Care, Denver, Colorado; St. Mary’s Family Practice, Grand Junction, Colorado; Brighton Salud Family Health Center, Brighton, Colorado; Rose Family Medicine Center, Denver, Colorado; Swedish Family Medicine Center, Littleton, Colorado; AF Williams Family Medicine Center, Denver, Colorado; and La CasaQuigg Newton Health Center, Denver, Colorado. We are also grateful to Elizabeth Staton and Michael Huiras, MD, for their comments on the manuscript. The authors deny any conflict of interest.

ABSTRACT

OBJECTIVES: The purpose of our study was to determine the frequency of smoking cessation counseling in relation to insurance status in a practice-based research network.

STUDY DESIGN: We administered a modified National Ambulatory Medical Care Survey (NAMCS), with an additional payment category to identify uninsured patients, quarterly to 100 random patients at each practice site for 1 year.

POPULATION: The study population included the patients at the 7 practices within the Colorado Research Network (CaReNet), associated with the Department of Family Medicine, University of Colorado Health Science Center.

OUTCOMES MEASURED: We measured the prevalence of smoking and the frequency of cessation counseling.

RESULTS: Of 2773 visits analyzed, 1443 were made by adults who were either was uninsured (39%), had Medicaid (22%), or had private or a health maintenance organization insurance (private/HMO; 40%). Smoking prevalence was significantly greater in uninsured patients (30%) and Medicaid patients (31%), compared with private/HMO patients (22%) (P =.008). However, those smokers with private/HMO insurance were more likely to receive tobacco counseling (50%) than Medicaid (41%) and uninsured (25%) patients (P <.001). After controlling for potential confounders, this difference remained significant.

CONCLUSIONS: Although smoking is more common among Medicaid and uninsured patients, these smokers are less likely to receive counseling. Possible explanations for this disparity include lack of access to cessation interventions or lower quality of care for underserved patients. This finding may have implications for achieving national public health goals on smoking cessation.

KEY POINTS FOR CLINICIANS

  • Prevalence of smoking is greater in patients who are uninsured or who have Medicaid insurance.
  • Advice on smoking cessation is given less frequently to these same patients.
  • Not providing cessation counseling is a missed opportunity in underserved patients.

Among underserved populations, the burden of tobacco is substantial. There is a clear association between poverty and high rates of tobacco use,1-3 and smoking is more prevalent among the uninsured (39%) than those with insurance (23%).4 Smoking cessation interventions can be successful among low-income and minority patients, especially when tailored to these populations.5-8 Tobacco counseling, including simple advice to quit, has been shown effective in primary care.9-11 Since disadvantaged patients, including 63% of the uninsured,12 are commonly seen in primary care settings, primary care providers are in a unique position to impact tobacco use in underserved patients.

Previous research on cessation counseling rates in low-income patients has yielded conflicting results. Taira and colleagues11 demonstrated that cessation advice by primary care providers was given more frequently to low-income groups. However, this study’s results were based on a written patient questionnaire, and recall may have been a significant limitation. Another study examined physician-reported rates of tobacco cessation counseling, and found that cessation was addressed more frequently with health maintenance organization (HMO)–insured patients (30%) than Medicaid patients (24%).13 However, this analysis did not differentiate between primary care providers and specialists, and neither of these studies identified low-income uninsured patients.

It thus remains unclear whether this effective intervention is routinely provided to underserved patients, including the uninsured, in primary care settings. Using a provider survey instrument that clearly identified medically indigent patients, this study examined the frequency with which primary care providers address tobacco use with their Medicaid-insured and uninsured patients compared with those with private or HMO insurance.

Methods

This study was conducted in the 7 primary care practices in the Colorado Research Network (CaReNet) in 1998 and 1999. CaReNet is a state-wide primary care, practice-based research network founded in 1997 with a particular focus on disadvantaged populations, including rural people, minorities, and the urban poor. The practices in CaReNet are affiliated with the University of Colorado Department of Family Medicine. Of the 7 practices, 4 are family medicine residency sites, 2 are federally-funded community health centers, and 1 is a clinic for the medically indigent. The provider mix in CaReNet includes 56% residents (residents average approximately 3 half-day clinics weekly), 21% full-time clinical faculty, 7% private physicians, and 15% other providers (nurse practitioners, physician assistants, and so forth). At the time of our study, none of the practices had a comprehensive tobacco cessation program on site. Colorado Medicaid recipients were eligible for a limited amount of smoking cessation products (this benefit required prior authorization), but Medicaid did not cover comprehensive programs.

A modified version of the 1994 National Ambulatory Medical Care Survey (NAMCS) was administered in each CaReNet practice. The NAMCS instrument is a physician survey that collects information about an ambulatory visit; it has been used by the National Center for Health Statistics since 1973 to analyze trends of ambulatory care. In the context of our study, the key modification was the addition of “uninsured” in the Expected Source of Payment category. This category included patients who were in 1 of several programs that discount charges on the basis of income, thus covering some of the costs of care. All providers received detailed instructions on completing this modified NAMCS form.

 

 

Each CaReNet practice collected data on a total of 400 patient visits in 1-week cycles (100 patients per cycle), quarterly, for 1 year. We used the typical NAMCS protocol of collecting data on every second patient presenting for medical care during the study period.14 The anonymous visit survey forms were coded using standard NAMCS nomenclature. Only patients aged between 13 years and 65 years were included in this analysis because there are almost no uninsured people older than 65 years. To identify patients with private insurance, the options “Private/commercial” and “HMO/other prepaid” were combined (hereafter referred to as “Private/HMO”).

For the present study, we examined the impact of patient insurance on 2 primary outcomes: (1) patient smoking status, and (2) whether smokers received smoking cessation counseling. Each provider coded smoking status as “Yes,” “No,” or “Unknown.” Only patients with a known smoking status (90% of sample) were included in the present analysis. For those patients coded as smokers, we determined whether providers checked the “Smoking Cessation” box.

Analysis

To examine the association between insurance group and study outcomes, we used chi-square tests to determine whether insurance group and other patient demographics (sex, age, ethnicity, and race) were reliably associated with smoking status and cessation counseling. Next, for each primary outcome, we conducted multivariate analyses to examine the effect of patient insurance, while controlling for other important demographic factors (ie, those with P values 0.20 in univariate analyses,15 as well as additional factors that may account for variability in this relationship. These factors included duration of visit, whether the patient had been seen before in the practice, and whether the patient had at least 1 of the chronic conditions listed on the NAMCS form (hypertension, depression, obesity, or hypercholesterolemia). Because initial random effects analyses revealed no significant practice site effects on the frequency of tobacco use and cessation counseling, all analyses include patient-level data.

The Colorado Multiple Institutional Review Board approved our study design.

Results

Description of sample

CaReNet providers completed NAMCS forms on 2773 patient encounters of 2800 eligible visits (99% completion rate). For this study, of the 2773 encounters, 1443 remained after excluding patients younger than 13 or older than 65 years, and those with sources of payment other than Medicaid, Uninsured, or Private/HMO. As shown in (Table 1), CaReNet patients in the present study were demographically diverse, with a high percentage who were Hispanic (26%), female (74%), or low-income (39% uninsured, 22% Medicaid).

TABLE 1
DEMOGRAPHIC CHARACTERISTICS OF CARENET STUDY SAMPLE

CharacteristicsN%*
Sex
  Female106374
  Male38026
Age
  13-17755
  18-4488661
  45-6448233
Ethnicity
  Hispanic36926
  Non-Hispanic106874
Race ‡†
  Asian-Pacific Islander10< 1
  Black1047
  Indian-Eskimo-Aleut322
  White128289
Insurance Status
  Uninsured56039
  Medicaid31122
  Private/HMO57240
*Percentages may not add to 100 because of rounding.
† Ethnic background is missing for 6 patients.
‡ Race is missing for 15 patients.
§ For all remaining analyses, we have re-coded race into “white” or “non-white.”

Univariate and multivariate analysis of smoking

A total of 351 patients in the study sample (24%) were identified as smokers. As expected, smoking was significantly more prevalent in the Medicaid and uninsured groups (Table W1*).

(Table 2) presents multivariate logistic regression results showing the significant relationship between insurance and smoking status after controlling for other important demographic and practice variables. Uninsured patients had similar rates of smoking as those with Medicaid; however, smoking among Private/HMO–insured patients was approximately half as frequent as among the uninsured.

In addition to patient insurance, ethnicity and clinical factors predicted whether patients smoked. Non-Hispanic patients were more than twice as likely to be identified as smokers compared with Hispanic patients (P <.001). Also, patients who were new to the practice or who had at least one chronic condition were significantly more likely to be identified as smokers (P = .011 and P = .001, respectively).

Table 2
LOGISTIC REGRESSION RESULTS: RELATIONSHIP OF PATIENT FACTORS WITH LIKELIHOOD OF SMOKING

Patient FactorOdds Ratio for Smoking (95% CI)P
Insurance
  Uninsured*1.00. 
  Medicaid1.01 (0.73 – 1.4).937
  Private/HMO0.55 (0.41 – 0.73)< .001
Sex
  Female*1.00 
  Male1.22 (0.92 – 1.6).164
Ethnicity
  Hispanic*1.00 
  Non-Hispanic2.1 (1.5 – 3.0)< .001
Patient Seen Before
  Yes*1.00 
  No1.6 (1.1 – 2.3).011
Duration of Visit1.00.990
Chronic Disease
  None*1.00 
  One or more1.6 (1.2 – 2.0).001
CI denotes confidence interval.
*Reference group.

Univeriate and multivariate analysis of cessation advice or counseling

The second primary analysis examined whether insurance is associated with how often smokers are counseled during visits. Out of 351 smokers, 129 (37%) received tobacco counseling during the medical encounter. Private/HMO insurance and duration of visit were the only factors univariately associated with whether a smoker received counseling (Table W2*).

Multivariate results indicate that patient insurance remained the only significant variable after controlling for other factors that might explain whether smokers received counseling. Smokers with Medicaid were more than twice as likely, and Private/HMO–insured smokers were more than 3 times as likely as uninsured patients (P <.001) to receive smoking cessation counseling (Table 3).

 

 

TABLE 3
LOGISTIC REGRESSION RESULTS: PATIENT FACTORS ASSOCIATED WITH LIKELIHOOD OF RECEIVING SMOKING CESSATION COUNSELING

Patient FactorOdds Ratio of Receiving Counseling (95% CI)P
Insurance
  Uninsured*1.00 
  Medicaid2.1 (1.2 – 3.7).011
  Private/HMO3.0 (1.8 – 5.3)< .001
Seen Patient Before
  Yes*1.00 
  No1.1 (0.6 – 2.1).707
Duration of Visit1.02 (0.99 – 1.0).158
Chronic Disease
  None*1.00 
  One or more1.1 (0.66 – 1.7).811
*Reference group.

Discussion

These findings demonstrate that although smoking is more common in CaReNet’s Medicaid and uninsured patients, providers gave cessation advice less often to these patients. The actual prevalence of tobacco use may be even greater than we think because providers may underreport it, but our results are similar to national trends.4 The decreased rate of tobacco counseling in underserved patients is in contrast to the findings in a study that were based on patient recall,11 rather than the provider-report methodology of NAMCS. However, our counseling results are consistent with a national NAMCS analysis, which found that tobacco use was addressed more frequently with HMO-insured patients than Medicaid patients.13 In that study, the overall primary care counseling rate (33%) was similar to that of CaReNet providers (37%). To the best of our knowledge, our finding of a lower rate of tobacco counseling in uninsured patients has not been previously reported.

Our study does not address why providers are less likely to advise Medicaid or uninsured patients to quit smoking. It is possible that tobacco interventions, such as pharmacologic aids and comprehensive cessation programs, may not be available to these groups because of cost. Providers may simply be reflecting this situation by not addressing cessation. Even so, cost and access barriers do not explain why providers would be less likely to give simple cessation advice to disadvantaged smokers. One possibility is that these findings may indicate a lower quality of care for these patients. Other preventive care measures have been shown to be performed less often in uninsured patients,16 and several studies have documented a lower quality of care for Medicaid and uninsured patients with chronic diseases.17-19

Limitations

A major limitation of our study is that the uninsured or Medicaid groups may have included sicker or more complex patients at the surveyed visits, thus there may have been less time to devote to tobacco cessation advice during that clinic visit. Unfortunately, the NAMCS instrument does not readily measure disease severity or case mix. In our analysis, we controlled for the presence of 1 or more chronic diseases (limited in NAMCS to 4 specific conditions), but this is only a crude measure of patient complexity. If patients in one of the payment groups were sicker, they might have had more frequent clinic visits, and tobacco cessation may have been addressed at higher rates over time than were found in this cross-sectional study. However, even in the presence of major morbidities, the uninsured often lack continuity because of their tenuous access to care.

If the payer mix of residents and faculty was significantly different, and residents addressed tobacco use at a different rate than faculty, this could explain some of the counseling differences. Unfortunately, this NAMCS instrument is anonymous and cannot identify the type of provider. Similarly, it is possible that the type of visit (acute care, chronic care, or prevention) may account for some of the findings. However, NAMCS also does not specify type of visit and there may be considerable overlap at any given encounter.

Our study administered NAMCS to the practices that make up CaReNet, and the results are not necessarily generalizable to other populations. There is substantial regional variation in health care access programs for the uninsured20; therefore the uninsured patients in CaReNet may not be representative of uninsured in primary care elsewhere. Also, the demographics of CaReNet include higher percentages of Hispanics and Medicaid recipients compared with a national analysis of primary care trends.21 CaReNet more closely resembles community health centers,22 except CaReNet has a greater number of Hispanic patients and fewer black patients, reflecting the particular demographics of Colorado. However, the smoking prevalence rates we found in the privately insured, Medicaid, and uninsured groups were similar to national patterns.

Conclusions

Our study argues for the inclusion of a separate payment category that clearly identifies the uninsured in NAMCS and other data collection instruments. Future studies on tobacco counseling rates should be designed to differentiate factors associated with the lower rate of counseling in disadvantaged populations, such as patient complexity, competing demands, lack of access to cessation resources, or lower standards of care. Identification of these factors may be valuable in implementing interventions to improve the rate of counseling for these patients.

 

 

If national tobacco goals are to be realized, then socioeconomic disparities in counseling need to be addressed. Our results show that primary care providers can substantially improve the tobacco counseling rate among disadvantaged smokers. As this occurs, the rate of smoking in these patients can be expected to decrease.

*Tables W1 and W2 are available on the JFP Web site, www.jfponline.com.

Acknowledgments

We appreciate the financial support of CaReNet by the University of Colorado School of Medicine Academic Enrichment Fund. We would also like to thank the faculty, residents, and staff at the following CaReNet sites for their assistance with this study: CU Care, Denver, Colorado; St. Mary’s Family Practice, Grand Junction, Colorado; Brighton Salud Family Health Center, Brighton, Colorado; Rose Family Medicine Center, Denver, Colorado; Swedish Family Medicine Center, Littleton, Colorado; AF Williams Family Medicine Center, Denver, Colorado; and La CasaQuigg Newton Health Center, Denver, Colorado. We are also grateful to Elizabeth Staton and Michael Huiras, MD, for their comments on the manuscript. The authors deny any conflict of interest.

References

1. Fiscella K. Is lower income associated with greater biopsychosocial morbidity? J Fam Pract 1999;48:372-7.

2. Flint AF, Novotny TE. Poverty status and cigarette smoking prevalence and cessation in the United States, 1983-1999. Tob Control 1997;6:5-6.

3. Hyman DJ, Simons-Morton DG, Dunn JK, Ho K. Smoking, smoking cessation, and understanding of the role of multiple cardiac risk factors among the urban poor. Prev Med 1996;25:653-9.

4. Anonymous. Self-assessed health status and selected behavioral risk factors among persons with and without health-care coverage— United States, 1994-1995. Morb Mortal Wkly Rep 1998;47:16-8.

5. Albrecht SA, Rosella JD, Patrick T. Smoking among low-income, pregnant women: prevalence rates, cessation interventions, and clinical implications. Birth 1994;21:155-62.

6. O’Loughlin J, Paradis G, Renaud L, Meshefedjian G, Barnett T. The “Yes, I Quit” smoking cessation course: does it help women in a low income community quit? J Community Health 1997;22:451-68.

7. Wadland WC, Stoffelmayr B, East KI. Enhancing smoking cessation of low-income smokers in managed care. J Fam Pract 2001;50:138-44.

8. Friedman DB, Williams AN, Levine BD. Compliance and efficacy of cardiac rehabilitation and risk factor modification in the medically indigent. Am J Card 1997;79:281-5.

9. Russell MA, Wilson C, Taylor C, Baker CD. Effect of general practitioners’ advice against smoking. Br Med J 1979;2:231-35.

10. Kreuter MW, Chheda SG, Bull FC. How does physician advice influence patient behavior? Evidence for a priming effect. Arch Fam Med 2000;9:426-33.

11. Taira DA, Safran DG, Seto TB, Rogers WH, Tarlov AR. The relationship between patient income and physician discussion of health risk behaviors. JAMA 1997;278:1412-7.

12. The Kaiser Commission on Medicaid and the Uninsured. Uninsured in Amerca: a chart book. June 1998; p.51.

13. Thorndike AN, Rigotti NA, Stafford RS, Singer DE. National patterns in the treatment of smokers by physicians. JAMA 1998;279:604-8.

14. Schappert SM, Nelson C. National Ambulatory Medical Care Survey, 1995-96 Summary. National Center for Health Statistics. Vital Health Stat [serial online] 13(142). 1999. [cited 2000 Dec 18] Available at www.cdc.gov/nchs/data/sr13_142.pdf.

15. Hosmer DW, Lemeshow S. Applied logistic regression. New York, NY: John Wiley and Sons. 1989.

16. Anonymous. Health insurance coverage and receipt of preventive health services—United States, 1993. MMWR Morb Mortal Wkly Rep 1995;44:219-25.

17. Ayanian JZ, Kohler BA, Abe T, Epstein AM. The relation between health insurance coverage and clinical outcomes among women with breast cancer. N Engl J Med 1993;329:326-31.

18. Carlisle DM, Leape LL, Bickel S, et al. Underuse and overuse of diagnostic testing for coronary artery disease in patients presenting with new-onset chest pain. Am J Med 1999;106:391-8.

19. Sada MJ, French WJ, Carlisle DM, Chandra NC, Gore JM, Rogers WJ. Influence of payor on use of invasive cardiac procedures and patient outcome after myocardial infarction in the United States. Participants in the National Registry of Myocardial Infarction. J Am Coll Cardiol 1998;31:1474-80.

20. Cunningham PJ, Kemper P. Ability to obtain medical care for the uninsured: How much does it vary across communities? JAMA 1998;280:921-7.

21. Stafford RS, Saglam D, Causino N, et al. Trends in adult visits to primary care physicians in the United States. Arch Fam Med 1999;8:26-32.

22. Forrest CB, Whelan EM. A comparison of community health centers, hospital outpatient departments, and physicians’ offices. JAMA 2000;284:2077-83

References

1. Fiscella K. Is lower income associated with greater biopsychosocial morbidity? J Fam Pract 1999;48:372-7.

2. Flint AF, Novotny TE. Poverty status and cigarette smoking prevalence and cessation in the United States, 1983-1999. Tob Control 1997;6:5-6.

3. Hyman DJ, Simons-Morton DG, Dunn JK, Ho K. Smoking, smoking cessation, and understanding of the role of multiple cardiac risk factors among the urban poor. Prev Med 1996;25:653-9.

4. Anonymous. Self-assessed health status and selected behavioral risk factors among persons with and without health-care coverage— United States, 1994-1995. Morb Mortal Wkly Rep 1998;47:16-8.

5. Albrecht SA, Rosella JD, Patrick T. Smoking among low-income, pregnant women: prevalence rates, cessation interventions, and clinical implications. Birth 1994;21:155-62.

6. O’Loughlin J, Paradis G, Renaud L, Meshefedjian G, Barnett T. The “Yes, I Quit” smoking cessation course: does it help women in a low income community quit? J Community Health 1997;22:451-68.

7. Wadland WC, Stoffelmayr B, East KI. Enhancing smoking cessation of low-income smokers in managed care. J Fam Pract 2001;50:138-44.

8. Friedman DB, Williams AN, Levine BD. Compliance and efficacy of cardiac rehabilitation and risk factor modification in the medically indigent. Am J Card 1997;79:281-5.

9. Russell MA, Wilson C, Taylor C, Baker CD. Effect of general practitioners’ advice against smoking. Br Med J 1979;2:231-35.

10. Kreuter MW, Chheda SG, Bull FC. How does physician advice influence patient behavior? Evidence for a priming effect. Arch Fam Med 2000;9:426-33.

11. Taira DA, Safran DG, Seto TB, Rogers WH, Tarlov AR. The relationship between patient income and physician discussion of health risk behaviors. JAMA 1997;278:1412-7.

12. The Kaiser Commission on Medicaid and the Uninsured. Uninsured in Amerca: a chart book. June 1998; p.51.

13. Thorndike AN, Rigotti NA, Stafford RS, Singer DE. National patterns in the treatment of smokers by physicians. JAMA 1998;279:604-8.

14. Schappert SM, Nelson C. National Ambulatory Medical Care Survey, 1995-96 Summary. National Center for Health Statistics. Vital Health Stat [serial online] 13(142). 1999. [cited 2000 Dec 18] Available at www.cdc.gov/nchs/data/sr13_142.pdf.

15. Hosmer DW, Lemeshow S. Applied logistic regression. New York, NY: John Wiley and Sons. 1989.

16. Anonymous. Health insurance coverage and receipt of preventive health services—United States, 1993. MMWR Morb Mortal Wkly Rep 1995;44:219-25.

17. Ayanian JZ, Kohler BA, Abe T, Epstein AM. The relation between health insurance coverage and clinical outcomes among women with breast cancer. N Engl J Med 1993;329:326-31.

18. Carlisle DM, Leape LL, Bickel S, et al. Underuse and overuse of diagnostic testing for coronary artery disease in patients presenting with new-onset chest pain. Am J Med 1999;106:391-8.

19. Sada MJ, French WJ, Carlisle DM, Chandra NC, Gore JM, Rogers WJ. Influence of payor on use of invasive cardiac procedures and patient outcome after myocardial infarction in the United States. Participants in the National Registry of Myocardial Infarction. J Am Coll Cardiol 1998;31:1474-80.

20. Cunningham PJ, Kemper P. Ability to obtain medical care for the uninsured: How much does it vary across communities? JAMA 1998;280:921-7.

21. Stafford RS, Saglam D, Causino N, et al. Trends in adult visits to primary care physicians in the United States. Arch Fam Med 1999;8:26-32.

22. Forrest CB, Whelan EM. A comparison of community health centers, hospital outpatient departments, and physicians’ offices. JAMA 2000;284:2077-83

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Do Parents and Physicians Differ in Making Decisions About Acute Otitis Media?

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Do Parents and Physicians Differ in Making Decisions About Acute Otitis Media?

ABSTRACT

OBJECTIVES: We wanted to discover how parents differ from physicians in making decisions about how to treat a child who may have acute otitis media (AOM).

STUDY DESIGN: We used questionnaires that required participants to judge the probability of AOM or choose treatment for 2 sets of 46 paper scenarios of hypothetical children aged 15 months who might have AOM, and they subsequently rated the importance of individual cues and described their attitudes and opinions related to health care and AOM.

POPULATION: Convenience samples of 19 US family physicians, 35 French generalists, 21 French pediatricians, 52 US parents, and 86 French parents were included.

OUTCOMES MEASURED: The primary outcomes were the judgment policies—the weights placed on each of the scenario cues when making decisions—that were derived for each individual and each group by multiple linear regression.

RESULTS: The mean judged probabilities of AOM were nearly the same for all groups: 50% for the US physicians, 51% for the US parents, 52.5% for the French physicians, and 52% for the French parents. The percentages of cases treated with antibiotics did not differ: 53% for US physicians, 45% for US parents, 53% for French physicians, and 51% for French parents. All groups gave greatest weight to the physical examination cues for decisions about both diagnosis and treatment. The parents paid little attention to the cues that reflected parental concerns.

CONCLUSIONS: US and French parents were very similar to physicians in their judgments and treatment choices regarding AOM. They appear to be able to adopt the physician’s point of view and to be selective in the use of antibiotics.

KEY POINTS FOR CLINICIANS

  • The appearance of the eardrum was the key factor in decision making about acute otitis media.
  • Both physicians and parents paid little attention to parental-sensitive factors when choosing treatment.
  • Parents were able to adopt the physician’s point of view.
  • Parents in the United States and France may be more willing to forgo antibiotics than physicians realize.

Acute otitis media (AOM) is the most frequent diagnosis for which antibiotics are prescribed in the United States1,2 and is among the most frequent in France.3 The benefit of antibiotic treatment is under scrutiny as physicians, parents, and policymakers in the United States,4-7 France,3,8-13 and elsewhere14-16 become increasingly worried about bacterial resistance to antibiotics.

Patients and caregivers often have a firm idea of what is wrong with their child and how it should be treated.17,18 Conflict with the physician may partially result from different interpretations that parents and physicians give to the same cues from the child’s history and physical examination, and the different implications derived from them. Despite evidence to the contrary,19-22 studies in the United States and Britain show that physicians feel parents are becoming increasingly demanding: They expect them to prescribe antibiotics even when they are not indicated and will be dissatisfied if they are not prescribed.22-29

The purpose of our study was to elucidate the differences between physicians and parents both in the United States and France by comparing their diagnostic judgments and treatment choices when dealing with children who might be suffering from AOM.

Methods

This study was primarily a “judgment analysis.”30 In judgment analyses, individuals make a series of judgments (eg, diagnoses or treatment choices about a series of patients) according to a set of varying cues (eg, signs and symptoms). Using multiple linear regression analyses, it is possible to determine how much weight they put on each cue when making their decisions. Separate regression analyses are performed for each person, with the cues as the predictors and the judgment (diagnoses or treatment decisions) as the variable predicted. The beta weights from the regression, suitably adjusted for differences in units of measurement, are estimates of the relative importance of the cues in making judgments. A higher beta weight for a cue indicates that it carried greater weight in making the diagnosis or treatment decision. Between- and within-group comparisons of these weights may reveal why people differ in their judgments of identical cases.

Participants

The samples consisted of 19 US family physicians, 35 French general practitioners, 21 French pediatricians, 52 US parents, and 86 French parents. The US physicians were recruited from the 62 family physicians in the region of Albany and Schenectady, New York, through written and oral appeals from the study team. The French practitioners consisted of personal contacts and members of 2 networks of research minded general practitioners. The French pediatricians were recruited by written appeals to the 30 practicing pediatricians in and around Tours, France. The US parents were responders to a recruitment letter sent to 100 randomly selected parents of young children of one primary care practice in suburban Albany. The French parents were recruited by students at the Université François-Rabelais in Tours.31

 

 

Procedure

We constructed 46 paper scenarios describing hypothetical children aged 15 months who might have ear infections. Each scenario displayed the values for 15 different cues, such as fever, redness of the tympanic membrane, or ear pain during examination (see the figure at www.jfponline.com for more information). These cues were selected after consultation with physicians and parents, who felt that they were important for decisions about the diagnosis and treatment of AOM. The diagnostic cues were based on our previous study of US pediatricians32 and on the medical literature in the United States,33-35 France,36,37 and elsewhere.14,38 We included the result of insufflation of the tympanic membrane, even though French primary care physicians are not taught to use pneumatic insufflation in their diagnosis of AOM.

The cue values for each case were generated randomly using a computer program. Very implausible combinations of cue values were excluded from the scenarios. We did not, however, attempt to create a set of cases that would reproduce the actual mix of cases seen by pediatricians in their offices. Indeed, the inclusion of unlikely combinations of cue values was useful in forcing the participants to choose which cues were most important. Intercorrelation between cues was small to moderate, ranging from -0.40 to 0.39. This resulted from the rules for excluding implausible cases (eg, 0.39 between bulging and mobility) or from chance (eg, -0.40 between a history of ear pain and ear pain during the examination). The participants were told to put themselves in the place of the examining physician. They were presented twice with the same 46 scenarios. For one set they were asked to judge the probability that each child had AOM. For the other, in which the cases were presented in a different order, they were to decide whether to treat with antibiotics or to observe the child. They were also asked in the second set to indicate on a 5-point scale their degree of certainty that this was the right choice. Half the participants completed the diagnosis set first; half did the treatment set first. They completed the study at home. They were instructed to refrain from looking back at the first set after finishing and to take a short break of up to a day between the 2 sets. At the end of the study session, the participants answered certain questions about attitudes toward health care and risk and about their background that might account for differences in their responses to the scenarios.*

Data analysis

Two multiple linear regression analyses were performed for each participant (physician or parent). In one regression, the predicted variable was the judgment of the probability of AOM; in the other, it was the choice of treatment. Before performing the second regression, the treatment choice was combined with the degree of certainty about it to create a treatment score on a 10-point scale ranging from -5 for “observe/completely sure” to 5 for “treat/completely sure” (with no 0). This score was used in the multiple linear regression analysis as the dependent variable. A participant was included in these analyses only if his or her R2 passed the F-test for fitness of the multiple regression model at P less than .05; failure of a model to pass the F-test meant that the individual’s judgments were not predictable by the cues. This could occur if he or she had answered randomly or had made mistakes.

Multivariate analysis of variance was used to test the differences in the mean responses to the questions on attitudes and opinions of each group of participants. The association of individual attitudes and opinions about AOM and health care with treatment choices was explored by correlating the percentage of cases treated with antibiotics with the responses to each of the questions about attitudes and opinions.

Results

The judgments of the parents were remarkably similar to those of the physicians, both in the United States and France. The means and the ranges of the mean probability judgments of the individual participants in all the groups were almost identical at 50% or just above (Table 1, row 1). The cue weights for diagnosis (Table 2) were also quite similar. The physical examination provided the key information for both the parents and physicians. The only differences were that French parents gave more weight than the other groups to a past history of ear infections (beta weight = 0.15) and focused more on fever (0.30) and ear pain during the examination (0.28) than on bulging (0.17).

In choosing treatment, the parents in each country had lower mean treatment scores (Table 1, row 2) than the physicians, but these differences were not statistically significant. Likewise, the overall percentages of cases judged as needing antibiotics, though lower for parents, were not significantly different between the physicians and parents of each country: 53.0% for US physicians, 44.6% for US parents, 53.4% for French physicians, and 51.1% for French parents. Among the 4 groups the important cues for treatment (Table 3) were similar, stressing the physical examination findings and de-emphasizing the history, including the parents’ report of ear pain. French parents gave atypical stress to the child’s temperature (beta weight = 0.52), so that they differed from other groups in their judgments of several individual scenarios. Both groups of parents assigned significantly less weight than the physicians of all groups to the cue of the parents’ position on antibiotics. Indeed, when asked to put themselves in the place of a physician, the parents did not give importance to any of the parent-sensitive cues, even when the question was treatment. These cues included ear pain noted by the parent, the parents’ personal position concerning antibiotics, whether caring for a sick child greatly upsets the parents’ ordinary schedule, and whether there are babies or other small children in the family.

 

 

Attitudes and opinions related to AOM and health care were not associated with the percentage of cases in which the participant of any group chose to treat with antibiotics. Among both the Americans and the French, the parents thought physicians worried more about charges of malpractice when making decisions about patient care than physicians claimed they did (P <.005 for both countries). Parents were bothered when their child was sick more than physicians were bothered when “one of my patients” was sick (P <.005 for both). If there was a possibility that their child had “a serious illness that is rare but curable,” parents were more willing than physicians to order diagnostic tests even when they would cost the parents “a great deal of time and/or money” (P <.005 for both). Interestingly, parents agreed less strongly than physicians with the statement “A doctor should pay close attention to the needs and preferences of a child’s parents” (P <.005 for both). US parents, but not those in France, agreed more strongly than physicians that if their child might have a serious illness that was rare but curable diagnostic tests should be ordered even when they were “very expensive for the child’s insurance plan” (P <.005). French parents, but not American ones, agreed more strongly than physicians that “all ear infections should be treated with antibiotics” (P <.005).

TABLE 1
DIFFERENCES AMONG GROUPS OF PARTICIPANTS

 US Physicians (n=19)US Parents (n=52)French Physicians (n=56)French Parents (n=86)
Mean judged probability of AOM (range)50 (28 to 75)51 (29 to 78)52.5 (33 to 85)52 (30 to 73)
Mean judged probability of treatment (range)0.22 (-2.4 to 3.0)-0.32 (-2.7 to 3.1)0.36 (-1.5 to 3.0)-0.42 (-2.9 to 2.6)
NOTE: The treatment score is the combination of the choice of observation versus antibiotics and the degree of certainty that this is the right choice, ranging from -5 (observe/completely sure) to 5 (antibiotics/completely sure).
AOM denotes acute otitis media.

TABLE 2
MEAN BETA WEIGHTS FOR CUES FOR JUDGING PROBABILITY OF ACUTE OTITIS MEDIA

CueUS Physicians (n=19)US Parents (n=52)French Physicians (n=55)French Parents (n=81)
History
  Past history of AOM0.020.080.03†0.15
  URI symptoms0.07-0.0100.03
  Ear pain noted by parent0.090.070.040.04
Findings on Examination
  Fever0.200.210.16†0.30†
  Redness of tympanic membrane0.48*0.34*0.400.36
  Bulging of tympanic membrane0.250.310.48†0.17†
  Mobility on insufflation0.37*0.22*0.130.14
  Asymmetry of tympanic membranes0.17*0.28*0.170.16
  Ear pain during examination0.090.140.09†0.28†
  General appearance of the child0.080.060.020.06
  Did the child start to cry just before the examination?0.02-0.03-0.01-0.02
Other Factors
  Parents’ personal position concerning antibiotics-0.050.030.020.02
  Ability of parents to provide effective care to a sick child0.01-0.04-0.02-0.01
  Does caring for sick child greatly upset parents’ ordinary schedule?00.0200.01
  Are there babies or other small children in the family?00.0200.02
Note: Higher values indicate a greater weight given to this cue in making the diagnosis of acute otitis media. Participants whose judgments failed to pass the F-test for multiple regression models (1 French pediatrician and 5 French parents) were excluded from the analysis.
AOM denotes acute otitis media; URI, upper respiratory infection.
*Significant comparison for US group, P <.05.
†Significant comparison for French group, P <.05.

TABLE 3
MEAN BETA WEIGHTS FOR CUES FOR CHOOSING TREATMENT OF ACUTE OTITIS MEDIA

CueUS Physicians (n=19)US Parents (n=49)French Physicians (n=50)French Parents (n=75)
History
  Past history of AOM0.020.040.040.08
  URI symptoms0.020.030.060.07
  Ear pain noted by parent0.110.100.07†0.0†
Findings on examination
  Fever0.210.280.25†0.52†
  Redness of Tympanic membrane0.360.290.34†0.23†
  Bulging of tympanic membrane0.190.230.41†0.08†
  Mobility on insufflation0.28*0.16*0.120.10
  Asymmetry of tympanic membranes0.200.230.130.09
  Ear pain during examination0.040.110.04†0.14†
  General appearance of the child0.160.100.10†0.04†
  Did the child start to cry just before the examination?0.0200.030
Other factors
  Parents’ personal position concerning antibiotics0.17*0.06*0.11†0.03†
  Ability of parents of provide effective care to a sick child-0.05-0.03-0.05-0.04
  Does caring for sick child greatly upset parents’ ordinary schedule?0.010.040.01-0.08
  Are there babies or other small children in the family?0.010.040.040.01
Note: Higher values indicate a greater weight given to this cue when deciding whether to treat with antibiotics. Participants whose judgments failed to pass the F-test for multiple regression models (3 French generalists, 3 French pediatricians, 3 US parents, and 11 French parents) were excluded from the analysis.
AOM denotes acute otitis media; URI, upper respiratory infection.
*Significant comparison for US group, P <.05.
†Significant comparison for French group, P <.05.

Discussion

Although physicians are aware that antibiotic resistance of bacteria is an increasing problem,39-41 they continue to prescribe antibiotics for patients who are unlikely to benefit from them.2,3,13,39,42,43 There are multiple plausible reasons for this.7,17,44-46 Some of these relate to physicians’ perceptions of the wants and needs of their patients and their caretakers. Physicians may47 (or may not48) make different decisions for individuals they are dealing with than for community groups. They know the public misunderstands the indications for antibiotics,25,49,50 and they may perceive, often incorrectly,19,21 that patients or parents want antibiotics and will be dissatisfied if they do not receive them.22,23,26,29,45,51 They may practice defensive medicine28 or believe that it takes less time and effort to prescribe antibiotics than to explain why they are withholding them.28,45 They may be sensitive to the socioeconomic pressures on patients and parents related to daycare policies, work schedules, and the costs of return visits.46

 

 

Our study results should be reassuring to physicians. The most striking finding was the similarity between parents’ diagnostic judgments and treatment choices and those of physicians. The only difference in judgment policies was that the French parents placed greater stress than the French physicians on fever and gave a lower weight to bulging when deciding about treatment, which may be understandable given their unfamiliarity with the technical aspects of examining an ear.31 Indeed, contrary to our expectations, parents in both countries gave less weight to parent-sensitive cues than did physicians. Overall, parents did not diagnose AOM in the scenario children or recommend treating them with antibiotics more frequently than physicians.

The parents’ restraint concerning antibiotics is surprising given physicians’ perceptions and the results of a recent US survey52 in which 96% of parents said that “ear infections can affect a baby’s hearing,” and only 11% thought that “most ear infections get better by themselves.” Also, recent experts have stressed the high indirect costs of an episode of AOM and the value to parents of reducing the duration of the illness.53-56 One explanation may be the rising parental worry that antibiotic treatment may lessen their child’s ability to fight off future infections, in particular because of the spread of antibiotic-resistant bacteria.27 The parents in our study worried as much as the physicians about the adverse effects of antibiotics and agreed just as strongly as the physicians that the resistance of bacteria to antibiotics is the most important threat to the future health of the public. The parents’ ability to adopt the physician’s point of view should encourage physicians to undertake the patient and parent education efforts recently recommended25,28,57 as the best way to reduce the excessive prescription of antibiotics.

We had anticipated incorrectly that the decision to treat would be influenced by attitudes toward uncertainty, ambiguity, and risk.59,63 We had also expected, again incorrectly, that a greater belief in the usefulness of antibiotics and (for the parents alone) in the contagious nature of ear infections — and a lesser worry about antibiotic side effects and bacterial resistance — would identify physicians and parents who opted more frequently for antibiotics. The explanation may be that our questions were insensitive or that general attitudes are poor predictors of individual case-by-case choices and behavior. It may also be that physicians—and even parents who take the role of physicians—believe that diagnosis is the first and determining step in managing a possible ear infection.

Limitations

Our study has several limitations. First, generalization is limited by the small samples, the inequality of the sample sizes, and the convenience nature of the samples. Second, the patients were hypothetical, presented on paper in schematic form, with neither the richness nor the vividness of the real children brought by parents to physicians’ offices. Although the use of “paper patients” has been questioned,64,65 it is practical and has been supported in other studies of clinical decision making.66-69 Third, comparisons between the French and Americans may have been influenced by unappreciated differences in meaning of the French and English versions of the scenarios and questions.

Conclusions

It is encouraging that parents in our study were able to adopt the physician’s perspective and to focus on medical indications rather than on parental needs in their treatment decisions, that they did not choose to prescribe antibiotics more frequently than the physicians, and that they were as concerned as the physicians about the adverse effects of antibiotics and the threat from resistant bacteria. Patients and parents may, therefore, be more willing to forgo antibiotics than physicians realize.

Acknowledgments

We thank the following for their invaluable advice and assistance: Bernard Grenier, MD; Héléne Touchon, MD; Joél Cogneau, MD; Marie-Ange Lecomte, MD; Sabine Maciaszczyk-Jedeau, PhD; Appleton Mason, MD; the Association des Pédiatres de Ville en Touraine; the Latham Medical Group; the Albany Medical Center Department of Family Practice; Héma Sandanam; and Roberta Sandler, RN.

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46. Pichichero ME. Understanding antibiotic overuse for respiratory tract infections in children. Pediatrics 1999;104:1384-88.

47. Redelmeier DA, Tversky A. Discrepancy between medical decisions for individual patients and for groups. N Engl J Med 1990;322:1162-64.

48. Nickerson CAE, Ubel PA, Hershey JC, Spranca MD, Asch DA. Further explorations of medical decisions for individuals and for groups. Med Decis Making 2000;20:39-44.

49. Mainous AG, III, Zoorob RJ, Oler MJ, Haynes DM. Patient knowledge of upper respiratory infections: implications for antibiotic expectations and unnecessary utilization. J Fam Pract 1997;45:75-83.

50. Wilson AA, Crane LA, Barrett PH, Jr, Gonzales R. Public beliefs and use of antibiotics for acute respiratory illlness. J Gen Intern Med 1999;14:658-62.

51. Cockburn J, Pit S. Prescribing behaviour in clinical practice: patients’ expectations and doctors’ perceptions of patients’ expectations—a questionnaire study. BMJ 1997;315:520-23.

52. Daly KA, Selvius RE, Lindgren B. Knowledge and attitudes about otitis media risk: implications for prevention. Pediatrics 1997;100:931-36.

53. Bauchner H, Adams W, Barnett E, Klein J. Therapy for acute otitis media: preference of parents for oral or parenteral antibiotic. Arch Pediatr Adolesc Med 1996;150:396-99.

54. Alsarraf R, Jung CJ, Perkins J, Crowley C, Alsarraf NW, Gates GA. Measuring the indirect and direct costs of acute otitis media. Arch Otolaryngol Head Neck Surg 1999;125:12-18.

55. Sorum PC. Measuring patient p by willingness to pay to avoid: the case of acute otitis media. Med Decis Making 1999;19:27-37.

56. Heymann SJ, Toomey S, Furstenberg F. Working parents: what factors are involved in their ability to take time off from work when their children are sick? Arch Pediatr Adolesc Med 1999;153:870-74.

57. Gonzales R, Steiner JF, Lum A, Barrett PH, Jr. Decreasing antibiotic use in ambulatory practice: impact of a multidimensional intervention on the treatment of uncomplicated acute bronchitis in adults. JAMA 1999;281:1512-19.

58. Poses RM, Chaput de Saintonge M, et al. An international comparison of physicians’ judgments of outcome rates of cardiac procedures and attitudes toward risk, uncertainty, justifiability, and regret. Med Decis Making 1998;18:131-40.

59. Gerrity MS. Conceptual models for understanding and measuring physicians’ reactions to uncertainty. In: Hibbard H, Nutting PA, Grady ML, eds. Primary care research: theory and methods. PB91-228130. Washington, DC: US Department of Health and Human Services, 1991;167-58.

60. Holtgrave DR, Lawler F, Spann SJ. Physicians’ risk attitudes, laboratory usage, and referral decisions: the case of an academic family practice center. Med Decis Making 1991;11:125-30.

61. Geller G, Tambor ES, Chase GA, Holtzman NA. Measuring physicians’ tolerance for ambiguity and its relationship to their reported practices regarding genetic testing. Med Care 1993;31:989-1001.

62. Kuhn KM, Budescu DV. The relative importance of probabilities, outcomes, and vagueness in hazard risk decisions. Organ Behav Hum Decis Process 1996;68:301-17.

63. Allison JJ, Kiefe CI, Cook F, Gerrity MS, Orav EJ, Centor R. The association of physician attitudes about uncertainty and risk taking with resource use in a Medicare HMO. Med Decis Making 1998;18:320-29.

64. Gorman CD, Clover WH, Doherty ME. Can we learn anything about interviewing real people from “interview” of paper people? Two studies of the external validity of a paradigm. Organ Behav Hum Perform 1978;22:165-92.

65. Jones TV, Gerrity MS, Earp J. Written case simulations: do they predict physicians’ behavior? J Clin Epidemiol 1990;43:805-15.

66. Chaput de Saintonge DM, Hathaway NR. Antibiotic use in otitis media: patient simulations as an aid to audit. BMJ 1981;283:883-84.

67. Kirwan JR, Chaput de Saintogne DM, Joyce CRB. Clinical judgment analysis. Q J Med 1990;76:935-49.

68. Chaput de Saintonge DM, Hattersley LA. Antibiotics for otitis media: can we help doctors agree? Fam Pract 1985;2:205-12.

69. Kirwan JP, Chaput de Saintogne DM, Joyce CRB, Currey HLF. Clinical judgment in rheumatoid arthritis. I. Rheumatologists’ opinions and the development of “paper patients”. Ann Rheum Dis 1983;42:648-51.

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Author and Disclosure Information

PAUL CLAY SORUM, MD, PHD
JUNSEOP SHIM, MPA
GÉRARD CHASSEIGNE, PHD
ETIENNE MULLET, PHD
MARIÍA TERESA MUÑOZ SASTRE, PHD
THOMAS STEWART, PHD
CLAUDIA GONZÁLEZ-VALLEJO, PHD
Albany, New York; Tours, Bruxelles, and Toulouse, France; and Athens, Ohio
Submitted, revised, May 29, 2001.
From the departments of Medicine and Pediatrics, Albany Medical Center, Albany (P.C.S.); the Center for Policy Research, State University of New York-Albany (J.S., T.S.); the Département de Psychologie, Université François-Rabelais, Tours (G.C.); Ecole Pratique des Hautes Etudes (E.M.); the Département de Psychologie Clinique et Pathologique, Université du Mirail, Toulouse (M.T.M.S.); and the Department of Psychology, Ohio University, Athens (C.G.). Reprint requests should be addressed to Paul Clay Sorum, MD, Albany Med Primary Care Network, 724 Watervliet-Shaker Road, Latham, NY 12110. E-mail: [email protected].

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The Journal of Family Practice - 51(1)
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51-57
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,Parents [non-MESH]decision makingacute otitis media [non-MESH]antibioticsFrance. (J Fam Pract 2002; 51:51-57)
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PAUL CLAY SORUM, MD, PHD
JUNSEOP SHIM, MPA
GÉRARD CHASSEIGNE, PHD
ETIENNE MULLET, PHD
MARIÍA TERESA MUÑOZ SASTRE, PHD
THOMAS STEWART, PHD
CLAUDIA GONZÁLEZ-VALLEJO, PHD
Albany, New York; Tours, Bruxelles, and Toulouse, France; and Athens, Ohio
Submitted, revised, May 29, 2001.
From the departments of Medicine and Pediatrics, Albany Medical Center, Albany (P.C.S.); the Center for Policy Research, State University of New York-Albany (J.S., T.S.); the Département de Psychologie, Université François-Rabelais, Tours (G.C.); Ecole Pratique des Hautes Etudes (E.M.); the Département de Psychologie Clinique et Pathologique, Université du Mirail, Toulouse (M.T.M.S.); and the Department of Psychology, Ohio University, Athens (C.G.). Reprint requests should be addressed to Paul Clay Sorum, MD, Albany Med Primary Care Network, 724 Watervliet-Shaker Road, Latham, NY 12110. E-mail: [email protected].

Author and Disclosure Information

PAUL CLAY SORUM, MD, PHD
JUNSEOP SHIM, MPA
GÉRARD CHASSEIGNE, PHD
ETIENNE MULLET, PHD
MARIÍA TERESA MUÑOZ SASTRE, PHD
THOMAS STEWART, PHD
CLAUDIA GONZÁLEZ-VALLEJO, PHD
Albany, New York; Tours, Bruxelles, and Toulouse, France; and Athens, Ohio
Submitted, revised, May 29, 2001.
From the departments of Medicine and Pediatrics, Albany Medical Center, Albany (P.C.S.); the Center for Policy Research, State University of New York-Albany (J.S., T.S.); the Département de Psychologie, Université François-Rabelais, Tours (G.C.); Ecole Pratique des Hautes Etudes (E.M.); the Département de Psychologie Clinique et Pathologique, Université du Mirail, Toulouse (M.T.M.S.); and the Department of Psychology, Ohio University, Athens (C.G.). Reprint requests should be addressed to Paul Clay Sorum, MD, Albany Med Primary Care Network, 724 Watervliet-Shaker Road, Latham, NY 12110. E-mail: [email protected].

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ABSTRACT

OBJECTIVES: We wanted to discover how parents differ from physicians in making decisions about how to treat a child who may have acute otitis media (AOM).

STUDY DESIGN: We used questionnaires that required participants to judge the probability of AOM or choose treatment for 2 sets of 46 paper scenarios of hypothetical children aged 15 months who might have AOM, and they subsequently rated the importance of individual cues and described their attitudes and opinions related to health care and AOM.

POPULATION: Convenience samples of 19 US family physicians, 35 French generalists, 21 French pediatricians, 52 US parents, and 86 French parents were included.

OUTCOMES MEASURED: The primary outcomes were the judgment policies—the weights placed on each of the scenario cues when making decisions—that were derived for each individual and each group by multiple linear regression.

RESULTS: The mean judged probabilities of AOM were nearly the same for all groups: 50% for the US physicians, 51% for the US parents, 52.5% for the French physicians, and 52% for the French parents. The percentages of cases treated with antibiotics did not differ: 53% for US physicians, 45% for US parents, 53% for French physicians, and 51% for French parents. All groups gave greatest weight to the physical examination cues for decisions about both diagnosis and treatment. The parents paid little attention to the cues that reflected parental concerns.

CONCLUSIONS: US and French parents were very similar to physicians in their judgments and treatment choices regarding AOM. They appear to be able to adopt the physician’s point of view and to be selective in the use of antibiotics.

KEY POINTS FOR CLINICIANS

  • The appearance of the eardrum was the key factor in decision making about acute otitis media.
  • Both physicians and parents paid little attention to parental-sensitive factors when choosing treatment.
  • Parents were able to adopt the physician’s point of view.
  • Parents in the United States and France may be more willing to forgo antibiotics than physicians realize.

Acute otitis media (AOM) is the most frequent diagnosis for which antibiotics are prescribed in the United States1,2 and is among the most frequent in France.3 The benefit of antibiotic treatment is under scrutiny as physicians, parents, and policymakers in the United States,4-7 France,3,8-13 and elsewhere14-16 become increasingly worried about bacterial resistance to antibiotics.

Patients and caregivers often have a firm idea of what is wrong with their child and how it should be treated.17,18 Conflict with the physician may partially result from different interpretations that parents and physicians give to the same cues from the child’s history and physical examination, and the different implications derived from them. Despite evidence to the contrary,19-22 studies in the United States and Britain show that physicians feel parents are becoming increasingly demanding: They expect them to prescribe antibiotics even when they are not indicated and will be dissatisfied if they are not prescribed.22-29

The purpose of our study was to elucidate the differences between physicians and parents both in the United States and France by comparing their diagnostic judgments and treatment choices when dealing with children who might be suffering from AOM.

Methods

This study was primarily a “judgment analysis.”30 In judgment analyses, individuals make a series of judgments (eg, diagnoses or treatment choices about a series of patients) according to a set of varying cues (eg, signs and symptoms). Using multiple linear regression analyses, it is possible to determine how much weight they put on each cue when making their decisions. Separate regression analyses are performed for each person, with the cues as the predictors and the judgment (diagnoses or treatment decisions) as the variable predicted. The beta weights from the regression, suitably adjusted for differences in units of measurement, are estimates of the relative importance of the cues in making judgments. A higher beta weight for a cue indicates that it carried greater weight in making the diagnosis or treatment decision. Between- and within-group comparisons of these weights may reveal why people differ in their judgments of identical cases.

Participants

The samples consisted of 19 US family physicians, 35 French general practitioners, 21 French pediatricians, 52 US parents, and 86 French parents. The US physicians were recruited from the 62 family physicians in the region of Albany and Schenectady, New York, through written and oral appeals from the study team. The French practitioners consisted of personal contacts and members of 2 networks of research minded general practitioners. The French pediatricians were recruited by written appeals to the 30 practicing pediatricians in and around Tours, France. The US parents were responders to a recruitment letter sent to 100 randomly selected parents of young children of one primary care practice in suburban Albany. The French parents were recruited by students at the Université François-Rabelais in Tours.31

 

 

Procedure

We constructed 46 paper scenarios describing hypothetical children aged 15 months who might have ear infections. Each scenario displayed the values for 15 different cues, such as fever, redness of the tympanic membrane, or ear pain during examination (see the figure at www.jfponline.com for more information). These cues were selected after consultation with physicians and parents, who felt that they were important for decisions about the diagnosis and treatment of AOM. The diagnostic cues were based on our previous study of US pediatricians32 and on the medical literature in the United States,33-35 France,36,37 and elsewhere.14,38 We included the result of insufflation of the tympanic membrane, even though French primary care physicians are not taught to use pneumatic insufflation in their diagnosis of AOM.

The cue values for each case were generated randomly using a computer program. Very implausible combinations of cue values were excluded from the scenarios. We did not, however, attempt to create a set of cases that would reproduce the actual mix of cases seen by pediatricians in their offices. Indeed, the inclusion of unlikely combinations of cue values was useful in forcing the participants to choose which cues were most important. Intercorrelation between cues was small to moderate, ranging from -0.40 to 0.39. This resulted from the rules for excluding implausible cases (eg, 0.39 between bulging and mobility) or from chance (eg, -0.40 between a history of ear pain and ear pain during the examination). The participants were told to put themselves in the place of the examining physician. They were presented twice with the same 46 scenarios. For one set they were asked to judge the probability that each child had AOM. For the other, in which the cases were presented in a different order, they were to decide whether to treat with antibiotics or to observe the child. They were also asked in the second set to indicate on a 5-point scale their degree of certainty that this was the right choice. Half the participants completed the diagnosis set first; half did the treatment set first. They completed the study at home. They were instructed to refrain from looking back at the first set after finishing and to take a short break of up to a day between the 2 sets. At the end of the study session, the participants answered certain questions about attitudes toward health care and risk and about their background that might account for differences in their responses to the scenarios.*

Data analysis

Two multiple linear regression analyses were performed for each participant (physician or parent). In one regression, the predicted variable was the judgment of the probability of AOM; in the other, it was the choice of treatment. Before performing the second regression, the treatment choice was combined with the degree of certainty about it to create a treatment score on a 10-point scale ranging from -5 for “observe/completely sure” to 5 for “treat/completely sure” (with no 0). This score was used in the multiple linear regression analysis as the dependent variable. A participant was included in these analyses only if his or her R2 passed the F-test for fitness of the multiple regression model at P less than .05; failure of a model to pass the F-test meant that the individual’s judgments were not predictable by the cues. This could occur if he or she had answered randomly or had made mistakes.

Multivariate analysis of variance was used to test the differences in the mean responses to the questions on attitudes and opinions of each group of participants. The association of individual attitudes and opinions about AOM and health care with treatment choices was explored by correlating the percentage of cases treated with antibiotics with the responses to each of the questions about attitudes and opinions.

Results

The judgments of the parents were remarkably similar to those of the physicians, both in the United States and France. The means and the ranges of the mean probability judgments of the individual participants in all the groups were almost identical at 50% or just above (Table 1, row 1). The cue weights for diagnosis (Table 2) were also quite similar. The physical examination provided the key information for both the parents and physicians. The only differences were that French parents gave more weight than the other groups to a past history of ear infections (beta weight = 0.15) and focused more on fever (0.30) and ear pain during the examination (0.28) than on bulging (0.17).

In choosing treatment, the parents in each country had lower mean treatment scores (Table 1, row 2) than the physicians, but these differences were not statistically significant. Likewise, the overall percentages of cases judged as needing antibiotics, though lower for parents, were not significantly different between the physicians and parents of each country: 53.0% for US physicians, 44.6% for US parents, 53.4% for French physicians, and 51.1% for French parents. Among the 4 groups the important cues for treatment (Table 3) were similar, stressing the physical examination findings and de-emphasizing the history, including the parents’ report of ear pain. French parents gave atypical stress to the child’s temperature (beta weight = 0.52), so that they differed from other groups in their judgments of several individual scenarios. Both groups of parents assigned significantly less weight than the physicians of all groups to the cue of the parents’ position on antibiotics. Indeed, when asked to put themselves in the place of a physician, the parents did not give importance to any of the parent-sensitive cues, even when the question was treatment. These cues included ear pain noted by the parent, the parents’ personal position concerning antibiotics, whether caring for a sick child greatly upsets the parents’ ordinary schedule, and whether there are babies or other small children in the family.

 

 

Attitudes and opinions related to AOM and health care were not associated with the percentage of cases in which the participant of any group chose to treat with antibiotics. Among both the Americans and the French, the parents thought physicians worried more about charges of malpractice when making decisions about patient care than physicians claimed they did (P <.005 for both countries). Parents were bothered when their child was sick more than physicians were bothered when “one of my patients” was sick (P <.005 for both). If there was a possibility that their child had “a serious illness that is rare but curable,” parents were more willing than physicians to order diagnostic tests even when they would cost the parents “a great deal of time and/or money” (P <.005 for both). Interestingly, parents agreed less strongly than physicians with the statement “A doctor should pay close attention to the needs and preferences of a child’s parents” (P <.005 for both). US parents, but not those in France, agreed more strongly than physicians that if their child might have a serious illness that was rare but curable diagnostic tests should be ordered even when they were “very expensive for the child’s insurance plan” (P <.005). French parents, but not American ones, agreed more strongly than physicians that “all ear infections should be treated with antibiotics” (P <.005).

TABLE 1
DIFFERENCES AMONG GROUPS OF PARTICIPANTS

 US Physicians (n=19)US Parents (n=52)French Physicians (n=56)French Parents (n=86)
Mean judged probability of AOM (range)50 (28 to 75)51 (29 to 78)52.5 (33 to 85)52 (30 to 73)
Mean judged probability of treatment (range)0.22 (-2.4 to 3.0)-0.32 (-2.7 to 3.1)0.36 (-1.5 to 3.0)-0.42 (-2.9 to 2.6)
NOTE: The treatment score is the combination of the choice of observation versus antibiotics and the degree of certainty that this is the right choice, ranging from -5 (observe/completely sure) to 5 (antibiotics/completely sure).
AOM denotes acute otitis media.

TABLE 2
MEAN BETA WEIGHTS FOR CUES FOR JUDGING PROBABILITY OF ACUTE OTITIS MEDIA

CueUS Physicians (n=19)US Parents (n=52)French Physicians (n=55)French Parents (n=81)
History
  Past history of AOM0.020.080.03†0.15
  URI symptoms0.07-0.0100.03
  Ear pain noted by parent0.090.070.040.04
Findings on Examination
  Fever0.200.210.16†0.30†
  Redness of tympanic membrane0.48*0.34*0.400.36
  Bulging of tympanic membrane0.250.310.48†0.17†
  Mobility on insufflation0.37*0.22*0.130.14
  Asymmetry of tympanic membranes0.17*0.28*0.170.16
  Ear pain during examination0.090.140.09†0.28†
  General appearance of the child0.080.060.020.06
  Did the child start to cry just before the examination?0.02-0.03-0.01-0.02
Other Factors
  Parents’ personal position concerning antibiotics-0.050.030.020.02
  Ability of parents to provide effective care to a sick child0.01-0.04-0.02-0.01
  Does caring for sick child greatly upset parents’ ordinary schedule?00.0200.01
  Are there babies or other small children in the family?00.0200.02
Note: Higher values indicate a greater weight given to this cue in making the diagnosis of acute otitis media. Participants whose judgments failed to pass the F-test for multiple regression models (1 French pediatrician and 5 French parents) were excluded from the analysis.
AOM denotes acute otitis media; URI, upper respiratory infection.
*Significant comparison for US group, P <.05.
†Significant comparison for French group, P <.05.

TABLE 3
MEAN BETA WEIGHTS FOR CUES FOR CHOOSING TREATMENT OF ACUTE OTITIS MEDIA

CueUS Physicians (n=19)US Parents (n=49)French Physicians (n=50)French Parents (n=75)
History
  Past history of AOM0.020.040.040.08
  URI symptoms0.020.030.060.07
  Ear pain noted by parent0.110.100.07†0.0†
Findings on examination
  Fever0.210.280.25†0.52†
  Redness of Tympanic membrane0.360.290.34†0.23†
  Bulging of tympanic membrane0.190.230.41†0.08†
  Mobility on insufflation0.28*0.16*0.120.10
  Asymmetry of tympanic membranes0.200.230.130.09
  Ear pain during examination0.040.110.04†0.14†
  General appearance of the child0.160.100.10†0.04†
  Did the child start to cry just before the examination?0.0200.030
Other factors
  Parents’ personal position concerning antibiotics0.17*0.06*0.11†0.03†
  Ability of parents of provide effective care to a sick child-0.05-0.03-0.05-0.04
  Does caring for sick child greatly upset parents’ ordinary schedule?0.010.040.01-0.08
  Are there babies or other small children in the family?0.010.040.040.01
Note: Higher values indicate a greater weight given to this cue when deciding whether to treat with antibiotics. Participants whose judgments failed to pass the F-test for multiple regression models (3 French generalists, 3 French pediatricians, 3 US parents, and 11 French parents) were excluded from the analysis.
AOM denotes acute otitis media; URI, upper respiratory infection.
*Significant comparison for US group, P <.05.
†Significant comparison for French group, P <.05.

Discussion

Although physicians are aware that antibiotic resistance of bacteria is an increasing problem,39-41 they continue to prescribe antibiotics for patients who are unlikely to benefit from them.2,3,13,39,42,43 There are multiple plausible reasons for this.7,17,44-46 Some of these relate to physicians’ perceptions of the wants and needs of their patients and their caretakers. Physicians may47 (or may not48) make different decisions for individuals they are dealing with than for community groups. They know the public misunderstands the indications for antibiotics,25,49,50 and they may perceive, often incorrectly,19,21 that patients or parents want antibiotics and will be dissatisfied if they do not receive them.22,23,26,29,45,51 They may practice defensive medicine28 or believe that it takes less time and effort to prescribe antibiotics than to explain why they are withholding them.28,45 They may be sensitive to the socioeconomic pressures on patients and parents related to daycare policies, work schedules, and the costs of return visits.46

 

 

Our study results should be reassuring to physicians. The most striking finding was the similarity between parents’ diagnostic judgments and treatment choices and those of physicians. The only difference in judgment policies was that the French parents placed greater stress than the French physicians on fever and gave a lower weight to bulging when deciding about treatment, which may be understandable given their unfamiliarity with the technical aspects of examining an ear.31 Indeed, contrary to our expectations, parents in both countries gave less weight to parent-sensitive cues than did physicians. Overall, parents did not diagnose AOM in the scenario children or recommend treating them with antibiotics more frequently than physicians.

The parents’ restraint concerning antibiotics is surprising given physicians’ perceptions and the results of a recent US survey52 in which 96% of parents said that “ear infections can affect a baby’s hearing,” and only 11% thought that “most ear infections get better by themselves.” Also, recent experts have stressed the high indirect costs of an episode of AOM and the value to parents of reducing the duration of the illness.53-56 One explanation may be the rising parental worry that antibiotic treatment may lessen their child’s ability to fight off future infections, in particular because of the spread of antibiotic-resistant bacteria.27 The parents in our study worried as much as the physicians about the adverse effects of antibiotics and agreed just as strongly as the physicians that the resistance of bacteria to antibiotics is the most important threat to the future health of the public. The parents’ ability to adopt the physician’s point of view should encourage physicians to undertake the patient and parent education efforts recently recommended25,28,57 as the best way to reduce the excessive prescription of antibiotics.

We had anticipated incorrectly that the decision to treat would be influenced by attitudes toward uncertainty, ambiguity, and risk.59,63 We had also expected, again incorrectly, that a greater belief in the usefulness of antibiotics and (for the parents alone) in the contagious nature of ear infections — and a lesser worry about antibiotic side effects and bacterial resistance — would identify physicians and parents who opted more frequently for antibiotics. The explanation may be that our questions were insensitive or that general attitudes are poor predictors of individual case-by-case choices and behavior. It may also be that physicians—and even parents who take the role of physicians—believe that diagnosis is the first and determining step in managing a possible ear infection.

Limitations

Our study has several limitations. First, generalization is limited by the small samples, the inequality of the sample sizes, and the convenience nature of the samples. Second, the patients were hypothetical, presented on paper in schematic form, with neither the richness nor the vividness of the real children brought by parents to physicians’ offices. Although the use of “paper patients” has been questioned,64,65 it is practical and has been supported in other studies of clinical decision making.66-69 Third, comparisons between the French and Americans may have been influenced by unappreciated differences in meaning of the French and English versions of the scenarios and questions.

Conclusions

It is encouraging that parents in our study were able to adopt the physician’s perspective and to focus on medical indications rather than on parental needs in their treatment decisions, that they did not choose to prescribe antibiotics more frequently than the physicians, and that they were as concerned as the physicians about the adverse effects of antibiotics and the threat from resistant bacteria. Patients and parents may, therefore, be more willing to forgo antibiotics than physicians realize.

Acknowledgments

We thank the following for their invaluable advice and assistance: Bernard Grenier, MD; Héléne Touchon, MD; Joél Cogneau, MD; Marie-Ange Lecomte, MD; Sabine Maciaszczyk-Jedeau, PhD; Appleton Mason, MD; the Association des Pédiatres de Ville en Touraine; the Latham Medical Group; the Albany Medical Center Department of Family Practice; Héma Sandanam; and Roberta Sandler, RN.

ABSTRACT

OBJECTIVES: We wanted to discover how parents differ from physicians in making decisions about how to treat a child who may have acute otitis media (AOM).

STUDY DESIGN: We used questionnaires that required participants to judge the probability of AOM or choose treatment for 2 sets of 46 paper scenarios of hypothetical children aged 15 months who might have AOM, and they subsequently rated the importance of individual cues and described their attitudes and opinions related to health care and AOM.

POPULATION: Convenience samples of 19 US family physicians, 35 French generalists, 21 French pediatricians, 52 US parents, and 86 French parents were included.

OUTCOMES MEASURED: The primary outcomes were the judgment policies—the weights placed on each of the scenario cues when making decisions—that were derived for each individual and each group by multiple linear regression.

RESULTS: The mean judged probabilities of AOM were nearly the same for all groups: 50% for the US physicians, 51% for the US parents, 52.5% for the French physicians, and 52% for the French parents. The percentages of cases treated with antibiotics did not differ: 53% for US physicians, 45% for US parents, 53% for French physicians, and 51% for French parents. All groups gave greatest weight to the physical examination cues for decisions about both diagnosis and treatment. The parents paid little attention to the cues that reflected parental concerns.

CONCLUSIONS: US and French parents were very similar to physicians in their judgments and treatment choices regarding AOM. They appear to be able to adopt the physician’s point of view and to be selective in the use of antibiotics.

KEY POINTS FOR CLINICIANS

  • The appearance of the eardrum was the key factor in decision making about acute otitis media.
  • Both physicians and parents paid little attention to parental-sensitive factors when choosing treatment.
  • Parents were able to adopt the physician’s point of view.
  • Parents in the United States and France may be more willing to forgo antibiotics than physicians realize.

Acute otitis media (AOM) is the most frequent diagnosis for which antibiotics are prescribed in the United States1,2 and is among the most frequent in France.3 The benefit of antibiotic treatment is under scrutiny as physicians, parents, and policymakers in the United States,4-7 France,3,8-13 and elsewhere14-16 become increasingly worried about bacterial resistance to antibiotics.

Patients and caregivers often have a firm idea of what is wrong with their child and how it should be treated.17,18 Conflict with the physician may partially result from different interpretations that parents and physicians give to the same cues from the child’s history and physical examination, and the different implications derived from them. Despite evidence to the contrary,19-22 studies in the United States and Britain show that physicians feel parents are becoming increasingly demanding: They expect them to prescribe antibiotics even when they are not indicated and will be dissatisfied if they are not prescribed.22-29

The purpose of our study was to elucidate the differences between physicians and parents both in the United States and France by comparing their diagnostic judgments and treatment choices when dealing with children who might be suffering from AOM.

Methods

This study was primarily a “judgment analysis.”30 In judgment analyses, individuals make a series of judgments (eg, diagnoses or treatment choices about a series of patients) according to a set of varying cues (eg, signs and symptoms). Using multiple linear regression analyses, it is possible to determine how much weight they put on each cue when making their decisions. Separate regression analyses are performed for each person, with the cues as the predictors and the judgment (diagnoses or treatment decisions) as the variable predicted. The beta weights from the regression, suitably adjusted for differences in units of measurement, are estimates of the relative importance of the cues in making judgments. A higher beta weight for a cue indicates that it carried greater weight in making the diagnosis or treatment decision. Between- and within-group comparisons of these weights may reveal why people differ in their judgments of identical cases.

Participants

The samples consisted of 19 US family physicians, 35 French general practitioners, 21 French pediatricians, 52 US parents, and 86 French parents. The US physicians were recruited from the 62 family physicians in the region of Albany and Schenectady, New York, through written and oral appeals from the study team. The French practitioners consisted of personal contacts and members of 2 networks of research minded general practitioners. The French pediatricians were recruited by written appeals to the 30 practicing pediatricians in and around Tours, France. The US parents were responders to a recruitment letter sent to 100 randomly selected parents of young children of one primary care practice in suburban Albany. The French parents were recruited by students at the Université François-Rabelais in Tours.31

 

 

Procedure

We constructed 46 paper scenarios describing hypothetical children aged 15 months who might have ear infections. Each scenario displayed the values for 15 different cues, such as fever, redness of the tympanic membrane, or ear pain during examination (see the figure at www.jfponline.com for more information). These cues were selected after consultation with physicians and parents, who felt that they were important for decisions about the diagnosis and treatment of AOM. The diagnostic cues were based on our previous study of US pediatricians32 and on the medical literature in the United States,33-35 France,36,37 and elsewhere.14,38 We included the result of insufflation of the tympanic membrane, even though French primary care physicians are not taught to use pneumatic insufflation in their diagnosis of AOM.

The cue values for each case were generated randomly using a computer program. Very implausible combinations of cue values were excluded from the scenarios. We did not, however, attempt to create a set of cases that would reproduce the actual mix of cases seen by pediatricians in their offices. Indeed, the inclusion of unlikely combinations of cue values was useful in forcing the participants to choose which cues were most important. Intercorrelation between cues was small to moderate, ranging from -0.40 to 0.39. This resulted from the rules for excluding implausible cases (eg, 0.39 between bulging and mobility) or from chance (eg, -0.40 between a history of ear pain and ear pain during the examination). The participants were told to put themselves in the place of the examining physician. They were presented twice with the same 46 scenarios. For one set they were asked to judge the probability that each child had AOM. For the other, in which the cases were presented in a different order, they were to decide whether to treat with antibiotics or to observe the child. They were also asked in the second set to indicate on a 5-point scale their degree of certainty that this was the right choice. Half the participants completed the diagnosis set first; half did the treatment set first. They completed the study at home. They were instructed to refrain from looking back at the first set after finishing and to take a short break of up to a day between the 2 sets. At the end of the study session, the participants answered certain questions about attitudes toward health care and risk and about their background that might account for differences in their responses to the scenarios.*

Data analysis

Two multiple linear regression analyses were performed for each participant (physician or parent). In one regression, the predicted variable was the judgment of the probability of AOM; in the other, it was the choice of treatment. Before performing the second regression, the treatment choice was combined with the degree of certainty about it to create a treatment score on a 10-point scale ranging from -5 for “observe/completely sure” to 5 for “treat/completely sure” (with no 0). This score was used in the multiple linear regression analysis as the dependent variable. A participant was included in these analyses only if his or her R2 passed the F-test for fitness of the multiple regression model at P less than .05; failure of a model to pass the F-test meant that the individual’s judgments were not predictable by the cues. This could occur if he or she had answered randomly or had made mistakes.

Multivariate analysis of variance was used to test the differences in the mean responses to the questions on attitudes and opinions of each group of participants. The association of individual attitudes and opinions about AOM and health care with treatment choices was explored by correlating the percentage of cases treated with antibiotics with the responses to each of the questions about attitudes and opinions.

Results

The judgments of the parents were remarkably similar to those of the physicians, both in the United States and France. The means and the ranges of the mean probability judgments of the individual participants in all the groups were almost identical at 50% or just above (Table 1, row 1). The cue weights for diagnosis (Table 2) were also quite similar. The physical examination provided the key information for both the parents and physicians. The only differences were that French parents gave more weight than the other groups to a past history of ear infections (beta weight = 0.15) and focused more on fever (0.30) and ear pain during the examination (0.28) than on bulging (0.17).

In choosing treatment, the parents in each country had lower mean treatment scores (Table 1, row 2) than the physicians, but these differences were not statistically significant. Likewise, the overall percentages of cases judged as needing antibiotics, though lower for parents, were not significantly different between the physicians and parents of each country: 53.0% for US physicians, 44.6% for US parents, 53.4% for French physicians, and 51.1% for French parents. Among the 4 groups the important cues for treatment (Table 3) were similar, stressing the physical examination findings and de-emphasizing the history, including the parents’ report of ear pain. French parents gave atypical stress to the child’s temperature (beta weight = 0.52), so that they differed from other groups in their judgments of several individual scenarios. Both groups of parents assigned significantly less weight than the physicians of all groups to the cue of the parents’ position on antibiotics. Indeed, when asked to put themselves in the place of a physician, the parents did not give importance to any of the parent-sensitive cues, even when the question was treatment. These cues included ear pain noted by the parent, the parents’ personal position concerning antibiotics, whether caring for a sick child greatly upsets the parents’ ordinary schedule, and whether there are babies or other small children in the family.

 

 

Attitudes and opinions related to AOM and health care were not associated with the percentage of cases in which the participant of any group chose to treat with antibiotics. Among both the Americans and the French, the parents thought physicians worried more about charges of malpractice when making decisions about patient care than physicians claimed they did (P <.005 for both countries). Parents were bothered when their child was sick more than physicians were bothered when “one of my patients” was sick (P <.005 for both). If there was a possibility that their child had “a serious illness that is rare but curable,” parents were more willing than physicians to order diagnostic tests even when they would cost the parents “a great deal of time and/or money” (P <.005 for both). Interestingly, parents agreed less strongly than physicians with the statement “A doctor should pay close attention to the needs and preferences of a child’s parents” (P <.005 for both). US parents, but not those in France, agreed more strongly than physicians that if their child might have a serious illness that was rare but curable diagnostic tests should be ordered even when they were “very expensive for the child’s insurance plan” (P <.005). French parents, but not American ones, agreed more strongly than physicians that “all ear infections should be treated with antibiotics” (P <.005).

TABLE 1
DIFFERENCES AMONG GROUPS OF PARTICIPANTS

 US Physicians (n=19)US Parents (n=52)French Physicians (n=56)French Parents (n=86)
Mean judged probability of AOM (range)50 (28 to 75)51 (29 to 78)52.5 (33 to 85)52 (30 to 73)
Mean judged probability of treatment (range)0.22 (-2.4 to 3.0)-0.32 (-2.7 to 3.1)0.36 (-1.5 to 3.0)-0.42 (-2.9 to 2.6)
NOTE: The treatment score is the combination of the choice of observation versus antibiotics and the degree of certainty that this is the right choice, ranging from -5 (observe/completely sure) to 5 (antibiotics/completely sure).
AOM denotes acute otitis media.

TABLE 2
MEAN BETA WEIGHTS FOR CUES FOR JUDGING PROBABILITY OF ACUTE OTITIS MEDIA

CueUS Physicians (n=19)US Parents (n=52)French Physicians (n=55)French Parents (n=81)
History
  Past history of AOM0.020.080.03†0.15
  URI symptoms0.07-0.0100.03
  Ear pain noted by parent0.090.070.040.04
Findings on Examination
  Fever0.200.210.16†0.30†
  Redness of tympanic membrane0.48*0.34*0.400.36
  Bulging of tympanic membrane0.250.310.48†0.17†
  Mobility on insufflation0.37*0.22*0.130.14
  Asymmetry of tympanic membranes0.17*0.28*0.170.16
  Ear pain during examination0.090.140.09†0.28†
  General appearance of the child0.080.060.020.06
  Did the child start to cry just before the examination?0.02-0.03-0.01-0.02
Other Factors
  Parents’ personal position concerning antibiotics-0.050.030.020.02
  Ability of parents to provide effective care to a sick child0.01-0.04-0.02-0.01
  Does caring for sick child greatly upset parents’ ordinary schedule?00.0200.01
  Are there babies or other small children in the family?00.0200.02
Note: Higher values indicate a greater weight given to this cue in making the diagnosis of acute otitis media. Participants whose judgments failed to pass the F-test for multiple regression models (1 French pediatrician and 5 French parents) were excluded from the analysis.
AOM denotes acute otitis media; URI, upper respiratory infection.
*Significant comparison for US group, P <.05.
†Significant comparison for French group, P <.05.

TABLE 3
MEAN BETA WEIGHTS FOR CUES FOR CHOOSING TREATMENT OF ACUTE OTITIS MEDIA

CueUS Physicians (n=19)US Parents (n=49)French Physicians (n=50)French Parents (n=75)
History
  Past history of AOM0.020.040.040.08
  URI symptoms0.020.030.060.07
  Ear pain noted by parent0.110.100.07†0.0†
Findings on examination
  Fever0.210.280.25†0.52†
  Redness of Tympanic membrane0.360.290.34†0.23†
  Bulging of tympanic membrane0.190.230.41†0.08†
  Mobility on insufflation0.28*0.16*0.120.10
  Asymmetry of tympanic membranes0.200.230.130.09
  Ear pain during examination0.040.110.04†0.14†
  General appearance of the child0.160.100.10†0.04†
  Did the child start to cry just before the examination?0.0200.030
Other factors
  Parents’ personal position concerning antibiotics0.17*0.06*0.11†0.03†
  Ability of parents of provide effective care to a sick child-0.05-0.03-0.05-0.04
  Does caring for sick child greatly upset parents’ ordinary schedule?0.010.040.01-0.08
  Are there babies or other small children in the family?0.010.040.040.01
Note: Higher values indicate a greater weight given to this cue when deciding whether to treat with antibiotics. Participants whose judgments failed to pass the F-test for multiple regression models (3 French generalists, 3 French pediatricians, 3 US parents, and 11 French parents) were excluded from the analysis.
AOM denotes acute otitis media; URI, upper respiratory infection.
*Significant comparison for US group, P <.05.
†Significant comparison for French group, P <.05.

Discussion

Although physicians are aware that antibiotic resistance of bacteria is an increasing problem,39-41 they continue to prescribe antibiotics for patients who are unlikely to benefit from them.2,3,13,39,42,43 There are multiple plausible reasons for this.7,17,44-46 Some of these relate to physicians’ perceptions of the wants and needs of their patients and their caretakers. Physicians may47 (or may not48) make different decisions for individuals they are dealing with than for community groups. They know the public misunderstands the indications for antibiotics,25,49,50 and they may perceive, often incorrectly,19,21 that patients or parents want antibiotics and will be dissatisfied if they do not receive them.22,23,26,29,45,51 They may practice defensive medicine28 or believe that it takes less time and effort to prescribe antibiotics than to explain why they are withholding them.28,45 They may be sensitive to the socioeconomic pressures on patients and parents related to daycare policies, work schedules, and the costs of return visits.46

 

 

Our study results should be reassuring to physicians. The most striking finding was the similarity between parents’ diagnostic judgments and treatment choices and those of physicians. The only difference in judgment policies was that the French parents placed greater stress than the French physicians on fever and gave a lower weight to bulging when deciding about treatment, which may be understandable given their unfamiliarity with the technical aspects of examining an ear.31 Indeed, contrary to our expectations, parents in both countries gave less weight to parent-sensitive cues than did physicians. Overall, parents did not diagnose AOM in the scenario children or recommend treating them with antibiotics more frequently than physicians.

The parents’ restraint concerning antibiotics is surprising given physicians’ perceptions and the results of a recent US survey52 in which 96% of parents said that “ear infections can affect a baby’s hearing,” and only 11% thought that “most ear infections get better by themselves.” Also, recent experts have stressed the high indirect costs of an episode of AOM and the value to parents of reducing the duration of the illness.53-56 One explanation may be the rising parental worry that antibiotic treatment may lessen their child’s ability to fight off future infections, in particular because of the spread of antibiotic-resistant bacteria.27 The parents in our study worried as much as the physicians about the adverse effects of antibiotics and agreed just as strongly as the physicians that the resistance of bacteria to antibiotics is the most important threat to the future health of the public. The parents’ ability to adopt the physician’s point of view should encourage physicians to undertake the patient and parent education efforts recently recommended25,28,57 as the best way to reduce the excessive prescription of antibiotics.

We had anticipated incorrectly that the decision to treat would be influenced by attitudes toward uncertainty, ambiguity, and risk.59,63 We had also expected, again incorrectly, that a greater belief in the usefulness of antibiotics and (for the parents alone) in the contagious nature of ear infections — and a lesser worry about antibiotic side effects and bacterial resistance — would identify physicians and parents who opted more frequently for antibiotics. The explanation may be that our questions were insensitive or that general attitudes are poor predictors of individual case-by-case choices and behavior. It may also be that physicians—and even parents who take the role of physicians—believe that diagnosis is the first and determining step in managing a possible ear infection.

Limitations

Our study has several limitations. First, generalization is limited by the small samples, the inequality of the sample sizes, and the convenience nature of the samples. Second, the patients were hypothetical, presented on paper in schematic form, with neither the richness nor the vividness of the real children brought by parents to physicians’ offices. Although the use of “paper patients” has been questioned,64,65 it is practical and has been supported in other studies of clinical decision making.66-69 Third, comparisons between the French and Americans may have been influenced by unappreciated differences in meaning of the French and English versions of the scenarios and questions.

Conclusions

It is encouraging that parents in our study were able to adopt the physician’s perspective and to focus on medical indications rather than on parental needs in their treatment decisions, that they did not choose to prescribe antibiotics more frequently than the physicians, and that they were as concerned as the physicians about the adverse effects of antibiotics and the threat from resistant bacteria. Patients and parents may, therefore, be more willing to forgo antibiotics than physicians realize.

Acknowledgments

We thank the following for their invaluable advice and assistance: Bernard Grenier, MD; Héléne Touchon, MD; Joél Cogneau, MD; Marie-Ange Lecomte, MD; Sabine Maciaszczyk-Jedeau, PhD; Appleton Mason, MD; the Association des Pédiatres de Ville en Touraine; the Latham Medical Group; the Albany Medical Center Department of Family Practice; Héma Sandanam; and Roberta Sandler, RN.

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12. Guillemot D. Are antibiotics over-consumed? Rev Prat 1998;48:585-86.

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References

1. McCaig LF, Hughes JM. Trends in antimicrobial drug prescribing among office-based physicians. JAMA 1995;273:214-19.

2. Nyquist A-C, Gonzales R, Steiner JF, Sande MA. Antibiotic prescribing for children with colds, upper respiratory tract infections, and bronchitis. JAMA 1998;279:875-77.

3. Guillemot D, Maison P, Carbon C, et al. Trends in antimicrobial drug use in the community—France, 1981-1992. J Infect Dis 1998;177:492-97.

4. Paradise JL. Managing otitis media: a time for change. Pediatrics 1995;96:712-15.

5. Culpepper L, Froom J. Routine antimicrobial treatment of acute otitis media: is it necessary? JAMA 1997;278:1643-45.

6. Dowell SF, Marcy SM, Phillips WR, Gerber MA, Schwartz B. Otitis media: principles of judicious use of antimicrobial agents. Pediatrics 1998;101:165-71.

7. Abramson JS, Givner LB. Bacterial resistance due to antimicrobial drug addiction among physicians: time for a cure! Arch Fam Med 1999;8:79-80.

8. Cohen R, Bingen E, Varon E, et al. Change in nasopharyngeal carriage of Streptoccus pneumoniae resulting from antibiotic therapy for acute otitis media in children. Pediatr Infect Dis J 1997;16:555-60.

9. Guillemot D, Carbon C, Balkau B, et al. Low dosage and long treatment duration of $-lactam: risk factors for carriage of penicillin-resistant Streptococcus pneumoniae. JAMA 1998;279:365-70.

10. Gehanno P, N’guyen L, Derriennic M, Pichon F, Goehrs J-M, Berche P. Pathogens isolated during treatment failures in otitis. Pediatr Infect Dis J 1998;17:885-90.

11. Geslin P, Fremaux A, Sissia G, Spicq C, Georges S. Development of resistance to beta-lactams and other antibiotics of pneumococci isolated from acute otitis media in France: statement of the National Reference Center 1995-1996. Arch Pediatr 1998;5:982-87.

12. Guillemot D. Are antibiotics over-consumed? Rev Prat 1998;48:585-86.

13. Agence du Medicament Antibiotherapie par voie generale en pratique courante: infections ORL et respiratoires basses. Paris; France: Agence du Medicament; 1999.

14. Froom J, Culpepper L, Grob P, et al. Diagnosis and antibiotic treatment of acute otitis media: report from International Primary Care Network. BMJ 1990;300:582-86.

15. Froom J, Culpepper L, Jacobs M, et al. Antimicrobials for acute otitis media? A review from the International Primary Care Network. BMJ 1997;315:98-102.

16. Del Mar C, Glasziou P, Hayem. Are antibiotics indicated as initial treatment for children with acute otitis media? A meta-analysis. BMJ 1997;314:1526-29.

17. Bauchner H, Philip B. Reducing inappropriate oral antibiotic use: a prescription for change. Pediatrics 1998;102:142-45.

18. Marple RL, Kroenke K, Lucey CR, Wilder J, Lucas CA. Concerns and expectations in patients presenting with physical complaints. Arch Intern Med 1997;157:1482-88.

19. Hamm RM, Hicks RJ, Bemben DA. Antibiotics and respiratory infections: are patients more satisfied when expectations are met? J Fam Pract 1996;43:56-62.

20. Hamm RM, Hicks RJ, Bemben DA. Antibiotics and respiratory infections: do antibiotic prescriptions improve outcomes? J Okla State Med Assoc 1996;89:267-74.

21. Himmel W, Lippert-Urbanke E, Kochen MM. Are patients more satisfied when they receive a prescription? The effect of patient expectations in general practice. Scand J Prim Health Care 1997;15:118-22.

22. Butler CC, Rollnick S, Pill R, Maggs-Rapport F, Stott N. Understanding the culture of prescribing: qualitative study of general practitioners’ and patients’ perceptions of antibiotics for sore throats. BMJ 1998;317:637-42

23. Vinson DC, Lutz LJ. The effect of parental expectations on treatment of children with a cough: a report from ASPN. J Fam Pract 1993;37:23-27.

24. Kai J. Parents’ difficulties and information needs in coping with acute illness in preschool children: a qualitative study. BMJ 1996;313:987-90.

25. Palmer DA, Bauchner H. Parents’ and physicians’ views on antibiotics. Pediatrics 1997;99:e6.-

26. Macfarlane J, Holmes W, Marfarlane R, Britten N. Influence of patients’ expectations on antibiotic management of acute lower respiratory tract illness in general practice: questionnaire study. BMJ 1997;315:1211-14.

27. Barden LS, Dowell SF, Schwartz B, Lackey C. Current attitudes regarding use of antimicrobial agents: results from physicians’ and parents’ focus group discussions. Clin Pediatr 1998;37:665-72.

28. Bauchner H, Pelton SI, Klein JO. Parents, physicians, and antibiotic use. Pediatrics 1999;103:395-401.

29. Mangione-Smith R, McGlynn EA, Elliott MN, Krogstad P, Brook RH. The relationship between perceived parental expectations and pediatrician antimicrobial prescribing behavior. Pediatrics 1999;103:711-18.

30. Cooksey RW. Judgment analysis. New York, NY: Academic Press; 1996.

31. Sandanam H. Etude du diagnostic de l’otite moyenne aigun par les parents. Memoire de MaTtrise. Tours, France: Universite Francois Rabelais; 1999.

32. Gonzalez-Vallejo C, Sorum PC, Stewart TR, Chessare JB, Mumpower JL. Physicians’ diagnostic judgments and treatment decisions for acute otitis media in children. Med Decis Making 1998;18:149-62.

33. Hayden GF. Acute suppurative otitis media in children: diversity of clinical diagnostic criteria. Clin Pediatr 1981;20:99-104.

34. Paradise JL. On classifying otitis media as suppurative or nonsuppurative, with a suggested clinical schema. J Pediatr 1987;111:948-51.

35. Bluestone CD, Klein JO. Otitis media in infants and children. 2nd ed. Philadelphia, Pa: WB Saunders; 1995.

36. Soussi T. Otite moyenne aigun. In: Bourrillon A, ed. Pediatrie pour le practicien. 2nd ed. Paris: Masson, 1996;456-458.

37. Mondain M. Symptomatologie clinique et diagnostic des otites et de leurs complications. Rev Prat 1998;48:843-47.

38. Heikkinen T, Ruuskanen O. Signs and symptoms predicting acute otitis media. Arch Pediatr Adolesc Med 1995;149:26-29.

39. Schwartz RA, Freij BJ, Ziai M, Sheridan MJ. Antimicrobial prescribing for acute purulent rhinitis in children: a survey of pediatricians and family practitioners. Pediatr Infect Dis J 1997;16:185-90.

40. Watson RL, Dowell SF, Jayaraman M, Keyserling H, Kolczak M, Schwartz B. Antimicrobial use for pediatric upper respiratory infections: reported practice, actual practice, and parent beliefs. Pediatrics 1999;104:1251-1257.

41. TremoliPres F. Antibiotherapie des infections ORL et respiratoires: de l’urgence des recommandations. Presse Med 1999;28:417-18.

42. Mainous AG, III, Hueston WJ. The cost of antibiotics in treating upper respiratory tract infections in a medicaid population. Arch Fam Med 1998;7:45-49.

43. Guillemot D, Carbon C, Vauzelle-KervoNdan Balkau B, Maison P, Bouvenot G, EschwPge E. Inappropriateness and variability of antibiotic prescription among French office-based physicians. J Clin Epidemiol 1998;51:61-68.

44. Richardson JP. Physician heal thyself: are antibiotics the cure of the disease? Arch Fam Med 1998;7:51-52.

45. Schwartz B, Mainous AG, III, Marcy SM. Why do physicians prescribe antibiotics for children with upper respiratory tract infections? JAMA 1998;279:881-82.

46. Pichichero ME. Understanding antibiotic overuse for respiratory tract infections in children. Pediatrics 1999;104:1384-88.

47. Redelmeier DA, Tversky A. Discrepancy between medical decisions for individual patients and for groups. N Engl J Med 1990;322:1162-64.

48. Nickerson CAE, Ubel PA, Hershey JC, Spranca MD, Asch DA. Further explorations of medical decisions for individuals and for groups. Med Decis Making 2000;20:39-44.

49. Mainous AG, III, Zoorob RJ, Oler MJ, Haynes DM. Patient knowledge of upper respiratory infections: implications for antibiotic expectations and unnecessary utilization. J Fam Pract 1997;45:75-83.

50. Wilson AA, Crane LA, Barrett PH, Jr, Gonzales R. Public beliefs and use of antibiotics for acute respiratory illlness. J Gen Intern Med 1999;14:658-62.

51. Cockburn J, Pit S. Prescribing behaviour in clinical practice: patients’ expectations and doctors’ perceptions of patients’ expectations—a questionnaire study. BMJ 1997;315:520-23.

52. Daly KA, Selvius RE, Lindgren B. Knowledge and attitudes about otitis media risk: implications for prevention. Pediatrics 1997;100:931-36.

53. Bauchner H, Adams W, Barnett E, Klein J. Therapy for acute otitis media: preference of parents for oral or parenteral antibiotic. Arch Pediatr Adolesc Med 1996;150:396-99.

54. Alsarraf R, Jung CJ, Perkins J, Crowley C, Alsarraf NW, Gates GA. Measuring the indirect and direct costs of acute otitis media. Arch Otolaryngol Head Neck Surg 1999;125:12-18.

55. Sorum PC. Measuring patient p by willingness to pay to avoid: the case of acute otitis media. Med Decis Making 1999;19:27-37.

56. Heymann SJ, Toomey S, Furstenberg F. Working parents: what factors are involved in their ability to take time off from work when their children are sick? Arch Pediatr Adolesc Med 1999;153:870-74.

57. Gonzales R, Steiner JF, Lum A, Barrett PH, Jr. Decreasing antibiotic use in ambulatory practice: impact of a multidimensional intervention on the treatment of uncomplicated acute bronchitis in adults. JAMA 1999;281:1512-19.

58. Poses RM, Chaput de Saintonge M, et al. An international comparison of physicians’ judgments of outcome rates of cardiac procedures and attitudes toward risk, uncertainty, justifiability, and regret. Med Decis Making 1998;18:131-40.

59. Gerrity MS. Conceptual models for understanding and measuring physicians’ reactions to uncertainty. In: Hibbard H, Nutting PA, Grady ML, eds. Primary care research: theory and methods. PB91-228130. Washington, DC: US Department of Health and Human Services, 1991;167-58.

60. Holtgrave DR, Lawler F, Spann SJ. Physicians’ risk attitudes, laboratory usage, and referral decisions: the case of an academic family practice center. Med Decis Making 1991;11:125-30.

61. Geller G, Tambor ES, Chase GA, Holtzman NA. Measuring physicians’ tolerance for ambiguity and its relationship to their reported practices regarding genetic testing. Med Care 1993;31:989-1001.

62. Kuhn KM, Budescu DV. The relative importance of probabilities, outcomes, and vagueness in hazard risk decisions. Organ Behav Hum Decis Process 1996;68:301-17.

63. Allison JJ, Kiefe CI, Cook F, Gerrity MS, Orav EJ, Centor R. The association of physician attitudes about uncertainty and risk taking with resource use in a Medicare HMO. Med Decis Making 1998;18:320-29.

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65. Jones TV, Gerrity MS, Earp J. Written case simulations: do they predict physicians’ behavior? J Clin Epidemiol 1990;43:805-15.

66. Chaput de Saintonge DM, Hathaway NR. Antibiotic use in otitis media: patient simulations as an aid to audit. BMJ 1981;283:883-84.

67. Kirwan JR, Chaput de Saintogne DM, Joyce CRB. Clinical judgment analysis. Q J Med 1990;76:935-49.

68. Chaput de Saintonge DM, Hattersley LA. Antibiotics for otitis media: can we help doctors agree? Fam Pract 1985;2:205-12.

69. Kirwan JP, Chaput de Saintogne DM, Joyce CRB, Currey HLF. Clinical judgment in rheumatoid arthritis. I. Rheumatologists’ opinions and the development of “paper patients”. Ann Rheum Dis 1983;42:648-51.

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Screening for Alcoholism in the Primary Care Setting

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Screening for Alcoholism in the Primary Care Setting

ABSTRACT

BACKGROUND: This study assessed which demographic groups were most likely to consume alcohol excessively, and which groups had received inquiries and discussion about alcohol use from their physicians compared with discussions about other health risks.

STUDY DESIGN: This was a cross-sectional study using data from the Centers for Disease Control Behavioral Risk Factors Surveillance System 1997 data set that represents a stratified random sample in the United States.

POPULATION: We selected 23,349 adults who reported a routine physical examination within the last 3 years.

OUTCOMES MEASURED: The main variables involved responses to questions about alcohol intake and whether the respondent’s physician had initiated discussions about drinking.

RESULTS: Physicians spoke to patients about alcohol use much less frequently than about other health-related behaviors. Discussions were roughly targeted to groups with the largest intake. However, physicians were least likely to speak with white patients, women, and widows who drank significantly.

CONCLUSIONS: Regularly asking patients about alcohol use could substantially reduce the under-recognition of alcoholism. Since brief counseling is effective, negative consequences of excessive alcohol intake may be avoided.

KEY POINTS FOR CLINICIANS

  • Alcohol screening occurs less frequently than screening about other health-related behaviors.
  • There were no demographic groups in which the prevalence of excessive drinking was so low that general screening was not appropriate.
  • Physicians frequently miss the opportunity to discuss alcohol use with patients in certain groups, such as white patients and women (widows, in particular).

Physicians and related health care workers are well positioned to detect possible alcohol-related problems during routine patient visits, provided the appropriate screening procedures are implemented.2-4 Ideally, the primary care clinician should be the most prominent source of alcohol abuse screening and referrals, rather than the provider of treatment after an alcohol-related incident.

Although screening for alcoholism adds another step to an already over-worked health care system, it can result in substantial benefits by reducing the burden of overall health care costs. According to recent information, alcohol abuse costs our society $184.6 billion.5 In 1997, an estimated 1.3 million hospital discharges reported an alcohol-related diagnosis.6 And an estimated 12,870 alcohol-related traffic fatalities accounted for nearly one third of all traffic deaths in that same year.7 Even when individuals reamin socially functional and do not meet the formal criteria for an alcohol-related disorder, excessive use of alcohol is associated with a variety of medical problems. Although cardioprotective effects have been reported with moderate use (ie, 1 to 2 drinks per day), the list of medical complications associated with longstanding alcoholism (hypertension, cardiomyopathy, cirrhosis, erosive gastris, pancreatitis, and esophageal varices, for example) account for considerable morbidity and mortality.8,9 Increased alcohol consumption over a 1-year period is also associated with accidents and injuries necessitating emergency services.10

Because the primary care physician is in a unique position to influence the preventive care of the community they serve, our study examined alcohol screening in the primary care setting. The following 2 questions were asked: (1) Which patients were assessed for excessive alcohol use, and what patient characteristics predicted the assessment? and (2) How often did discussions about alcohol occur compared with other health risk discussions (eg, eating habits or smoking)?

Methods

Subjects

This is a secondary analysis of data from an epidemiologic telephone interview conducted by the Behavioral Risk Factors Surveillance System (BRFSS)1 involving a random stratified sample of people living within the United States. In the 1997 interviews, all state interviews included questions about alcohol consumption. Alaska, Colorado, Idaho, Louisiana, Missouri, New York, North Carolina, Oklahoma, Pennsylvania, Virginia, and Wyoming included a counseling module that asked, “Has a doctor or other health professional ever talked with you about alcohol use?”

The 1997 BRFSS data set represents 135,582 interviews. The sample reported here includes only respondents who reported a routine physical examination within the last 3 years and who were asked questions from the counseling module (n = 23,349), as well as questions about other health habits. There were 9106 men (mean age = 45.82 years; SD = 16.86) and 14,203 women (mean age = 46.90; SD = 17.44) who responded.

Excessive drinking was defined as consuming 60 alcoholic beverages per month or 5 on a single occasion (binge drinking) in the month prior to the interview (n = 2772). The 60 beverages per month threshold follows recommendations by the National Institute for Alcohol Abuse and Alcoholism and the US Department of Health and Human Services’ Dietary Guidelines for Americans.11,12

Statistical analysis

Analysis used the sampling weights provided by the Centers for Disease Control. The data were weighted so that the summary statistics, standard errors, and test statistics took into account the sampling design and represented estimates in the total US. population. We used the procedures described by Levy and Lemeshow,13 and implemented them using STATA.14 These included simple chi-square tests, logistic regression with F- or t-approximations. The F- and t-approximations for the logistic regression were necessary to adjust for the complex survey design.13 Hierarchical (protected) testing procedures helped correct for multiple comparisons. We used omnibus tests for variables with multiple options (eg, marital status), and only considered follow-up tests when the overall test result was significant. Furthermore, a conservative threshold for significance (P < .01) was a compromise to the Bonferroni correction for multiple comparisons.

 

 

Results

Approximately 1 in 6 patients (16.1%; 95% confidence interval [CI], 15.4 - 16.8) reported that a physician or other health care worker had initiated a discussion about alcohol use. Table W1* compares patients who reported such a discussion with those who did not. Physicians talked to male patients about alcohol use most frequently. This corresponds to men reporting nearly 3 times more drinks consumed (12.9 drinks/month) than women (4.7 drinks/month; t = 16.26, df = 18,323, P < .001).

In general, physicians spoke about alcohol more often to younger people. There was a significant interaction, however, between sex and age, as shown in Figure 1. Discussions with women demonstrated a clear decrease in frequency with age; discussions with men decreased with age more slowly.

The amount of drinking in our sample, indexed by the number of drinks per month, is shown in Figure 2. Both the frequency of discussions and amount of alcohol consumed declined with advancing age. Women drank less alcohol than men overall and showed a moderate decline in use with age.

Nonwhite respondents reported more physician discussions about alcohol than did whites. However, white patients reported greater consumption (8.9 drinks/month) than nonwhites (6.8 drinks; t = 2.79, df = 18253, P < .005).

The lowest income group reported being advised about alcohol most frequently. Interestingly, the highest income respondents tended to drink more than those with less income. Marital status also predicted alcohol discussions. Physicians discussed alcohol more often with patients who were unmarried. While divorced patients reported discussions about alcohol use frequently, widowed patients reported them least often.

Discussions about alcohol occurred more often with respondents who had consumed alcohol within the month prior to the interview. Respondents who reported having these discussions also consumed more (14.6 drinks/month) than those who did not report a discussion (7.3 drinks/month, t = 7.20, df = 17985, P < .001). People who binge drank were more than twice as likely (OR = 2.25; 95% CI, 1.94 - 2.60) to report such a discussion (27.5%).

All of these predictors of a discussion about alcohol use were entered into a multivariate logistic regression. Backward elimination removed items that failed to provide independent information. The final model appears in the Table. Factors other than drinking behaviors that uniquely increased the chance of such a discussion about alcohol were being young, male, nonwhite, and of lower income.

More than 1 in 9 people (12.3%; n = 2768) in the sample met criteria for excessive drinking (ie, consuming 60 drinks per month or drinking 5 or more drinks on a single occasion in the last month). Within this group, 11.9% engaged in binge drinking and 3.5% consumed 60 drinks a month. Most were men (71.3%) with a mean age of 35 years, and they averaged 39.7 (95% CI, 36.74 - 42.74) drinks per month. They binged an average of 3.44 (95% CI, 3.18 - 3.70) times during the last month.

Slightly more than 1 in 4 excessive drinkers (28%; 95% CI, 25.41 - 30.69) reported a discussion about alcohol with a physician. While none of the demographic factors met our stringent criteria for significance (P < .01), many would have met the more usual threshold of P < .05. For example, among the respondents with excessive alcohol use, 23.9% of the women had been screened by their physicians compared with 29.9% of the men (P < .013). This suggests that these women may receive less preventive discussions and screening than they require. While the percent of nonwhite respondents who drank excessively and were successfully screened was above the mean rate overall (35.6%), the percent of white patients was lower than the mean (26.4%; P < .012). Compared with married patients, excessive drinkers who were divorced (35.2%; P < .019) or separated (38.1%; P < .091) reported a higher frequency of screening. Widowed patients with excessive alcohol use rarely reported screening (13%; P < .032).

TABLE
FINAL LOGISTIC REGRESSION PREDICTING A DISCUSSION ABOUT ALCOHOL

PredictorBetaOR(95% CI)
Sex-0.480.62 (0.44 - 0.87)*
Age-0.0280.97 (0.97 - 0.98)†
Age, by sex interaction0.021.02 (1.01 - 1.03)†
Did not drink ‡-0.570.57 (0.44 - 0.73)†
Drinks per month §0.281.33 (1.24 - 1.43)†
Income-0.080.93 (0.90 - 0.96)†
Race ¶0.341.41 (1.22 - 1.63)†
Constant-0.24 
OR denotes odds ratio from univariate logistic regression; CI, confidence interval.
*P< .01
†P< .001
Respondent reported not drinking any alcohol in the last month.
§ Log transform of the number of drinks per month.
¶ Race coded as white versus nonwhite.

FIGURE 1
LOGISTIC REGRESSION PREDICTING THE OCCURRENCE OF PHYSICIAN DISCUSSION ABOUT ALCOHOL FROM THE PATIENTS’ AGE AND SEX (WOMEN =o MEN =●).


FIGURE 2
MEAN NUMBERS OF ALCOHOLIC DRINKS CONSUMED BY AGE AND SEX (WOMEN = o MEN = ● ).

 

 

Other preventive services

We also assessed the rate of talks about healthy eating as a comparison for the alcohol discussions. A total of 44.6% of patients reported having a conversation about healthy eating compared with 16.1% having an alcohol discussion. On the chance that people who drank excessively might differentially remember conversations with their physicians, we assessed the association between alcohol use and reports of experiencing counseling on healthy eating. No significant associations were found between reported conversations about eating and any variable related to alcohol consumption even after controlling for sex, age, sex-by-age interaction, race, income, and education. Similar results were obtained for discussions about exercise, AIDS, and illegal drugs. Discussions about illegal drug use occurred less frequently than discussions about alcohol use (12.5%). Discussions about AIDS were reported by 26.1% of the respondents, exercise-related conversations were reported 47.4% of the time, and smoking was mentioned by 49.2% of the respondents. Considering any preventive health discussions (smoking, drinking, drug abuse, exercise, healthy eating, or AIDS), 97.4% of the respondents reported a discussion of at least 1 topic.

Discussion

Physicians currently incorporate preventive counseling about behavioral health risks as part of standard clinical care. In a recent survey of general practitioners, 97% of those surveyed thought that members of their profession should inquire about drinking behaviors.15 Moreover, brief office visit screening followed by physician advice has been documented as effective in reducing alcohol consumption.16,17 Despite the general positive opinion of alcohol screening, however, discussions about AIDS and other health-related behaviors were discussed much more frequently than alcohol-related behaviors.

Our analysis identified patients who consume a significant amount of alcohol, yet did not report being screened or counseled by their physicians. We gathered information about the magnitude of use, as well as about the presence or absence of a discussion regarding alcohol. This allowed us to examine 2 important aspects of alcohol screening: (1) the demographic features that predicted it, and (2) whether these demographic features represented patients who actually consumed large amounts of alcohol and could therefore benefit from counseling.

Although the BRFSS did not assess alcohol dependence or abuse directly, the goal of the study was to designate which patients might be appropriate candidates for screening or preventive counseling. Assessments of alcohol abuse or dependence using strict Diagnostic and Statistical Manual of Mental Disorders – 4th Edition (DSM-IV) criteria require insight, as well as a willingness to share this information with the interviewer. Kosten and Rounsaville18 found that DSM-based diagnostic interviews for alcoholism and substance abuse showed the lowest sensitivity relative to other psychiatric diagnoses. Therefore, more recent surveys of alcohol abuse assess the quantity of consumption before applying strict DSM-IV criteria.19,20 Although participants may also minimize actual consumption, the screening for quantity requires less insight than a formal diagnosis and may more effectively identify candidates for counseling. However, our findings suggest that physicians do not routinely attempt to ascertain alcohol use quantitatively.21,22 Implementing alcohol screening as a routine preventive health care practice would allow physicians to detect problems without relying on insightful spontaneous reporting from patients.

The most successful strategy to identify more candidates for treatment involves simply screening a larger number of patients, especially high-risk patients. Our data suggest that physicians do target discussions somewhat toward people who report excessive alcohol consumption. Approximately 16% of the general patient population reported such a discussion, but this rate was greater (27.8%) among heavy or binge drinkers. Unfortunately, these data also suggest that the majority of patients who might benefit from such counseling, did not report a discussion about alcohol use. Individuals who are likely to be appropriate candidates but who were not counseled include white patients and women (widows, in particular).

The frequency of discussions about alcohol for women and widows who drank excessively was low. This finding is consistent with current research demonstrating that alcohol problems among women, and widowed women in particular, are under-recognized. Physiologically, the lower body water volume in women, especially in elderly women, increases the detrimental effects of alcohol.23 Physicians also appear to have more difficulty recognizing alcohol problems among the elderly.24 Alcohol-related symptoms among elderly women may be misinterpreted as caused by depression, anxiety, or other psychiatric problems.25 Elderly women taking psychoactive medications or medications with sedative effects may be even more difficult to assess. Moreover, our analysis categorized excessive drinking using a single criterion for all respondents. Evidence is mounting that indicates that women26 and the elderly23 are more at risk from lower levels of drinking. Had we lowered our criterion for these patients, the magnitude of problem drinking would have appeared even greater.

 

 

Limitations

There are several limitations to our study. A number of reasons, including forgetfulness or inattention, may account for under-reporting. An overall problem with memory is unlikely, since almost all respondents remembered at least one discussion about some kind of health risk. Nonetheless, patients may be selectively less likely to recall a discussion about alcohol because of emotional associations with the topic. However, it is unclear why memory would be less reliable about alcohol use than memory about another potentially emotionally-charged topic, such as AIDS.

Additionally, our information was self-reported through a telephone interview. There have been positive study results published that validate the BRFSS survey data on alcohol consumption.27,28 Nevertheless, the potential remains that respondents underestimate their alcohol use, and this might lead to false-negatives. Furthermore, the nature of the BRFSS question for alcohol discussions is somewhat ambiguous since we do not know if the discussion was a screening for excessive drinking or simply educational counseling.

Conclusions

Ideally, alcohol screening should occur in all primary care office visits, but given the extreme time constraints in the clinic setting, identification of under-recognized groups for targeted screening may enhance the recognition of alcohol abuse in a most time-effective manner.

References

1. Centers for Disease Control and Prevention. Behavioral Risk Factor Surveillance System. Atlanta: National Center for Chronic Disease Prevention and Health Promotion, US Department of Health and Human Services, 1997.

2. The National Center on Addiction and Substance Abuse at Columbia University. Missed Opportunity: National Survey of Primary Care Physicians and Patients on Substance Abuse. New York: The National Center on Addiction and Substance Abuse at Columbia University, 2000.

3. Leshner AI. Science-based views of drug addiction and its treatment. JAMA 1999;282:1314-16.

4. Weisner C, Schmidt LA. Expanding the frame of health services research in the drug abuse field. Health Serv Res 1995;30:707-26.

5. Department of Health and Human Services, National Institutes of Health. Disease-Specific Estimates of Direct and Indirect Costs of Illness and NIH Support: Fiscal Year 2000 Update. Washington, DC: Department of Health and Human Services, National Institutes of Health, 2000.

6. Whitmore CC, Stinson FS, Dufour MC. Surveillance Report #50. Trends in Alcohol-Related Morbidity Among Short-Stay Community Hospital Discharges, United States, 1979-97. Washington, DC: National Institute on Alcohol Abuse and Alcoholism, 1999.

7. National Institute of Alcohol Abuse and Alcoholism. Traffic crashes, traffic crash fatalities, and alcohol-related traffic crash fatalities, United States, 1977-97. Washington, DC: National Institute of Alcohol Abuse and Alcoholism, 1999.

8. Thun MJ, Peto R, Lopez AD, et al. Alcohol consumption and mortality among middle-aged and elderly US adults. New Eng J Med 1997;337:1705-14.

9. Gorelick PB, Erkinjuntti T, Hofman A, Rocca WA, Skoog I, Winblad B. Prevention of vascular dementia. Alzheimer Dis Assoc Disord 1999;3:S131-39

10. Borges G, Cherpitel CJ, Medina-Mora ME, Mondragon L, Casanova L. Alcohol consumption in emergency room patients and the general population: a population based study. Alcohol Clin Exp Res 1998;22:1986-91.

11. National Institute on Alcohol Abuse and Alcoholism. Physicians’ guide to helping patients with alcohol problems. Washington, DC: US Department of Health and Human Services, 1995.

12. US Department of Health and Human Services. Nutrition and your health: dietary guidelines for Americans. 3rd edition. Washington, DC: Supt of Docs, US Govt Print Office, 1990.

13. Levy PS, Lemeshow S. Sampling of populations. New York: John Wiley & Sons, 1999.

14. Stata Corporation. Stata 6.0. College Station, TX: Stata Corporation, 2000.

15. Herbert C, Bass F. Early at-risk alcohol intake. Definitions and physicians’ role in modifying behavior. Can Fam Phys 1997;43:639-44.

16. Fleming MG, Barry KL, Manwell LB, Johnson K, London R. Brief physician advice from problem alcohol drinkers. J Am Med Assoc 1997;277:1039-45.

17. Wallace P, Cutler S, Haines A. Randomized controlled trial of general practitioner intervention in patients with excessive alcohol consumption. Br Med J 1988;297:663-68.

18. Kosten TA, Rounsaville BJ. Sensitivity of psychiatric diagnosis based on the best estimate procedure. Am J Psych 1992;149:1225-27.

19. First MB, Spitzer RL, Gibbon M, Williams JB. Structured clinical interview for DSM-IV axis I disorders—clinician version. New York: Biometrics Research Department, New York State Psychiatric Institute, 1997.

20. Kessler RC, McGonagle KA, Zhao S, et al. Lifetime and 12-month prevalence of DSM-III-R psychiatric disorders in the United States. Results from the National Comorbidity Study. Arch Gen Psych 1994;51:8-19.

21. Brown RL, Leonard T, Saunders LA, Papasouliotis O. A 2-item conjoint screen for alcohol and other drug problems. J Am Board Fam Pract 2001;14:95-106.

22. Hays RD, Ellickson PL. Comparison of the Rost and the CAGE alcohol screening instruments in young adults. Subst Use Misuse 2001;36:639-51.

23. National Institute on Alcohol Abuse and Alcoholism. Alcohol alert #40: alcohol and aging. Washington, DC: National Institute on Alcohol Abuse and Alcoholism, 1998.

24. Curtis JR, Geller G, Stokes EJ, Levine DM, Moore RD. Characteristics, diagnosis, and treatment of alcoholism in elderly patients. J Am Geriatrics Soc 1989;37:310-16.

25. The National Center on Addiction and Substance Abuse at Columbia University. Under the rug: substance abuse and the mature woman. New York: The National Center on Addiction and Substance Abuse at Columbia University, 1998.

26. National Institute on Alcohol Abuse and Alcoholism. Alcohol alert #46: are women more vulnerable to alcohol’s effects? Washington, DC: National Institute on Alcohol Abuse and Alcoholism, 1999.

27. Smith PF, Remington PL, Williamson DF, Anda RF. A comparison of alcohol sales data with survey data on self-reported alcohol use in 21 states. Am J Public Health 1990;0:309-12.

28. Anda RF, Williamson DF, Dodson D, Remington PL. Telephone versus in-person reporting of smoking and alcohol use: a comparison of 2 statewide surveys. Am J Health Promotion 1989;4:32-36.

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STEPHAN ARNDT, PHD
SUSAN K. SCHULTZ, MD
CAROLYN TURVEY, PHD
AMY PETERSEN, PHD
Iowa City, Iowa
Submitted, August 4, 2001.
From the departments of Psychiatry (S.A., S.K.S, C.T.) and Biostatistics (S.A.), and the Iowa Consortium for Substance Abuse Research and Evaluation (S.A., S.K.S., A.P.), University of Iowa, Iowa City. All requests for reprints should be addressed to Stephan Arndt, University of Iowa College of Medicine, Psychiatry Research – MEB, Iowa City, Iowa 52242. E-mail: [email protected]

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STEPHAN ARNDT, PHD
SUSAN K. SCHULTZ, MD
CAROLYN TURVEY, PHD
AMY PETERSEN, PHD
Iowa City, Iowa
Submitted, August 4, 2001.
From the departments of Psychiatry (S.A., S.K.S, C.T.) and Biostatistics (S.A.), and the Iowa Consortium for Substance Abuse Research and Evaluation (S.A., S.K.S., A.P.), University of Iowa, Iowa City. All requests for reprints should be addressed to Stephan Arndt, University of Iowa College of Medicine, Psychiatry Research – MEB, Iowa City, Iowa 52242. E-mail: [email protected]

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STEPHAN ARNDT, PHD
SUSAN K. SCHULTZ, MD
CAROLYN TURVEY, PHD
AMY PETERSEN, PHD
Iowa City, Iowa
Submitted, August 4, 2001.
From the departments of Psychiatry (S.A., S.K.S, C.T.) and Biostatistics (S.A.), and the Iowa Consortium for Substance Abuse Research and Evaluation (S.A., S.K.S., A.P.), University of Iowa, Iowa City. All requests for reprints should be addressed to Stephan Arndt, University of Iowa College of Medicine, Psychiatry Research – MEB, Iowa City, Iowa 52242. E-mail: [email protected]

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ABSTRACT

BACKGROUND: This study assessed which demographic groups were most likely to consume alcohol excessively, and which groups had received inquiries and discussion about alcohol use from their physicians compared with discussions about other health risks.

STUDY DESIGN: This was a cross-sectional study using data from the Centers for Disease Control Behavioral Risk Factors Surveillance System 1997 data set that represents a stratified random sample in the United States.

POPULATION: We selected 23,349 adults who reported a routine physical examination within the last 3 years.

OUTCOMES MEASURED: The main variables involved responses to questions about alcohol intake and whether the respondent’s physician had initiated discussions about drinking.

RESULTS: Physicians spoke to patients about alcohol use much less frequently than about other health-related behaviors. Discussions were roughly targeted to groups with the largest intake. However, physicians were least likely to speak with white patients, women, and widows who drank significantly.

CONCLUSIONS: Regularly asking patients about alcohol use could substantially reduce the under-recognition of alcoholism. Since brief counseling is effective, negative consequences of excessive alcohol intake may be avoided.

KEY POINTS FOR CLINICIANS

  • Alcohol screening occurs less frequently than screening about other health-related behaviors.
  • There were no demographic groups in which the prevalence of excessive drinking was so low that general screening was not appropriate.
  • Physicians frequently miss the opportunity to discuss alcohol use with patients in certain groups, such as white patients and women (widows, in particular).

Physicians and related health care workers are well positioned to detect possible alcohol-related problems during routine patient visits, provided the appropriate screening procedures are implemented.2-4 Ideally, the primary care clinician should be the most prominent source of alcohol abuse screening and referrals, rather than the provider of treatment after an alcohol-related incident.

Although screening for alcoholism adds another step to an already over-worked health care system, it can result in substantial benefits by reducing the burden of overall health care costs. According to recent information, alcohol abuse costs our society $184.6 billion.5 In 1997, an estimated 1.3 million hospital discharges reported an alcohol-related diagnosis.6 And an estimated 12,870 alcohol-related traffic fatalities accounted for nearly one third of all traffic deaths in that same year.7 Even when individuals reamin socially functional and do not meet the formal criteria for an alcohol-related disorder, excessive use of alcohol is associated with a variety of medical problems. Although cardioprotective effects have been reported with moderate use (ie, 1 to 2 drinks per day), the list of medical complications associated with longstanding alcoholism (hypertension, cardiomyopathy, cirrhosis, erosive gastris, pancreatitis, and esophageal varices, for example) account for considerable morbidity and mortality.8,9 Increased alcohol consumption over a 1-year period is also associated with accidents and injuries necessitating emergency services.10

Because the primary care physician is in a unique position to influence the preventive care of the community they serve, our study examined alcohol screening in the primary care setting. The following 2 questions were asked: (1) Which patients were assessed for excessive alcohol use, and what patient characteristics predicted the assessment? and (2) How often did discussions about alcohol occur compared with other health risk discussions (eg, eating habits or smoking)?

Methods

Subjects

This is a secondary analysis of data from an epidemiologic telephone interview conducted by the Behavioral Risk Factors Surveillance System (BRFSS)1 involving a random stratified sample of people living within the United States. In the 1997 interviews, all state interviews included questions about alcohol consumption. Alaska, Colorado, Idaho, Louisiana, Missouri, New York, North Carolina, Oklahoma, Pennsylvania, Virginia, and Wyoming included a counseling module that asked, “Has a doctor or other health professional ever talked with you about alcohol use?”

The 1997 BRFSS data set represents 135,582 interviews. The sample reported here includes only respondents who reported a routine physical examination within the last 3 years and who were asked questions from the counseling module (n = 23,349), as well as questions about other health habits. There were 9106 men (mean age = 45.82 years; SD = 16.86) and 14,203 women (mean age = 46.90; SD = 17.44) who responded.

Excessive drinking was defined as consuming 60 alcoholic beverages per month or 5 on a single occasion (binge drinking) in the month prior to the interview (n = 2772). The 60 beverages per month threshold follows recommendations by the National Institute for Alcohol Abuse and Alcoholism and the US Department of Health and Human Services’ Dietary Guidelines for Americans.11,12

Statistical analysis

Analysis used the sampling weights provided by the Centers for Disease Control. The data were weighted so that the summary statistics, standard errors, and test statistics took into account the sampling design and represented estimates in the total US. population. We used the procedures described by Levy and Lemeshow,13 and implemented them using STATA.14 These included simple chi-square tests, logistic regression with F- or t-approximations. The F- and t-approximations for the logistic regression were necessary to adjust for the complex survey design.13 Hierarchical (protected) testing procedures helped correct for multiple comparisons. We used omnibus tests for variables with multiple options (eg, marital status), and only considered follow-up tests when the overall test result was significant. Furthermore, a conservative threshold for significance (P < .01) was a compromise to the Bonferroni correction for multiple comparisons.

 

 

Results

Approximately 1 in 6 patients (16.1%; 95% confidence interval [CI], 15.4 - 16.8) reported that a physician or other health care worker had initiated a discussion about alcohol use. Table W1* compares patients who reported such a discussion with those who did not. Physicians talked to male patients about alcohol use most frequently. This corresponds to men reporting nearly 3 times more drinks consumed (12.9 drinks/month) than women (4.7 drinks/month; t = 16.26, df = 18,323, P < .001).

In general, physicians spoke about alcohol more often to younger people. There was a significant interaction, however, between sex and age, as shown in Figure 1. Discussions with women demonstrated a clear decrease in frequency with age; discussions with men decreased with age more slowly.

The amount of drinking in our sample, indexed by the number of drinks per month, is shown in Figure 2. Both the frequency of discussions and amount of alcohol consumed declined with advancing age. Women drank less alcohol than men overall and showed a moderate decline in use with age.

Nonwhite respondents reported more physician discussions about alcohol than did whites. However, white patients reported greater consumption (8.9 drinks/month) than nonwhites (6.8 drinks; t = 2.79, df = 18253, P < .005).

The lowest income group reported being advised about alcohol most frequently. Interestingly, the highest income respondents tended to drink more than those with less income. Marital status also predicted alcohol discussions. Physicians discussed alcohol more often with patients who were unmarried. While divorced patients reported discussions about alcohol use frequently, widowed patients reported them least often.

Discussions about alcohol occurred more often with respondents who had consumed alcohol within the month prior to the interview. Respondents who reported having these discussions also consumed more (14.6 drinks/month) than those who did not report a discussion (7.3 drinks/month, t = 7.20, df = 17985, P < .001). People who binge drank were more than twice as likely (OR = 2.25; 95% CI, 1.94 - 2.60) to report such a discussion (27.5%).

All of these predictors of a discussion about alcohol use were entered into a multivariate logistic regression. Backward elimination removed items that failed to provide independent information. The final model appears in the Table. Factors other than drinking behaviors that uniquely increased the chance of such a discussion about alcohol were being young, male, nonwhite, and of lower income.

More than 1 in 9 people (12.3%; n = 2768) in the sample met criteria for excessive drinking (ie, consuming 60 drinks per month or drinking 5 or more drinks on a single occasion in the last month). Within this group, 11.9% engaged in binge drinking and 3.5% consumed 60 drinks a month. Most were men (71.3%) with a mean age of 35 years, and they averaged 39.7 (95% CI, 36.74 - 42.74) drinks per month. They binged an average of 3.44 (95% CI, 3.18 - 3.70) times during the last month.

Slightly more than 1 in 4 excessive drinkers (28%; 95% CI, 25.41 - 30.69) reported a discussion about alcohol with a physician. While none of the demographic factors met our stringent criteria for significance (P < .01), many would have met the more usual threshold of P < .05. For example, among the respondents with excessive alcohol use, 23.9% of the women had been screened by their physicians compared with 29.9% of the men (P < .013). This suggests that these women may receive less preventive discussions and screening than they require. While the percent of nonwhite respondents who drank excessively and were successfully screened was above the mean rate overall (35.6%), the percent of white patients was lower than the mean (26.4%; P < .012). Compared with married patients, excessive drinkers who were divorced (35.2%; P < .019) or separated (38.1%; P < .091) reported a higher frequency of screening. Widowed patients with excessive alcohol use rarely reported screening (13%; P < .032).

TABLE
FINAL LOGISTIC REGRESSION PREDICTING A DISCUSSION ABOUT ALCOHOL

PredictorBetaOR(95% CI)
Sex-0.480.62 (0.44 - 0.87)*
Age-0.0280.97 (0.97 - 0.98)†
Age, by sex interaction0.021.02 (1.01 - 1.03)†
Did not drink ‡-0.570.57 (0.44 - 0.73)†
Drinks per month §0.281.33 (1.24 - 1.43)†
Income-0.080.93 (0.90 - 0.96)†
Race ¶0.341.41 (1.22 - 1.63)†
Constant-0.24 
OR denotes odds ratio from univariate logistic regression; CI, confidence interval.
*P< .01
†P< .001
Respondent reported not drinking any alcohol in the last month.
§ Log transform of the number of drinks per month.
¶ Race coded as white versus nonwhite.

FIGURE 1
LOGISTIC REGRESSION PREDICTING THE OCCURRENCE OF PHYSICIAN DISCUSSION ABOUT ALCOHOL FROM THE PATIENTS’ AGE AND SEX (WOMEN =o MEN =●).


FIGURE 2
MEAN NUMBERS OF ALCOHOLIC DRINKS CONSUMED BY AGE AND SEX (WOMEN = o MEN = ● ).

 

 

Other preventive services

We also assessed the rate of talks about healthy eating as a comparison for the alcohol discussions. A total of 44.6% of patients reported having a conversation about healthy eating compared with 16.1% having an alcohol discussion. On the chance that people who drank excessively might differentially remember conversations with their physicians, we assessed the association between alcohol use and reports of experiencing counseling on healthy eating. No significant associations were found between reported conversations about eating and any variable related to alcohol consumption even after controlling for sex, age, sex-by-age interaction, race, income, and education. Similar results were obtained for discussions about exercise, AIDS, and illegal drugs. Discussions about illegal drug use occurred less frequently than discussions about alcohol use (12.5%). Discussions about AIDS were reported by 26.1% of the respondents, exercise-related conversations were reported 47.4% of the time, and smoking was mentioned by 49.2% of the respondents. Considering any preventive health discussions (smoking, drinking, drug abuse, exercise, healthy eating, or AIDS), 97.4% of the respondents reported a discussion of at least 1 topic.

Discussion

Physicians currently incorporate preventive counseling about behavioral health risks as part of standard clinical care. In a recent survey of general practitioners, 97% of those surveyed thought that members of their profession should inquire about drinking behaviors.15 Moreover, brief office visit screening followed by physician advice has been documented as effective in reducing alcohol consumption.16,17 Despite the general positive opinion of alcohol screening, however, discussions about AIDS and other health-related behaviors were discussed much more frequently than alcohol-related behaviors.

Our analysis identified patients who consume a significant amount of alcohol, yet did not report being screened or counseled by their physicians. We gathered information about the magnitude of use, as well as about the presence or absence of a discussion regarding alcohol. This allowed us to examine 2 important aspects of alcohol screening: (1) the demographic features that predicted it, and (2) whether these demographic features represented patients who actually consumed large amounts of alcohol and could therefore benefit from counseling.

Although the BRFSS did not assess alcohol dependence or abuse directly, the goal of the study was to designate which patients might be appropriate candidates for screening or preventive counseling. Assessments of alcohol abuse or dependence using strict Diagnostic and Statistical Manual of Mental Disorders – 4th Edition (DSM-IV) criteria require insight, as well as a willingness to share this information with the interviewer. Kosten and Rounsaville18 found that DSM-based diagnostic interviews for alcoholism and substance abuse showed the lowest sensitivity relative to other psychiatric diagnoses. Therefore, more recent surveys of alcohol abuse assess the quantity of consumption before applying strict DSM-IV criteria.19,20 Although participants may also minimize actual consumption, the screening for quantity requires less insight than a formal diagnosis and may more effectively identify candidates for counseling. However, our findings suggest that physicians do not routinely attempt to ascertain alcohol use quantitatively.21,22 Implementing alcohol screening as a routine preventive health care practice would allow physicians to detect problems without relying on insightful spontaneous reporting from patients.

The most successful strategy to identify more candidates for treatment involves simply screening a larger number of patients, especially high-risk patients. Our data suggest that physicians do target discussions somewhat toward people who report excessive alcohol consumption. Approximately 16% of the general patient population reported such a discussion, but this rate was greater (27.8%) among heavy or binge drinkers. Unfortunately, these data also suggest that the majority of patients who might benefit from such counseling, did not report a discussion about alcohol use. Individuals who are likely to be appropriate candidates but who were not counseled include white patients and women (widows, in particular).

The frequency of discussions about alcohol for women and widows who drank excessively was low. This finding is consistent with current research demonstrating that alcohol problems among women, and widowed women in particular, are under-recognized. Physiologically, the lower body water volume in women, especially in elderly women, increases the detrimental effects of alcohol.23 Physicians also appear to have more difficulty recognizing alcohol problems among the elderly.24 Alcohol-related symptoms among elderly women may be misinterpreted as caused by depression, anxiety, or other psychiatric problems.25 Elderly women taking psychoactive medications or medications with sedative effects may be even more difficult to assess. Moreover, our analysis categorized excessive drinking using a single criterion for all respondents. Evidence is mounting that indicates that women26 and the elderly23 are more at risk from lower levels of drinking. Had we lowered our criterion for these patients, the magnitude of problem drinking would have appeared even greater.

 

 

Limitations

There are several limitations to our study. A number of reasons, including forgetfulness or inattention, may account for under-reporting. An overall problem with memory is unlikely, since almost all respondents remembered at least one discussion about some kind of health risk. Nonetheless, patients may be selectively less likely to recall a discussion about alcohol because of emotional associations with the topic. However, it is unclear why memory would be less reliable about alcohol use than memory about another potentially emotionally-charged topic, such as AIDS.

Additionally, our information was self-reported through a telephone interview. There have been positive study results published that validate the BRFSS survey data on alcohol consumption.27,28 Nevertheless, the potential remains that respondents underestimate their alcohol use, and this might lead to false-negatives. Furthermore, the nature of the BRFSS question for alcohol discussions is somewhat ambiguous since we do not know if the discussion was a screening for excessive drinking or simply educational counseling.

Conclusions

Ideally, alcohol screening should occur in all primary care office visits, but given the extreme time constraints in the clinic setting, identification of under-recognized groups for targeted screening may enhance the recognition of alcohol abuse in a most time-effective manner.

ABSTRACT

BACKGROUND: This study assessed which demographic groups were most likely to consume alcohol excessively, and which groups had received inquiries and discussion about alcohol use from their physicians compared with discussions about other health risks.

STUDY DESIGN: This was a cross-sectional study using data from the Centers for Disease Control Behavioral Risk Factors Surveillance System 1997 data set that represents a stratified random sample in the United States.

POPULATION: We selected 23,349 adults who reported a routine physical examination within the last 3 years.

OUTCOMES MEASURED: The main variables involved responses to questions about alcohol intake and whether the respondent’s physician had initiated discussions about drinking.

RESULTS: Physicians spoke to patients about alcohol use much less frequently than about other health-related behaviors. Discussions were roughly targeted to groups with the largest intake. However, physicians were least likely to speak with white patients, women, and widows who drank significantly.

CONCLUSIONS: Regularly asking patients about alcohol use could substantially reduce the under-recognition of alcoholism. Since brief counseling is effective, negative consequences of excessive alcohol intake may be avoided.

KEY POINTS FOR CLINICIANS

  • Alcohol screening occurs less frequently than screening about other health-related behaviors.
  • There were no demographic groups in which the prevalence of excessive drinking was so low that general screening was not appropriate.
  • Physicians frequently miss the opportunity to discuss alcohol use with patients in certain groups, such as white patients and women (widows, in particular).

Physicians and related health care workers are well positioned to detect possible alcohol-related problems during routine patient visits, provided the appropriate screening procedures are implemented.2-4 Ideally, the primary care clinician should be the most prominent source of alcohol abuse screening and referrals, rather than the provider of treatment after an alcohol-related incident.

Although screening for alcoholism adds another step to an already over-worked health care system, it can result in substantial benefits by reducing the burden of overall health care costs. According to recent information, alcohol abuse costs our society $184.6 billion.5 In 1997, an estimated 1.3 million hospital discharges reported an alcohol-related diagnosis.6 And an estimated 12,870 alcohol-related traffic fatalities accounted for nearly one third of all traffic deaths in that same year.7 Even when individuals reamin socially functional and do not meet the formal criteria for an alcohol-related disorder, excessive use of alcohol is associated with a variety of medical problems. Although cardioprotective effects have been reported with moderate use (ie, 1 to 2 drinks per day), the list of medical complications associated with longstanding alcoholism (hypertension, cardiomyopathy, cirrhosis, erosive gastris, pancreatitis, and esophageal varices, for example) account for considerable morbidity and mortality.8,9 Increased alcohol consumption over a 1-year period is also associated with accidents and injuries necessitating emergency services.10

Because the primary care physician is in a unique position to influence the preventive care of the community they serve, our study examined alcohol screening in the primary care setting. The following 2 questions were asked: (1) Which patients were assessed for excessive alcohol use, and what patient characteristics predicted the assessment? and (2) How often did discussions about alcohol occur compared with other health risk discussions (eg, eating habits or smoking)?

Methods

Subjects

This is a secondary analysis of data from an epidemiologic telephone interview conducted by the Behavioral Risk Factors Surveillance System (BRFSS)1 involving a random stratified sample of people living within the United States. In the 1997 interviews, all state interviews included questions about alcohol consumption. Alaska, Colorado, Idaho, Louisiana, Missouri, New York, North Carolina, Oklahoma, Pennsylvania, Virginia, and Wyoming included a counseling module that asked, “Has a doctor or other health professional ever talked with you about alcohol use?”

The 1997 BRFSS data set represents 135,582 interviews. The sample reported here includes only respondents who reported a routine physical examination within the last 3 years and who were asked questions from the counseling module (n = 23,349), as well as questions about other health habits. There were 9106 men (mean age = 45.82 years; SD = 16.86) and 14,203 women (mean age = 46.90; SD = 17.44) who responded.

Excessive drinking was defined as consuming 60 alcoholic beverages per month or 5 on a single occasion (binge drinking) in the month prior to the interview (n = 2772). The 60 beverages per month threshold follows recommendations by the National Institute for Alcohol Abuse and Alcoholism and the US Department of Health and Human Services’ Dietary Guidelines for Americans.11,12

Statistical analysis

Analysis used the sampling weights provided by the Centers for Disease Control. The data were weighted so that the summary statistics, standard errors, and test statistics took into account the sampling design and represented estimates in the total US. population. We used the procedures described by Levy and Lemeshow,13 and implemented them using STATA.14 These included simple chi-square tests, logistic regression with F- or t-approximations. The F- and t-approximations for the logistic regression were necessary to adjust for the complex survey design.13 Hierarchical (protected) testing procedures helped correct for multiple comparisons. We used omnibus tests for variables with multiple options (eg, marital status), and only considered follow-up tests when the overall test result was significant. Furthermore, a conservative threshold for significance (P < .01) was a compromise to the Bonferroni correction for multiple comparisons.

 

 

Results

Approximately 1 in 6 patients (16.1%; 95% confidence interval [CI], 15.4 - 16.8) reported that a physician or other health care worker had initiated a discussion about alcohol use. Table W1* compares patients who reported such a discussion with those who did not. Physicians talked to male patients about alcohol use most frequently. This corresponds to men reporting nearly 3 times more drinks consumed (12.9 drinks/month) than women (4.7 drinks/month; t = 16.26, df = 18,323, P < .001).

In general, physicians spoke about alcohol more often to younger people. There was a significant interaction, however, between sex and age, as shown in Figure 1. Discussions with women demonstrated a clear decrease in frequency with age; discussions with men decreased with age more slowly.

The amount of drinking in our sample, indexed by the number of drinks per month, is shown in Figure 2. Both the frequency of discussions and amount of alcohol consumed declined with advancing age. Women drank less alcohol than men overall and showed a moderate decline in use with age.

Nonwhite respondents reported more physician discussions about alcohol than did whites. However, white patients reported greater consumption (8.9 drinks/month) than nonwhites (6.8 drinks; t = 2.79, df = 18253, P < .005).

The lowest income group reported being advised about alcohol most frequently. Interestingly, the highest income respondents tended to drink more than those with less income. Marital status also predicted alcohol discussions. Physicians discussed alcohol more often with patients who were unmarried. While divorced patients reported discussions about alcohol use frequently, widowed patients reported them least often.

Discussions about alcohol occurred more often with respondents who had consumed alcohol within the month prior to the interview. Respondents who reported having these discussions also consumed more (14.6 drinks/month) than those who did not report a discussion (7.3 drinks/month, t = 7.20, df = 17985, P < .001). People who binge drank were more than twice as likely (OR = 2.25; 95% CI, 1.94 - 2.60) to report such a discussion (27.5%).

All of these predictors of a discussion about alcohol use were entered into a multivariate logistic regression. Backward elimination removed items that failed to provide independent information. The final model appears in the Table. Factors other than drinking behaviors that uniquely increased the chance of such a discussion about alcohol were being young, male, nonwhite, and of lower income.

More than 1 in 9 people (12.3%; n = 2768) in the sample met criteria for excessive drinking (ie, consuming 60 drinks per month or drinking 5 or more drinks on a single occasion in the last month). Within this group, 11.9% engaged in binge drinking and 3.5% consumed 60 drinks a month. Most were men (71.3%) with a mean age of 35 years, and they averaged 39.7 (95% CI, 36.74 - 42.74) drinks per month. They binged an average of 3.44 (95% CI, 3.18 - 3.70) times during the last month.

Slightly more than 1 in 4 excessive drinkers (28%; 95% CI, 25.41 - 30.69) reported a discussion about alcohol with a physician. While none of the demographic factors met our stringent criteria for significance (P < .01), many would have met the more usual threshold of P < .05. For example, among the respondents with excessive alcohol use, 23.9% of the women had been screened by their physicians compared with 29.9% of the men (P < .013). This suggests that these women may receive less preventive discussions and screening than they require. While the percent of nonwhite respondents who drank excessively and were successfully screened was above the mean rate overall (35.6%), the percent of white patients was lower than the mean (26.4%; P < .012). Compared with married patients, excessive drinkers who were divorced (35.2%; P < .019) or separated (38.1%; P < .091) reported a higher frequency of screening. Widowed patients with excessive alcohol use rarely reported screening (13%; P < .032).

TABLE
FINAL LOGISTIC REGRESSION PREDICTING A DISCUSSION ABOUT ALCOHOL

PredictorBetaOR(95% CI)
Sex-0.480.62 (0.44 - 0.87)*
Age-0.0280.97 (0.97 - 0.98)†
Age, by sex interaction0.021.02 (1.01 - 1.03)†
Did not drink ‡-0.570.57 (0.44 - 0.73)†
Drinks per month §0.281.33 (1.24 - 1.43)†
Income-0.080.93 (0.90 - 0.96)†
Race ¶0.341.41 (1.22 - 1.63)†
Constant-0.24 
OR denotes odds ratio from univariate logistic regression; CI, confidence interval.
*P< .01
†P< .001
Respondent reported not drinking any alcohol in the last month.
§ Log transform of the number of drinks per month.
¶ Race coded as white versus nonwhite.

FIGURE 1
LOGISTIC REGRESSION PREDICTING THE OCCURRENCE OF PHYSICIAN DISCUSSION ABOUT ALCOHOL FROM THE PATIENTS’ AGE AND SEX (WOMEN =o MEN =●).


FIGURE 2
MEAN NUMBERS OF ALCOHOLIC DRINKS CONSUMED BY AGE AND SEX (WOMEN = o MEN = ● ).

 

 

Other preventive services

We also assessed the rate of talks about healthy eating as a comparison for the alcohol discussions. A total of 44.6% of patients reported having a conversation about healthy eating compared with 16.1% having an alcohol discussion. On the chance that people who drank excessively might differentially remember conversations with their physicians, we assessed the association between alcohol use and reports of experiencing counseling on healthy eating. No significant associations were found between reported conversations about eating and any variable related to alcohol consumption even after controlling for sex, age, sex-by-age interaction, race, income, and education. Similar results were obtained for discussions about exercise, AIDS, and illegal drugs. Discussions about illegal drug use occurred less frequently than discussions about alcohol use (12.5%). Discussions about AIDS were reported by 26.1% of the respondents, exercise-related conversations were reported 47.4% of the time, and smoking was mentioned by 49.2% of the respondents. Considering any preventive health discussions (smoking, drinking, drug abuse, exercise, healthy eating, or AIDS), 97.4% of the respondents reported a discussion of at least 1 topic.

Discussion

Physicians currently incorporate preventive counseling about behavioral health risks as part of standard clinical care. In a recent survey of general practitioners, 97% of those surveyed thought that members of their profession should inquire about drinking behaviors.15 Moreover, brief office visit screening followed by physician advice has been documented as effective in reducing alcohol consumption.16,17 Despite the general positive opinion of alcohol screening, however, discussions about AIDS and other health-related behaviors were discussed much more frequently than alcohol-related behaviors.

Our analysis identified patients who consume a significant amount of alcohol, yet did not report being screened or counseled by their physicians. We gathered information about the magnitude of use, as well as about the presence or absence of a discussion regarding alcohol. This allowed us to examine 2 important aspects of alcohol screening: (1) the demographic features that predicted it, and (2) whether these demographic features represented patients who actually consumed large amounts of alcohol and could therefore benefit from counseling.

Although the BRFSS did not assess alcohol dependence or abuse directly, the goal of the study was to designate which patients might be appropriate candidates for screening or preventive counseling. Assessments of alcohol abuse or dependence using strict Diagnostic and Statistical Manual of Mental Disorders – 4th Edition (DSM-IV) criteria require insight, as well as a willingness to share this information with the interviewer. Kosten and Rounsaville18 found that DSM-based diagnostic interviews for alcoholism and substance abuse showed the lowest sensitivity relative to other psychiatric diagnoses. Therefore, more recent surveys of alcohol abuse assess the quantity of consumption before applying strict DSM-IV criteria.19,20 Although participants may also minimize actual consumption, the screening for quantity requires less insight than a formal diagnosis and may more effectively identify candidates for counseling. However, our findings suggest that physicians do not routinely attempt to ascertain alcohol use quantitatively.21,22 Implementing alcohol screening as a routine preventive health care practice would allow physicians to detect problems without relying on insightful spontaneous reporting from patients.

The most successful strategy to identify more candidates for treatment involves simply screening a larger number of patients, especially high-risk patients. Our data suggest that physicians do target discussions somewhat toward people who report excessive alcohol consumption. Approximately 16% of the general patient population reported such a discussion, but this rate was greater (27.8%) among heavy or binge drinkers. Unfortunately, these data also suggest that the majority of patients who might benefit from such counseling, did not report a discussion about alcohol use. Individuals who are likely to be appropriate candidates but who were not counseled include white patients and women (widows, in particular).

The frequency of discussions about alcohol for women and widows who drank excessively was low. This finding is consistent with current research demonstrating that alcohol problems among women, and widowed women in particular, are under-recognized. Physiologically, the lower body water volume in women, especially in elderly women, increases the detrimental effects of alcohol.23 Physicians also appear to have more difficulty recognizing alcohol problems among the elderly.24 Alcohol-related symptoms among elderly women may be misinterpreted as caused by depression, anxiety, or other psychiatric problems.25 Elderly women taking psychoactive medications or medications with sedative effects may be even more difficult to assess. Moreover, our analysis categorized excessive drinking using a single criterion for all respondents. Evidence is mounting that indicates that women26 and the elderly23 are more at risk from lower levels of drinking. Had we lowered our criterion for these patients, the magnitude of problem drinking would have appeared even greater.

 

 

Limitations

There are several limitations to our study. A number of reasons, including forgetfulness or inattention, may account for under-reporting. An overall problem with memory is unlikely, since almost all respondents remembered at least one discussion about some kind of health risk. Nonetheless, patients may be selectively less likely to recall a discussion about alcohol because of emotional associations with the topic. However, it is unclear why memory would be less reliable about alcohol use than memory about another potentially emotionally-charged topic, such as AIDS.

Additionally, our information was self-reported through a telephone interview. There have been positive study results published that validate the BRFSS survey data on alcohol consumption.27,28 Nevertheless, the potential remains that respondents underestimate their alcohol use, and this might lead to false-negatives. Furthermore, the nature of the BRFSS question for alcohol discussions is somewhat ambiguous since we do not know if the discussion was a screening for excessive drinking or simply educational counseling.

Conclusions

Ideally, alcohol screening should occur in all primary care office visits, but given the extreme time constraints in the clinic setting, identification of under-recognized groups for targeted screening may enhance the recognition of alcohol abuse in a most time-effective manner.

References

1. Centers for Disease Control and Prevention. Behavioral Risk Factor Surveillance System. Atlanta: National Center for Chronic Disease Prevention and Health Promotion, US Department of Health and Human Services, 1997.

2. The National Center on Addiction and Substance Abuse at Columbia University. Missed Opportunity: National Survey of Primary Care Physicians and Patients on Substance Abuse. New York: The National Center on Addiction and Substance Abuse at Columbia University, 2000.

3. Leshner AI. Science-based views of drug addiction and its treatment. JAMA 1999;282:1314-16.

4. Weisner C, Schmidt LA. Expanding the frame of health services research in the drug abuse field. Health Serv Res 1995;30:707-26.

5. Department of Health and Human Services, National Institutes of Health. Disease-Specific Estimates of Direct and Indirect Costs of Illness and NIH Support: Fiscal Year 2000 Update. Washington, DC: Department of Health and Human Services, National Institutes of Health, 2000.

6. Whitmore CC, Stinson FS, Dufour MC. Surveillance Report #50. Trends in Alcohol-Related Morbidity Among Short-Stay Community Hospital Discharges, United States, 1979-97. Washington, DC: National Institute on Alcohol Abuse and Alcoholism, 1999.

7. National Institute of Alcohol Abuse and Alcoholism. Traffic crashes, traffic crash fatalities, and alcohol-related traffic crash fatalities, United States, 1977-97. Washington, DC: National Institute of Alcohol Abuse and Alcoholism, 1999.

8. Thun MJ, Peto R, Lopez AD, et al. Alcohol consumption and mortality among middle-aged and elderly US adults. New Eng J Med 1997;337:1705-14.

9. Gorelick PB, Erkinjuntti T, Hofman A, Rocca WA, Skoog I, Winblad B. Prevention of vascular dementia. Alzheimer Dis Assoc Disord 1999;3:S131-39

10. Borges G, Cherpitel CJ, Medina-Mora ME, Mondragon L, Casanova L. Alcohol consumption in emergency room patients and the general population: a population based study. Alcohol Clin Exp Res 1998;22:1986-91.

11. National Institute on Alcohol Abuse and Alcoholism. Physicians’ guide to helping patients with alcohol problems. Washington, DC: US Department of Health and Human Services, 1995.

12. US Department of Health and Human Services. Nutrition and your health: dietary guidelines for Americans. 3rd edition. Washington, DC: Supt of Docs, US Govt Print Office, 1990.

13. Levy PS, Lemeshow S. Sampling of populations. New York: John Wiley & Sons, 1999.

14. Stata Corporation. Stata 6.0. College Station, TX: Stata Corporation, 2000.

15. Herbert C, Bass F. Early at-risk alcohol intake. Definitions and physicians’ role in modifying behavior. Can Fam Phys 1997;43:639-44.

16. Fleming MG, Barry KL, Manwell LB, Johnson K, London R. Brief physician advice from problem alcohol drinkers. J Am Med Assoc 1997;277:1039-45.

17. Wallace P, Cutler S, Haines A. Randomized controlled trial of general practitioner intervention in patients with excessive alcohol consumption. Br Med J 1988;297:663-68.

18. Kosten TA, Rounsaville BJ. Sensitivity of psychiatric diagnosis based on the best estimate procedure. Am J Psych 1992;149:1225-27.

19. First MB, Spitzer RL, Gibbon M, Williams JB. Structured clinical interview for DSM-IV axis I disorders—clinician version. New York: Biometrics Research Department, New York State Psychiatric Institute, 1997.

20. Kessler RC, McGonagle KA, Zhao S, et al. Lifetime and 12-month prevalence of DSM-III-R psychiatric disorders in the United States. Results from the National Comorbidity Study. Arch Gen Psych 1994;51:8-19.

21. Brown RL, Leonard T, Saunders LA, Papasouliotis O. A 2-item conjoint screen for alcohol and other drug problems. J Am Board Fam Pract 2001;14:95-106.

22. Hays RD, Ellickson PL. Comparison of the Rost and the CAGE alcohol screening instruments in young adults. Subst Use Misuse 2001;36:639-51.

23. National Institute on Alcohol Abuse and Alcoholism. Alcohol alert #40: alcohol and aging. Washington, DC: National Institute on Alcohol Abuse and Alcoholism, 1998.

24. Curtis JR, Geller G, Stokes EJ, Levine DM, Moore RD. Characteristics, diagnosis, and treatment of alcoholism in elderly patients. J Am Geriatrics Soc 1989;37:310-16.

25. The National Center on Addiction and Substance Abuse at Columbia University. Under the rug: substance abuse and the mature woman. New York: The National Center on Addiction and Substance Abuse at Columbia University, 1998.

26. National Institute on Alcohol Abuse and Alcoholism. Alcohol alert #46: are women more vulnerable to alcohol’s effects? Washington, DC: National Institute on Alcohol Abuse and Alcoholism, 1999.

27. Smith PF, Remington PL, Williamson DF, Anda RF. A comparison of alcohol sales data with survey data on self-reported alcohol use in 21 states. Am J Public Health 1990;0:309-12.

28. Anda RF, Williamson DF, Dodson D, Remington PL. Telephone versus in-person reporting of smoking and alcohol use: a comparison of 2 statewide surveys. Am J Health Promotion 1989;4:32-36.

References

1. Centers for Disease Control and Prevention. Behavioral Risk Factor Surveillance System. Atlanta: National Center for Chronic Disease Prevention and Health Promotion, US Department of Health and Human Services, 1997.

2. The National Center on Addiction and Substance Abuse at Columbia University. Missed Opportunity: National Survey of Primary Care Physicians and Patients on Substance Abuse. New York: The National Center on Addiction and Substance Abuse at Columbia University, 2000.

3. Leshner AI. Science-based views of drug addiction and its treatment. JAMA 1999;282:1314-16.

4. Weisner C, Schmidt LA. Expanding the frame of health services research in the drug abuse field. Health Serv Res 1995;30:707-26.

5. Department of Health and Human Services, National Institutes of Health. Disease-Specific Estimates of Direct and Indirect Costs of Illness and NIH Support: Fiscal Year 2000 Update. Washington, DC: Department of Health and Human Services, National Institutes of Health, 2000.

6. Whitmore CC, Stinson FS, Dufour MC. Surveillance Report #50. Trends in Alcohol-Related Morbidity Among Short-Stay Community Hospital Discharges, United States, 1979-97. Washington, DC: National Institute on Alcohol Abuse and Alcoholism, 1999.

7. National Institute of Alcohol Abuse and Alcoholism. Traffic crashes, traffic crash fatalities, and alcohol-related traffic crash fatalities, United States, 1977-97. Washington, DC: National Institute of Alcohol Abuse and Alcoholism, 1999.

8. Thun MJ, Peto R, Lopez AD, et al. Alcohol consumption and mortality among middle-aged and elderly US adults. New Eng J Med 1997;337:1705-14.

9. Gorelick PB, Erkinjuntti T, Hofman A, Rocca WA, Skoog I, Winblad B. Prevention of vascular dementia. Alzheimer Dis Assoc Disord 1999;3:S131-39

10. Borges G, Cherpitel CJ, Medina-Mora ME, Mondragon L, Casanova L. Alcohol consumption in emergency room patients and the general population: a population based study. Alcohol Clin Exp Res 1998;22:1986-91.

11. National Institute on Alcohol Abuse and Alcoholism. Physicians’ guide to helping patients with alcohol problems. Washington, DC: US Department of Health and Human Services, 1995.

12. US Department of Health and Human Services. Nutrition and your health: dietary guidelines for Americans. 3rd edition. Washington, DC: Supt of Docs, US Govt Print Office, 1990.

13. Levy PS, Lemeshow S. Sampling of populations. New York: John Wiley & Sons, 1999.

14. Stata Corporation. Stata 6.0. College Station, TX: Stata Corporation, 2000.

15. Herbert C, Bass F. Early at-risk alcohol intake. Definitions and physicians’ role in modifying behavior. Can Fam Phys 1997;43:639-44.

16. Fleming MG, Barry KL, Manwell LB, Johnson K, London R. Brief physician advice from problem alcohol drinkers. J Am Med Assoc 1997;277:1039-45.

17. Wallace P, Cutler S, Haines A. Randomized controlled trial of general practitioner intervention in patients with excessive alcohol consumption. Br Med J 1988;297:663-68.

18. Kosten TA, Rounsaville BJ. Sensitivity of psychiatric diagnosis based on the best estimate procedure. Am J Psych 1992;149:1225-27.

19. First MB, Spitzer RL, Gibbon M, Williams JB. Structured clinical interview for DSM-IV axis I disorders—clinician version. New York: Biometrics Research Department, New York State Psychiatric Institute, 1997.

20. Kessler RC, McGonagle KA, Zhao S, et al. Lifetime and 12-month prevalence of DSM-III-R psychiatric disorders in the United States. Results from the National Comorbidity Study. Arch Gen Psych 1994;51:8-19.

21. Brown RL, Leonard T, Saunders LA, Papasouliotis O. A 2-item conjoint screen for alcohol and other drug problems. J Am Board Fam Pract 2001;14:95-106.

22. Hays RD, Ellickson PL. Comparison of the Rost and the CAGE alcohol screening instruments in young adults. Subst Use Misuse 2001;36:639-51.

23. National Institute on Alcohol Abuse and Alcoholism. Alcohol alert #40: alcohol and aging. Washington, DC: National Institute on Alcohol Abuse and Alcoholism, 1998.

24. Curtis JR, Geller G, Stokes EJ, Levine DM, Moore RD. Characteristics, diagnosis, and treatment of alcoholism in elderly patients. J Am Geriatrics Soc 1989;37:310-16.

25. The National Center on Addiction and Substance Abuse at Columbia University. Under the rug: substance abuse and the mature woman. New York: The National Center on Addiction and Substance Abuse at Columbia University, 1998.

26. National Institute on Alcohol Abuse and Alcoholism. Alcohol alert #46: are women more vulnerable to alcohol’s effects? Washington, DC: National Institute on Alcohol Abuse and Alcoholism, 1999.

27. Smith PF, Remington PL, Williamson DF, Anda RF. A comparison of alcohol sales data with survey data on self-reported alcohol use in 21 states. Am J Public Health 1990;0:309-12.

28. Anda RF, Williamson DF, Dodson D, Remington PL. Telephone versus in-person reporting of smoking and alcohol use: a comparison of 2 statewide surveys. Am J Health Promotion 1989;4:32-36.

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The Effectiveness of Magnet Therapy for Treatment of Wrist Pain Attributed to Carpal Tunnel Syndrome

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The Effectiveness of Magnet Therapy for Treatment of Wrist Pain Attributed to Carpal Tunnel Syndrome

We conducted a double-blind placebo-controlled randomized clinical trial in which 30 patients with pain attributed to carpal tunnel syndrome had either a 1000 gauss magnet or a placebo metal disk applied to the carpal tunnel area using a Velcro wrap for a period of 45 minutes. Pain was measured on a visual analogue scale using 0 and 10 as anchors.

Presenting symptoms including numbness, tingling, burning, and pain did not differ significantly between the 2 groups. There was significant pain reduction across the 45-minute period for both groups. However, t test comparisons found no significant differences between the groups for beginning pain, pain at 15 minutes, pain at 30 minutes, or pain at 45 minutes. The use of a magnet for reducing pain attributed to carpal tunnel syndrome was no more effective than use of the placebo device.

Four recent randomized trials have provided conflicting results concerning the efficacy of magnets in relieving pain. Two double-blind randomized trials have found that magnets relieve pain in postpolio subjects1 and in patients with postoperative wounds.2 However, double-blind randomized studies of magnet therapy for treatment of low back pain3 and foot pain4 showed no benefit.

In an attempt to find alternate forms of therapy,5,6 many chronic sufferers of carpal tunnel syndrome have resorted to using magnets to alleviate their symptoms. The purpose of our study was to determine the efficacy of magnet therapy on pain attributed to carpal tunnel syndrome when compared with a placebo device.

Methods

Subjects

We contacted 160 patients who had wrist pain attributed to carpal tunnel syndrome by their primary care physicians. These patients were identified from the billing databases at a university-operated family practice clinic and a rural private practitioner’s office. The inclusion criteria for participation were presence of chronic wrist pain in the area of the carpal tunnel and the willingness to accept randomization into treatment or control group. Individuals were excluded before randomization if the source of pain had been attributed to some cause other than carpal tunnel syndrome, if they had taken pain medication within 4 hours of beginning treatment, if their body mass index was greater than 35, or if they were not experiencing pain at the time treatment was started.

Treatment intervention

The magnets and placebo devices used in our study were custom made by Medical Magnetics of Houston, Texas. The devices consisted of 5 stacked magnetic pads. Four of these were flexible (2500 gauss, residual induction). The fifth pad was a neodymium disk (10,000 gauss, residual induction). The flexible pads were 1.7 inches in diameter, and the neodymium disk was 0.5 inches in diameter. All 5 pads were glued together to form a single unit. Actual magnetic energy was determined to be 1000 gauss at the surface of the center of the magnet, and depth of penetration was estimated to be adequate for the carpal tunnel area. The placebo disks appeared identical to the magnets. Each magnet and placebo was labeled with a computer-generated random number, wrapped in foam, and boxed individually. Individual boxes were selected at the time of the patient appointment without regard for the order or numerical identifier, which served as a blinding device. Codes identifying placebo or control were not broken until the completion of the study.

After giving written consent, patients were asked to complete a short questionnaire collecting demographic and symptom information. They were then asked to rate the pain at the most painful point in the wrist using the visual analog scale (VAS) of the McGill Pain Questionnaire.7 The VAS consisted of a standard length line labeled 0 on the left and 10 on the right. The patient was instructed to place a mark on that line at the appropriate position relative to the degree of pain experienced (0 = no pain; 10 = the worst pain ever experienced). The distance of the mark from 0 was then measured in millimeters to provide the pain score. A new pain scale was provided for each measurement, and patients were not allowed to view previous measurements, to insure the objectivity of the patient’s pain perception.

A device, either magnet or placebo, was then placed on the wrist overlying the carpal tunnel. The device was secured with foam and a wrist bracelet fastened with Velcro. Each patient was then asked to remain seated and to keep the device in place for the next 45 minutes. This time period was selected based on the experience of the postpolio pain trial.1 Throughout the 45 minutes a research assistant observed the patients to ensure that they did not tamper with their device. The patients were asked to rate their pain on the VAS at 15-minute intervals. After 45 minutes the device was removed, and the patient again rated his or her pain on a VAS.

 

 

Patients were sent home with a postcard that served as a 2-week follow-up. Two weeks after treatment patients rated their current pain, maximum pain over the 2-week period, and typical pain over the 2-week period, using the previously described VAS.

Data analysis

Previous research on the effect of magnets on pain has shown reduction in pain on a 10-point VAS ranging from 1.1 to 4.4 points with standard deviations of 1.6 and 3.1, respectively.1 Corresponding sample sizes to detect these differences would range from 34 per group to 8 per group. Standard sample size formulas for power equal to 0.80, ( equal to 0.05, and a standard deviation of 2.5 estimated that a sample size of 15 per group could detect a difference of 2.6 points between groups.

Data were analyzed using chi-square analysis for categorical data, paired t tests for within group comparisons, and independent t tests for between group comparisons on age and pain. Confirmation of normal distributions for the VAS variables was made using the Kolmogorov-Smirnov goodness-of-fit test.

Results

Of the 160 patients contacted by mail, 45 replied, 38 qualified for participation, and 30 patients completed the 45-minute treatment protocol: 15 with a magnetic device and 15 with a placebo. Descriptive statistics for the 2 groups are provided in Table 1. Groups did not differ significantly in age or any of the presenting symptoms including numbness, tingling, burning, and pain. There were no men in the magnet group and 4 in the placebo group ( P =.01).

Table 2 contains the mean pain scores for both groups at different points in time. There were no significant differences for any of the pain variables. Twenty of the participants in this study completed a 2-week follow-up questionnaire, 10 in each group. There were no significant differences between groups in the pain at 2 weeks post-treatment, the greatest pain experienced during the 2 weeks, and the typical pain experienced during the 2 weeks. The mean pain score at 2 weeks post-treatment and their typical pain across the 2 weeks had not returned to their baseline pain levels measured before device application.

The Figure shows the pain trend across the 45-minute treatment for both groups. The steep decline across each pain measurement period was almost identical for each group but illustrates the significant pain relief provided by both the magnet and the placebo devices. Paired t test analysis revealed that the mean change between pre- and post-treatment was -2.4 ( P =.004) for the magnet group and -2.4 ( P =.003) for the placebo group.

TABLE 1
BASELINE CHARACTERISTICS OF THE STUDY GROUPS

 

CharacteristicMagnet N (%)Placebo N (%)P
Mean age, years, N (SD)50.7 (15.5)48.5 (11.7).67*
Women15 (100)11 (73).01†
Repetitive work11 (73)13 (87).36†
Numbness
  None5 (33)7 (49).13†
  Some2 (13)5 (33) 
  A great deal8 (53)3 (20) 
Tingling
  None8 (47)9 (60).68†
  Some2 (13)3 (20) 
  A great deal5 (33)3 (20) 
Burning
  None12 (80)11 (73).22†
  Some0 (0)2 (13) 
  A great deal3 (20)2 (13) 
Pain
  None5 (33)6 (40).25†
  Some3 (20)6 (40) 
  A great deal7 (47)3 (20) 
*t test analysis
† Chi-square analysis
SD denotes standard deviation.

TABLE 2
COMPARISON OF GROUP VISUAL ANALOG SCALE MEANS BEFORE, DURING, AND AFTER DEVICE APPLICATION

 

Pain ScoreMagnet Mean (SD)Placebo Mean (SD)Difference (95% CI)*
Pretreatment pain†5.9 (2.6)5.0 (2.4)0.9 (-.90 to 2.84)
Pain at 15 minutes†4.5 (2.6)3.9 (2.8)0.6 (-1.49 to 2.47)
Pain at 30 minutes†3.7 (2.6)3.2 (2.6)0.5 (-1.47 to 2.36)
Post-treatment pain†3.6 (3.1)2.6 (2.7)1.0 (-1.21 to 3.15)
Total pain decrease†-2.4 (2.7)-2.4 (2.6)0.0 (-2.02 to 1.97)
Pain at 2 week follow-up‡4.3 (2.9)4.3 (3.5)0.0 (-3.0 to 3.03)
Greatest pain during 2 weeks‡5.5 (2.7)4.9 (2.8)0.6 (-2.07 to 3.15)
Typical pain during 2 weeks‡4.1 (2.7)3.7 (2.4)0.4 (-1.99 to 2.83)
*95% confidence interval for the difference between the mean pain scores. None of the differences were statistically significant.
SD denotes standard deviation; CI, confidence interval.
† N=150
‡ N=100

 

FIGURE
PAIN TREND BY GROUP

Discussion

The delivery of a unipolar static magnetic field through a magnetized device directly applied to the point of greatest wrist pain resulted in no significant difference in relief of pain when compared with an identical placebo device. However, both magnet and placebo produced a significant decrease in pain during the 45-minute application that was still detectable at the 2-week follow-up. The decrease in pain observed in both experimental and control groups could be attributed to a variety of causes. Most likely, this is a placebo effect due to the patients’ belief in the efficacy of the device. Also, it is possible that pressure over the area of pain, due to application of the bracelet, somehow reduces the amount of pain experienced.

A limitation of this study is the small sample size. It is possible that a larger study would detect small improvements in outcomes, but it is questionable whether these would be clinically significant.

 

 

Conclusions

Collacott and colleagues3 found that magnets were not effective in treating low back pain. Although they proposed that the depth of the pain source might have played a role in the outcome of their research project, such an issue would not be a significant factor in our study because of the relatively short distance from the surface of the wrist to the median nerve. Future research might include a measure of belief in magnets as healing devices to determine the impact of the placebo device. The addition of another arm of the study to include magnet placement adjacent to, but not touching, the point of pain to determine the pressure effect might be interesting. Although this study did not show magnets to be more effective than the placebo, the reduction in pain with this simple intervention was remarkable.

Acknowledgments

Funding for this project was provided by The Oklahoma Center for Family Medicine Research, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma; Dr. James Mold, director. Thanks to Deborah Cacy, PhD, for her assistance in the development of our project.

References

 

1. Vallbona C, Hazlewood CF, Jurida G. Response of pain to static magnetic fields in postpolio patients: a double-blind pilot study. Arch Phys Med Rehabil 1997;78:1200-03.

2. Man D, Man B, Plosker H. The influence of permanent magnetic field therapy on wound healing in suction lipectomy patients: a double-blind study. Plastic Reconstruct Surg 1999;104:2261-66.

3. Collacott EA, Zimmerman JT, White DW, Rindone JP. Bipolar permanent magnets for the treatment of chronic low back pain. JAMA 1999;283:1322-25.

4. Caselli MA, Clark N, Lazarus S, Velez Z, Venegas L. Evaluation of magnetic foil and PPT insoles in the treatment of heel pain. J Am Podiatr Med Assoc 1997;87:11-16.

5. Lawrence MD, Rosch PJ, Plowden J. Magnet therapy. Rocklin, Calif: Prima Publishers; 1998.

6. Howells B. Magnet therapy’s strong attractions. Available online: outside.starwave.com/magazine/0897/9708bodypres.html.

7. Melzack R. The McGill Pain Questionnaire: major properties and scoring methods. Pain 1975;1:277-99.

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RICHARD CARTER
THOMAS HALL
CHERYL B. ASPY, PHD
JAMES MOLD, MD, MPH
Oklahoma City, Oklahoma
Submitted, revised, August 28, 2001.
From the Department of Family and Preventive Medicine (C.B.A., J.M.), University of Oklahoma College of Medicine (R.C., T.H.). Reprint requests should be addressed to Cheryl B. Aspy, PhD, Family & Preventive Medicine, 900 NE 10th St, Oklahoma City, OK 73104. E-mail: [email protected]

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RICHARD CARTER
THOMAS HALL
CHERYL B. ASPY, PHD
JAMES MOLD, MD, MPH
Oklahoma City, Oklahoma
Submitted, revised, August 28, 2001.
From the Department of Family and Preventive Medicine (C.B.A., J.M.), University of Oklahoma College of Medicine (R.C., T.H.). Reprint requests should be addressed to Cheryl B. Aspy, PhD, Family & Preventive Medicine, 900 NE 10th St, Oklahoma City, OK 73104. E-mail: [email protected]

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RICHARD CARTER
THOMAS HALL
CHERYL B. ASPY, PHD
JAMES MOLD, MD, MPH
Oklahoma City, Oklahoma
Submitted, revised, August 28, 2001.
From the Department of Family and Preventive Medicine (C.B.A., J.M.), University of Oklahoma College of Medicine (R.C., T.H.). Reprint requests should be addressed to Cheryl B. Aspy, PhD, Family & Preventive Medicine, 900 NE 10th St, Oklahoma City, OK 73104. E-mail: [email protected]

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We conducted a double-blind placebo-controlled randomized clinical trial in which 30 patients with pain attributed to carpal tunnel syndrome had either a 1000 gauss magnet or a placebo metal disk applied to the carpal tunnel area using a Velcro wrap for a period of 45 minutes. Pain was measured on a visual analogue scale using 0 and 10 as anchors.

Presenting symptoms including numbness, tingling, burning, and pain did not differ significantly between the 2 groups. There was significant pain reduction across the 45-minute period for both groups. However, t test comparisons found no significant differences between the groups for beginning pain, pain at 15 minutes, pain at 30 minutes, or pain at 45 minutes. The use of a magnet for reducing pain attributed to carpal tunnel syndrome was no more effective than use of the placebo device.

Four recent randomized trials have provided conflicting results concerning the efficacy of magnets in relieving pain. Two double-blind randomized trials have found that magnets relieve pain in postpolio subjects1 and in patients with postoperative wounds.2 However, double-blind randomized studies of magnet therapy for treatment of low back pain3 and foot pain4 showed no benefit.

In an attempt to find alternate forms of therapy,5,6 many chronic sufferers of carpal tunnel syndrome have resorted to using magnets to alleviate their symptoms. The purpose of our study was to determine the efficacy of magnet therapy on pain attributed to carpal tunnel syndrome when compared with a placebo device.

Methods

Subjects

We contacted 160 patients who had wrist pain attributed to carpal tunnel syndrome by their primary care physicians. These patients were identified from the billing databases at a university-operated family practice clinic and a rural private practitioner’s office. The inclusion criteria for participation were presence of chronic wrist pain in the area of the carpal tunnel and the willingness to accept randomization into treatment or control group. Individuals were excluded before randomization if the source of pain had been attributed to some cause other than carpal tunnel syndrome, if they had taken pain medication within 4 hours of beginning treatment, if their body mass index was greater than 35, or if they were not experiencing pain at the time treatment was started.

Treatment intervention

The magnets and placebo devices used in our study were custom made by Medical Magnetics of Houston, Texas. The devices consisted of 5 stacked magnetic pads. Four of these were flexible (2500 gauss, residual induction). The fifth pad was a neodymium disk (10,000 gauss, residual induction). The flexible pads were 1.7 inches in diameter, and the neodymium disk was 0.5 inches in diameter. All 5 pads were glued together to form a single unit. Actual magnetic energy was determined to be 1000 gauss at the surface of the center of the magnet, and depth of penetration was estimated to be adequate for the carpal tunnel area. The placebo disks appeared identical to the magnets. Each magnet and placebo was labeled with a computer-generated random number, wrapped in foam, and boxed individually. Individual boxes were selected at the time of the patient appointment without regard for the order or numerical identifier, which served as a blinding device. Codes identifying placebo or control were not broken until the completion of the study.

After giving written consent, patients were asked to complete a short questionnaire collecting demographic and symptom information. They were then asked to rate the pain at the most painful point in the wrist using the visual analog scale (VAS) of the McGill Pain Questionnaire.7 The VAS consisted of a standard length line labeled 0 on the left and 10 on the right. The patient was instructed to place a mark on that line at the appropriate position relative to the degree of pain experienced (0 = no pain; 10 = the worst pain ever experienced). The distance of the mark from 0 was then measured in millimeters to provide the pain score. A new pain scale was provided for each measurement, and patients were not allowed to view previous measurements, to insure the objectivity of the patient’s pain perception.

A device, either magnet or placebo, was then placed on the wrist overlying the carpal tunnel. The device was secured with foam and a wrist bracelet fastened with Velcro. Each patient was then asked to remain seated and to keep the device in place for the next 45 minutes. This time period was selected based on the experience of the postpolio pain trial.1 Throughout the 45 minutes a research assistant observed the patients to ensure that they did not tamper with their device. The patients were asked to rate their pain on the VAS at 15-minute intervals. After 45 minutes the device was removed, and the patient again rated his or her pain on a VAS.

 

 

Patients were sent home with a postcard that served as a 2-week follow-up. Two weeks after treatment patients rated their current pain, maximum pain over the 2-week period, and typical pain over the 2-week period, using the previously described VAS.

Data analysis

Previous research on the effect of magnets on pain has shown reduction in pain on a 10-point VAS ranging from 1.1 to 4.4 points with standard deviations of 1.6 and 3.1, respectively.1 Corresponding sample sizes to detect these differences would range from 34 per group to 8 per group. Standard sample size formulas for power equal to 0.80, ( equal to 0.05, and a standard deviation of 2.5 estimated that a sample size of 15 per group could detect a difference of 2.6 points between groups.

Data were analyzed using chi-square analysis for categorical data, paired t tests for within group comparisons, and independent t tests for between group comparisons on age and pain. Confirmation of normal distributions for the VAS variables was made using the Kolmogorov-Smirnov goodness-of-fit test.

Results

Of the 160 patients contacted by mail, 45 replied, 38 qualified for participation, and 30 patients completed the 45-minute treatment protocol: 15 with a magnetic device and 15 with a placebo. Descriptive statistics for the 2 groups are provided in Table 1. Groups did not differ significantly in age or any of the presenting symptoms including numbness, tingling, burning, and pain. There were no men in the magnet group and 4 in the placebo group ( P =.01).

Table 2 contains the mean pain scores for both groups at different points in time. There were no significant differences for any of the pain variables. Twenty of the participants in this study completed a 2-week follow-up questionnaire, 10 in each group. There were no significant differences between groups in the pain at 2 weeks post-treatment, the greatest pain experienced during the 2 weeks, and the typical pain experienced during the 2 weeks. The mean pain score at 2 weeks post-treatment and their typical pain across the 2 weeks had not returned to their baseline pain levels measured before device application.

The Figure shows the pain trend across the 45-minute treatment for both groups. The steep decline across each pain measurement period was almost identical for each group but illustrates the significant pain relief provided by both the magnet and the placebo devices. Paired t test analysis revealed that the mean change between pre- and post-treatment was -2.4 ( P =.004) for the magnet group and -2.4 ( P =.003) for the placebo group.

TABLE 1
BASELINE CHARACTERISTICS OF THE STUDY GROUPS

 

CharacteristicMagnet N (%)Placebo N (%)P
Mean age, years, N (SD)50.7 (15.5)48.5 (11.7).67*
Women15 (100)11 (73).01†
Repetitive work11 (73)13 (87).36†
Numbness
  None5 (33)7 (49).13†
  Some2 (13)5 (33) 
  A great deal8 (53)3 (20) 
Tingling
  None8 (47)9 (60).68†
  Some2 (13)3 (20) 
  A great deal5 (33)3 (20) 
Burning
  None12 (80)11 (73).22†
  Some0 (0)2 (13) 
  A great deal3 (20)2 (13) 
Pain
  None5 (33)6 (40).25†
  Some3 (20)6 (40) 
  A great deal7 (47)3 (20) 
*t test analysis
† Chi-square analysis
SD denotes standard deviation.

TABLE 2
COMPARISON OF GROUP VISUAL ANALOG SCALE MEANS BEFORE, DURING, AND AFTER DEVICE APPLICATION

 

Pain ScoreMagnet Mean (SD)Placebo Mean (SD)Difference (95% CI)*
Pretreatment pain†5.9 (2.6)5.0 (2.4)0.9 (-.90 to 2.84)
Pain at 15 minutes†4.5 (2.6)3.9 (2.8)0.6 (-1.49 to 2.47)
Pain at 30 minutes†3.7 (2.6)3.2 (2.6)0.5 (-1.47 to 2.36)
Post-treatment pain†3.6 (3.1)2.6 (2.7)1.0 (-1.21 to 3.15)
Total pain decrease†-2.4 (2.7)-2.4 (2.6)0.0 (-2.02 to 1.97)
Pain at 2 week follow-up‡4.3 (2.9)4.3 (3.5)0.0 (-3.0 to 3.03)
Greatest pain during 2 weeks‡5.5 (2.7)4.9 (2.8)0.6 (-2.07 to 3.15)
Typical pain during 2 weeks‡4.1 (2.7)3.7 (2.4)0.4 (-1.99 to 2.83)
*95% confidence interval for the difference between the mean pain scores. None of the differences were statistically significant.
SD denotes standard deviation; CI, confidence interval.
† N=150
‡ N=100

 

FIGURE
PAIN TREND BY GROUP

Discussion

The delivery of a unipolar static magnetic field through a magnetized device directly applied to the point of greatest wrist pain resulted in no significant difference in relief of pain when compared with an identical placebo device. However, both magnet and placebo produced a significant decrease in pain during the 45-minute application that was still detectable at the 2-week follow-up. The decrease in pain observed in both experimental and control groups could be attributed to a variety of causes. Most likely, this is a placebo effect due to the patients’ belief in the efficacy of the device. Also, it is possible that pressure over the area of pain, due to application of the bracelet, somehow reduces the amount of pain experienced.

A limitation of this study is the small sample size. It is possible that a larger study would detect small improvements in outcomes, but it is questionable whether these would be clinically significant.

 

 

Conclusions

Collacott and colleagues3 found that magnets were not effective in treating low back pain. Although they proposed that the depth of the pain source might have played a role in the outcome of their research project, such an issue would not be a significant factor in our study because of the relatively short distance from the surface of the wrist to the median nerve. Future research might include a measure of belief in magnets as healing devices to determine the impact of the placebo device. The addition of another arm of the study to include magnet placement adjacent to, but not touching, the point of pain to determine the pressure effect might be interesting. Although this study did not show magnets to be more effective than the placebo, the reduction in pain with this simple intervention was remarkable.

Acknowledgments

Funding for this project was provided by The Oklahoma Center for Family Medicine Research, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma; Dr. James Mold, director. Thanks to Deborah Cacy, PhD, for her assistance in the development of our project.

We conducted a double-blind placebo-controlled randomized clinical trial in which 30 patients with pain attributed to carpal tunnel syndrome had either a 1000 gauss magnet or a placebo metal disk applied to the carpal tunnel area using a Velcro wrap for a period of 45 minutes. Pain was measured on a visual analogue scale using 0 and 10 as anchors.

Presenting symptoms including numbness, tingling, burning, and pain did not differ significantly between the 2 groups. There was significant pain reduction across the 45-minute period for both groups. However, t test comparisons found no significant differences between the groups for beginning pain, pain at 15 minutes, pain at 30 minutes, or pain at 45 minutes. The use of a magnet for reducing pain attributed to carpal tunnel syndrome was no more effective than use of the placebo device.

Four recent randomized trials have provided conflicting results concerning the efficacy of magnets in relieving pain. Two double-blind randomized trials have found that magnets relieve pain in postpolio subjects1 and in patients with postoperative wounds.2 However, double-blind randomized studies of magnet therapy for treatment of low back pain3 and foot pain4 showed no benefit.

In an attempt to find alternate forms of therapy,5,6 many chronic sufferers of carpal tunnel syndrome have resorted to using magnets to alleviate their symptoms. The purpose of our study was to determine the efficacy of magnet therapy on pain attributed to carpal tunnel syndrome when compared with a placebo device.

Methods

Subjects

We contacted 160 patients who had wrist pain attributed to carpal tunnel syndrome by their primary care physicians. These patients were identified from the billing databases at a university-operated family practice clinic and a rural private practitioner’s office. The inclusion criteria for participation were presence of chronic wrist pain in the area of the carpal tunnel and the willingness to accept randomization into treatment or control group. Individuals were excluded before randomization if the source of pain had been attributed to some cause other than carpal tunnel syndrome, if they had taken pain medication within 4 hours of beginning treatment, if their body mass index was greater than 35, or if they were not experiencing pain at the time treatment was started.

Treatment intervention

The magnets and placebo devices used in our study were custom made by Medical Magnetics of Houston, Texas. The devices consisted of 5 stacked magnetic pads. Four of these were flexible (2500 gauss, residual induction). The fifth pad was a neodymium disk (10,000 gauss, residual induction). The flexible pads were 1.7 inches in diameter, and the neodymium disk was 0.5 inches in diameter. All 5 pads were glued together to form a single unit. Actual magnetic energy was determined to be 1000 gauss at the surface of the center of the magnet, and depth of penetration was estimated to be adequate for the carpal tunnel area. The placebo disks appeared identical to the magnets. Each magnet and placebo was labeled with a computer-generated random number, wrapped in foam, and boxed individually. Individual boxes were selected at the time of the patient appointment without regard for the order or numerical identifier, which served as a blinding device. Codes identifying placebo or control were not broken until the completion of the study.

After giving written consent, patients were asked to complete a short questionnaire collecting demographic and symptom information. They were then asked to rate the pain at the most painful point in the wrist using the visual analog scale (VAS) of the McGill Pain Questionnaire.7 The VAS consisted of a standard length line labeled 0 on the left and 10 on the right. The patient was instructed to place a mark on that line at the appropriate position relative to the degree of pain experienced (0 = no pain; 10 = the worst pain ever experienced). The distance of the mark from 0 was then measured in millimeters to provide the pain score. A new pain scale was provided for each measurement, and patients were not allowed to view previous measurements, to insure the objectivity of the patient’s pain perception.

A device, either magnet or placebo, was then placed on the wrist overlying the carpal tunnel. The device was secured with foam and a wrist bracelet fastened with Velcro. Each patient was then asked to remain seated and to keep the device in place for the next 45 minutes. This time period was selected based on the experience of the postpolio pain trial.1 Throughout the 45 minutes a research assistant observed the patients to ensure that they did not tamper with their device. The patients were asked to rate their pain on the VAS at 15-minute intervals. After 45 minutes the device was removed, and the patient again rated his or her pain on a VAS.

 

 

Patients were sent home with a postcard that served as a 2-week follow-up. Two weeks after treatment patients rated their current pain, maximum pain over the 2-week period, and typical pain over the 2-week period, using the previously described VAS.

Data analysis

Previous research on the effect of magnets on pain has shown reduction in pain on a 10-point VAS ranging from 1.1 to 4.4 points with standard deviations of 1.6 and 3.1, respectively.1 Corresponding sample sizes to detect these differences would range from 34 per group to 8 per group. Standard sample size formulas for power equal to 0.80, ( equal to 0.05, and a standard deviation of 2.5 estimated that a sample size of 15 per group could detect a difference of 2.6 points between groups.

Data were analyzed using chi-square analysis for categorical data, paired t tests for within group comparisons, and independent t tests for between group comparisons on age and pain. Confirmation of normal distributions for the VAS variables was made using the Kolmogorov-Smirnov goodness-of-fit test.

Results

Of the 160 patients contacted by mail, 45 replied, 38 qualified for participation, and 30 patients completed the 45-minute treatment protocol: 15 with a magnetic device and 15 with a placebo. Descriptive statistics for the 2 groups are provided in Table 1. Groups did not differ significantly in age or any of the presenting symptoms including numbness, tingling, burning, and pain. There were no men in the magnet group and 4 in the placebo group ( P =.01).

Table 2 contains the mean pain scores for both groups at different points in time. There were no significant differences for any of the pain variables. Twenty of the participants in this study completed a 2-week follow-up questionnaire, 10 in each group. There were no significant differences between groups in the pain at 2 weeks post-treatment, the greatest pain experienced during the 2 weeks, and the typical pain experienced during the 2 weeks. The mean pain score at 2 weeks post-treatment and their typical pain across the 2 weeks had not returned to their baseline pain levels measured before device application.

The Figure shows the pain trend across the 45-minute treatment for both groups. The steep decline across each pain measurement period was almost identical for each group but illustrates the significant pain relief provided by both the magnet and the placebo devices. Paired t test analysis revealed that the mean change between pre- and post-treatment was -2.4 ( P =.004) for the magnet group and -2.4 ( P =.003) for the placebo group.

TABLE 1
BASELINE CHARACTERISTICS OF THE STUDY GROUPS

 

CharacteristicMagnet N (%)Placebo N (%)P
Mean age, years, N (SD)50.7 (15.5)48.5 (11.7).67*
Women15 (100)11 (73).01†
Repetitive work11 (73)13 (87).36†
Numbness
  None5 (33)7 (49).13†
  Some2 (13)5 (33) 
  A great deal8 (53)3 (20) 
Tingling
  None8 (47)9 (60).68†
  Some2 (13)3 (20) 
  A great deal5 (33)3 (20) 
Burning
  None12 (80)11 (73).22†
  Some0 (0)2 (13) 
  A great deal3 (20)2 (13) 
Pain
  None5 (33)6 (40).25†
  Some3 (20)6 (40) 
  A great deal7 (47)3 (20) 
*t test analysis
† Chi-square analysis
SD denotes standard deviation.

TABLE 2
COMPARISON OF GROUP VISUAL ANALOG SCALE MEANS BEFORE, DURING, AND AFTER DEVICE APPLICATION

 

Pain ScoreMagnet Mean (SD)Placebo Mean (SD)Difference (95% CI)*
Pretreatment pain†5.9 (2.6)5.0 (2.4)0.9 (-.90 to 2.84)
Pain at 15 minutes†4.5 (2.6)3.9 (2.8)0.6 (-1.49 to 2.47)
Pain at 30 minutes†3.7 (2.6)3.2 (2.6)0.5 (-1.47 to 2.36)
Post-treatment pain†3.6 (3.1)2.6 (2.7)1.0 (-1.21 to 3.15)
Total pain decrease†-2.4 (2.7)-2.4 (2.6)0.0 (-2.02 to 1.97)
Pain at 2 week follow-up‡4.3 (2.9)4.3 (3.5)0.0 (-3.0 to 3.03)
Greatest pain during 2 weeks‡5.5 (2.7)4.9 (2.8)0.6 (-2.07 to 3.15)
Typical pain during 2 weeks‡4.1 (2.7)3.7 (2.4)0.4 (-1.99 to 2.83)
*95% confidence interval for the difference between the mean pain scores. None of the differences were statistically significant.
SD denotes standard deviation; CI, confidence interval.
† N=150
‡ N=100

 

FIGURE
PAIN TREND BY GROUP

Discussion

The delivery of a unipolar static magnetic field through a magnetized device directly applied to the point of greatest wrist pain resulted in no significant difference in relief of pain when compared with an identical placebo device. However, both magnet and placebo produced a significant decrease in pain during the 45-minute application that was still detectable at the 2-week follow-up. The decrease in pain observed in both experimental and control groups could be attributed to a variety of causes. Most likely, this is a placebo effect due to the patients’ belief in the efficacy of the device. Also, it is possible that pressure over the area of pain, due to application of the bracelet, somehow reduces the amount of pain experienced.

A limitation of this study is the small sample size. It is possible that a larger study would detect small improvements in outcomes, but it is questionable whether these would be clinically significant.

 

 

Conclusions

Collacott and colleagues3 found that magnets were not effective in treating low back pain. Although they proposed that the depth of the pain source might have played a role in the outcome of their research project, such an issue would not be a significant factor in our study because of the relatively short distance from the surface of the wrist to the median nerve. Future research might include a measure of belief in magnets as healing devices to determine the impact of the placebo device. The addition of another arm of the study to include magnet placement adjacent to, but not touching, the point of pain to determine the pressure effect might be interesting. Although this study did not show magnets to be more effective than the placebo, the reduction in pain with this simple intervention was remarkable.

Acknowledgments

Funding for this project was provided by The Oklahoma Center for Family Medicine Research, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma; Dr. James Mold, director. Thanks to Deborah Cacy, PhD, for her assistance in the development of our project.

References

 

1. Vallbona C, Hazlewood CF, Jurida G. Response of pain to static magnetic fields in postpolio patients: a double-blind pilot study. Arch Phys Med Rehabil 1997;78:1200-03.

2. Man D, Man B, Plosker H. The influence of permanent magnetic field therapy on wound healing in suction lipectomy patients: a double-blind study. Plastic Reconstruct Surg 1999;104:2261-66.

3. Collacott EA, Zimmerman JT, White DW, Rindone JP. Bipolar permanent magnets for the treatment of chronic low back pain. JAMA 1999;283:1322-25.

4. Caselli MA, Clark N, Lazarus S, Velez Z, Venegas L. Evaluation of magnetic foil and PPT insoles in the treatment of heel pain. J Am Podiatr Med Assoc 1997;87:11-16.

5. Lawrence MD, Rosch PJ, Plowden J. Magnet therapy. Rocklin, Calif: Prima Publishers; 1998.

6. Howells B. Magnet therapy’s strong attractions. Available online: outside.starwave.com/magazine/0897/9708bodypres.html.

7. Melzack R. The McGill Pain Questionnaire: major properties and scoring methods. Pain 1975;1:277-99.

References

 

1. Vallbona C, Hazlewood CF, Jurida G. Response of pain to static magnetic fields in postpolio patients: a double-blind pilot study. Arch Phys Med Rehabil 1997;78:1200-03.

2. Man D, Man B, Plosker H. The influence of permanent magnetic field therapy on wound healing in suction lipectomy patients: a double-blind study. Plastic Reconstruct Surg 1999;104:2261-66.

3. Collacott EA, Zimmerman JT, White DW, Rindone JP. Bipolar permanent magnets for the treatment of chronic low back pain. JAMA 1999;283:1322-25.

4. Caselli MA, Clark N, Lazarus S, Velez Z, Venegas L. Evaluation of magnetic foil and PPT insoles in the treatment of heel pain. J Am Podiatr Med Assoc 1997;87:11-16.

5. Lawrence MD, Rosch PJ, Plowden J. Magnet therapy. Rocklin, Calif: Prima Publishers; 1998.

6. Howells B. Magnet therapy’s strong attractions. Available online: outside.starwave.com/magazine/0897/9708bodypres.html.

7. Melzack R. The McGill Pain Questionnaire: major properties and scoring methods. Pain 1975;1:277-99.

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The Probability of Specific Diagnoses for Patients Presenting with Common Symptoms to Dutch Family Physicians

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The Probability of Specific Diagnoses for Patients Presenting with Common Symptoms to Dutch Family Physicians

ABSTRACT

OBJECTIVE: Our goal was to develop reliable data on the probability of specific diagnoses among patients of family physicians (FPs) presenting with common symptoms.

STUDY DESIGN: A group of 54 Dutch FPs recorded the reasons for encounter, diagnoses, and interventions for all episodes of care between 1985 and 1995. Diagnoses could be modified during the episode of care, and a modified diagnosis was applied to all episode data.

POPULATION: We used the listed patient populations of the 54 Dutch FPs, representing 93,297 patient years, 236,027 episodes of care, and 267,897 patient encounters.

OUTCOMES MEASURED: The top 20 diagnoses related to 4 selected presenting symptoms (cough, shortness of breath, general weakness/tiredness, and low back symptom /complaints without radiation), per 100 patients, with 95% confidence intervals, stratified by age. In the standard tables, age-specific cells with fewer than 10 observations were excluded.

RESULTS: The availability of an accurate estimate of prior (pretest) probabilities for common symptoms/complaints has great potential value for family practice as an academic discipline and for family physicians in that it can support their medical decision making. Stratifying data by age groups increases the clinical relevance of the prior probabilities.

CONCLUSIONS: Though collected by Dutch FPs, the data in our study have a high face validity for other clinicians. Still, FPs in other countries should give priority to collecting their own probability databases.

KEY POINTS FOR CLINICIANS

  • The pretest probability is the likelihood of disease before tests are ordered in a patient with a specific symptom or complaint.
  • It is very helpful in the diagnosis and management of problems common in family practice.
  • We have identified the pretest probability for the most common final diagnoses in patients with many common presenting symptom /complaints.

Estimating the probability of disease in unselected patients lies at the heart of the clinical competence of family physicians (FPs).1,2 This probability is called the prior or pretest probability, because it precedes any diagnostic testing, including the history and physical examination. Based on this knowledge, FPs often decide that since the probability of a serious disease is low, the best thing to do is watchful waiting, thus preventing unnecessary harm and cost to the patient.3,4 Moderate probabilities may trigger a diagnostic evaluation, and high probabilities may warrant empiric therapy without further diagnostic confirmation. For example, knowing that the probability of gastric carcinoma is exceedingly rare for a dyspeptic patient younger than 40 years supports a diagnostic approach that does not include initial endoscopy. A moderate probability of strep throat is a situation where a rapid strep test may be helpful.5 Understanding the high prior probability of a urinary tract infection in young healthy women with dysuria might allow an FP to confidently institute a telephone-based protocol, including empiric treatment for selected patients.

The development of a prior probabilities database requires access to large diverse practice populations with adequate continuity of care and good documentation of all episodes of care.2,6,7 An “episode of care” is defined as a health problem from the first encounter with a health care provider through the completion of the last encounter related to that particular problem.2,8.10,11 Early in an episode of care, FPs will often assign a symptom diagnosis such as chest pain, gastric pain, or otalgia; a substantial number of these diagnoses will change over time. Therefore, the observation period should allow for the most important modifications of diagnoses to become visible. Because 1-year incidence and prevalence rates are usually calculated, at least a 1-year study period is required; however, a longer period is preferable, especially for information on chronic diseases.12

Episodes of care are based on the relationship between the patient’s reasons for encounter (RFE), the physician’s diagnostic interpretation, and the related interventions over time.13 Episodes of care are clearly distinguished from episodes of disease and episodes of illness. An episode of disease begins with its onset and continues until its resolution or the patient’s death, while an episode of illness refers to the period that someone suffers from symptoms or complaints experienced as illness. Not every disease, and certainly not every illness, results in an episode of care.2,13,14 Most episodes of care, however, are part of an episode of disease and/or illness. Health maintenance episodes can be considered a special form of episodes of care.10,11 For example, screening for breast cancer (an episode of health maintenance) may prove the existence of the episode of disease well before the patient has symptoms (an episode of illness); an episode of care will follow.

The development of a prior probabilities database also requires a primary care–specific system of classification. The International Classification of Primary Care (ICPC) includes approximately 200 symptoms/complaints and 300 diagnoses common in family practice. Almost all have an incidence of at least 1 per 1000 patients per year.13,15

 

 

Since 1985, the members of the Transition Project of the University of Amsterdam Department of Family Practice have been contributing to episodeoriented epidemiology in family practice. We have developed a large prospective database that provides reliable probabilities of diagnoses for symptoms or complaints in a patient of a specific age and sex, suitable for the support of family physicians’ decision making.2,7,9,16 This article describes the database in more detail and presents data for 4 common symptoms.*

Methods

From 1985 to 1995, 54 Dutch FPs in 23 locations routinely coded episode data for all direct (face-to-face) encounters with their listed patients; within the Dutch health care system, all citizens are listed (registered) with an FP. Each participating FP collected data during a period of at least 1 year; the registration period for patients ranged from 1 to 10 years (mean 2.4).2,9,16 For each encounter, the patient’s reasons for the encounter, the diagnoses, and the interventions ordered by the physician were coded according to the ICPC. Data were entered on a special encounter form with a copy for a central data entry and included 93,297 patient years, with 236,027 episodes of care and 267,897 direct patient encounters.

Since the FPs had a well-defined practice with listed patients, a precise denominator could be established for the calculation of rates. In the Netherlands, patients can, in principle, not seek specialist care without a referral by their FP. Therefore, especially in a longer observation period, an FP will document a close approximation of the distribution of episodes of care in the Dutch population. In 1 year, 73% of listed patients have a direct encounter with their family physician; in a 2-year observation period this is 92%. It is therefore unlikely that a substantial group of listed patients receive specialist care without their FP’s being aware of it.12

A diagnosis could be modified during the course of an episode. If that occurred, the modified diagnosis was applied to all episode data in the analysis. New and ongoing episodes of care were included in a registration year if dealt with at least once; in case of a follow-up encounter in a later registration year, the episode was included again as an ongoing episode. As a consequence, an episode of a chronic disease (eg, diabetes, hypertension) coded in 2 or more registration years was included more than once in the annual prevalence.17 The average yearly practice population served as the denominator.

As is the case in all time-consuming morbidity studies, the participating FPs were selected, highly motivated, and in this respect, not representative of the average Dutch FP. The database was used in numerous studies, however, and its reliability consistently proved to be high: Approximately 2% of all episodes appeared to be missing in the paper record, and another 2% were erroneously included in the database. The complete reference database is available in Dutch on a CD-ROM attached to a family practice textbook. It includes all combinations of an RFE, a diagnosis, and an intervention for 7 standard age groups at the start of episodes and during follow-up, together with data on comorbidity.9

Our paper focuses on prior probabilities, expressed as the top 20 final diagnoses for 4 common reasons for encounter presented at the start of an episode of care18,19: cough, shortness of breath, general weakness/tiredness, and low back pain without radiation. All probabilities are presented for the total population and for 7 standard age groups, as percentages with 95% symmetric confidence intervals.20 Cells with fewer than 10 observations were excluded. Incidences standardized for the 1995 Dutch population were provided.

Results

(Table 1) shows that for the RFE “cough,” the patient’s age had a substantial impact on the probabilities. The diagnosis of acute bronchitis was common overall but especially in the very young and the very old. This table illustrates the relationships between a common symptom and several diseases with a relatively high incidence (the last column). The prior probabilities were well distributed over the standard table: empty cells occur infrequently.

“Shortness of breath/dyspnoea” as an RFE is associated with a very different distribution of diagnoses than found with cough, especially in the very young and the very old (Table 2). Asthma and acute laryngitis typically occur in the young, while chronic obstructive pulmonary disease, ischemic heart disease, and heart failure occur in the old. Hyperventilation had a peak in young adults. In this table, the relation between a less common symptom and several less common diseases is illustrated. In this case, more empty cells are found. Both cough and shortness of breath mainly relate to respiratory and cardiovascular diseases.

 

 

The RFE “tiredness” (Table 3) was associated with several common diseases, but a much wider range of clinical possibilities is apparent. Quite often, this RFE resulted in the symptom diagnosis “tiredness.” Major age differences existed for several diagnoses.

(Table 4) shows that the RFE “low back symptoms/complaints without radiation” quite often led to the same symptom diagnosis. Also, the rather skewed age distribution of low back complaints shows: most cells were insufficiently filled, while age differences for the most common diagnoses appeared to be relatively small.

TABLE 1
FINAL DIAGNOSES FOR EPISODES OF CARE STARTING WITH THE REASON FOR ENCOUNTER COUGH (R05) N=11092

 Percentage of Patients Presenting with Cough Who Had This Final Diagnosis 
  Age (Years) 
RankICPC CodeDiagnosisTotal0-45-1415-2425-4445-6465-7475+Standardized Incidence(Cases/100 Patients/Year)
1R74URI (head cold)32.9±.940.1±2.135.6±2.636.7±3.334.4±1.929.9±2.127.4±2.223.9±2.49.7
2R78Acute bronchitis/bronchiolitis25.4±.823.7±1.820.4±2.217.8±2.618.5±1.626.1±2.034.3±2.439.5±2.84.7
3R05Cough13.7±.610.5±1.312.4±1.815.9±2.515.8±1.515.4±1.612.4±1.714.0±2.01.9
4R77Acute laryngitis/tracheitis9.0±.56.6±1.18.6±1.510.0±2.012.4±1.311.1±1.47.3±1.35.2±1.31.8
5R75Sinusitis acute/chronic3.5±.31.2±.52.3±.84.2±1.45.7±.94.8±1.04.1±1.01.2±.63.1
6A77Viral diseases NOS2.3±.33.3±.84.2±1.11.9±.92.1±.61.7±.61.4±.61.2±.62.8
7R80Influenza (proven)2.0±.3.9±.41.9±.73.1±1.22.6±.72.4±.71.6±.62.3±.9.8
8R96Asthma1.9±.32.9±.73.1±1.01.9±.91.4±.51.1±.51.6±.61.2±.6.7
9R81Pneumonia1.9±.31.9±.62.6±.91.7±.91.5±.51.0±.51.4±.63.7±1.1.5
10R83Other infection respiratory system.6±.1.4±.3-1.4±.8.6±.3.7±.4.9±.5.6±.4.2
11R76Tonsillitis acute.6±.11.8±.61.3±.6-.3±.2---1.6
12R91Chronic bronchitis/bronchiectasis.6±.1---.3±.2.8±.41.1±.51.6±.7.1
13R95Emphysema/COPD.5±.1-.6±.4-.3±.2.9±.4.9±.5.8±.5.2
14R71Whooping cough.4±.1.6±.31.7±.7-----.1
15R90Hypertrophy/chronic infection T&A.4±.11.1±.41.6±.7-----.3
16A97No disease/prevention.4±.1.4±.3--.5±.3.4±.3.6±.4-8.5
17H71Acute otitis media/myringitis.4±.11.7±.5.5±.4-----2.3
18K77Heart failure.3±.1-----1.4±.6.9±.5.5
19R99Other disease respiratory system.3±.1.3±.2--.3±.2.4±.3--.3
20R27Fear of other respiratory disease.3±.1.4±.3--.4±.3---.2
  Absolute number of observations11,092209012818322320184215291199 
URI denotes upper respiratory infection; COPD, chronic obstructive pulmonary disease.

TABLE 2
FINAL DIAGNOSES FOR EPISODES OF CARE STARTING WITH THE REASON FOR ENCOUNTER SHORTNESS OF BREATH (R02); N=2505

 Percentage of Patients Presenting with Cough Who Had This Final Diagnosis 
  Age (Years) 
RankICPC CodeDiagnosisTotal0-45-1415-2425-4445-6465-7475+Standardized Incidence (Cases/100 Patients/Year)
1R748Acute bronchitis/bronchiolitis27.3±1.728.7±5.630.0±7.918.1±6.223.7±4.028.7±4.633.5±4.325.8±3.24.7
2R96Asthma9.7±1.217.4±4.722.3±7.223.5±6.813.9±3.27.2±2.65.4±2.13.4±1.3.7
3K77Heart failure8.9±1.1----4.5±2.112.6±3.021.0±3.0.5
4R02Shortness of breath/dyspnoea8.6±1.1-7.7±4.66.0±3.810.5±2.910.1±3.08.0±2.59.9±2.2.3
5R98Hyperventilation8.0±1.1-7.7±4.618.8±6.313.0±3.215.7±3.75.9±2.12.8±1.2.9
6R74URI (head cold)6.6±1.019.0±4.98.5±4.88.1±4.49.8±2.83.7±1.93.0±1.63.5±1.49.7
7R77Acute laryngitis/tracheitis3.2±.715.0±4.58.5±4.8-2.5±1.51.9±1.4-1.3±.81.8
8R81Pneumonia3.2±.73.2±2.2--2.1±1.32.1±1.52.6±1.55.4±1.7.5
9R95Emphysema/COPD2.8±.6----3.5±1.85.2±.2.03.5±1.4.2
10K76Ischemic heart disease2.1±.6----2.4±1.52.6±1.54.1±1.5.8
11A97No disease/prevention1.9±.53.2±2.2--1.8±1.32.1±1.51.3±1.01.6±.98.5
12A77Viral disease NOS1.3±42.8±2.1--1.8±.1.3-1.3±1.0.9±.72.8
13R91Chronic bronchitis/bronchiectasis1.3±.4----2.1±1.52.6±1.51.3±.8.1
14R75Sinusitis acute/chronic1.0±.4---2.3±1.4---3.1
15K78Atrial fibrillation/flutter.8±.4-----2.4±1.41.3±.8.2
16A85Adv effect medical agent in proper dose.8±.3----1.6±1.31.5±1.11.0±.72.6
17R99Other disease respiratory system.8±.3---1.6±1.2---.3
18P01Feeling anxious/nervous/tense.5±.3-------1.4
19P02Acute stress reaction.4±.3-------.9
20A96Death.4±.2------.9±.7.5
  Absolute number of observations2505247130149438376460705 
URI denotes upper respiratory infection; COPD, chronic obstructive pulmonary disease; NOS, not otherwise specified.

TABLE 3
FINAL DIAGNOSES FOR EPISODES OF CARE STARTING WITH THE REASON FOR ENCOUNTER GENERAL WEAKNESS/TIREDNESS (A04); N=5915

 Percentage of Patients Presenting with Cough Who Had This Final Diagnosis 
  Age (Years) 
RankICPC CodeDiagnosisTotal0-45-1415-2425-4445-6465-7475+Standardized Incidence (Cases/100 Patients/Year)
1A04General weakness/tiredness37.5±1.216.9±3.732.3±4.643.3±3.640.5±2.338.0±3.038.4±3.637.5±3.02.7
2A77Viral disease NOS8.2±.714.9±3.512.0±3.28.0±2.07.9±1.37.0±1.68.0±2.06.0±1.52.8
3R74URI (head cold)4.3±.511.7±3.18.8±2.83.6±1.33.4±.93.3±1.14.2±1.52.5±1.09.7
4B80Iron deficiency anemia3.5±54.0±1.97.2±2.54.4±1.54.2±1.01.6±.82.4±1.12.7±1.0.7
5A97No disease/prevention2.8±.43.7±1.93.5±1.85.2±1.62.6±.82.9±1.01.0±.71.9±.88.5
6R78Acute bronchitis/bronchiolitis2.7±.44.7±2.14.0±1.91.1±.81.4±.63.0±1.12.4±1.14.3±1.34.7
7A85Adv effect medical agent in proper dose2.1±.4--1.1±.8.8±.43.4±1.14.2±1.53.8±1.22.6
8P76Depressive disorder1.9±.3--.8±.71.3±.53.2±1.13.4±1.42.7±1.0.7
9P99Other mental disorder1.8±.3--3.4±1.33.6±.91.5±.8--.7
10R75Sinusitis acute/chronic1.8±.3-3.2±1.71.4±.82.5±.71.7±.81.6±.9.8±.53.1
11R80Influenza (proven)1.7±.3--1.7±.91.6±.62.6±1.02.3±1.11.3±.7.8
12P02Acute stress reaction1.6±.3--1.9±1.02.8±.81.6±.81.1±.8.6±.5.9
13P01Feeling anxious/nervous/tense1.5±.3--1.2±.81.8±.62.2±.91.7±1.0.8±.51.4
14Z05Problem working conditions1.4±.3--2.5±1.12.7±.81.5±.8--1.2
15R76Tonsillitis acute1.0±.37.0±2.52.0±1.4.8±.7.9±.4---1.6
16P03Feeling depressed1.0±.2---1.1±.51.4±.71.3±.8.8±.5.5
17A75Infectious mononucleosis.8±.2-1.8±1.33.2±1.31.0±.5---.2
18R81Pneumonia.8±.2-3.2±1.7--.7±.51.3±.81.0±.6.5
19H71Acute otitis media/myringitis.8±.28.7±2.82.2±1.5-----2.3
20R98Hyperventilation.8±.2---1.0±.51.0±.61.1±.8.6±.5.9
  Absolute number of observations591540240072616809966971014 
NOS, not otherwise specified; URI, upper respiratory infection.

TABLE 4
FINAL DIAGNOSES FOR EPISODES OF CARE STARTING WITH THE REASON FOR ENCOUNTER LOW BACK SYMPTOMS/COMPLAINTS WITHOUT RADIATION (L03); N=4238

 Percentage of Patients Presenting with Cough Who Had This Final Diagnosis 
  Age (Years) 
RankICPC CodeDiagnosisTotal0-45-1415-2425-4445-6465-7475+Standardized Incidence (Cases/100 Patients/Year)
1L03Low back symptoms/complaints without radiation69.9±1.4-56.9±12.772.2±4.371.2±2.273.9±2.566.7±4.058.1±4.73.7
2L18Muscle pain/fibrositis6.2±.7-10.3±7.87.9±2.67.5±1.35.2±1.33.7±1.64.9±2.02.6
3L86Lumbar disc lesion/radiation6.0±.7--2.2±1.46.7±1.26.8±1.45.8±2.05.6±2.2.7
4L85Acquired deformities of spine3.2±.5-10.3±7.85.0±2.13.9±.91.8±.81.9±1.13.0±1.6.4
5L84Osteoarthritis of spine1.6±.4---.4±.3.9±.64.1±1.76.5±2.3.2
6L81Other musculoskeletal injury1.6±.4--2.9±1.61.0±.5.9±.51.3±1.04.4±1.92.4
7L99Other musculoskeletal disease1.3±.3---.9±.5.9±.62.6±1.32.3±1.41.4
8L89Osteoarthritis1.0±.3----.8±.52.4±1.34.0±1.81.4
9N99Other neurological disease.8±.3---.8±.41.3±.6--1.1
10U95Urinary calculus.4±.2---.4±.3.6±.4--.3
11L02Back symptoms/complaints.4±.2---.5±.3---1
12L19Other multiple/unspecif muscle symptoms/complaints.4±.2---.4±.3.7±.5--.4
13L79Sprains & strains NOS.4±.2-------1.3
14U70Pyelonephritis/pyelitis acute.4±.2-------.2
15L88Rheumatoid arthritis/allied conditions.3±.2---.4±.3---.2
16L95Osteoporosis.3±.2-----1.1±.9-.1
17U71Cystitis.3±.2-------2.2
18A77Viral diseases NOS.2±.1---.4±.3---2.8
19D06Other localized abdominal pain.2±.1-------1.3
20L76Other fracture.2±.1-------.3
  Absolute number of observations423855841816261163538430 
NOS denotes not otherwise specified.

Discussion

Family practice can be characterized by the specific distribution of health problems and disease in its population, as distinct from the distributions in the general population and in specialists’ populations. Increasingly, empiric data indicate how morbidity patterns and the distribution of reasons for visit in family practice differ from those in hospitals. The availability of age-specific prior probabilities of common symptoms and complaints for diagnoses in family practice has great potential. Diagnostic labels often have the disadvantage of a relative uncertainty, caused by a more or less arbitrary attribution of different symptoms and signs (eg, syndrome diagnoses, psychiatric diagnoses). Symptoms and complaints on the other hand have the advantage of relative certainty, because they represent the patient’s ill health irrespective of the diagnostic label they are given. In the daily work of FPs the importance of the absence or presence of a symptom must be considered in light of the distribution of disease in the family practice setting.

Therefore, such data would not only seem to be crucial for the further development of family practice as an academic discipline or for the design of intervention studies but also has direct practical consequences for clinicians. This information can directly support FPs’ medical decision making and improve communication with patients. The process of finding common ground about diagnosis and management could especially profit from a realistic estimate of probabilities, bridging the gap between the patient’s perspective, as reflected in the presenting symptoms, and the clinical perspective of the FP who wants to provide optimal care.21

Several important types of distributions are shown in (Table1) through 4: a very common symptom in relation to highly incident diagnoses, a less common symptom leading to less incident diagnoses, and symptoms primarily resulting in a symptom diagnosis with the same label. Also, it is evident that the range of clinical considerations resulting from a presenting symptom can vary from a relatively limited to a very wide morbidity spectrum.

Sex-specific symptoms and complaints (eg, menstrual problems) typically result in rather specific distributions, as is illustrated in the database available on the JFP Web site. The distribution of diagnoses for symptoms that occur in both sexes may be different not only for age groups but also for sex /age groups.

 

 

The value of prior probabilities increases with the availability of data on incidence of diseases in the same population, allowing an estimation of the positive and negative predictive values. Since 1995, data collection has occurred electronically. Later in 2001, a database twice the size will become available that allows more precise estimations for finer age/sex distributions and symptom combinations. Although from Dutch family practice, these data have a high face validity for clinicians wherever they work.8 Nevertheless, it would seem that FPs in the United States and other countries should give priority to collecting their own reliable probability databases.4,22,23

In the Netherlands, Japan, and Poland, an international comparative study has taken place with an electronic patient record, using ICPC. (See page xxx in this issue for the abstract of this article.) Based on a comparison of these databases with US NAMCS data (1995-1997), tables similar to those presented in this paper for the most frequent symptoms and complaints have been made available on the JFP Web site.24.25

References

1. Sox HC, Jr. Decision making: a comparison of referral practice and primary care. J Fam Pract 1996;42:155-60.

2. Lamberts H, Hofmans-Okkes IM. Episode of care: a core concept in family practice. J Fam Pract 1996;42:161-67.

3. Kerr LW. Fundamental research at primary care level. Lancet 2000;355:1904-06.

4. Stange KC, Jaén CR, Flocke SA, Miller WL, Crabtree BF, Zyzanski SJ. The value of a family physician. J Fam Pract 1998;46:363-68.

5. Ebell MH, Smith MA, Barry HC, Ives K, Carey M. Does this patient have strep throat? JAMA 2000;284:2912-18.

6. Nutting PA, Green LA. Practice-based research networks: reuniting practice and research around the problems most of the people have most of the time. J Fam Pract 1994;38:335-36.

7. Lamberts H. Episode-oriented epidemiology in family practice: the practical use of the International Classification of Primary Care (ICPC) as illustrated in patients with headache. In: Norton PG, Stewart M, Tudiver F, Bass MJ, Dunn EV, eds. Primary care research: traditional and innovative approaches. Newbury Park, Calif: Sage; 1991;20-72.

8. Donaldson MS, Yordy KD, Lohr KN, Vanselow NA, eds. Primary care: America’s health in a new era. Washington, DC: National Academy Press; 1996.

9. Okkes IM, Oskam SK, Lamberts H. Van Klacht naar Diagnose. Episodegegevens uit de huisartspraktijk. [From complaint to diagnosis: episode data from family practice]. With a CD-ROM. Bussum, the Netherlands: Coutinho; 1998.

10. Hornbrook MC, Hurtado RV, Johnson RE. Health care episodes: definition, measurement and use. Med Care Rev 1985;42:163-218.

11. Solon JA, Feeney JJ, Jones SH, Rigg RD, Sheps CG. Delineating episodes of medical care. Am J Public Health 1967;57:401-08.

12. Okkes IM, Groen A, Oskam SK, Lamberts H. Advantages of a long observation period in episode oriented electronic patient records in family practice. Methods Inf Med 2001;40:229-35

13. Lamberts H, Wood M, eds. ICPC. International classification of primary care. Oxford, England: Oxford University Press; 1987.

14. Lamberts H, Hofmans-Okkes IM. The core of computer based patient records in family practice: episodes of care classified with ICPC. Int J Biomed Comp 1996;42:35-41.

15. Klinkman MS, Green LA. Using ICPC in a computer-based primary care information system. Fam Med 1995;27:449-56.

16. Van Boven C, Dijksterhuis PH, Lamberts H. Defensive testing in Dutch family practice. J Fam Pract 1997;44:468-72.

17. Bentzen N ed. An international glossary for general/family practice. Fam Pract 1995;12:341-69.

18. National Center for Health Statistics. A reason for visit classification for ambulatory care. Hyattsville, Md: National Center for Health Statistics, US Public Health Service; 1979

19. Hofmans-Okkes IM. An international study into concept and validity of the “reason for encounter”. In: Lamberts H, Wood M, Hofmans-Okkes IM, eds. The international classification of primary care in the European community. Oxford, England: Oxford University Press; 1993.

20. Gardner MJ, Altman DG, eds Statistics with confidence. London, England: BMJ; 1989 (floppy disk).

21. Stewart M, Belle Brown J, Weston WW, McWhinney IR, McWilliam CL, Freeman TR. Patient-centered medicine: transforming the clinical method. Thousand Oaks, Calif: Sage; 1995.

22. Green LA, Nutting PA. Family physicians as researchers in their own practices. J Am Board Fam Pract 1994;7:261-63.

23. Franks P, Clancy CM, Nutting PA. Defining primary care: empirical analysis of the National Ambulatory Medical Care Survey. Med Care 1997;35:655-68.

24. National Center for Health Statistics. 1995, 1996, 1997 Ambulatory Medical Care Survey: CD-ROM-series 13. Hyattsville, Md: National Center for Heath Statistics; 1997, 1998, 1999.

25. Okkes IM, Polderman GO, Fryer E, Yamada T, Bujak M, Oskam SK, Green LA, Lamberts H. The role of family practice in different health care systems: a comparison of morbidity data from primary care populations in the Netherlands, Japan, Poland and the US. J Fam Pract 2002;51:72-73.

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S. K. OSKAM, MSC, PHD
H. LAMBERTS, MD, PHD
Amsterdam, the Netherlands
Submitted, revised, July 24, 2001.
From the Academic Medical Centre/University of Amsterdam, Division of Public Health, Department of Family Practice. Reprint requests should be addressed to I.M. Okkes, MA, PhD, Academic Medical Centre/University of Amsterdam, Division Public Health, Department of Family Practice, Meibergdreef 15, 1105 AZ Amsterdam, the Netherlands. E-mail: [email protected].

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Submitted, revised, July 24, 2001.
From the Academic Medical Centre/University of Amsterdam, Division of Public Health, Department of Family Practice. Reprint requests should be addressed to I.M. Okkes, MA, PhD, Academic Medical Centre/University of Amsterdam, Division Public Health, Department of Family Practice, Meibergdreef 15, 1105 AZ Amsterdam, the Netherlands. E-mail: [email protected].

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S. K. OSKAM, MSC, PHD
H. LAMBERTS, MD, PHD
Amsterdam, the Netherlands
Submitted, revised, July 24, 2001.
From the Academic Medical Centre/University of Amsterdam, Division of Public Health, Department of Family Practice. Reprint requests should be addressed to I.M. Okkes, MA, PhD, Academic Medical Centre/University of Amsterdam, Division Public Health, Department of Family Practice, Meibergdreef 15, 1105 AZ Amsterdam, the Netherlands. E-mail: [email protected].

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ABSTRACT

OBJECTIVE: Our goal was to develop reliable data on the probability of specific diagnoses among patients of family physicians (FPs) presenting with common symptoms.

STUDY DESIGN: A group of 54 Dutch FPs recorded the reasons for encounter, diagnoses, and interventions for all episodes of care between 1985 and 1995. Diagnoses could be modified during the episode of care, and a modified diagnosis was applied to all episode data.

POPULATION: We used the listed patient populations of the 54 Dutch FPs, representing 93,297 patient years, 236,027 episodes of care, and 267,897 patient encounters.

OUTCOMES MEASURED: The top 20 diagnoses related to 4 selected presenting symptoms (cough, shortness of breath, general weakness/tiredness, and low back symptom /complaints without radiation), per 100 patients, with 95% confidence intervals, stratified by age. In the standard tables, age-specific cells with fewer than 10 observations were excluded.

RESULTS: The availability of an accurate estimate of prior (pretest) probabilities for common symptoms/complaints has great potential value for family practice as an academic discipline and for family physicians in that it can support their medical decision making. Stratifying data by age groups increases the clinical relevance of the prior probabilities.

CONCLUSIONS: Though collected by Dutch FPs, the data in our study have a high face validity for other clinicians. Still, FPs in other countries should give priority to collecting their own probability databases.

KEY POINTS FOR CLINICIANS

  • The pretest probability is the likelihood of disease before tests are ordered in a patient with a specific symptom or complaint.
  • It is very helpful in the diagnosis and management of problems common in family practice.
  • We have identified the pretest probability for the most common final diagnoses in patients with many common presenting symptom /complaints.

Estimating the probability of disease in unselected patients lies at the heart of the clinical competence of family physicians (FPs).1,2 This probability is called the prior or pretest probability, because it precedes any diagnostic testing, including the history and physical examination. Based on this knowledge, FPs often decide that since the probability of a serious disease is low, the best thing to do is watchful waiting, thus preventing unnecessary harm and cost to the patient.3,4 Moderate probabilities may trigger a diagnostic evaluation, and high probabilities may warrant empiric therapy without further diagnostic confirmation. For example, knowing that the probability of gastric carcinoma is exceedingly rare for a dyspeptic patient younger than 40 years supports a diagnostic approach that does not include initial endoscopy. A moderate probability of strep throat is a situation where a rapid strep test may be helpful.5 Understanding the high prior probability of a urinary tract infection in young healthy women with dysuria might allow an FP to confidently institute a telephone-based protocol, including empiric treatment for selected patients.

The development of a prior probabilities database requires access to large diverse practice populations with adequate continuity of care and good documentation of all episodes of care.2,6,7 An “episode of care” is defined as a health problem from the first encounter with a health care provider through the completion of the last encounter related to that particular problem.2,8.10,11 Early in an episode of care, FPs will often assign a symptom diagnosis such as chest pain, gastric pain, or otalgia; a substantial number of these diagnoses will change over time. Therefore, the observation period should allow for the most important modifications of diagnoses to become visible. Because 1-year incidence and prevalence rates are usually calculated, at least a 1-year study period is required; however, a longer period is preferable, especially for information on chronic diseases.12

Episodes of care are based on the relationship between the patient’s reasons for encounter (RFE), the physician’s diagnostic interpretation, and the related interventions over time.13 Episodes of care are clearly distinguished from episodes of disease and episodes of illness. An episode of disease begins with its onset and continues until its resolution or the patient’s death, while an episode of illness refers to the period that someone suffers from symptoms or complaints experienced as illness. Not every disease, and certainly not every illness, results in an episode of care.2,13,14 Most episodes of care, however, are part of an episode of disease and/or illness. Health maintenance episodes can be considered a special form of episodes of care.10,11 For example, screening for breast cancer (an episode of health maintenance) may prove the existence of the episode of disease well before the patient has symptoms (an episode of illness); an episode of care will follow.

The development of a prior probabilities database also requires a primary care–specific system of classification. The International Classification of Primary Care (ICPC) includes approximately 200 symptoms/complaints and 300 diagnoses common in family practice. Almost all have an incidence of at least 1 per 1000 patients per year.13,15

 

 

Since 1985, the members of the Transition Project of the University of Amsterdam Department of Family Practice have been contributing to episodeoriented epidemiology in family practice. We have developed a large prospective database that provides reliable probabilities of diagnoses for symptoms or complaints in a patient of a specific age and sex, suitable for the support of family physicians’ decision making.2,7,9,16 This article describes the database in more detail and presents data for 4 common symptoms.*

Methods

From 1985 to 1995, 54 Dutch FPs in 23 locations routinely coded episode data for all direct (face-to-face) encounters with their listed patients; within the Dutch health care system, all citizens are listed (registered) with an FP. Each participating FP collected data during a period of at least 1 year; the registration period for patients ranged from 1 to 10 years (mean 2.4).2,9,16 For each encounter, the patient’s reasons for the encounter, the diagnoses, and the interventions ordered by the physician were coded according to the ICPC. Data were entered on a special encounter form with a copy for a central data entry and included 93,297 patient years, with 236,027 episodes of care and 267,897 direct patient encounters.

Since the FPs had a well-defined practice with listed patients, a precise denominator could be established for the calculation of rates. In the Netherlands, patients can, in principle, not seek specialist care without a referral by their FP. Therefore, especially in a longer observation period, an FP will document a close approximation of the distribution of episodes of care in the Dutch population. In 1 year, 73% of listed patients have a direct encounter with their family physician; in a 2-year observation period this is 92%. It is therefore unlikely that a substantial group of listed patients receive specialist care without their FP’s being aware of it.12

A diagnosis could be modified during the course of an episode. If that occurred, the modified diagnosis was applied to all episode data in the analysis. New and ongoing episodes of care were included in a registration year if dealt with at least once; in case of a follow-up encounter in a later registration year, the episode was included again as an ongoing episode. As a consequence, an episode of a chronic disease (eg, diabetes, hypertension) coded in 2 or more registration years was included more than once in the annual prevalence.17 The average yearly practice population served as the denominator.

As is the case in all time-consuming morbidity studies, the participating FPs were selected, highly motivated, and in this respect, not representative of the average Dutch FP. The database was used in numerous studies, however, and its reliability consistently proved to be high: Approximately 2% of all episodes appeared to be missing in the paper record, and another 2% were erroneously included in the database. The complete reference database is available in Dutch on a CD-ROM attached to a family practice textbook. It includes all combinations of an RFE, a diagnosis, and an intervention for 7 standard age groups at the start of episodes and during follow-up, together with data on comorbidity.9

Our paper focuses on prior probabilities, expressed as the top 20 final diagnoses for 4 common reasons for encounter presented at the start of an episode of care18,19: cough, shortness of breath, general weakness/tiredness, and low back pain without radiation. All probabilities are presented for the total population and for 7 standard age groups, as percentages with 95% symmetric confidence intervals.20 Cells with fewer than 10 observations were excluded. Incidences standardized for the 1995 Dutch population were provided.

Results

(Table 1) shows that for the RFE “cough,” the patient’s age had a substantial impact on the probabilities. The diagnosis of acute bronchitis was common overall but especially in the very young and the very old. This table illustrates the relationships between a common symptom and several diseases with a relatively high incidence (the last column). The prior probabilities were well distributed over the standard table: empty cells occur infrequently.

“Shortness of breath/dyspnoea” as an RFE is associated with a very different distribution of diagnoses than found with cough, especially in the very young and the very old (Table 2). Asthma and acute laryngitis typically occur in the young, while chronic obstructive pulmonary disease, ischemic heart disease, and heart failure occur in the old. Hyperventilation had a peak in young adults. In this table, the relation between a less common symptom and several less common diseases is illustrated. In this case, more empty cells are found. Both cough and shortness of breath mainly relate to respiratory and cardiovascular diseases.

 

 

The RFE “tiredness” (Table 3) was associated with several common diseases, but a much wider range of clinical possibilities is apparent. Quite often, this RFE resulted in the symptom diagnosis “tiredness.” Major age differences existed for several diagnoses.

(Table 4) shows that the RFE “low back symptoms/complaints without radiation” quite often led to the same symptom diagnosis. Also, the rather skewed age distribution of low back complaints shows: most cells were insufficiently filled, while age differences for the most common diagnoses appeared to be relatively small.

TABLE 1
FINAL DIAGNOSES FOR EPISODES OF CARE STARTING WITH THE REASON FOR ENCOUNTER COUGH (R05) N=11092

 Percentage of Patients Presenting with Cough Who Had This Final Diagnosis 
  Age (Years) 
RankICPC CodeDiagnosisTotal0-45-1415-2425-4445-6465-7475+Standardized Incidence(Cases/100 Patients/Year)
1R74URI (head cold)32.9±.940.1±2.135.6±2.636.7±3.334.4±1.929.9±2.127.4±2.223.9±2.49.7
2R78Acute bronchitis/bronchiolitis25.4±.823.7±1.820.4±2.217.8±2.618.5±1.626.1±2.034.3±2.439.5±2.84.7
3R05Cough13.7±.610.5±1.312.4±1.815.9±2.515.8±1.515.4±1.612.4±1.714.0±2.01.9
4R77Acute laryngitis/tracheitis9.0±.56.6±1.18.6±1.510.0±2.012.4±1.311.1±1.47.3±1.35.2±1.31.8
5R75Sinusitis acute/chronic3.5±.31.2±.52.3±.84.2±1.45.7±.94.8±1.04.1±1.01.2±.63.1
6A77Viral diseases NOS2.3±.33.3±.84.2±1.11.9±.92.1±.61.7±.61.4±.61.2±.62.8
7R80Influenza (proven)2.0±.3.9±.41.9±.73.1±1.22.6±.72.4±.71.6±.62.3±.9.8
8R96Asthma1.9±.32.9±.73.1±1.01.9±.91.4±.51.1±.51.6±.61.2±.6.7
9R81Pneumonia1.9±.31.9±.62.6±.91.7±.91.5±.51.0±.51.4±.63.7±1.1.5
10R83Other infection respiratory system.6±.1.4±.3-1.4±.8.6±.3.7±.4.9±.5.6±.4.2
11R76Tonsillitis acute.6±.11.8±.61.3±.6-.3±.2---1.6
12R91Chronic bronchitis/bronchiectasis.6±.1---.3±.2.8±.41.1±.51.6±.7.1
13R95Emphysema/COPD.5±.1-.6±.4-.3±.2.9±.4.9±.5.8±.5.2
14R71Whooping cough.4±.1.6±.31.7±.7-----.1
15R90Hypertrophy/chronic infection T&A.4±.11.1±.41.6±.7-----.3
16A97No disease/prevention.4±.1.4±.3--.5±.3.4±.3.6±.4-8.5
17H71Acute otitis media/myringitis.4±.11.7±.5.5±.4-----2.3
18K77Heart failure.3±.1-----1.4±.6.9±.5.5
19R99Other disease respiratory system.3±.1.3±.2--.3±.2.4±.3--.3
20R27Fear of other respiratory disease.3±.1.4±.3--.4±.3---.2
  Absolute number of observations11,092209012818322320184215291199 
URI denotes upper respiratory infection; COPD, chronic obstructive pulmonary disease.

TABLE 2
FINAL DIAGNOSES FOR EPISODES OF CARE STARTING WITH THE REASON FOR ENCOUNTER SHORTNESS OF BREATH (R02); N=2505

 Percentage of Patients Presenting with Cough Who Had This Final Diagnosis 
  Age (Years) 
RankICPC CodeDiagnosisTotal0-45-1415-2425-4445-6465-7475+Standardized Incidence (Cases/100 Patients/Year)
1R748Acute bronchitis/bronchiolitis27.3±1.728.7±5.630.0±7.918.1±6.223.7±4.028.7±4.633.5±4.325.8±3.24.7
2R96Asthma9.7±1.217.4±4.722.3±7.223.5±6.813.9±3.27.2±2.65.4±2.13.4±1.3.7
3K77Heart failure8.9±1.1----4.5±2.112.6±3.021.0±3.0.5
4R02Shortness of breath/dyspnoea8.6±1.1-7.7±4.66.0±3.810.5±2.910.1±3.08.0±2.59.9±2.2.3
5R98Hyperventilation8.0±1.1-7.7±4.618.8±6.313.0±3.215.7±3.75.9±2.12.8±1.2.9
6R74URI (head cold)6.6±1.019.0±4.98.5±4.88.1±4.49.8±2.83.7±1.93.0±1.63.5±1.49.7
7R77Acute laryngitis/tracheitis3.2±.715.0±4.58.5±4.8-2.5±1.51.9±1.4-1.3±.81.8
8R81Pneumonia3.2±.73.2±2.2--2.1±1.32.1±1.52.6±1.55.4±1.7.5
9R95Emphysema/COPD2.8±.6----3.5±1.85.2±.2.03.5±1.4.2
10K76Ischemic heart disease2.1±.6----2.4±1.52.6±1.54.1±1.5.8
11A97No disease/prevention1.9±.53.2±2.2--1.8±1.32.1±1.51.3±1.01.6±.98.5
12A77Viral disease NOS1.3±42.8±2.1--1.8±.1.3-1.3±1.0.9±.72.8
13R91Chronic bronchitis/bronchiectasis1.3±.4----2.1±1.52.6±1.51.3±.8.1
14R75Sinusitis acute/chronic1.0±.4---2.3±1.4---3.1
15K78Atrial fibrillation/flutter.8±.4-----2.4±1.41.3±.8.2
16A85Adv effect medical agent in proper dose.8±.3----1.6±1.31.5±1.11.0±.72.6
17R99Other disease respiratory system.8±.3---1.6±1.2---.3
18P01Feeling anxious/nervous/tense.5±.3-------1.4
19P02Acute stress reaction.4±.3-------.9
20A96Death.4±.2------.9±.7.5
  Absolute number of observations2505247130149438376460705 
URI denotes upper respiratory infection; COPD, chronic obstructive pulmonary disease; NOS, not otherwise specified.

TABLE 3
FINAL DIAGNOSES FOR EPISODES OF CARE STARTING WITH THE REASON FOR ENCOUNTER GENERAL WEAKNESS/TIREDNESS (A04); N=5915

 Percentage of Patients Presenting with Cough Who Had This Final Diagnosis 
  Age (Years) 
RankICPC CodeDiagnosisTotal0-45-1415-2425-4445-6465-7475+Standardized Incidence (Cases/100 Patients/Year)
1A04General weakness/tiredness37.5±1.216.9±3.732.3±4.643.3±3.640.5±2.338.0±3.038.4±3.637.5±3.02.7
2A77Viral disease NOS8.2±.714.9±3.512.0±3.28.0±2.07.9±1.37.0±1.68.0±2.06.0±1.52.8
3R74URI (head cold)4.3±.511.7±3.18.8±2.83.6±1.33.4±.93.3±1.14.2±1.52.5±1.09.7
4B80Iron deficiency anemia3.5±54.0±1.97.2±2.54.4±1.54.2±1.01.6±.82.4±1.12.7±1.0.7
5A97No disease/prevention2.8±.43.7±1.93.5±1.85.2±1.62.6±.82.9±1.01.0±.71.9±.88.5
6R78Acute bronchitis/bronchiolitis2.7±.44.7±2.14.0±1.91.1±.81.4±.63.0±1.12.4±1.14.3±1.34.7
7A85Adv effect medical agent in proper dose2.1±.4--1.1±.8.8±.43.4±1.14.2±1.53.8±1.22.6
8P76Depressive disorder1.9±.3--.8±.71.3±.53.2±1.13.4±1.42.7±1.0.7
9P99Other mental disorder1.8±.3--3.4±1.33.6±.91.5±.8--.7
10R75Sinusitis acute/chronic1.8±.3-3.2±1.71.4±.82.5±.71.7±.81.6±.9.8±.53.1
11R80Influenza (proven)1.7±.3--1.7±.91.6±.62.6±1.02.3±1.11.3±.7.8
12P02Acute stress reaction1.6±.3--1.9±1.02.8±.81.6±.81.1±.8.6±.5.9
13P01Feeling anxious/nervous/tense1.5±.3--1.2±.81.8±.62.2±.91.7±1.0.8±.51.4
14Z05Problem working conditions1.4±.3--2.5±1.12.7±.81.5±.8--1.2
15R76Tonsillitis acute1.0±.37.0±2.52.0±1.4.8±.7.9±.4---1.6
16P03Feeling depressed1.0±.2---1.1±.51.4±.71.3±.8.8±.5.5
17A75Infectious mononucleosis.8±.2-1.8±1.33.2±1.31.0±.5---.2
18R81Pneumonia.8±.2-3.2±1.7--.7±.51.3±.81.0±.6.5
19H71Acute otitis media/myringitis.8±.28.7±2.82.2±1.5-----2.3
20R98Hyperventilation.8±.2---1.0±.51.0±.61.1±.8.6±.5.9
  Absolute number of observations591540240072616809966971014 
NOS, not otherwise specified; URI, upper respiratory infection.

TABLE 4
FINAL DIAGNOSES FOR EPISODES OF CARE STARTING WITH THE REASON FOR ENCOUNTER LOW BACK SYMPTOMS/COMPLAINTS WITHOUT RADIATION (L03); N=4238

 Percentage of Patients Presenting with Cough Who Had This Final Diagnosis 
  Age (Years) 
RankICPC CodeDiagnosisTotal0-45-1415-2425-4445-6465-7475+Standardized Incidence (Cases/100 Patients/Year)
1L03Low back symptoms/complaints without radiation69.9±1.4-56.9±12.772.2±4.371.2±2.273.9±2.566.7±4.058.1±4.73.7
2L18Muscle pain/fibrositis6.2±.7-10.3±7.87.9±2.67.5±1.35.2±1.33.7±1.64.9±2.02.6
3L86Lumbar disc lesion/radiation6.0±.7--2.2±1.46.7±1.26.8±1.45.8±2.05.6±2.2.7
4L85Acquired deformities of spine3.2±.5-10.3±7.85.0±2.13.9±.91.8±.81.9±1.13.0±1.6.4
5L84Osteoarthritis of spine1.6±.4---.4±.3.9±.64.1±1.76.5±2.3.2
6L81Other musculoskeletal injury1.6±.4--2.9±1.61.0±.5.9±.51.3±1.04.4±1.92.4
7L99Other musculoskeletal disease1.3±.3---.9±.5.9±.62.6±1.32.3±1.41.4
8L89Osteoarthritis1.0±.3----.8±.52.4±1.34.0±1.81.4
9N99Other neurological disease.8±.3---.8±.41.3±.6--1.1
10U95Urinary calculus.4±.2---.4±.3.6±.4--.3
11L02Back symptoms/complaints.4±.2---.5±.3---1
12L19Other multiple/unspecif muscle symptoms/complaints.4±.2---.4±.3.7±.5--.4
13L79Sprains & strains NOS.4±.2-------1.3
14U70Pyelonephritis/pyelitis acute.4±.2-------.2
15L88Rheumatoid arthritis/allied conditions.3±.2---.4±.3---.2
16L95Osteoporosis.3±.2-----1.1±.9-.1
17U71Cystitis.3±.2-------2.2
18A77Viral diseases NOS.2±.1---.4±.3---2.8
19D06Other localized abdominal pain.2±.1-------1.3
20L76Other fracture.2±.1-------.3
  Absolute number of observations423855841816261163538430 
NOS denotes not otherwise specified.

Discussion

Family practice can be characterized by the specific distribution of health problems and disease in its population, as distinct from the distributions in the general population and in specialists’ populations. Increasingly, empiric data indicate how morbidity patterns and the distribution of reasons for visit in family practice differ from those in hospitals. The availability of age-specific prior probabilities of common symptoms and complaints for diagnoses in family practice has great potential. Diagnostic labels often have the disadvantage of a relative uncertainty, caused by a more or less arbitrary attribution of different symptoms and signs (eg, syndrome diagnoses, psychiatric diagnoses). Symptoms and complaints on the other hand have the advantage of relative certainty, because they represent the patient’s ill health irrespective of the diagnostic label they are given. In the daily work of FPs the importance of the absence or presence of a symptom must be considered in light of the distribution of disease in the family practice setting.

Therefore, such data would not only seem to be crucial for the further development of family practice as an academic discipline or for the design of intervention studies but also has direct practical consequences for clinicians. This information can directly support FPs’ medical decision making and improve communication with patients. The process of finding common ground about diagnosis and management could especially profit from a realistic estimate of probabilities, bridging the gap between the patient’s perspective, as reflected in the presenting symptoms, and the clinical perspective of the FP who wants to provide optimal care.21

Several important types of distributions are shown in (Table1) through 4: a very common symptom in relation to highly incident diagnoses, a less common symptom leading to less incident diagnoses, and symptoms primarily resulting in a symptom diagnosis with the same label. Also, it is evident that the range of clinical considerations resulting from a presenting symptom can vary from a relatively limited to a very wide morbidity spectrum.

Sex-specific symptoms and complaints (eg, menstrual problems) typically result in rather specific distributions, as is illustrated in the database available on the JFP Web site. The distribution of diagnoses for symptoms that occur in both sexes may be different not only for age groups but also for sex /age groups.

 

 

The value of prior probabilities increases with the availability of data on incidence of diseases in the same population, allowing an estimation of the positive and negative predictive values. Since 1995, data collection has occurred electronically. Later in 2001, a database twice the size will become available that allows more precise estimations for finer age/sex distributions and symptom combinations. Although from Dutch family practice, these data have a high face validity for clinicians wherever they work.8 Nevertheless, it would seem that FPs in the United States and other countries should give priority to collecting their own reliable probability databases.4,22,23

In the Netherlands, Japan, and Poland, an international comparative study has taken place with an electronic patient record, using ICPC. (See page xxx in this issue for the abstract of this article.) Based on a comparison of these databases with US NAMCS data (1995-1997), tables similar to those presented in this paper for the most frequent symptoms and complaints have been made available on the JFP Web site.24.25

ABSTRACT

OBJECTIVE: Our goal was to develop reliable data on the probability of specific diagnoses among patients of family physicians (FPs) presenting with common symptoms.

STUDY DESIGN: A group of 54 Dutch FPs recorded the reasons for encounter, diagnoses, and interventions for all episodes of care between 1985 and 1995. Diagnoses could be modified during the episode of care, and a modified diagnosis was applied to all episode data.

POPULATION: We used the listed patient populations of the 54 Dutch FPs, representing 93,297 patient years, 236,027 episodes of care, and 267,897 patient encounters.

OUTCOMES MEASURED: The top 20 diagnoses related to 4 selected presenting symptoms (cough, shortness of breath, general weakness/tiredness, and low back symptom /complaints without radiation), per 100 patients, with 95% confidence intervals, stratified by age. In the standard tables, age-specific cells with fewer than 10 observations were excluded.

RESULTS: The availability of an accurate estimate of prior (pretest) probabilities for common symptoms/complaints has great potential value for family practice as an academic discipline and for family physicians in that it can support their medical decision making. Stratifying data by age groups increases the clinical relevance of the prior probabilities.

CONCLUSIONS: Though collected by Dutch FPs, the data in our study have a high face validity for other clinicians. Still, FPs in other countries should give priority to collecting their own probability databases.

KEY POINTS FOR CLINICIANS

  • The pretest probability is the likelihood of disease before tests are ordered in a patient with a specific symptom or complaint.
  • It is very helpful in the diagnosis and management of problems common in family practice.
  • We have identified the pretest probability for the most common final diagnoses in patients with many common presenting symptom /complaints.

Estimating the probability of disease in unselected patients lies at the heart of the clinical competence of family physicians (FPs).1,2 This probability is called the prior or pretest probability, because it precedes any diagnostic testing, including the history and physical examination. Based on this knowledge, FPs often decide that since the probability of a serious disease is low, the best thing to do is watchful waiting, thus preventing unnecessary harm and cost to the patient.3,4 Moderate probabilities may trigger a diagnostic evaluation, and high probabilities may warrant empiric therapy without further diagnostic confirmation. For example, knowing that the probability of gastric carcinoma is exceedingly rare for a dyspeptic patient younger than 40 years supports a diagnostic approach that does not include initial endoscopy. A moderate probability of strep throat is a situation where a rapid strep test may be helpful.5 Understanding the high prior probability of a urinary tract infection in young healthy women with dysuria might allow an FP to confidently institute a telephone-based protocol, including empiric treatment for selected patients.

The development of a prior probabilities database requires access to large diverse practice populations with adequate continuity of care and good documentation of all episodes of care.2,6,7 An “episode of care” is defined as a health problem from the first encounter with a health care provider through the completion of the last encounter related to that particular problem.2,8.10,11 Early in an episode of care, FPs will often assign a symptom diagnosis such as chest pain, gastric pain, or otalgia; a substantial number of these diagnoses will change over time. Therefore, the observation period should allow for the most important modifications of diagnoses to become visible. Because 1-year incidence and prevalence rates are usually calculated, at least a 1-year study period is required; however, a longer period is preferable, especially for information on chronic diseases.12

Episodes of care are based on the relationship between the patient’s reasons for encounter (RFE), the physician’s diagnostic interpretation, and the related interventions over time.13 Episodes of care are clearly distinguished from episodes of disease and episodes of illness. An episode of disease begins with its onset and continues until its resolution or the patient’s death, while an episode of illness refers to the period that someone suffers from symptoms or complaints experienced as illness. Not every disease, and certainly not every illness, results in an episode of care.2,13,14 Most episodes of care, however, are part of an episode of disease and/or illness. Health maintenance episodes can be considered a special form of episodes of care.10,11 For example, screening for breast cancer (an episode of health maintenance) may prove the existence of the episode of disease well before the patient has symptoms (an episode of illness); an episode of care will follow.

The development of a prior probabilities database also requires a primary care–specific system of classification. The International Classification of Primary Care (ICPC) includes approximately 200 symptoms/complaints and 300 diagnoses common in family practice. Almost all have an incidence of at least 1 per 1000 patients per year.13,15

 

 

Since 1985, the members of the Transition Project of the University of Amsterdam Department of Family Practice have been contributing to episodeoriented epidemiology in family practice. We have developed a large prospective database that provides reliable probabilities of diagnoses for symptoms or complaints in a patient of a specific age and sex, suitable for the support of family physicians’ decision making.2,7,9,16 This article describes the database in more detail and presents data for 4 common symptoms.*

Methods

From 1985 to 1995, 54 Dutch FPs in 23 locations routinely coded episode data for all direct (face-to-face) encounters with their listed patients; within the Dutch health care system, all citizens are listed (registered) with an FP. Each participating FP collected data during a period of at least 1 year; the registration period for patients ranged from 1 to 10 years (mean 2.4).2,9,16 For each encounter, the patient’s reasons for the encounter, the diagnoses, and the interventions ordered by the physician were coded according to the ICPC. Data were entered on a special encounter form with a copy for a central data entry and included 93,297 patient years, with 236,027 episodes of care and 267,897 direct patient encounters.

Since the FPs had a well-defined practice with listed patients, a precise denominator could be established for the calculation of rates. In the Netherlands, patients can, in principle, not seek specialist care without a referral by their FP. Therefore, especially in a longer observation period, an FP will document a close approximation of the distribution of episodes of care in the Dutch population. In 1 year, 73% of listed patients have a direct encounter with their family physician; in a 2-year observation period this is 92%. It is therefore unlikely that a substantial group of listed patients receive specialist care without their FP’s being aware of it.12

A diagnosis could be modified during the course of an episode. If that occurred, the modified diagnosis was applied to all episode data in the analysis. New and ongoing episodes of care were included in a registration year if dealt with at least once; in case of a follow-up encounter in a later registration year, the episode was included again as an ongoing episode. As a consequence, an episode of a chronic disease (eg, diabetes, hypertension) coded in 2 or more registration years was included more than once in the annual prevalence.17 The average yearly practice population served as the denominator.

As is the case in all time-consuming morbidity studies, the participating FPs were selected, highly motivated, and in this respect, not representative of the average Dutch FP. The database was used in numerous studies, however, and its reliability consistently proved to be high: Approximately 2% of all episodes appeared to be missing in the paper record, and another 2% were erroneously included in the database. The complete reference database is available in Dutch on a CD-ROM attached to a family practice textbook. It includes all combinations of an RFE, a diagnosis, and an intervention for 7 standard age groups at the start of episodes and during follow-up, together with data on comorbidity.9

Our paper focuses on prior probabilities, expressed as the top 20 final diagnoses for 4 common reasons for encounter presented at the start of an episode of care18,19: cough, shortness of breath, general weakness/tiredness, and low back pain without radiation. All probabilities are presented for the total population and for 7 standard age groups, as percentages with 95% symmetric confidence intervals.20 Cells with fewer than 10 observations were excluded. Incidences standardized for the 1995 Dutch population were provided.

Results

(Table 1) shows that for the RFE “cough,” the patient’s age had a substantial impact on the probabilities. The diagnosis of acute bronchitis was common overall but especially in the very young and the very old. This table illustrates the relationships between a common symptom and several diseases with a relatively high incidence (the last column). The prior probabilities were well distributed over the standard table: empty cells occur infrequently.

“Shortness of breath/dyspnoea” as an RFE is associated with a very different distribution of diagnoses than found with cough, especially in the very young and the very old (Table 2). Asthma and acute laryngitis typically occur in the young, while chronic obstructive pulmonary disease, ischemic heart disease, and heart failure occur in the old. Hyperventilation had a peak in young adults. In this table, the relation between a less common symptom and several less common diseases is illustrated. In this case, more empty cells are found. Both cough and shortness of breath mainly relate to respiratory and cardiovascular diseases.

 

 

The RFE “tiredness” (Table 3) was associated with several common diseases, but a much wider range of clinical possibilities is apparent. Quite often, this RFE resulted in the symptom diagnosis “tiredness.” Major age differences existed for several diagnoses.

(Table 4) shows that the RFE “low back symptoms/complaints without radiation” quite often led to the same symptom diagnosis. Also, the rather skewed age distribution of low back complaints shows: most cells were insufficiently filled, while age differences for the most common diagnoses appeared to be relatively small.

TABLE 1
FINAL DIAGNOSES FOR EPISODES OF CARE STARTING WITH THE REASON FOR ENCOUNTER COUGH (R05) N=11092

 Percentage of Patients Presenting with Cough Who Had This Final Diagnosis 
  Age (Years) 
RankICPC CodeDiagnosisTotal0-45-1415-2425-4445-6465-7475+Standardized Incidence(Cases/100 Patients/Year)
1R74URI (head cold)32.9±.940.1±2.135.6±2.636.7±3.334.4±1.929.9±2.127.4±2.223.9±2.49.7
2R78Acute bronchitis/bronchiolitis25.4±.823.7±1.820.4±2.217.8±2.618.5±1.626.1±2.034.3±2.439.5±2.84.7
3R05Cough13.7±.610.5±1.312.4±1.815.9±2.515.8±1.515.4±1.612.4±1.714.0±2.01.9
4R77Acute laryngitis/tracheitis9.0±.56.6±1.18.6±1.510.0±2.012.4±1.311.1±1.47.3±1.35.2±1.31.8
5R75Sinusitis acute/chronic3.5±.31.2±.52.3±.84.2±1.45.7±.94.8±1.04.1±1.01.2±.63.1
6A77Viral diseases NOS2.3±.33.3±.84.2±1.11.9±.92.1±.61.7±.61.4±.61.2±.62.8
7R80Influenza (proven)2.0±.3.9±.41.9±.73.1±1.22.6±.72.4±.71.6±.62.3±.9.8
8R96Asthma1.9±.32.9±.73.1±1.01.9±.91.4±.51.1±.51.6±.61.2±.6.7
9R81Pneumonia1.9±.31.9±.62.6±.91.7±.91.5±.51.0±.51.4±.63.7±1.1.5
10R83Other infection respiratory system.6±.1.4±.3-1.4±.8.6±.3.7±.4.9±.5.6±.4.2
11R76Tonsillitis acute.6±.11.8±.61.3±.6-.3±.2---1.6
12R91Chronic bronchitis/bronchiectasis.6±.1---.3±.2.8±.41.1±.51.6±.7.1
13R95Emphysema/COPD.5±.1-.6±.4-.3±.2.9±.4.9±.5.8±.5.2
14R71Whooping cough.4±.1.6±.31.7±.7-----.1
15R90Hypertrophy/chronic infection T&A.4±.11.1±.41.6±.7-----.3
16A97No disease/prevention.4±.1.4±.3--.5±.3.4±.3.6±.4-8.5
17H71Acute otitis media/myringitis.4±.11.7±.5.5±.4-----2.3
18K77Heart failure.3±.1-----1.4±.6.9±.5.5
19R99Other disease respiratory system.3±.1.3±.2--.3±.2.4±.3--.3
20R27Fear of other respiratory disease.3±.1.4±.3--.4±.3---.2
  Absolute number of observations11,092209012818322320184215291199 
URI denotes upper respiratory infection; COPD, chronic obstructive pulmonary disease.

TABLE 2
FINAL DIAGNOSES FOR EPISODES OF CARE STARTING WITH THE REASON FOR ENCOUNTER SHORTNESS OF BREATH (R02); N=2505

 Percentage of Patients Presenting with Cough Who Had This Final Diagnosis 
  Age (Years) 
RankICPC CodeDiagnosisTotal0-45-1415-2425-4445-6465-7475+Standardized Incidence (Cases/100 Patients/Year)
1R748Acute bronchitis/bronchiolitis27.3±1.728.7±5.630.0±7.918.1±6.223.7±4.028.7±4.633.5±4.325.8±3.24.7
2R96Asthma9.7±1.217.4±4.722.3±7.223.5±6.813.9±3.27.2±2.65.4±2.13.4±1.3.7
3K77Heart failure8.9±1.1----4.5±2.112.6±3.021.0±3.0.5
4R02Shortness of breath/dyspnoea8.6±1.1-7.7±4.66.0±3.810.5±2.910.1±3.08.0±2.59.9±2.2.3
5R98Hyperventilation8.0±1.1-7.7±4.618.8±6.313.0±3.215.7±3.75.9±2.12.8±1.2.9
6R74URI (head cold)6.6±1.019.0±4.98.5±4.88.1±4.49.8±2.83.7±1.93.0±1.63.5±1.49.7
7R77Acute laryngitis/tracheitis3.2±.715.0±4.58.5±4.8-2.5±1.51.9±1.4-1.3±.81.8
8R81Pneumonia3.2±.73.2±2.2--2.1±1.32.1±1.52.6±1.55.4±1.7.5
9R95Emphysema/COPD2.8±.6----3.5±1.85.2±.2.03.5±1.4.2
10K76Ischemic heart disease2.1±.6----2.4±1.52.6±1.54.1±1.5.8
11A97No disease/prevention1.9±.53.2±2.2--1.8±1.32.1±1.51.3±1.01.6±.98.5
12A77Viral disease NOS1.3±42.8±2.1--1.8±.1.3-1.3±1.0.9±.72.8
13R91Chronic bronchitis/bronchiectasis1.3±.4----2.1±1.52.6±1.51.3±.8.1
14R75Sinusitis acute/chronic1.0±.4---2.3±1.4---3.1
15K78Atrial fibrillation/flutter.8±.4-----2.4±1.41.3±.8.2
16A85Adv effect medical agent in proper dose.8±.3----1.6±1.31.5±1.11.0±.72.6
17R99Other disease respiratory system.8±.3---1.6±1.2---.3
18P01Feeling anxious/nervous/tense.5±.3-------1.4
19P02Acute stress reaction.4±.3-------.9
20A96Death.4±.2------.9±.7.5
  Absolute number of observations2505247130149438376460705 
URI denotes upper respiratory infection; COPD, chronic obstructive pulmonary disease; NOS, not otherwise specified.

TABLE 3
FINAL DIAGNOSES FOR EPISODES OF CARE STARTING WITH THE REASON FOR ENCOUNTER GENERAL WEAKNESS/TIREDNESS (A04); N=5915

 Percentage of Patients Presenting with Cough Who Had This Final Diagnosis 
  Age (Years) 
RankICPC CodeDiagnosisTotal0-45-1415-2425-4445-6465-7475+Standardized Incidence (Cases/100 Patients/Year)
1A04General weakness/tiredness37.5±1.216.9±3.732.3±4.643.3±3.640.5±2.338.0±3.038.4±3.637.5±3.02.7
2A77Viral disease NOS8.2±.714.9±3.512.0±3.28.0±2.07.9±1.37.0±1.68.0±2.06.0±1.52.8
3R74URI (head cold)4.3±.511.7±3.18.8±2.83.6±1.33.4±.93.3±1.14.2±1.52.5±1.09.7
4B80Iron deficiency anemia3.5±54.0±1.97.2±2.54.4±1.54.2±1.01.6±.82.4±1.12.7±1.0.7
5A97No disease/prevention2.8±.43.7±1.93.5±1.85.2±1.62.6±.82.9±1.01.0±.71.9±.88.5
6R78Acute bronchitis/bronchiolitis2.7±.44.7±2.14.0±1.91.1±.81.4±.63.0±1.12.4±1.14.3±1.34.7
7A85Adv effect medical agent in proper dose2.1±.4--1.1±.8.8±.43.4±1.14.2±1.53.8±1.22.6
8P76Depressive disorder1.9±.3--.8±.71.3±.53.2±1.13.4±1.42.7±1.0.7
9P99Other mental disorder1.8±.3--3.4±1.33.6±.91.5±.8--.7
10R75Sinusitis acute/chronic1.8±.3-3.2±1.71.4±.82.5±.71.7±.81.6±.9.8±.53.1
11R80Influenza (proven)1.7±.3--1.7±.91.6±.62.6±1.02.3±1.11.3±.7.8
12P02Acute stress reaction1.6±.3--1.9±1.02.8±.81.6±.81.1±.8.6±.5.9
13P01Feeling anxious/nervous/tense1.5±.3--1.2±.81.8±.62.2±.91.7±1.0.8±.51.4
14Z05Problem working conditions1.4±.3--2.5±1.12.7±.81.5±.8--1.2
15R76Tonsillitis acute1.0±.37.0±2.52.0±1.4.8±.7.9±.4---1.6
16P03Feeling depressed1.0±.2---1.1±.51.4±.71.3±.8.8±.5.5
17A75Infectious mononucleosis.8±.2-1.8±1.33.2±1.31.0±.5---.2
18R81Pneumonia.8±.2-3.2±1.7--.7±.51.3±.81.0±.6.5
19H71Acute otitis media/myringitis.8±.28.7±2.82.2±1.5-----2.3
20R98Hyperventilation.8±.2---1.0±.51.0±.61.1±.8.6±.5.9
  Absolute number of observations591540240072616809966971014 
NOS, not otherwise specified; URI, upper respiratory infection.

TABLE 4
FINAL DIAGNOSES FOR EPISODES OF CARE STARTING WITH THE REASON FOR ENCOUNTER LOW BACK SYMPTOMS/COMPLAINTS WITHOUT RADIATION (L03); N=4238

 Percentage of Patients Presenting with Cough Who Had This Final Diagnosis 
  Age (Years) 
RankICPC CodeDiagnosisTotal0-45-1415-2425-4445-6465-7475+Standardized Incidence (Cases/100 Patients/Year)
1L03Low back symptoms/complaints without radiation69.9±1.4-56.9±12.772.2±4.371.2±2.273.9±2.566.7±4.058.1±4.73.7
2L18Muscle pain/fibrositis6.2±.7-10.3±7.87.9±2.67.5±1.35.2±1.33.7±1.64.9±2.02.6
3L86Lumbar disc lesion/radiation6.0±.7--2.2±1.46.7±1.26.8±1.45.8±2.05.6±2.2.7
4L85Acquired deformities of spine3.2±.5-10.3±7.85.0±2.13.9±.91.8±.81.9±1.13.0±1.6.4
5L84Osteoarthritis of spine1.6±.4---.4±.3.9±.64.1±1.76.5±2.3.2
6L81Other musculoskeletal injury1.6±.4--2.9±1.61.0±.5.9±.51.3±1.04.4±1.92.4
7L99Other musculoskeletal disease1.3±.3---.9±.5.9±.62.6±1.32.3±1.41.4
8L89Osteoarthritis1.0±.3----.8±.52.4±1.34.0±1.81.4
9N99Other neurological disease.8±.3---.8±.41.3±.6--1.1
10U95Urinary calculus.4±.2---.4±.3.6±.4--.3
11L02Back symptoms/complaints.4±.2---.5±.3---1
12L19Other multiple/unspecif muscle symptoms/complaints.4±.2---.4±.3.7±.5--.4
13L79Sprains & strains NOS.4±.2-------1.3
14U70Pyelonephritis/pyelitis acute.4±.2-------.2
15L88Rheumatoid arthritis/allied conditions.3±.2---.4±.3---.2
16L95Osteoporosis.3±.2-----1.1±.9-.1
17U71Cystitis.3±.2-------2.2
18A77Viral diseases NOS.2±.1---.4±.3---2.8
19D06Other localized abdominal pain.2±.1-------1.3
20L76Other fracture.2±.1-------.3
  Absolute number of observations423855841816261163538430 
NOS denotes not otherwise specified.

Discussion

Family practice can be characterized by the specific distribution of health problems and disease in its population, as distinct from the distributions in the general population and in specialists’ populations. Increasingly, empiric data indicate how morbidity patterns and the distribution of reasons for visit in family practice differ from those in hospitals. The availability of age-specific prior probabilities of common symptoms and complaints for diagnoses in family practice has great potential. Diagnostic labels often have the disadvantage of a relative uncertainty, caused by a more or less arbitrary attribution of different symptoms and signs (eg, syndrome diagnoses, psychiatric diagnoses). Symptoms and complaints on the other hand have the advantage of relative certainty, because they represent the patient’s ill health irrespective of the diagnostic label they are given. In the daily work of FPs the importance of the absence or presence of a symptom must be considered in light of the distribution of disease in the family practice setting.

Therefore, such data would not only seem to be crucial for the further development of family practice as an academic discipline or for the design of intervention studies but also has direct practical consequences for clinicians. This information can directly support FPs’ medical decision making and improve communication with patients. The process of finding common ground about diagnosis and management could especially profit from a realistic estimate of probabilities, bridging the gap between the patient’s perspective, as reflected in the presenting symptoms, and the clinical perspective of the FP who wants to provide optimal care.21

Several important types of distributions are shown in (Table1) through 4: a very common symptom in relation to highly incident diagnoses, a less common symptom leading to less incident diagnoses, and symptoms primarily resulting in a symptom diagnosis with the same label. Also, it is evident that the range of clinical considerations resulting from a presenting symptom can vary from a relatively limited to a very wide morbidity spectrum.

Sex-specific symptoms and complaints (eg, menstrual problems) typically result in rather specific distributions, as is illustrated in the database available on the JFP Web site. The distribution of diagnoses for symptoms that occur in both sexes may be different not only for age groups but also for sex /age groups.

 

 

The value of prior probabilities increases with the availability of data on incidence of diseases in the same population, allowing an estimation of the positive and negative predictive values. Since 1995, data collection has occurred electronically. Later in 2001, a database twice the size will become available that allows more precise estimations for finer age/sex distributions and symptom combinations. Although from Dutch family practice, these data have a high face validity for clinicians wherever they work.8 Nevertheless, it would seem that FPs in the United States and other countries should give priority to collecting their own reliable probability databases.4,22,23

In the Netherlands, Japan, and Poland, an international comparative study has taken place with an electronic patient record, using ICPC. (See page xxx in this issue for the abstract of this article.) Based on a comparison of these databases with US NAMCS data (1995-1997), tables similar to those presented in this paper for the most frequent symptoms and complaints have been made available on the JFP Web site.24.25

References

1. Sox HC, Jr. Decision making: a comparison of referral practice and primary care. J Fam Pract 1996;42:155-60.

2. Lamberts H, Hofmans-Okkes IM. Episode of care: a core concept in family practice. J Fam Pract 1996;42:161-67.

3. Kerr LW. Fundamental research at primary care level. Lancet 2000;355:1904-06.

4. Stange KC, Jaén CR, Flocke SA, Miller WL, Crabtree BF, Zyzanski SJ. The value of a family physician. J Fam Pract 1998;46:363-68.

5. Ebell MH, Smith MA, Barry HC, Ives K, Carey M. Does this patient have strep throat? JAMA 2000;284:2912-18.

6. Nutting PA, Green LA. Practice-based research networks: reuniting practice and research around the problems most of the people have most of the time. J Fam Pract 1994;38:335-36.

7. Lamberts H. Episode-oriented epidemiology in family practice: the practical use of the International Classification of Primary Care (ICPC) as illustrated in patients with headache. In: Norton PG, Stewart M, Tudiver F, Bass MJ, Dunn EV, eds. Primary care research: traditional and innovative approaches. Newbury Park, Calif: Sage; 1991;20-72.

8. Donaldson MS, Yordy KD, Lohr KN, Vanselow NA, eds. Primary care: America’s health in a new era. Washington, DC: National Academy Press; 1996.

9. Okkes IM, Oskam SK, Lamberts H. Van Klacht naar Diagnose. Episodegegevens uit de huisartspraktijk. [From complaint to diagnosis: episode data from family practice]. With a CD-ROM. Bussum, the Netherlands: Coutinho; 1998.

10. Hornbrook MC, Hurtado RV, Johnson RE. Health care episodes: definition, measurement and use. Med Care Rev 1985;42:163-218.

11. Solon JA, Feeney JJ, Jones SH, Rigg RD, Sheps CG. Delineating episodes of medical care. Am J Public Health 1967;57:401-08.

12. Okkes IM, Groen A, Oskam SK, Lamberts H. Advantages of a long observation period in episode oriented electronic patient records in family practice. Methods Inf Med 2001;40:229-35

13. Lamberts H, Wood M, eds. ICPC. International classification of primary care. Oxford, England: Oxford University Press; 1987.

14. Lamberts H, Hofmans-Okkes IM. The core of computer based patient records in family practice: episodes of care classified with ICPC. Int J Biomed Comp 1996;42:35-41.

15. Klinkman MS, Green LA. Using ICPC in a computer-based primary care information system. Fam Med 1995;27:449-56.

16. Van Boven C, Dijksterhuis PH, Lamberts H. Defensive testing in Dutch family practice. J Fam Pract 1997;44:468-72.

17. Bentzen N ed. An international glossary for general/family practice. Fam Pract 1995;12:341-69.

18. National Center for Health Statistics. A reason for visit classification for ambulatory care. Hyattsville, Md: National Center for Health Statistics, US Public Health Service; 1979

19. Hofmans-Okkes IM. An international study into concept and validity of the “reason for encounter”. In: Lamberts H, Wood M, Hofmans-Okkes IM, eds. The international classification of primary care in the European community. Oxford, England: Oxford University Press; 1993.

20. Gardner MJ, Altman DG, eds Statistics with confidence. London, England: BMJ; 1989 (floppy disk).

21. Stewart M, Belle Brown J, Weston WW, McWhinney IR, McWilliam CL, Freeman TR. Patient-centered medicine: transforming the clinical method. Thousand Oaks, Calif: Sage; 1995.

22. Green LA, Nutting PA. Family physicians as researchers in their own practices. J Am Board Fam Pract 1994;7:261-63.

23. Franks P, Clancy CM, Nutting PA. Defining primary care: empirical analysis of the National Ambulatory Medical Care Survey. Med Care 1997;35:655-68.

24. National Center for Health Statistics. 1995, 1996, 1997 Ambulatory Medical Care Survey: CD-ROM-series 13. Hyattsville, Md: National Center for Heath Statistics; 1997, 1998, 1999.

25. Okkes IM, Polderman GO, Fryer E, Yamada T, Bujak M, Oskam SK, Green LA, Lamberts H. The role of family practice in different health care systems: a comparison of morbidity data from primary care populations in the Netherlands, Japan, Poland and the US. J Fam Pract 2002;51:72-73.

References

1. Sox HC, Jr. Decision making: a comparison of referral practice and primary care. J Fam Pract 1996;42:155-60.

2. Lamberts H, Hofmans-Okkes IM. Episode of care: a core concept in family practice. J Fam Pract 1996;42:161-67.

3. Kerr LW. Fundamental research at primary care level. Lancet 2000;355:1904-06.

4. Stange KC, Jaén CR, Flocke SA, Miller WL, Crabtree BF, Zyzanski SJ. The value of a family physician. J Fam Pract 1998;46:363-68.

5. Ebell MH, Smith MA, Barry HC, Ives K, Carey M. Does this patient have strep throat? JAMA 2000;284:2912-18.

6. Nutting PA, Green LA. Practice-based research networks: reuniting practice and research around the problems most of the people have most of the time. J Fam Pract 1994;38:335-36.

7. Lamberts H. Episode-oriented epidemiology in family practice: the practical use of the International Classification of Primary Care (ICPC) as illustrated in patients with headache. In: Norton PG, Stewart M, Tudiver F, Bass MJ, Dunn EV, eds. Primary care research: traditional and innovative approaches. Newbury Park, Calif: Sage; 1991;20-72.

8. Donaldson MS, Yordy KD, Lohr KN, Vanselow NA, eds. Primary care: America’s health in a new era. Washington, DC: National Academy Press; 1996.

9. Okkes IM, Oskam SK, Lamberts H. Van Klacht naar Diagnose. Episodegegevens uit de huisartspraktijk. [From complaint to diagnosis: episode data from family practice]. With a CD-ROM. Bussum, the Netherlands: Coutinho; 1998.

10. Hornbrook MC, Hurtado RV, Johnson RE. Health care episodes: definition, measurement and use. Med Care Rev 1985;42:163-218.

11. Solon JA, Feeney JJ, Jones SH, Rigg RD, Sheps CG. Delineating episodes of medical care. Am J Public Health 1967;57:401-08.

12. Okkes IM, Groen A, Oskam SK, Lamberts H. Advantages of a long observation period in episode oriented electronic patient records in family practice. Methods Inf Med 2001;40:229-35

13. Lamberts H, Wood M, eds. ICPC. International classification of primary care. Oxford, England: Oxford University Press; 1987.

14. Lamberts H, Hofmans-Okkes IM. The core of computer based patient records in family practice: episodes of care classified with ICPC. Int J Biomed Comp 1996;42:35-41.

15. Klinkman MS, Green LA. Using ICPC in a computer-based primary care information system. Fam Med 1995;27:449-56.

16. Van Boven C, Dijksterhuis PH, Lamberts H. Defensive testing in Dutch family practice. J Fam Pract 1997;44:468-72.

17. Bentzen N ed. An international glossary for general/family practice. Fam Pract 1995;12:341-69.

18. National Center for Health Statistics. A reason for visit classification for ambulatory care. Hyattsville, Md: National Center for Health Statistics, US Public Health Service; 1979

19. Hofmans-Okkes IM. An international study into concept and validity of the “reason for encounter”. In: Lamberts H, Wood M, Hofmans-Okkes IM, eds. The international classification of primary care in the European community. Oxford, England: Oxford University Press; 1993.

20. Gardner MJ, Altman DG, eds Statistics with confidence. London, England: BMJ; 1989 (floppy disk).

21. Stewart M, Belle Brown J, Weston WW, McWhinney IR, McWilliam CL, Freeman TR. Patient-centered medicine: transforming the clinical method. Thousand Oaks, Calif: Sage; 1995.

22. Green LA, Nutting PA. Family physicians as researchers in their own practices. J Am Board Fam Pract 1994;7:261-63.

23. Franks P, Clancy CM, Nutting PA. Defining primary care: empirical analysis of the National Ambulatory Medical Care Survey. Med Care 1997;35:655-68.

24. National Center for Health Statistics. 1995, 1996, 1997 Ambulatory Medical Care Survey: CD-ROM-series 13. Hyattsville, Md: National Center for Heath Statistics; 1997, 1998, 1999.

25. Okkes IM, Polderman GO, Fryer E, Yamada T, Bujak M, Oskam SK, Green LA, Lamberts H. The role of family practice in different health care systems: a comparison of morbidity data from primary care populations in the Netherlands, Japan, Poland and the US. J Fam Pract 2002;51:72-73.

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Minor Acute Illness: A Preliminary Research Report on the “Worried Well”

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Minor Acute Illness: A Preliminary Research Report on the “Worried Well”

ABSTRACT

OBJECTIVES: Our objectives were to determine how patients who make frequent use of the medical system (high users) with medically unexplained symptoms met our chart-rating criteria for somatization and minor acute illness and what the stability of such diagnoses were over time.

STUDY DESIGN: A chart review was performed at baseline and 1 and 2 years; we re-rated the charts of patients initially rated as having somatization, as well as a 15% sample of those with minor acute illness.

POPULATION: We obtained a random sample of high-use patients (6 visits/year) aged 21 to 55 years who were identified from the management information system.

OUTCOMES: We measured chart review designations as organic disease, somatization, or minor acute illness.

RESULTS: Among 883 high users at baseline, 35% had organic diseases; 14% had somatization; and 51% had minor acute illness as their primary problems. No patients with initial minor acute diagnoses were reclassified as having somatization 1 or 2 years later, and all but 2 patients had minor acute illness in 1 or both follow-up years.

CONCLUSIONS: Minor acute illness was more common among high users than somatization and organic diseases combined. It has not previously been studied but probably has been recognized by clinicians as the “worried well.” Diagnoses of somatization were unstable over 2 years’ follow-up, while minor acute diagnoses were stable, supporting the latter as a valid entity.

KEY POINTS FOR CLINICIANS

  • Many high-use patients with medically unexplained symptoms have a syndrome characterized by minor but recurring symptoms that we call minor acute illness.
  • Minor acute illness has not been previously described as a research entity, but there are some similarities to what is referred to as the “worried well” in the nonresearch literature.

Using this preliminary research, we report on patients with medically unexplained physical symptoms, who have what we call “minor acute illness.” In contrast to the well-studied chronic somatizing patient in whom medically unexplained symptoms1,2 are of at least 6 months’ duration,3 we define minor acute illness as unexplained symptoms of any type (eg, sore throats, minor sprains, “sinuses”) that resolve completely in less than 6 months (usually days or weeks). Although most patients would not seek care for these minor complaints, some patients with minor acute illness have exaggerated responses to common symptoms and become high users of medical care.4-8 These are probably recognized by many physicians as the “worried well.”

Our review of the literature and discussion with several experts reveal that no research group has given consideration to defining the diagnostic features of minor acute illness or to describing it over time.9,10 Not surprisingly, studies of treatment are nonexistent, and reported treatments are ineffective.5,6,11 We present preliminary research defining minor acute illness, distinguishing it from somatization and organic disease, and evaluating its persistence over time among high-use patients.

Methods

Subjects

All patients were members of a largely primary care, staff model health maintenance organization (HMO) in Lansing, Michigan (Blue Cross Network). Only computerized descriptive information in the HMO’s management information system (MIS) and data in patients’ clinical charts were involved in our study. The MIS includes administrative information on age, sex, all patient encounters with the system, primary diagnoses made at each physician/nurse practitioner/physician assistant visit (International Classification of Diseases—Ninth Revision codes), revenue codes, and charges for services. Subjects whose visits for the year were primarily because of pregnancy, substance abuse, or other recognized psychiatric problems/diagnoses (eg, bipolar disorder, eating disorder) were excluded.

Screening to identify somatizing and minor acute illness patients

We first identified all patients aged 21 to 55 years in the Lansing, Michigan, area who had at least 1 visit during 1995 to a physician, physician assistant, nurse practitioner, specialist, or emergency room; each hospitalization was counted as 1 visit. We did not use older patients, because our goal for another project was to identify chronic somatizing patients with minimal organic disease; the discovery of minor acute illness patients was an unexpected byproduct. Of 15,505 members in 1995, 5423 had 6 or more visits, and 1000 of these patients were randomly selected for further evaluation; to obtain the greatest possible sample, we arbitrarily defined 6 or more visits (65th percentile) as high users. Of the 1000, 94 were excluded because of pregnancy, substance abuse, visits for psychiatric care, or because they were employees of the HMO, and 23 were excluded because of incomplete data. We excluded patients under regular psychiatric care, because we wanted to obtain (for a treatment intervention) patients not receiving psychologic attention. The remaining 883 patients constituted the study population. Excluded patients differed from those in the study group in age, sex, and employer group but not on the amount of copay (P =.58) and relationship to the subscriber (P =.23). Excluded patients were on average younger (35.7 years vs 40.3 years, P <.001), and 88% were women as opposed to 68% for patients included in the overall study (P <.001).

 

 

Reference standard diagnoses were established by a resident physician (emergency medicine) rating the 883 charts according to specific criteria, reported previously12 and summarized in the report’s Appendix.* The rater classified patients by their primary/predominant problem for the entire year as organic disease, somatization, or minor acute illness. The designation of the primary problem was based on the largest number of visits for a problem. For example, a patient with a documented urinary infection at the first visit with 1 follow-up visit, documented pneumonia at the third visit with 2 follow-up visits, 1 visit for chronic low back pain with a negative computed tomography scan, and 2 visits for minor ligamentous strain, with no objective manifestations and no investigation, would be rated as organic for this year; similarly, a patient would be considered to have minor acute illness with 6 visits for minor complaints with no work-up and no objective manifestations of disease on examination, as well as 1 visit for documented urinary infection and 3 for diabetes mellitus. The same rule was used for follow-up ratings 1 and 2 years later, and the rater for follow-up ratings was unaware of the baseline ratings.

Organic disease was diagnosed by standard medical criteria and based on clear physical signs of disease (eg, laceration, enlarged liver) or, almost always, definitive laboratory investigation; the rater relied on expert judgment and referred to text material as needed.13 Somatization was rated when, following objectively based diagnostic evaluation (definitive testing), patients were free of organic disease that contributed significantly to at least 1 physical symptom of at least 6 months’ duration. Minor acute illness was rated when all physical symptoms were of less than 6 months’ duration, as judged by the rater from explicit mention in the chart or from observation that symptoms cleared and did not recur, and there was no documentation of an organic disease explanation for the symptom or its degree of severity. Because minor acute problems typically were not severe or disabling (in contrast to somatization), definitive testing often had not been performed. From the 1995 baseline sample of 122 somatizers and 450 minor acute patients identified by our rating procedure, we re-rated a sample of all available somatizers (N=104; 85%) and a 15% random sample of minor acute illness patients (N=66) both 1 and 2 years later.

After 10 hours of initial training, including practice rating on nonstudy charts, the rater rated 20 charts of non-study high-utilizing patients. A priori, we set an agreement rate with the trainer (one of the authors [R.C.S.]) for primary diagnosis of 90% (18 of 20 charts) before the rater began rating study patients. During the study, the trainer rated sets of 20 study charts already evaluated by the rater, once each during 1995, 1996, and 1997. The rater had high levels of agreement with the trainer throughout, varying from 90% to 95%. This level of agreement is not surprising, because the trainer trained the rater, which was also reflected in the of 0.93.

Statistical analysis

We also reviewed the 1996 and 1997 use for the same 104 somatizers identified in our initial 1995 baseline chart review of high-utilizing patients. Again, the same 15% random sample (N=66) was selected from those classified as having minor acute illness in 1995. In the follow-up years, some patients had relocated or were no longer receiving their medical care at our HMO. However, nearly 85% of the patients in our selected sample were continuously enrolled in the HMO in the 2 subsequent years of our study. Similar to chart rating, the final sample consisted of 104 somatizers and 66 patients with minor acute illness. For these patients we ascertained their status (somatization, organic disease, or minor acute illness) and their use (<6 visits; 6 visits) in 1996 and 1997. The 2 groups of patients were compared using chi-square tests for categoric variables and by t tests for continuous variables. Confidence intervals for binomial proportions were calculated by the exact method.

Results

The characteristics of 170 patients (104 somatization; 66 minor acute) studied at all data collection points are shown in Table 1. The mean age (as of 1995) was 41.3 years among somatizers versus 39.7 years among patients with minor acute illness (P =.19) The 2 groups differed only by sex, with nearly 83% of the somatizers being women, compared with 65% among minor acute illness patients (P=.009).

Among 883 high-use patients at baseline, 311 had organic diseases (35%); 122 had somatization (14%); and 450 had minor acute illness (51%) as their primary problems. No patients with initial minor acute illness diagnoses were reclassified as somatization 1 and 2 years later, and all but 2 patients had minor acute illness in 1 or both follow-up years.

 

 

The follow-up status in 1996 and 1997 of the 104 somatizers and the 66 minor acute illness patients initially rated in 1995 appears in Figure 1. It shows the (percentage) distribution of organic diseases, somatization, and minor acute illness diagnoses for 1995, 1996, and 1997, using all available data in each year. Because our rating of patients in 1 of the 3 categories followed a strict protocol (for 13 patients in 1996 and 40 patients in 1997), and because of insufficient information in their charts, we were unable to definitely ascertain their status. None of the patients with minor acute illness were reclassified as somatizers in the follow-up years, and 83% of these patients continued to have minor acute illness in 1996. All but 2 patients had minor acute illness either in 1996 or 1997 or both. Approximately 27% of the somatizers remained somatizers in 1996, 54% were rated as having minor acute illness, and 13% had organic disease. In 27% of these somatizers minor acute illness developed in the 2 subsequent years. Among patients in 1995, the probabilities of somatization for 2 and 3 consecutive years were 3.7% and 1.1%, respectively. In contrast, for minor acute illness these probabilities were 42.5% and 27%, respectively.

Use status of the groups (somatization, minor acute illness) combined is shown in Figure 2. It shows the (percentage) distribution of use (<6 visits; 6 visits) for 1996 and 1997. Approximately 57% of the somatizers were high users in 1996, compared with 29% of the minor acute illness patients. High use in either 1996 or 1997 was more prevalent among somatizers (70%) than in minor acute illness patients (45%). Persistent high use in both years among minor acute illness patients was 16.7% (95% confidence interval [CI], 8.6%-27.9%) and among somatizers 32.7% (95% CI, 23.8%-42.6%).

TABLE 1
CHARACTERISTICS OF FOLLOW-UP STUDY (N=70)

CharacteristicSubgroupSomatizers % (N)Minor Acute % (N)
Age, years20-2913.5 (14)9.1 (6)
30-3924.0 (25)43.9 (29)
40-4951.0 (53)36.4 (24)
50+11.5 (12)10.6 (7)
SexMen17.3 (18)34.9 (23)
Women82.7 (86)65.2 (43)
EmployerMSU6.7 (7)4.6 (3)
State28.9 (30)24.2 (16)
GM22.1 (23)30.3 (20)
Other42.3 (44)41.0 (27)
Copay, $058.7 (61)57.6 (38)
528.9 (30)25.8 (17)
711.5 (12)10.6 (7)
101.0 (1)6.1 (4)
Relationship to subscriberSelf59.6 (62)62.1 (41)
Spouse37.5 (39)34.9 (23)
Dependent2.9 (3)3.0 (2)
MSU denotes Michigan State University; GM, General Motors.

FIGURE 1
FOLLOW-UP DIAGNOSES IN INTIALLY IDENTIFIED MINOR ACUTE ILLNESS AND SOMATIZATION PATIENTS


FIGURE 2
FOLLOW-UP USE PATTERNS OF INTIALLY HIGH-USE PATIENTS WITH MINOR ACUTE ILLNESS AND SOMATIZATION PATIENTS

Discussion

Among high users of care, patients with minor acute symptoms were more common than those with symptoms of somatization and organic diseases combined. On follow-up study, diagnoses of somatization often changed to minor acute status, while minor acute illness diagnoses were more persistent. Although use did not remain high in either group, regression to the mean must be considered, because we sampled only on high use in 1995.

We found no research data to compare with our results in patients with minor acute illness. However, the presumed stability and chronicity of somatization diagnoses3,14 have been questioned.14-16 For example, great variability in symptoms was seen in repeated hospitalizations of somatizers, as judged by chart review.17 Others found that only 21 of 70 cases identified as somatization disorder at baseline had the same diagnosis 12 months later and that only approximately one half of all somatization symptoms were present 1 year later.16,18

Limitations

This first preliminary study of high users with minor acute illness was limited by the shortcomings of any retrospective chart review. These include the facts that: (1) We depended on how aggressively a physician attempted to diagnose organic disease and on how completely clinicians recorded their findings; (2) Without access to patients, we were unable to identify their unique perspective or to diagnose specific somatoform disorders3 using, for example, the Diagnostic Interview Schedule,19 the World Health Organization Composite Diagnostic Interview,20 or direct patient interviews; (3) Our designation of somatization depended on identifying symptoms of 6 or more months’ duration, the minimum criterion specified for Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) somatoform diagnoses3; if busy clinicians did not record duration, overcoding of minor acute status could have occurred.4 We cannot be certain that patients in either the medically unexplained group (ie, somatization, minor acute illness) did not in fact have organic diseases, because we were unable to investigate each patient ourselves from a biomedical perspective. However, during follow-up chart review, we observed no instances of an important organic disease having been missed when a diagnosis of minor acute illness or somatization status was made initially.15,21

In spite of these expected problems when chart review is the only available source of data, this research on previously unstudied patients provides a starting point for further research. Based on clinical descriptions, our observations on somatizing patients did in fact indicate that we had studied a typical chronic somatizing population (eg, those with low back pain, pelvic pain, irritable bowel syndrome). Also, others have found that chart review may be superior in identifying somatizing patients17,18 and that the DSM-IV has important shortcomings, notwithstanding the latter’s prominent present role in diagnosing conditions with medically unexplained symptoms.22,23 Further, because minor acute illness symptoms are short term, it is arguable that few minor acute patients will receive DSM-IV somatoform diagnoses. Still, as the next research step DSM-IV diagnoses should be sought to better delineate this new group, as should other standard psychiatric measures, especially for depression, anxiety, and unique personality traits.

 

 

Although psychiatric measures are needed to better define this high-use population, their absence in this initial study does not negate the importance of our findings from medical patients’ charts. They presumably reflect what patients actually reported to providers as their major reasons for seeking care, in contrast to questionnaire and lay interviewer data obtained unrelated to care seeking. Complementing our chart-based data with these standard psychiatric measures is the necessary next step.

Others have considered the problem of minor symptoms. Barsky and Borus7 seemed to distinguish short-term symptoms from the chronic symptoms of somatization and somatoform disorders by including many symptoms that often are brief and self-limited in what they called functional somatic syndromes (eg, palpitations, dizziness, lightheadedness, sore throat, and dry mouth). To avoid compounding the severe nosology problem in somatization24,25 and because the term functional somatic syndromes has been used by others to encompass all types of somatization,26,27 we are using the purely descriptive term “minor acute illness” to identify the patients reported here with short-term symptoms, recognizing that there may be considerable overlap with the group of patients identified by Barsky and Borus,7 Katon and colleagues,28 the acute and subacute somatizers of Kleinman,8 and the somatoform “not otherwise specified” category in DSM-IV.29 We prefer minor acute illness also to the common but pejorative term “worried well,” which we believe has never been defined. The “minor acute” label also has been used previously in a closely related context.30 Similar to Katon and coworkers,28 we propose that minor acute illness fits into the mild end of a multidimensional classification scheme for patients with medically unexplained symptoms—with abridged somatization disorder31 as moderate and full somatization disorder as severe.3 The latter 2 diagnoses are based on DSM-IV criteria only.

CONCLUSIONS

More research is needed to assist the field in better addressing patients with medically unexplained symptoms. The long-range goal for minor acute illness (as well as somatization) is to determine if it is a distinct and valid entity. In what is a complex task for psychiatric epidemiologists in the absence of organic disease and pathophysiologic changes,32-37 we can use the recommendations of Guze and colleagues38,39 for establishing the validity of a psychiatric diagnosis to guide us (Table 2). At this point, we can say only that there is evidence from our initial research study that we can use to describe and define minor acute illness and that it persists over 2 years (criteria 1 and 4 from Table 2). These are key determinants of validity, but they require much confirmation.38,39 Extensive work lies ahead in achieving our ultimate goal, providing effective treatment for a group that often receives inappropriate treatment, such as unnecessary antibiotics.

TABLE 2
VALIDITY CRITERIA FOR PSYCHIATRIC ENTITIES

  1. Describe the syndrome/disorder through unique symptoms and symptom patterns as well as by other features (eg, age and precipitating factors), and delineate clinical exclusion criteria to differentiate from other disorders.
  2. Identify distinctive laboratory diagnostic features, including psychologic testing.
  3. Identify uniform etiology, pathogenesis, and epidemiology to distinguish from other syndromes/diseases.
  4. Show uniform clinical course and persistence of diagnosis over time.
  5. Show increased prevalence among close family members.
NOTE: From Guze and colleagues,38,39 who comment that we often know little about criteria 2 and 3, and a careful description must focus on criteria 4 and 5, follow-up studies, and family studies.

Acknowledgments

Our work was supported by a generous grant from the Institute for Managed Care, Michigan State University, East Lansing.

References

1. Lipowski ZJ. Somatization: the concept and its clinical application. Am J Psychiatry 1988;145:1358-68.

2. Kirmayer LJ, Robbins JM. Introduction: concepts of somatization. In: Kirmayer LJ, Robbins JM, eds. Current concepts of somatization: research and clinical perspectives. Washington, DC: American Psychiatric Press, Inc; 1991;1-19.

3. American Psychiatric Association. Diagnostic and statistical manual of mental disorders. 4th ed. Washington, DC: American Psychiatric Association; 1994.

4. Komaroff AL. ‘Minor’ illness symptoms—the magnitude of their burden and of our ignorance. Arch Intern Med 1990;150:1586-87.

5. Kroenke K, Mangelsdorff AD. Common symptoms in ambulatory care: incidence, evaluation, therapy, and outcome. Am J Med 1989;86:262-66.

6. Connelly JE, Smith GR, Philbrick JT, Kaiser DL. Healthy patients who perceive poor health and their use of primary care services. J Gen Int Med 1991;6:47-51.

7. Barsky AJ, Borus JF. Functional somatic syndromes. Ann Intern Med 1999;130:910-21.

8. Kleinman A. Social origins of distress and disease—depression, neurasthenia, and pain in modern China. New Haven, Conn: Yale University Press; 1986.

9. Andersson S-O, Mattsson B, Lynoe N. Patients frequently consulting general practitioners at a primary health care centre in Sweden—a comparative study. Scand J Soc Med 1995;23:251-57.

10. Verbrugge LM, Ascione FJ. Exploring the iceberg—common symptoms and how people care for them. Med Care 1987;25:539-69.

11. Kroenke K, Arrington ME, Mangelsdorff AD. The prevalence of symptoms in medical outpatients and the adequacy of therapy. Arch Intern Med 1990;150:1685-89.

12. Smith RC, Gardiner JC, Armatti S, et al. Screening for high utilizing somatizing patients using a prediction rule derived from the management information system of an HMO—a preliminary study. In press.

13. Humes HD, DuPont HL, Gardner LB, et al, eds. Kelley’s textbook of internal medicine. 4th ed. Philadelphia, Pa: Lippincott Williams and Wilkins; 2000.

14. Cloninger CR, Martin RL, Guze SB, Clayton PJ. A prospective follow-up and family study of somatization in men and women. Am J Psychiatry 1986;143:873-78.

15. Rief W, Hiller W, Geissner E, Fichter MM. A two-year follow-up study of patients with somatoform disorders. Psychosomatics 1995;36:376-86.

16. Gara MA, Escobar JI. The stability of somatization syndromes over time. Arch Gen Psychiatry 2001;58:94.-

17. Fink P. Physical complaints and symptoms of somatizing patients. J Psychosom Res 1992;36:125-36.

18. Simon GE, Gureje O. Stability of somatization disorder and somatization symptoms among primary care patients. Arch Gen Psychiatry 1999;56:90-95.

19. Robins LN, Helzer JE, Croughan J, Ratcliff KS. National Institute of Mental Health Diagnostic Interview Schedule: its history, characteristics, and validity. Arch Gen Psychiatry 1981;38:381-89.

20. Sartorius N. Composite International Diagnostic Interview (CIDI)—core version 1.1. Geneva, Switzerland: World Health Organization.

21. Kroenke K, Spitzer RL, deGruy FV, Hahn SR, Linzer M, Williams JBW, Brody D, Davies M. Multisomatoform disorder—an alternative to undifferentiated somatoform disorder for the somatizing patient in primary care. Arch Gen Psychiatry 1997;54:352-58.

22. Norquist G, Hyman SE. Advances in understanding and treating mental illness: implications for policy. Health Aff 1999;18:32-47.

23. Krueger RF. The structure of common mental disorders. Arch Gen Psychiatry 1999;56:921-26.

24. Murphy MR. Classification of the somatoform disorders. In: Bass CM, ed. Somatization: physical symptoms and psychological illness. Oxford, England: Blackwell; 1990;10-39.

25. Mayou R, Bass C, Sharpe M. Overview of epidemiology, classification, and aetiology. In: Mayou R, Bass C, Sharpe M, eds. Treatment of functional somatic symptoms. Oxford, England: Oxford University Press, 1995;42-65.

26. Kirmayer LJ, Robbins JM. Functional somatic syndromes. In: Kirmayer LJ, Robbins JM, eds. Current concepts of somatization: research and clinical perspectives. Washington, DC: American Psychiatric Press, Inc; 1991;79-106.

27. Sharpe M, Mayou R, Bass C. Concepts, theories, and terminology. In: Mayou R, Bass C, Sharpe M, eds. Treatment of functional somatic symptoms. Oxford, England: Oxford University Press; 1995;1-16.

28. Katon W, Lin E, von Korff M, Russo J, Lipscomb P, Bush T. Somatization: a spectrum of severity. Am J Psychiatry 1991;148:34-40.

29. Kroenke K, Spitzer RL, deGruy FV, Swindle R. A symptom checklist to screen for somatoform disorders in primary care. Psychosomatics 1998;39:263-72.

30. Weiner JP, Starfield BH, Steinwachs DM, Mumford LM. Development and application of a population-oriented measure of ambulatory care case-mix. Med Care 1991;29:452-72.

31. Escobar JI, Swartz M, Rubio-Stipec M, Manu P. Medically unexplained symptoms: distribution, risk factors, and comorbidity. In: Kirmayer LJ, Robbins JM, eds. Current concepts of somatization: research and clinical perspectives. Washington, DC: American Psychiatric Press, Inc; 1991;63-78.

32. Kovacs M, Gatsonis C. Stability and change in childhood-onset depressive disorders: longitudinal course as a diagnostic validator. In: Robins LN, Barrett JE, eds. The validity of psychiatric diagnosis. New York, NY: Raven Press, Ltd; 1989;57-73.

33. Tyrer P, Alexander J, Remington M, Riley P. Relationship between neurotic symptoms and neurotic diagnosis: a longitudinal study. J Affect Dis 1987;13:13-21.

34. Beiser M, Iacono WG, Erickson D. Temporal stability in the major mental disorders. In: Robins LN, Barrett JE, eds. The validity of psychiatric diagnosis. New York, NY: Raven Press, Ltd; 1989;77-97.

35. Andreason NC. The validation of psychiatric diagnosis: new models and approaches. Am J Psychiatry 1995;152:161-62.

36. Cloninger CR. Establishment of diagnostic validity in psychiatric illness: Robins and Guze’s method revisited. In: Robins LN, Barrett JE, eds. The validity of psychiatric diagnoses. New York, NY: Raven Press; 1989;9-16.

37. Grove WM, Andreasen NC. Quantitative and qualitative distinctions between psychiatric disorders. In: Robins LN, Barrett JE, eds. The validity of psychiatric diagnoses. New York, NY: Raven Press; 1989;127-39.

38. Guze SB. The diagnosis of hysteria: what are we trying to do? Am J Psychiatry 1967;124:491-98.

39. Robins E, Guze SB. Establishment of diagnostic validity in psychiatric illness: its application to schizophrenia. Am J Psychiatry 1970;126:983-87.

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ROBERT C. SMITH, MD, SCM
JOSEPH C. GARDINER, PHD
JUDITH S. LYLES, PHD
MONICA JOHNSON, MD
KATHRYN M. ROST, PHD
ZHEHUI LUO, MS
JOHN GODDEERIS, PHD
CATHERINE LEIN, RN, MS
WILLIAM C. GIVEN, PHD
BARBARA GIVEN, RN, PHD
FRANCESCA DWAMENA, MD
CLARE COLLINS, RN, PHD
LAWRENCE F. VANEGEREN, PHD
ELIE KORBAN, MD
MOHAMMED KANJ, MD
ROBERT HADDAD, MD
East Lansing, Michigan, and Fayetteville, Arkansas
Submitted, revised, July 13, 2001.
From the Department of Medicine, the College of Human Medicine, Michigan State University (R.C.S., J.C.G., J.SL., M.J., Z.L., J.G., C.L., C.W.G., B.G., F.D., C.C., L.F.V.E, E.K., M.K.) and the Department of Psychiatry, University of Arkansas (K.M.R.), Fayetteville. Dr Rost is currently in the Department of Family Medicine, University of Colorado School of Medicine, Denver. Dr Johnson now is in private practice. Reprint requests should be addressed to Robert C. Smith, MD, ScM, B312 Clinical Center, 138 Service Road, East Lansing, MI 48824.
E-mail: [email protected]

Issue
The Journal of Family Practice - 51(1)
Publications
Page Number
24-29
Legacy Keywords
,Somatoform disordersphysical symptoms [non-MESH]medically unexplained symptoms [non-MESH]worried well [non-MESH]. (J Fam Pract 2002; 51:24-29)
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ROBERT C. SMITH, MD, SCM
JOSEPH C. GARDINER, PHD
JUDITH S. LYLES, PHD
MONICA JOHNSON, MD
KATHRYN M. ROST, PHD
ZHEHUI LUO, MS
JOHN GODDEERIS, PHD
CATHERINE LEIN, RN, MS
WILLIAM C. GIVEN, PHD
BARBARA GIVEN, RN, PHD
FRANCESCA DWAMENA, MD
CLARE COLLINS, RN, PHD
LAWRENCE F. VANEGEREN, PHD
ELIE KORBAN, MD
MOHAMMED KANJ, MD
ROBERT HADDAD, MD
East Lansing, Michigan, and Fayetteville, Arkansas
Submitted, revised, July 13, 2001.
From the Department of Medicine, the College of Human Medicine, Michigan State University (R.C.S., J.C.G., J.SL., M.J., Z.L., J.G., C.L., C.W.G., B.G., F.D., C.C., L.F.V.E, E.K., M.K.) and the Department of Psychiatry, University of Arkansas (K.M.R.), Fayetteville. Dr Rost is currently in the Department of Family Medicine, University of Colorado School of Medicine, Denver. Dr Johnson now is in private practice. Reprint requests should be addressed to Robert C. Smith, MD, ScM, B312 Clinical Center, 138 Service Road, East Lansing, MI 48824.
E-mail: [email protected]

Author and Disclosure Information

ROBERT C. SMITH, MD, SCM
JOSEPH C. GARDINER, PHD
JUDITH S. LYLES, PHD
MONICA JOHNSON, MD
KATHRYN M. ROST, PHD
ZHEHUI LUO, MS
JOHN GODDEERIS, PHD
CATHERINE LEIN, RN, MS
WILLIAM C. GIVEN, PHD
BARBARA GIVEN, RN, PHD
FRANCESCA DWAMENA, MD
CLARE COLLINS, RN, PHD
LAWRENCE F. VANEGEREN, PHD
ELIE KORBAN, MD
MOHAMMED KANJ, MD
ROBERT HADDAD, MD
East Lansing, Michigan, and Fayetteville, Arkansas
Submitted, revised, July 13, 2001.
From the Department of Medicine, the College of Human Medicine, Michigan State University (R.C.S., J.C.G., J.SL., M.J., Z.L., J.G., C.L., C.W.G., B.G., F.D., C.C., L.F.V.E, E.K., M.K.) and the Department of Psychiatry, University of Arkansas (K.M.R.), Fayetteville. Dr Rost is currently in the Department of Family Medicine, University of Colorado School of Medicine, Denver. Dr Johnson now is in private practice. Reprint requests should be addressed to Robert C. Smith, MD, ScM, B312 Clinical Center, 138 Service Road, East Lansing, MI 48824.
E-mail: [email protected]

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ABSTRACT

OBJECTIVES: Our objectives were to determine how patients who make frequent use of the medical system (high users) with medically unexplained symptoms met our chart-rating criteria for somatization and minor acute illness and what the stability of such diagnoses were over time.

STUDY DESIGN: A chart review was performed at baseline and 1 and 2 years; we re-rated the charts of patients initially rated as having somatization, as well as a 15% sample of those with minor acute illness.

POPULATION: We obtained a random sample of high-use patients (6 visits/year) aged 21 to 55 years who were identified from the management information system.

OUTCOMES: We measured chart review designations as organic disease, somatization, or minor acute illness.

RESULTS: Among 883 high users at baseline, 35% had organic diseases; 14% had somatization; and 51% had minor acute illness as their primary problems. No patients with initial minor acute diagnoses were reclassified as having somatization 1 or 2 years later, and all but 2 patients had minor acute illness in 1 or both follow-up years.

CONCLUSIONS: Minor acute illness was more common among high users than somatization and organic diseases combined. It has not previously been studied but probably has been recognized by clinicians as the “worried well.” Diagnoses of somatization were unstable over 2 years’ follow-up, while minor acute diagnoses were stable, supporting the latter as a valid entity.

KEY POINTS FOR CLINICIANS

  • Many high-use patients with medically unexplained symptoms have a syndrome characterized by minor but recurring symptoms that we call minor acute illness.
  • Minor acute illness has not been previously described as a research entity, but there are some similarities to what is referred to as the “worried well” in the nonresearch literature.

Using this preliminary research, we report on patients with medically unexplained physical symptoms, who have what we call “minor acute illness.” In contrast to the well-studied chronic somatizing patient in whom medically unexplained symptoms1,2 are of at least 6 months’ duration,3 we define minor acute illness as unexplained symptoms of any type (eg, sore throats, minor sprains, “sinuses”) that resolve completely in less than 6 months (usually days or weeks). Although most patients would not seek care for these minor complaints, some patients with minor acute illness have exaggerated responses to common symptoms and become high users of medical care.4-8 These are probably recognized by many physicians as the “worried well.”

Our review of the literature and discussion with several experts reveal that no research group has given consideration to defining the diagnostic features of minor acute illness or to describing it over time.9,10 Not surprisingly, studies of treatment are nonexistent, and reported treatments are ineffective.5,6,11 We present preliminary research defining minor acute illness, distinguishing it from somatization and organic disease, and evaluating its persistence over time among high-use patients.

Methods

Subjects

All patients were members of a largely primary care, staff model health maintenance organization (HMO) in Lansing, Michigan (Blue Cross Network). Only computerized descriptive information in the HMO’s management information system (MIS) and data in patients’ clinical charts were involved in our study. The MIS includes administrative information on age, sex, all patient encounters with the system, primary diagnoses made at each physician/nurse practitioner/physician assistant visit (International Classification of Diseases—Ninth Revision codes), revenue codes, and charges for services. Subjects whose visits for the year were primarily because of pregnancy, substance abuse, or other recognized psychiatric problems/diagnoses (eg, bipolar disorder, eating disorder) were excluded.

Screening to identify somatizing and minor acute illness patients

We first identified all patients aged 21 to 55 years in the Lansing, Michigan, area who had at least 1 visit during 1995 to a physician, physician assistant, nurse practitioner, specialist, or emergency room; each hospitalization was counted as 1 visit. We did not use older patients, because our goal for another project was to identify chronic somatizing patients with minimal organic disease; the discovery of minor acute illness patients was an unexpected byproduct. Of 15,505 members in 1995, 5423 had 6 or more visits, and 1000 of these patients were randomly selected for further evaluation; to obtain the greatest possible sample, we arbitrarily defined 6 or more visits (65th percentile) as high users. Of the 1000, 94 were excluded because of pregnancy, substance abuse, visits for psychiatric care, or because they were employees of the HMO, and 23 were excluded because of incomplete data. We excluded patients under regular psychiatric care, because we wanted to obtain (for a treatment intervention) patients not receiving psychologic attention. The remaining 883 patients constituted the study population. Excluded patients differed from those in the study group in age, sex, and employer group but not on the amount of copay (P =.58) and relationship to the subscriber (P =.23). Excluded patients were on average younger (35.7 years vs 40.3 years, P <.001), and 88% were women as opposed to 68% for patients included in the overall study (P <.001).

 

 

Reference standard diagnoses were established by a resident physician (emergency medicine) rating the 883 charts according to specific criteria, reported previously12 and summarized in the report’s Appendix.* The rater classified patients by their primary/predominant problem for the entire year as organic disease, somatization, or minor acute illness. The designation of the primary problem was based on the largest number of visits for a problem. For example, a patient with a documented urinary infection at the first visit with 1 follow-up visit, documented pneumonia at the third visit with 2 follow-up visits, 1 visit for chronic low back pain with a negative computed tomography scan, and 2 visits for minor ligamentous strain, with no objective manifestations and no investigation, would be rated as organic for this year; similarly, a patient would be considered to have minor acute illness with 6 visits for minor complaints with no work-up and no objective manifestations of disease on examination, as well as 1 visit for documented urinary infection and 3 for diabetes mellitus. The same rule was used for follow-up ratings 1 and 2 years later, and the rater for follow-up ratings was unaware of the baseline ratings.

Organic disease was diagnosed by standard medical criteria and based on clear physical signs of disease (eg, laceration, enlarged liver) or, almost always, definitive laboratory investigation; the rater relied on expert judgment and referred to text material as needed.13 Somatization was rated when, following objectively based diagnostic evaluation (definitive testing), patients were free of organic disease that contributed significantly to at least 1 physical symptom of at least 6 months’ duration. Minor acute illness was rated when all physical symptoms were of less than 6 months’ duration, as judged by the rater from explicit mention in the chart or from observation that symptoms cleared and did not recur, and there was no documentation of an organic disease explanation for the symptom or its degree of severity. Because minor acute problems typically were not severe or disabling (in contrast to somatization), definitive testing often had not been performed. From the 1995 baseline sample of 122 somatizers and 450 minor acute patients identified by our rating procedure, we re-rated a sample of all available somatizers (N=104; 85%) and a 15% random sample of minor acute illness patients (N=66) both 1 and 2 years later.

After 10 hours of initial training, including practice rating on nonstudy charts, the rater rated 20 charts of non-study high-utilizing patients. A priori, we set an agreement rate with the trainer (one of the authors [R.C.S.]) for primary diagnosis of 90% (18 of 20 charts) before the rater began rating study patients. During the study, the trainer rated sets of 20 study charts already evaluated by the rater, once each during 1995, 1996, and 1997. The rater had high levels of agreement with the trainer throughout, varying from 90% to 95%. This level of agreement is not surprising, because the trainer trained the rater, which was also reflected in the of 0.93.

Statistical analysis

We also reviewed the 1996 and 1997 use for the same 104 somatizers identified in our initial 1995 baseline chart review of high-utilizing patients. Again, the same 15% random sample (N=66) was selected from those classified as having minor acute illness in 1995. In the follow-up years, some patients had relocated or were no longer receiving their medical care at our HMO. However, nearly 85% of the patients in our selected sample were continuously enrolled in the HMO in the 2 subsequent years of our study. Similar to chart rating, the final sample consisted of 104 somatizers and 66 patients with minor acute illness. For these patients we ascertained their status (somatization, organic disease, or minor acute illness) and their use (<6 visits; 6 visits) in 1996 and 1997. The 2 groups of patients were compared using chi-square tests for categoric variables and by t tests for continuous variables. Confidence intervals for binomial proportions were calculated by the exact method.

Results

The characteristics of 170 patients (104 somatization; 66 minor acute) studied at all data collection points are shown in Table 1. The mean age (as of 1995) was 41.3 years among somatizers versus 39.7 years among patients with minor acute illness (P =.19) The 2 groups differed only by sex, with nearly 83% of the somatizers being women, compared with 65% among minor acute illness patients (P=.009).

Among 883 high-use patients at baseline, 311 had organic diseases (35%); 122 had somatization (14%); and 450 had minor acute illness (51%) as their primary problems. No patients with initial minor acute illness diagnoses were reclassified as somatization 1 and 2 years later, and all but 2 patients had minor acute illness in 1 or both follow-up years.

 

 

The follow-up status in 1996 and 1997 of the 104 somatizers and the 66 minor acute illness patients initially rated in 1995 appears in Figure 1. It shows the (percentage) distribution of organic diseases, somatization, and minor acute illness diagnoses for 1995, 1996, and 1997, using all available data in each year. Because our rating of patients in 1 of the 3 categories followed a strict protocol (for 13 patients in 1996 and 40 patients in 1997), and because of insufficient information in their charts, we were unable to definitely ascertain their status. None of the patients with minor acute illness were reclassified as somatizers in the follow-up years, and 83% of these patients continued to have minor acute illness in 1996. All but 2 patients had minor acute illness either in 1996 or 1997 or both. Approximately 27% of the somatizers remained somatizers in 1996, 54% were rated as having minor acute illness, and 13% had organic disease. In 27% of these somatizers minor acute illness developed in the 2 subsequent years. Among patients in 1995, the probabilities of somatization for 2 and 3 consecutive years were 3.7% and 1.1%, respectively. In contrast, for minor acute illness these probabilities were 42.5% and 27%, respectively.

Use status of the groups (somatization, minor acute illness) combined is shown in Figure 2. It shows the (percentage) distribution of use (<6 visits; 6 visits) for 1996 and 1997. Approximately 57% of the somatizers were high users in 1996, compared with 29% of the minor acute illness patients. High use in either 1996 or 1997 was more prevalent among somatizers (70%) than in minor acute illness patients (45%). Persistent high use in both years among minor acute illness patients was 16.7% (95% confidence interval [CI], 8.6%-27.9%) and among somatizers 32.7% (95% CI, 23.8%-42.6%).

TABLE 1
CHARACTERISTICS OF FOLLOW-UP STUDY (N=70)

CharacteristicSubgroupSomatizers % (N)Minor Acute % (N)
Age, years20-2913.5 (14)9.1 (6)
30-3924.0 (25)43.9 (29)
40-4951.0 (53)36.4 (24)
50+11.5 (12)10.6 (7)
SexMen17.3 (18)34.9 (23)
Women82.7 (86)65.2 (43)
EmployerMSU6.7 (7)4.6 (3)
State28.9 (30)24.2 (16)
GM22.1 (23)30.3 (20)
Other42.3 (44)41.0 (27)
Copay, $058.7 (61)57.6 (38)
528.9 (30)25.8 (17)
711.5 (12)10.6 (7)
101.0 (1)6.1 (4)
Relationship to subscriberSelf59.6 (62)62.1 (41)
Spouse37.5 (39)34.9 (23)
Dependent2.9 (3)3.0 (2)
MSU denotes Michigan State University; GM, General Motors.

FIGURE 1
FOLLOW-UP DIAGNOSES IN INTIALLY IDENTIFIED MINOR ACUTE ILLNESS AND SOMATIZATION PATIENTS


FIGURE 2
FOLLOW-UP USE PATTERNS OF INTIALLY HIGH-USE PATIENTS WITH MINOR ACUTE ILLNESS AND SOMATIZATION PATIENTS

Discussion

Among high users of care, patients with minor acute symptoms were more common than those with symptoms of somatization and organic diseases combined. On follow-up study, diagnoses of somatization often changed to minor acute status, while minor acute illness diagnoses were more persistent. Although use did not remain high in either group, regression to the mean must be considered, because we sampled only on high use in 1995.

We found no research data to compare with our results in patients with minor acute illness. However, the presumed stability and chronicity of somatization diagnoses3,14 have been questioned.14-16 For example, great variability in symptoms was seen in repeated hospitalizations of somatizers, as judged by chart review.17 Others found that only 21 of 70 cases identified as somatization disorder at baseline had the same diagnosis 12 months later and that only approximately one half of all somatization symptoms were present 1 year later.16,18

Limitations

This first preliminary study of high users with minor acute illness was limited by the shortcomings of any retrospective chart review. These include the facts that: (1) We depended on how aggressively a physician attempted to diagnose organic disease and on how completely clinicians recorded their findings; (2) Without access to patients, we were unable to identify their unique perspective or to diagnose specific somatoform disorders3 using, for example, the Diagnostic Interview Schedule,19 the World Health Organization Composite Diagnostic Interview,20 or direct patient interviews; (3) Our designation of somatization depended on identifying symptoms of 6 or more months’ duration, the minimum criterion specified for Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) somatoform diagnoses3; if busy clinicians did not record duration, overcoding of minor acute status could have occurred.4 We cannot be certain that patients in either the medically unexplained group (ie, somatization, minor acute illness) did not in fact have organic diseases, because we were unable to investigate each patient ourselves from a biomedical perspective. However, during follow-up chart review, we observed no instances of an important organic disease having been missed when a diagnosis of minor acute illness or somatization status was made initially.15,21

In spite of these expected problems when chart review is the only available source of data, this research on previously unstudied patients provides a starting point for further research. Based on clinical descriptions, our observations on somatizing patients did in fact indicate that we had studied a typical chronic somatizing population (eg, those with low back pain, pelvic pain, irritable bowel syndrome). Also, others have found that chart review may be superior in identifying somatizing patients17,18 and that the DSM-IV has important shortcomings, notwithstanding the latter’s prominent present role in diagnosing conditions with medically unexplained symptoms.22,23 Further, because minor acute illness symptoms are short term, it is arguable that few minor acute patients will receive DSM-IV somatoform diagnoses. Still, as the next research step DSM-IV diagnoses should be sought to better delineate this new group, as should other standard psychiatric measures, especially for depression, anxiety, and unique personality traits.

 

 

Although psychiatric measures are needed to better define this high-use population, their absence in this initial study does not negate the importance of our findings from medical patients’ charts. They presumably reflect what patients actually reported to providers as their major reasons for seeking care, in contrast to questionnaire and lay interviewer data obtained unrelated to care seeking. Complementing our chart-based data with these standard psychiatric measures is the necessary next step.

Others have considered the problem of minor symptoms. Barsky and Borus7 seemed to distinguish short-term symptoms from the chronic symptoms of somatization and somatoform disorders by including many symptoms that often are brief and self-limited in what they called functional somatic syndromes (eg, palpitations, dizziness, lightheadedness, sore throat, and dry mouth). To avoid compounding the severe nosology problem in somatization24,25 and because the term functional somatic syndromes has been used by others to encompass all types of somatization,26,27 we are using the purely descriptive term “minor acute illness” to identify the patients reported here with short-term symptoms, recognizing that there may be considerable overlap with the group of patients identified by Barsky and Borus,7 Katon and colleagues,28 the acute and subacute somatizers of Kleinman,8 and the somatoform “not otherwise specified” category in DSM-IV.29 We prefer minor acute illness also to the common but pejorative term “worried well,” which we believe has never been defined. The “minor acute” label also has been used previously in a closely related context.30 Similar to Katon and coworkers,28 we propose that minor acute illness fits into the mild end of a multidimensional classification scheme for patients with medically unexplained symptoms—with abridged somatization disorder31 as moderate and full somatization disorder as severe.3 The latter 2 diagnoses are based on DSM-IV criteria only.

CONCLUSIONS

More research is needed to assist the field in better addressing patients with medically unexplained symptoms. The long-range goal for minor acute illness (as well as somatization) is to determine if it is a distinct and valid entity. In what is a complex task for psychiatric epidemiologists in the absence of organic disease and pathophysiologic changes,32-37 we can use the recommendations of Guze and colleagues38,39 for establishing the validity of a psychiatric diagnosis to guide us (Table 2). At this point, we can say only that there is evidence from our initial research study that we can use to describe and define minor acute illness and that it persists over 2 years (criteria 1 and 4 from Table 2). These are key determinants of validity, but they require much confirmation.38,39 Extensive work lies ahead in achieving our ultimate goal, providing effective treatment for a group that often receives inappropriate treatment, such as unnecessary antibiotics.

TABLE 2
VALIDITY CRITERIA FOR PSYCHIATRIC ENTITIES

  1. Describe the syndrome/disorder through unique symptoms and symptom patterns as well as by other features (eg, age and precipitating factors), and delineate clinical exclusion criteria to differentiate from other disorders.
  2. Identify distinctive laboratory diagnostic features, including psychologic testing.
  3. Identify uniform etiology, pathogenesis, and epidemiology to distinguish from other syndromes/diseases.
  4. Show uniform clinical course and persistence of diagnosis over time.
  5. Show increased prevalence among close family members.
NOTE: From Guze and colleagues,38,39 who comment that we often know little about criteria 2 and 3, and a careful description must focus on criteria 4 and 5, follow-up studies, and family studies.

Acknowledgments

Our work was supported by a generous grant from the Institute for Managed Care, Michigan State University, East Lansing.

ABSTRACT

OBJECTIVES: Our objectives were to determine how patients who make frequent use of the medical system (high users) with medically unexplained symptoms met our chart-rating criteria for somatization and minor acute illness and what the stability of such diagnoses were over time.

STUDY DESIGN: A chart review was performed at baseline and 1 and 2 years; we re-rated the charts of patients initially rated as having somatization, as well as a 15% sample of those with minor acute illness.

POPULATION: We obtained a random sample of high-use patients (6 visits/year) aged 21 to 55 years who were identified from the management information system.

OUTCOMES: We measured chart review designations as organic disease, somatization, or minor acute illness.

RESULTS: Among 883 high users at baseline, 35% had organic diseases; 14% had somatization; and 51% had minor acute illness as their primary problems. No patients with initial minor acute diagnoses were reclassified as having somatization 1 or 2 years later, and all but 2 patients had minor acute illness in 1 or both follow-up years.

CONCLUSIONS: Minor acute illness was more common among high users than somatization and organic diseases combined. It has not previously been studied but probably has been recognized by clinicians as the “worried well.” Diagnoses of somatization were unstable over 2 years’ follow-up, while minor acute diagnoses were stable, supporting the latter as a valid entity.

KEY POINTS FOR CLINICIANS

  • Many high-use patients with medically unexplained symptoms have a syndrome characterized by minor but recurring symptoms that we call minor acute illness.
  • Minor acute illness has not been previously described as a research entity, but there are some similarities to what is referred to as the “worried well” in the nonresearch literature.

Using this preliminary research, we report on patients with medically unexplained physical symptoms, who have what we call “minor acute illness.” In contrast to the well-studied chronic somatizing patient in whom medically unexplained symptoms1,2 are of at least 6 months’ duration,3 we define minor acute illness as unexplained symptoms of any type (eg, sore throats, minor sprains, “sinuses”) that resolve completely in less than 6 months (usually days or weeks). Although most patients would not seek care for these minor complaints, some patients with minor acute illness have exaggerated responses to common symptoms and become high users of medical care.4-8 These are probably recognized by many physicians as the “worried well.”

Our review of the literature and discussion with several experts reveal that no research group has given consideration to defining the diagnostic features of minor acute illness or to describing it over time.9,10 Not surprisingly, studies of treatment are nonexistent, and reported treatments are ineffective.5,6,11 We present preliminary research defining minor acute illness, distinguishing it from somatization and organic disease, and evaluating its persistence over time among high-use patients.

Methods

Subjects

All patients were members of a largely primary care, staff model health maintenance organization (HMO) in Lansing, Michigan (Blue Cross Network). Only computerized descriptive information in the HMO’s management information system (MIS) and data in patients’ clinical charts were involved in our study. The MIS includes administrative information on age, sex, all patient encounters with the system, primary diagnoses made at each physician/nurse practitioner/physician assistant visit (International Classification of Diseases—Ninth Revision codes), revenue codes, and charges for services. Subjects whose visits for the year were primarily because of pregnancy, substance abuse, or other recognized psychiatric problems/diagnoses (eg, bipolar disorder, eating disorder) were excluded.

Screening to identify somatizing and minor acute illness patients

We first identified all patients aged 21 to 55 years in the Lansing, Michigan, area who had at least 1 visit during 1995 to a physician, physician assistant, nurse practitioner, specialist, or emergency room; each hospitalization was counted as 1 visit. We did not use older patients, because our goal for another project was to identify chronic somatizing patients with minimal organic disease; the discovery of minor acute illness patients was an unexpected byproduct. Of 15,505 members in 1995, 5423 had 6 or more visits, and 1000 of these patients were randomly selected for further evaluation; to obtain the greatest possible sample, we arbitrarily defined 6 or more visits (65th percentile) as high users. Of the 1000, 94 were excluded because of pregnancy, substance abuse, visits for psychiatric care, or because they were employees of the HMO, and 23 were excluded because of incomplete data. We excluded patients under regular psychiatric care, because we wanted to obtain (for a treatment intervention) patients not receiving psychologic attention. The remaining 883 patients constituted the study population. Excluded patients differed from those in the study group in age, sex, and employer group but not on the amount of copay (P =.58) and relationship to the subscriber (P =.23). Excluded patients were on average younger (35.7 years vs 40.3 years, P <.001), and 88% were women as opposed to 68% for patients included in the overall study (P <.001).

 

 

Reference standard diagnoses were established by a resident physician (emergency medicine) rating the 883 charts according to specific criteria, reported previously12 and summarized in the report’s Appendix.* The rater classified patients by their primary/predominant problem for the entire year as organic disease, somatization, or minor acute illness. The designation of the primary problem was based on the largest number of visits for a problem. For example, a patient with a documented urinary infection at the first visit with 1 follow-up visit, documented pneumonia at the third visit with 2 follow-up visits, 1 visit for chronic low back pain with a negative computed tomography scan, and 2 visits for minor ligamentous strain, with no objective manifestations and no investigation, would be rated as organic for this year; similarly, a patient would be considered to have minor acute illness with 6 visits for minor complaints with no work-up and no objective manifestations of disease on examination, as well as 1 visit for documented urinary infection and 3 for diabetes mellitus. The same rule was used for follow-up ratings 1 and 2 years later, and the rater for follow-up ratings was unaware of the baseline ratings.

Organic disease was diagnosed by standard medical criteria and based on clear physical signs of disease (eg, laceration, enlarged liver) or, almost always, definitive laboratory investigation; the rater relied on expert judgment and referred to text material as needed.13 Somatization was rated when, following objectively based diagnostic evaluation (definitive testing), patients were free of organic disease that contributed significantly to at least 1 physical symptom of at least 6 months’ duration. Minor acute illness was rated when all physical symptoms were of less than 6 months’ duration, as judged by the rater from explicit mention in the chart or from observation that symptoms cleared and did not recur, and there was no documentation of an organic disease explanation for the symptom or its degree of severity. Because minor acute problems typically were not severe or disabling (in contrast to somatization), definitive testing often had not been performed. From the 1995 baseline sample of 122 somatizers and 450 minor acute patients identified by our rating procedure, we re-rated a sample of all available somatizers (N=104; 85%) and a 15% random sample of minor acute illness patients (N=66) both 1 and 2 years later.

After 10 hours of initial training, including practice rating on nonstudy charts, the rater rated 20 charts of non-study high-utilizing patients. A priori, we set an agreement rate with the trainer (one of the authors [R.C.S.]) for primary diagnosis of 90% (18 of 20 charts) before the rater began rating study patients. During the study, the trainer rated sets of 20 study charts already evaluated by the rater, once each during 1995, 1996, and 1997. The rater had high levels of agreement with the trainer throughout, varying from 90% to 95%. This level of agreement is not surprising, because the trainer trained the rater, which was also reflected in the of 0.93.

Statistical analysis

We also reviewed the 1996 and 1997 use for the same 104 somatizers identified in our initial 1995 baseline chart review of high-utilizing patients. Again, the same 15% random sample (N=66) was selected from those classified as having minor acute illness in 1995. In the follow-up years, some patients had relocated or were no longer receiving their medical care at our HMO. However, nearly 85% of the patients in our selected sample were continuously enrolled in the HMO in the 2 subsequent years of our study. Similar to chart rating, the final sample consisted of 104 somatizers and 66 patients with minor acute illness. For these patients we ascertained their status (somatization, organic disease, or minor acute illness) and their use (<6 visits; 6 visits) in 1996 and 1997. The 2 groups of patients were compared using chi-square tests for categoric variables and by t tests for continuous variables. Confidence intervals for binomial proportions were calculated by the exact method.

Results

The characteristics of 170 patients (104 somatization; 66 minor acute) studied at all data collection points are shown in Table 1. The mean age (as of 1995) was 41.3 years among somatizers versus 39.7 years among patients with minor acute illness (P =.19) The 2 groups differed only by sex, with nearly 83% of the somatizers being women, compared with 65% among minor acute illness patients (P=.009).

Among 883 high-use patients at baseline, 311 had organic diseases (35%); 122 had somatization (14%); and 450 had minor acute illness (51%) as their primary problems. No patients with initial minor acute illness diagnoses were reclassified as somatization 1 and 2 years later, and all but 2 patients had minor acute illness in 1 or both follow-up years.

 

 

The follow-up status in 1996 and 1997 of the 104 somatizers and the 66 minor acute illness patients initially rated in 1995 appears in Figure 1. It shows the (percentage) distribution of organic diseases, somatization, and minor acute illness diagnoses for 1995, 1996, and 1997, using all available data in each year. Because our rating of patients in 1 of the 3 categories followed a strict protocol (for 13 patients in 1996 and 40 patients in 1997), and because of insufficient information in their charts, we were unable to definitely ascertain their status. None of the patients with minor acute illness were reclassified as somatizers in the follow-up years, and 83% of these patients continued to have minor acute illness in 1996. All but 2 patients had minor acute illness either in 1996 or 1997 or both. Approximately 27% of the somatizers remained somatizers in 1996, 54% were rated as having minor acute illness, and 13% had organic disease. In 27% of these somatizers minor acute illness developed in the 2 subsequent years. Among patients in 1995, the probabilities of somatization for 2 and 3 consecutive years were 3.7% and 1.1%, respectively. In contrast, for minor acute illness these probabilities were 42.5% and 27%, respectively.

Use status of the groups (somatization, minor acute illness) combined is shown in Figure 2. It shows the (percentage) distribution of use (<6 visits; 6 visits) for 1996 and 1997. Approximately 57% of the somatizers were high users in 1996, compared with 29% of the minor acute illness patients. High use in either 1996 or 1997 was more prevalent among somatizers (70%) than in minor acute illness patients (45%). Persistent high use in both years among minor acute illness patients was 16.7% (95% confidence interval [CI], 8.6%-27.9%) and among somatizers 32.7% (95% CI, 23.8%-42.6%).

TABLE 1
CHARACTERISTICS OF FOLLOW-UP STUDY (N=70)

CharacteristicSubgroupSomatizers % (N)Minor Acute % (N)
Age, years20-2913.5 (14)9.1 (6)
30-3924.0 (25)43.9 (29)
40-4951.0 (53)36.4 (24)
50+11.5 (12)10.6 (7)
SexMen17.3 (18)34.9 (23)
Women82.7 (86)65.2 (43)
EmployerMSU6.7 (7)4.6 (3)
State28.9 (30)24.2 (16)
GM22.1 (23)30.3 (20)
Other42.3 (44)41.0 (27)
Copay, $058.7 (61)57.6 (38)
528.9 (30)25.8 (17)
711.5 (12)10.6 (7)
101.0 (1)6.1 (4)
Relationship to subscriberSelf59.6 (62)62.1 (41)
Spouse37.5 (39)34.9 (23)
Dependent2.9 (3)3.0 (2)
MSU denotes Michigan State University; GM, General Motors.

FIGURE 1
FOLLOW-UP DIAGNOSES IN INTIALLY IDENTIFIED MINOR ACUTE ILLNESS AND SOMATIZATION PATIENTS


FIGURE 2
FOLLOW-UP USE PATTERNS OF INTIALLY HIGH-USE PATIENTS WITH MINOR ACUTE ILLNESS AND SOMATIZATION PATIENTS

Discussion

Among high users of care, patients with minor acute symptoms were more common than those with symptoms of somatization and organic diseases combined. On follow-up study, diagnoses of somatization often changed to minor acute status, while minor acute illness diagnoses were more persistent. Although use did not remain high in either group, regression to the mean must be considered, because we sampled only on high use in 1995.

We found no research data to compare with our results in patients with minor acute illness. However, the presumed stability and chronicity of somatization diagnoses3,14 have been questioned.14-16 For example, great variability in symptoms was seen in repeated hospitalizations of somatizers, as judged by chart review.17 Others found that only 21 of 70 cases identified as somatization disorder at baseline had the same diagnosis 12 months later and that only approximately one half of all somatization symptoms were present 1 year later.16,18

Limitations

This first preliminary study of high users with minor acute illness was limited by the shortcomings of any retrospective chart review. These include the facts that: (1) We depended on how aggressively a physician attempted to diagnose organic disease and on how completely clinicians recorded their findings; (2) Without access to patients, we were unable to identify their unique perspective or to diagnose specific somatoform disorders3 using, for example, the Diagnostic Interview Schedule,19 the World Health Organization Composite Diagnostic Interview,20 or direct patient interviews; (3) Our designation of somatization depended on identifying symptoms of 6 or more months’ duration, the minimum criterion specified for Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) somatoform diagnoses3; if busy clinicians did not record duration, overcoding of minor acute status could have occurred.4 We cannot be certain that patients in either the medically unexplained group (ie, somatization, minor acute illness) did not in fact have organic diseases, because we were unable to investigate each patient ourselves from a biomedical perspective. However, during follow-up chart review, we observed no instances of an important organic disease having been missed when a diagnosis of minor acute illness or somatization status was made initially.15,21

In spite of these expected problems when chart review is the only available source of data, this research on previously unstudied patients provides a starting point for further research. Based on clinical descriptions, our observations on somatizing patients did in fact indicate that we had studied a typical chronic somatizing population (eg, those with low back pain, pelvic pain, irritable bowel syndrome). Also, others have found that chart review may be superior in identifying somatizing patients17,18 and that the DSM-IV has important shortcomings, notwithstanding the latter’s prominent present role in diagnosing conditions with medically unexplained symptoms.22,23 Further, because minor acute illness symptoms are short term, it is arguable that few minor acute patients will receive DSM-IV somatoform diagnoses. Still, as the next research step DSM-IV diagnoses should be sought to better delineate this new group, as should other standard psychiatric measures, especially for depression, anxiety, and unique personality traits.

 

 

Although psychiatric measures are needed to better define this high-use population, their absence in this initial study does not negate the importance of our findings from medical patients’ charts. They presumably reflect what patients actually reported to providers as their major reasons for seeking care, in contrast to questionnaire and lay interviewer data obtained unrelated to care seeking. Complementing our chart-based data with these standard psychiatric measures is the necessary next step.

Others have considered the problem of minor symptoms. Barsky and Borus7 seemed to distinguish short-term symptoms from the chronic symptoms of somatization and somatoform disorders by including many symptoms that often are brief and self-limited in what they called functional somatic syndromes (eg, palpitations, dizziness, lightheadedness, sore throat, and dry mouth). To avoid compounding the severe nosology problem in somatization24,25 and because the term functional somatic syndromes has been used by others to encompass all types of somatization,26,27 we are using the purely descriptive term “minor acute illness” to identify the patients reported here with short-term symptoms, recognizing that there may be considerable overlap with the group of patients identified by Barsky and Borus,7 Katon and colleagues,28 the acute and subacute somatizers of Kleinman,8 and the somatoform “not otherwise specified” category in DSM-IV.29 We prefer minor acute illness also to the common but pejorative term “worried well,” which we believe has never been defined. The “minor acute” label also has been used previously in a closely related context.30 Similar to Katon and coworkers,28 we propose that minor acute illness fits into the mild end of a multidimensional classification scheme for patients with medically unexplained symptoms—with abridged somatization disorder31 as moderate and full somatization disorder as severe.3 The latter 2 diagnoses are based on DSM-IV criteria only.

CONCLUSIONS

More research is needed to assist the field in better addressing patients with medically unexplained symptoms. The long-range goal for minor acute illness (as well as somatization) is to determine if it is a distinct and valid entity. In what is a complex task for psychiatric epidemiologists in the absence of organic disease and pathophysiologic changes,32-37 we can use the recommendations of Guze and colleagues38,39 for establishing the validity of a psychiatric diagnosis to guide us (Table 2). At this point, we can say only that there is evidence from our initial research study that we can use to describe and define minor acute illness and that it persists over 2 years (criteria 1 and 4 from Table 2). These are key determinants of validity, but they require much confirmation.38,39 Extensive work lies ahead in achieving our ultimate goal, providing effective treatment for a group that often receives inappropriate treatment, such as unnecessary antibiotics.

TABLE 2
VALIDITY CRITERIA FOR PSYCHIATRIC ENTITIES

  1. Describe the syndrome/disorder through unique symptoms and symptom patterns as well as by other features (eg, age and precipitating factors), and delineate clinical exclusion criteria to differentiate from other disorders.
  2. Identify distinctive laboratory diagnostic features, including psychologic testing.
  3. Identify uniform etiology, pathogenesis, and epidemiology to distinguish from other syndromes/diseases.
  4. Show uniform clinical course and persistence of diagnosis over time.
  5. Show increased prevalence among close family members.
NOTE: From Guze and colleagues,38,39 who comment that we often know little about criteria 2 and 3, and a careful description must focus on criteria 4 and 5, follow-up studies, and family studies.

Acknowledgments

Our work was supported by a generous grant from the Institute for Managed Care, Michigan State University, East Lansing.

References

1. Lipowski ZJ. Somatization: the concept and its clinical application. Am J Psychiatry 1988;145:1358-68.

2. Kirmayer LJ, Robbins JM. Introduction: concepts of somatization. In: Kirmayer LJ, Robbins JM, eds. Current concepts of somatization: research and clinical perspectives. Washington, DC: American Psychiatric Press, Inc; 1991;1-19.

3. American Psychiatric Association. Diagnostic and statistical manual of mental disorders. 4th ed. Washington, DC: American Psychiatric Association; 1994.

4. Komaroff AL. ‘Minor’ illness symptoms—the magnitude of their burden and of our ignorance. Arch Intern Med 1990;150:1586-87.

5. Kroenke K, Mangelsdorff AD. Common symptoms in ambulatory care: incidence, evaluation, therapy, and outcome. Am J Med 1989;86:262-66.

6. Connelly JE, Smith GR, Philbrick JT, Kaiser DL. Healthy patients who perceive poor health and their use of primary care services. J Gen Int Med 1991;6:47-51.

7. Barsky AJ, Borus JF. Functional somatic syndromes. Ann Intern Med 1999;130:910-21.

8. Kleinman A. Social origins of distress and disease—depression, neurasthenia, and pain in modern China. New Haven, Conn: Yale University Press; 1986.

9. Andersson S-O, Mattsson B, Lynoe N. Patients frequently consulting general practitioners at a primary health care centre in Sweden—a comparative study. Scand J Soc Med 1995;23:251-57.

10. Verbrugge LM, Ascione FJ. Exploring the iceberg—common symptoms and how people care for them. Med Care 1987;25:539-69.

11. Kroenke K, Arrington ME, Mangelsdorff AD. The prevalence of symptoms in medical outpatients and the adequacy of therapy. Arch Intern Med 1990;150:1685-89.

12. Smith RC, Gardiner JC, Armatti S, et al. Screening for high utilizing somatizing patients using a prediction rule derived from the management information system of an HMO—a preliminary study. In press.

13. Humes HD, DuPont HL, Gardner LB, et al, eds. Kelley’s textbook of internal medicine. 4th ed. Philadelphia, Pa: Lippincott Williams and Wilkins; 2000.

14. Cloninger CR, Martin RL, Guze SB, Clayton PJ. A prospective follow-up and family study of somatization in men and women. Am J Psychiatry 1986;143:873-78.

15. Rief W, Hiller W, Geissner E, Fichter MM. A two-year follow-up study of patients with somatoform disorders. Psychosomatics 1995;36:376-86.

16. Gara MA, Escobar JI. The stability of somatization syndromes over time. Arch Gen Psychiatry 2001;58:94.-

17. Fink P. Physical complaints and symptoms of somatizing patients. J Psychosom Res 1992;36:125-36.

18. Simon GE, Gureje O. Stability of somatization disorder and somatization symptoms among primary care patients. Arch Gen Psychiatry 1999;56:90-95.

19. Robins LN, Helzer JE, Croughan J, Ratcliff KS. National Institute of Mental Health Diagnostic Interview Schedule: its history, characteristics, and validity. Arch Gen Psychiatry 1981;38:381-89.

20. Sartorius N. Composite International Diagnostic Interview (CIDI)—core version 1.1. Geneva, Switzerland: World Health Organization.

21. Kroenke K, Spitzer RL, deGruy FV, Hahn SR, Linzer M, Williams JBW, Brody D, Davies M. Multisomatoform disorder—an alternative to undifferentiated somatoform disorder for the somatizing patient in primary care. Arch Gen Psychiatry 1997;54:352-58.

22. Norquist G, Hyman SE. Advances in understanding and treating mental illness: implications for policy. Health Aff 1999;18:32-47.

23. Krueger RF. The structure of common mental disorders. Arch Gen Psychiatry 1999;56:921-26.

24. Murphy MR. Classification of the somatoform disorders. In: Bass CM, ed. Somatization: physical symptoms and psychological illness. Oxford, England: Blackwell; 1990;10-39.

25. Mayou R, Bass C, Sharpe M. Overview of epidemiology, classification, and aetiology. In: Mayou R, Bass C, Sharpe M, eds. Treatment of functional somatic symptoms. Oxford, England: Oxford University Press, 1995;42-65.

26. Kirmayer LJ, Robbins JM. Functional somatic syndromes. In: Kirmayer LJ, Robbins JM, eds. Current concepts of somatization: research and clinical perspectives. Washington, DC: American Psychiatric Press, Inc; 1991;79-106.

27. Sharpe M, Mayou R, Bass C. Concepts, theories, and terminology. In: Mayou R, Bass C, Sharpe M, eds. Treatment of functional somatic symptoms. Oxford, England: Oxford University Press; 1995;1-16.

28. Katon W, Lin E, von Korff M, Russo J, Lipscomb P, Bush T. Somatization: a spectrum of severity. Am J Psychiatry 1991;148:34-40.

29. Kroenke K, Spitzer RL, deGruy FV, Swindle R. A symptom checklist to screen for somatoform disorders in primary care. Psychosomatics 1998;39:263-72.

30. Weiner JP, Starfield BH, Steinwachs DM, Mumford LM. Development and application of a population-oriented measure of ambulatory care case-mix. Med Care 1991;29:452-72.

31. Escobar JI, Swartz M, Rubio-Stipec M, Manu P. Medically unexplained symptoms: distribution, risk factors, and comorbidity. In: Kirmayer LJ, Robbins JM, eds. Current concepts of somatization: research and clinical perspectives. Washington, DC: American Psychiatric Press, Inc; 1991;63-78.

32. Kovacs M, Gatsonis C. Stability and change in childhood-onset depressive disorders: longitudinal course as a diagnostic validator. In: Robins LN, Barrett JE, eds. The validity of psychiatric diagnosis. New York, NY: Raven Press, Ltd; 1989;57-73.

33. Tyrer P, Alexander J, Remington M, Riley P. Relationship between neurotic symptoms and neurotic diagnosis: a longitudinal study. J Affect Dis 1987;13:13-21.

34. Beiser M, Iacono WG, Erickson D. Temporal stability in the major mental disorders. In: Robins LN, Barrett JE, eds. The validity of psychiatric diagnosis. New York, NY: Raven Press, Ltd; 1989;77-97.

35. Andreason NC. The validation of psychiatric diagnosis: new models and approaches. Am J Psychiatry 1995;152:161-62.

36. Cloninger CR. Establishment of diagnostic validity in psychiatric illness: Robins and Guze’s method revisited. In: Robins LN, Barrett JE, eds. The validity of psychiatric diagnoses. New York, NY: Raven Press; 1989;9-16.

37. Grove WM, Andreasen NC. Quantitative and qualitative distinctions between psychiatric disorders. In: Robins LN, Barrett JE, eds. The validity of psychiatric diagnoses. New York, NY: Raven Press; 1989;127-39.

38. Guze SB. The diagnosis of hysteria: what are we trying to do? Am J Psychiatry 1967;124:491-98.

39. Robins E, Guze SB. Establishment of diagnostic validity in psychiatric illness: its application to schizophrenia. Am J Psychiatry 1970;126:983-87.

References

1. Lipowski ZJ. Somatization: the concept and its clinical application. Am J Psychiatry 1988;145:1358-68.

2. Kirmayer LJ, Robbins JM. Introduction: concepts of somatization. In: Kirmayer LJ, Robbins JM, eds. Current concepts of somatization: research and clinical perspectives. Washington, DC: American Psychiatric Press, Inc; 1991;1-19.

3. American Psychiatric Association. Diagnostic and statistical manual of mental disorders. 4th ed. Washington, DC: American Psychiatric Association; 1994.

4. Komaroff AL. ‘Minor’ illness symptoms—the magnitude of their burden and of our ignorance. Arch Intern Med 1990;150:1586-87.

5. Kroenke K, Mangelsdorff AD. Common symptoms in ambulatory care: incidence, evaluation, therapy, and outcome. Am J Med 1989;86:262-66.

6. Connelly JE, Smith GR, Philbrick JT, Kaiser DL. Healthy patients who perceive poor health and their use of primary care services. J Gen Int Med 1991;6:47-51.

7. Barsky AJ, Borus JF. Functional somatic syndromes. Ann Intern Med 1999;130:910-21.

8. Kleinman A. Social origins of distress and disease—depression, neurasthenia, and pain in modern China. New Haven, Conn: Yale University Press; 1986.

9. Andersson S-O, Mattsson B, Lynoe N. Patients frequently consulting general practitioners at a primary health care centre in Sweden—a comparative study. Scand J Soc Med 1995;23:251-57.

10. Verbrugge LM, Ascione FJ. Exploring the iceberg—common symptoms and how people care for them. Med Care 1987;25:539-69.

11. Kroenke K, Arrington ME, Mangelsdorff AD. The prevalence of symptoms in medical outpatients and the adequacy of therapy. Arch Intern Med 1990;150:1685-89.

12. Smith RC, Gardiner JC, Armatti S, et al. Screening for high utilizing somatizing patients using a prediction rule derived from the management information system of an HMO—a preliminary study. In press.

13. Humes HD, DuPont HL, Gardner LB, et al, eds. Kelley’s textbook of internal medicine. 4th ed. Philadelphia, Pa: Lippincott Williams and Wilkins; 2000.

14. Cloninger CR, Martin RL, Guze SB, Clayton PJ. A prospective follow-up and family study of somatization in men and women. Am J Psychiatry 1986;143:873-78.

15. Rief W, Hiller W, Geissner E, Fichter MM. A two-year follow-up study of patients with somatoform disorders. Psychosomatics 1995;36:376-86.

16. Gara MA, Escobar JI. The stability of somatization syndromes over time. Arch Gen Psychiatry 2001;58:94.-

17. Fink P. Physical complaints and symptoms of somatizing patients. J Psychosom Res 1992;36:125-36.

18. Simon GE, Gureje O. Stability of somatization disorder and somatization symptoms among primary care patients. Arch Gen Psychiatry 1999;56:90-95.

19. Robins LN, Helzer JE, Croughan J, Ratcliff KS. National Institute of Mental Health Diagnostic Interview Schedule: its history, characteristics, and validity. Arch Gen Psychiatry 1981;38:381-89.

20. Sartorius N. Composite International Diagnostic Interview (CIDI)—core version 1.1. Geneva, Switzerland: World Health Organization.

21. Kroenke K, Spitzer RL, deGruy FV, Hahn SR, Linzer M, Williams JBW, Brody D, Davies M. Multisomatoform disorder—an alternative to undifferentiated somatoform disorder for the somatizing patient in primary care. Arch Gen Psychiatry 1997;54:352-58.

22. Norquist G, Hyman SE. Advances in understanding and treating mental illness: implications for policy. Health Aff 1999;18:32-47.

23. Krueger RF. The structure of common mental disorders. Arch Gen Psychiatry 1999;56:921-26.

24. Murphy MR. Classification of the somatoform disorders. In: Bass CM, ed. Somatization: physical symptoms and psychological illness. Oxford, England: Blackwell; 1990;10-39.

25. Mayou R, Bass C, Sharpe M. Overview of epidemiology, classification, and aetiology. In: Mayou R, Bass C, Sharpe M, eds. Treatment of functional somatic symptoms. Oxford, England: Oxford University Press, 1995;42-65.

26. Kirmayer LJ, Robbins JM. Functional somatic syndromes. In: Kirmayer LJ, Robbins JM, eds. Current concepts of somatization: research and clinical perspectives. Washington, DC: American Psychiatric Press, Inc; 1991;79-106.

27. Sharpe M, Mayou R, Bass C. Concepts, theories, and terminology. In: Mayou R, Bass C, Sharpe M, eds. Treatment of functional somatic symptoms. Oxford, England: Oxford University Press; 1995;1-16.

28. Katon W, Lin E, von Korff M, Russo J, Lipscomb P, Bush T. Somatization: a spectrum of severity. Am J Psychiatry 1991;148:34-40.

29. Kroenke K, Spitzer RL, deGruy FV, Swindle R. A symptom checklist to screen for somatoform disorders in primary care. Psychosomatics 1998;39:263-72.

30. Weiner JP, Starfield BH, Steinwachs DM, Mumford LM. Development and application of a population-oriented measure of ambulatory care case-mix. Med Care 1991;29:452-72.

31. Escobar JI, Swartz M, Rubio-Stipec M, Manu P. Medically unexplained symptoms: distribution, risk factors, and comorbidity. In: Kirmayer LJ, Robbins JM, eds. Current concepts of somatization: research and clinical perspectives. Washington, DC: American Psychiatric Press, Inc; 1991;63-78.

32. Kovacs M, Gatsonis C. Stability and change in childhood-onset depressive disorders: longitudinal course as a diagnostic validator. In: Robins LN, Barrett JE, eds. The validity of psychiatric diagnosis. New York, NY: Raven Press, Ltd; 1989;57-73.

33. Tyrer P, Alexander J, Remington M, Riley P. Relationship between neurotic symptoms and neurotic diagnosis: a longitudinal study. J Affect Dis 1987;13:13-21.

34. Beiser M, Iacono WG, Erickson D. Temporal stability in the major mental disorders. In: Robins LN, Barrett JE, eds. The validity of psychiatric diagnosis. New York, NY: Raven Press, Ltd; 1989;77-97.

35. Andreason NC. The validation of psychiatric diagnosis: new models and approaches. Am J Psychiatry 1995;152:161-62.

36. Cloninger CR. Establishment of diagnostic validity in psychiatric illness: Robins and Guze’s method revisited. In: Robins LN, Barrett JE, eds. The validity of psychiatric diagnoses. New York, NY: Raven Press; 1989;9-16.

37. Grove WM, Andreasen NC. Quantitative and qualitative distinctions between psychiatric disorders. In: Robins LN, Barrett JE, eds. The validity of psychiatric diagnoses. New York, NY: Raven Press; 1989;127-39.

38. Guze SB. The diagnosis of hysteria: what are we trying to do? Am J Psychiatry 1967;124:491-98.

39. Robins E, Guze SB. Establishment of diagnostic validity in psychiatric illness: its application to schizophrenia. Am J Psychiatry 1970;126:983-87.

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ABSTRACT

OBJECTIVE: Our goal was to compare the content of family practice in different countries using databases containing information on reasons for encounter, diagnoses, and interventions that are coded with or can be addressed by the International Classification of Primary Care (ICPC).

STUDY DESIGN: In the Netherlands, Japan, and Poland data were collected identically with an electronic patient record (Transhis). For all face-to-face encounters the reasons for encounter, diagnoses, and interventions were coded according to the ICPC within an episode of care structure; prescriptions were coded with the ICPC drug code. Data were collected for research purposes and cannot be considered representative for family practice in these countries. We derived comparable estimates for the United States using visit data from the National Ambulatory Care Survey (NAMCS), with specific emphasis on the contribution of family physicians. NAMCS data were mapped to the ICPC and the ICPC drug code, and Dutch, Polish, and Japanese data were directly standardized for the 1996 US population. Data on utilization, reasons for encounter, encounters per episode of care, new episodes of care, and prescriptions were compared. We also present World Health Organization and Organisation for Economic Co-operation and Development data on health care delivery, efficiency, expenditure, and health status for each country.

POPULATION: We included the following: from the Netherlands: 10 family physicians, 48.640 patient years, 1995-2000; from Japan: 6 family physicians, 17.082 patient years, 1996-1999; from Poland: 22 family physicians, 11.315 patient years, 1997-1999; and from the United States: NAMCS 1995-97 30 991 patient years 91395 visits (26% with a family physician).

RESULTS: We found important differences and striking similarities. Differences in the numbers of episodes and of encounters per patient per year were small compared with differences in utilization per episode of care, including diagnostic and therapeutic interventions. Substantial differences were found in prescribing antibiotics, oral contraceptives, cardiovascular medications, and gastrointestinal therapies. Prescribing behavior in the Netherlands and the United States was similar, while very different patterns were found in Japan and Poland. Similarities were much higher in patients’ reasons for encounter than in diagnoses. Only 35 groups of symptoms/complaints covered the top 30s in all databases, at the same time including 45% to 60% of all symptom/complaint reasons for encounter.

CONCLUSIONS: Even under very different conditions there was substantial overlap in the top 30 symptom/complaint reasons for encounter, incidence rates, and encounters per diagnosis in the 4 countries we studied. This striking resemblance supports the concept of the reason for encounter as a core element of the consultation with a family physician. Similarities between the databases are much better reflected by the way patients formulate their demand for care than in the diagnoses by the family physician. Patients from the US also see providers other than family physicians for common problems; it remains unclear whether a limited group brings most of their health problems to a family physician or whether most people visit a series of primary care physicians. Possibilities to further develop episode-oriented epidemiology in family practice have considerably increased with this study. The potential for comparative studies has also increased with the introduction of complete electronic patient records based on the documentation of episodes of care with the ICPC and with its mapping to International Classification of Diseases-10th revision (or the 9th revision clinical modification).

Internationally, family practice receives increasing emphasis. The World Organization of Family Doctors (WONCA) now has members from more than 80 countries, in several of which family practice has developed into a core element of health care delivery and a well-defined academic discipline. In the United Kingdom, Ireland, Australia, New Zealand, Scandinavia and the Netherlands, the development of family practice has benefited from a health care policy that arranged for direct access for all, and for a gatekeeping function of the family physician that has also resulted in the availability of databases reflecting the distribution of morbidity in family practice populations.1-5 In the US, Japan and many European countries, however, the development of family practice is handicapped by a health care policy less favorable to the discipline. This has resulted in a paucity of information on the distribution of morbidity in the population.6-12

Primary care/family practice is characterized in the 1997 Institute of Medicine (IOM) definition as: “…the provision of integrated accessible health care services by physicians who are accountable for addressing a large majority of personal health care needs, developing a sustained partnership with patients and practicing in the context of family and community.”12 A particularly relevant unit of assessment for this definition is the episode of care, defined as a health problem from its first presentation to a health care provider till the last encounter for it.12-14 This implies that morbidity and mortality rates are insufficient to characterize the content of health care; one must include the patient’s perspective during episodes of illness and episodes of care.7

 

 

Over the years, WONCA has developed the International Classification of Primary Care ICPC as the ordering principle of the family practice domain. The ICPC describes episodes of care by reasons for encounter (reflecting the patient’s perspective), diagnoses (reflecting the physician’s perspective), and interventions.3,15-19 On this basis, family practice databases can be created that allow international comparison.

Unfortunately, national representative databases fulfilling the requirements formulated by White and colleagues in 1961 are still not available.7 The goal of this study is to compare the content of family practice in different countries, using existing databases that (minimally) contain data on reasons for encounter, diagnoses and interventions that are coded with, or can be addressed by ICPC in an episode of care structure.7,18

Family physicians in the Netherlands, Japan, and Poland have been collecting episode of care data over several years in listed populations, with an ICPC -based electronic patient record for all encounters, for research purposes and under controlled conditions. Recently, Green and colleagues noted a serious lack of such data in the US, resulting in problems when estimating essential indicators from available sources.9 Since the publication of the IOM Report on Primary Care in 1996, pointing out that the available information in the US did not allow episode of care analysis, the increasing use of electronic patient records in family practice networks has not yet resulted in databases fulfilling all criteria for this study. However, the National Ambulatory Medical Care Survey (NAMCS) records reasons for visit and diagnoses, allowing an estimation of the family physician’s contribution to ambulatory care; no episodes of care could be identified from NAMCS data.20-23 It was decided to use these four databases in this study.

Obviously, comparative studies must take into account the major differences in the national health care systems; global data from these four countries indicate substantial differences in health care delivery, expenditure and health status Table 1.24-26 Although it is impossible to directly relate these differences to the available databases, and family practice does not have a major impact on all of these outcomes, they can be helpful to better understand the study’s results.

Dutch family physicians are gatekeepers for listed and relatively healthy practice populations with universal access. This contrasts sharply with the US, where far more is spent on health care with disappointing health status indicators, and without a central position for family practice. Dutch family practice data on reasons for encounter, diagnoses and interventions are, by their nature, a close proxy for the population’s demand, clinical need, and supply. Most Dutch family physicians use an electronic patient record, in which the use of ICPC for coding diagnoses is mandatory.27-29

In Poland, little is spent on a health care system with general access; health status is unsatisfactory. Over the past decade, Polish health care policy has strongly supported family practice, deploying a family practice retraining program for general internists, gynecologists, and pediatricians with an often-longstanding experience in hospitals.27-29

In Japan, health care contributes to a relatively long and healthy life at moderate costs; family practice has a weak position, being well developed in rural areas only. Family physician training is much like that in general internal medicine. Although the Japanese have freedom of choice and complete coverage, patients in the participating rural practices bring most health problems to their family physician, with the exception of practically all obstetric/gynecologic and most pediatric and psychiatric care for which they see specialists in the nearest cities.10,11,30-36

Methods

Data from the Netherlands, Japan, and Poland were collected identically with an electronic patient record (“Transhis”) as a part of the Transition Project of the Amsterdam University. For all face-to-face encounters, the reasons for encounter, diagnoses and interventions were coded with ICPC within an episode of care structure. Prescriptions were coded with the ICPC drug-code (derived from the Anatomical Therapeutic Classification [ATC]).29,37

In the Netherlands, 10 family physicians in 6 practices participated from 1995 to 2000. In Japan, 6 family physicians in rural health centers related to Jichi Medical School participated from 1996 to 1999. In Poland, the Family Practice Department of Katowice Medical School organized the study from 1997 to 1999) with 22 family physicians in 2 practices. Their population was assigned to them on the basis of census data. Therefore, families without 1 of its members having had at least 1 encounter with a participating family physician were excluded from the Polish data.

Since no such US data existed, we derived where possible comparable estimates using visit data from the NAMCS database.20 Sample physicians completed forms for a systematic random sample of office visits during a random 1-week period, coding up to 3 reasons for visit and diagnoses using the Reason for Visit Classification for Ambulatory Care (RVC) and the International Classification of Diseases-9th revision (ICD-9-CM).38 Prescribed drugs were classified with the National Drug Code Directory.39 Data included all ages, all races, and both sexes. The 1995-1997 data were used (91,395 visits), with 2955 ambulatory care visits per 1000 US citizens (26% with a family physician).20,22 Data were recoded with ICPC through mappings with RVC and ICD-9-CM, and ICPC drug codes were mapped with the major pharmaceutical groupings in NAMCS.37,39,40

 

 

The content of family practice was established by:

  1. utilization indicators per patient/visit, per patient year, per encounter, per episode of care and per patient per year;
  2. the distribution of reasons for encounter/visit by ICPC-chapter; most frequent (groups of) reasons for encounter expressed as a symptom/complaint; most frequent (groups of) diagnoses in new episodes of care; most frequent (groups of) diagnoses in encounters per episode. While the incidence of chronic health problems is considerably smaller than their prevalence it is more representative considerably smaller than their prevalence, it is more representative for the content of family practice. Therefore, for selected major chronic diseases cumulative prevalences for the complete observation period were calculated;
  3. prescriptions per 1000 direct encounters and per 1000 patients per year.

To improve comparability, all Transhis data were directly standardized for the sex/age distribution of the 1996 US population, in effect using the NAMCS data (that we could not recalculate) as the standard. Utilization indicators and epidemiological rates were calculated using definitions from WONCA’s International Glossary of Primary Care.42

Results

Utilization

Substantial differences and similarities in utilization existed Table 2 often comparable NAMCS data were unavailable to us. Differences in the numbers of episodes of care and of encounters per patient per year were smaller than those in utilization per episode. In Japan, utilization per episode was relatively high, as was the use of physiotherapy and additional testing; in Poland, counseling, electrocardiograms and laboratory tests were rather prominent. Home visits appeared to be common only in the Netherlands; however, the proportion of out-of-hours encounters was quite similar in the 3 Transhis databases. In the Netherlands and Poland, family physicians were actively involved in referring to specialists, as opposed to the situation in Japan.

Reasons for Encounter

The distribution of reasons for encounter by ICPC-chapter illustrates the wide scope of family practice, as well as differences resulting from national health care systems Table 3. Digestive, circulatory, musculoskeletal, respiratory, and skin problems were frequent in all databases. Psychological problems were frequent in Dutch and US primary care, while digestive problems were very prominent in Japan. However, general problems, including prevention, were less frequent. The very limited contribution of Japanese family physicians to gynecologic/obstetric care and psychological and social problems is clear.

The top 30 reasons for an encounter expressed as a symptom/complaint are presented in Table 4. The rank order is derived from the highest frequency per 1000 listed patients (NAMCS: per 1000 US-population). In the US (last column), the relative contribution of family practice to care for common symptoms/complaints appears to be generally high but unevenly distributed; the overall US distribution was rather similar to the Dutch data. Only 35 groups of symptoms/complaints covered the top-thirties in all databases, at the same time including 45% to 60% of all symptom/complaint reasons for encounter.

Diagnoses

Table 5 and Table 6 present the diagnoses in the same format as Table 4; NAMCS-data on new episodes of care per 1000 patients per year were unavailable. The distribution of the incidences of common conditions in Table 5 reflects disease presented to a family physician: respiratory infections, prevention, trauma, gastrointestinal, musculoskeletal and skin problems were frequent in the 3 databases. Approximately 50 diagnoses covered 45% to 60% of all new episodes of care. Large differences, again, existed in the contribution of family practice to gynecology/obstetrics and to psychosocial problems.

Upper respiratory tract infections were far more often diagnosed in Japan and Poland than in the Netherlands, and Polish family physicians diagnosed more tonsillitis and strep throat. In Japan, the family physician’s contribution to prevention was very low, and very high to care for intestinal problems.

Table 6 shows the most frequent face-to-face encounters per episode of care per 1000 patients per year for all four databases, together with the family physician’s contribution to the NAMCS data. Again, data from the Netherlands and NAMCS were relatively similar, and family physicians in the US had a relatively important contribution to care for most common episodes of care. The very high overall number of face-to-face encounters per 1000 patients per year in Japan was rather evenly distributed over the most common episodes of care. The proportion of all encounters per 1000 patients per year covered by the top thirty for each country was 70% to 75%.

Prescribing

Only information on prescriptions by a family physician per 1000 encounters was available for the US. The same rate was calculated for the other 3 database, also, in addition to the number of prescriptions per 1000 patients per year Table 7. Data on prescriptions per 1000 direct encounters in the four countries indicated both similarities and differences. For example, family physicians in the US prescribed more antimicrobial agents than the Dutch, while the choice of antibiotics strongly differed. Dutch physicians prescribed many laxatives, while Polish physicians prescribed many antidiarrheals Cardiovascular treatment in the Netherlands and the US was rather similar, although the choice of drugs differed. Data per 1000 patients per year provided a rather different perspective on prescribing; especially in Japan, and to a lesser extent, in Poland, the large number of encounters per episode resulted in large differences between data per year versus per encounter.

 

 

Discussion

Considerable progress has been made in the methods for the analysis of the content of family practice.42-48 Episodes of care are a critical unit of analysis, and it is timely to recognize the importance and feasibility of using episodes of care prospectively in electronic patient records.12

A major limitation of international comparative studies on the content of family practice is that no nationally representative data on reasons for encounter, diagnoses and episode of care over time are available. The Dutch, Japanese, and Polish data used in this study reflect the contribution of highly motivated, research-oriented family physicians who were not representative for their respective national family practice conditions; rather, they documented in much clinical detail what the content of family practice could be in these countries under optimal conditions. The US NAMCS data were representative for the national health care system, but they lacked data on episodes of care over time. The increasing use of electronic patient records in US practice networks is a very encouraging development, but has not resulted yet in a database that fulfills the criteria for this comparative study.

It is clear that under very different conditions, substantial proportions of all symptom/complaint reasons for encounter, incidence rates, and encounters per diagnosis are covered with the respective top thirty distributions for the four countries studied. Reasons for encounter as a representation of the patient’s demand for care and the diagnoses as the physician’s interpretation of the need for care follow a common pattern. Given the limitations of the study, they allow us to globally characterize the family physician’s contribution to national health care systems in different countries. The striking resemblance in the distribution of common symptoms and complaints supports the concept of the reason for encounter as a core element of the consultation with a family physician. Similarities between the four databases are much better reflected in the manner that patients formulate and express their demand for care than in the diagnoses assigned by family physicians.

Family practice appears to become what the profession, the patients, and the national conditions permit; it is akin to an antibody reacting to the specific antigens of a nation.44-51 Given the substantial variations across countries, several of the “resulting antibodies” appear to be remarkably similar, which suggests a coherence derived from the way people become sick and seek care. The substantial differences in incidence and utilization in episodes of care for common diseases usually allowed an interpretation along these lines. In the discussions with the national project leaders, their interpretations and explanations had high face validity, allowing a better understanding of the data as characteristic for the position of family practice in the four countries. For example, the high utilization for hypertension in Japan can be explained by legal limitations to the amount of medication per prescription, while the high utilization for upper respiratory tract infection and prevention in Poland also reflects formal requirements. Also, the important role of psychological problems in Dutch, and to a lesser extent in US family practice, reflects its importance in training programs, in contrast with the near absence of such problems in Japan and Poland.

Gynecology and obstetrics are in the core business of family practice in the Netherlands, but in the US, gynecologists provide a substantial contribution in addition to the role of family physicians. Japanese family physicians play practically no role in this area, while in Poland the role of family physicians is limited to gynecology with only a small contribution to contraception and pregnancy. Although respiratory problems are important for family physicians wherever they work, the high incidence and utilization in Poland can also be explained by the need for sickness certification from the first day of illness.

The Japanese system requires multiple encounters per episode during a short period of time. For example, patients with sinusitis, bronchitis, gastritis, or a self-limiting musculoskeletal problem, are often seen 3 or more times per week. In the Netherlands, the health care system requires a large number of repeat prescriptions or refills by the family physician. A trained medical secretary practically always deals with this, and these encounters are considered as “indirect encounters.” The utilization per episode of care in Poland and the Netherlands is rather similar.

The uneven distribution in the relative contribution of family practice to the care for common conditions indicated that US patients also see other providers for common problems. The NAMCS data cannot tell us whether a limited group of the population brings most of their health problems to a family physician, or whether most people visit a series of physicians (Ob/Gyn, Eye, ENT, Psychiatry) depending on who they consider most fit for each problem.51,52

 

 

The ample use of endoscopy, x-ray, and ultrasound in Japan and of electrocardiograms in Poland contrasts with a relatively modest use of diagnostics in the Netherlands. The referral rate to specialists in the Netherlands is a reliable indicator of the role of secondary care; the very low referral rate in Japan reflects how in a rural area patients seek care either of their family physician or of specialists in a nearby city, to whom they have direct access. The high referral rate in Poland also probably reflects the attitude of former hospital specialists who were practically overnight transformed into family physicians.

It is difficult to interpret all differences in prescribing, because the US data do not include the quantity of medication; in the Transition Project’s data, “Defined Daily Doses” are used to better understand prescribing patterns. This study’s data reflect substantial differences in prescribing antibiotics, oral contraceptives, and cardiovascular and gastrointestinal therapies.53-56 Prescribing behavior in the Netherlands and the US is rather similar, while very different patterns are found in Japan and Poland. Antibiotic use in upper respiratory tract infections differs largely: the Dutch prescribe infrequently and almost always use penicillins, the Japanese rarely prescribe penicillins.

Conclusions

The main conclusion of our study is that family practice varies as a customized service, determined by a combination of factors, including the burden of disease; the habits, customs, and training of physicians; the regulations promulgated by government and guilds; the way people understand their symptoms; and the availability of money, services, tools, and goods. Another important conclusion is that, paradoxically, while the need to document reasons for visit was first acknowledged in the US many years ago, US family practice still has not been in the position to document their contribution to national health care in sufficient clinical detail focussing on episodes of care over time.7,9

The recommendation of the IOM to “foster the development of standards for data collection that will ensure the consistency of data elements and definitions of terms, improve coding, permit analysis of episodes of care, and reflect the content of primary care” has not yet resulted in the availability of such (nationally representative) data to be included in a comparative international study.

The possibilities for international cooperation to further develop episode-oriented epidemiology in family practice have, however, increased considerably over the past decade. Further, the potential for international comparative studies in family practice has increased with the introduction of complete electronic patient records based on a standardized documentation of episodes of care with ICPC together with its mapping to ICD-10 (or, for the time being, ICD-9-CM). Now is the time to make a wide use of the new possibilities in routine documentation of patient-physician encounters in family practice by family physicians, wherever they work.57-60

Acknowledgments

The first and last author gratefully acknowledge the opportunity to work on this paper during their scholarship, in late 1999, at AHCPR (now the Agency for Healthcare and Research Quality) in Washington, DC. The authors do not report any competing interests

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Author and Disclosure Information

 

I.M. Okkes, MA, PhD
G.O. Polderman, MD
G.E. Fryer, PhD
T. Yamada, MD
M. Bujak, MD
S.K. Oskam, MSc, PhD
L.A. Green, MD
H. Lamberts, MD, PhD
Amsterdam and Amstelveen, the Netherlands; Tokyo, Japan; Katowice, Poland; and Washington, DC
Submitted, revised, October 21, 2001.
From the Academic Medical Center/University of Amsterdam, Division of Public Health, Department of Family Practice, Amsterdam (I.M.O., S.K.O., H.L.); Family Physician, Transition Project, Amstelveen (G.O.P.); the Robert Graham Center: Policy Studies in Family Practice and Primary Care, Washington, DC (G.E.F., L.A.G.); the Japanese Association for Development of Community Medicine, Tokyo (T.Y.); and the Silesian Medical School, Katowice (M.B.). Reprint requests should be addressed to I.M. Okkes, MA, PhD, Academic Medical Center/University of Amsterdam, Division Public Health, Department of Family Practice, Meibergdreef 15, 1105 AZ Amsterdam, the Netherlands. E-mail: [email protected].

Issue
The Journal of Family Practice - 51(1)
Publications
Page Number
1
Legacy Keywords
,Episode of carefamily practicereason for encounter [non-MESH]diagnosisclassificationprescribing [non-MESH]comparative study (J Fam Pract 2002; 51:72)
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I.M. Okkes, MA, PhD
G.O. Polderman, MD
G.E. Fryer, PhD
T. Yamada, MD
M. Bujak, MD
S.K. Oskam, MSc, PhD
L.A. Green, MD
H. Lamberts, MD, PhD
Amsterdam and Amstelveen, the Netherlands; Tokyo, Japan; Katowice, Poland; and Washington, DC
Submitted, revised, October 21, 2001.
From the Academic Medical Center/University of Amsterdam, Division of Public Health, Department of Family Practice, Amsterdam (I.M.O., S.K.O., H.L.); Family Physician, Transition Project, Amstelveen (G.O.P.); the Robert Graham Center: Policy Studies in Family Practice and Primary Care, Washington, DC (G.E.F., L.A.G.); the Japanese Association for Development of Community Medicine, Tokyo (T.Y.); and the Silesian Medical School, Katowice (M.B.). Reprint requests should be addressed to I.M. Okkes, MA, PhD, Academic Medical Center/University of Amsterdam, Division Public Health, Department of Family Practice, Meibergdreef 15, 1105 AZ Amsterdam, the Netherlands. E-mail: [email protected].

Author and Disclosure Information

 

I.M. Okkes, MA, PhD
G.O. Polderman, MD
G.E. Fryer, PhD
T. Yamada, MD
M. Bujak, MD
S.K. Oskam, MSc, PhD
L.A. Green, MD
H. Lamberts, MD, PhD
Amsterdam and Amstelveen, the Netherlands; Tokyo, Japan; Katowice, Poland; and Washington, DC
Submitted, revised, October 21, 2001.
From the Academic Medical Center/University of Amsterdam, Division of Public Health, Department of Family Practice, Amsterdam (I.M.O., S.K.O., H.L.); Family Physician, Transition Project, Amstelveen (G.O.P.); the Robert Graham Center: Policy Studies in Family Practice and Primary Care, Washington, DC (G.E.F., L.A.G.); the Japanese Association for Development of Community Medicine, Tokyo (T.Y.); and the Silesian Medical School, Katowice (M.B.). Reprint requests should be addressed to I.M. Okkes, MA, PhD, Academic Medical Center/University of Amsterdam, Division Public Health, Department of Family Practice, Meibergdreef 15, 1105 AZ Amsterdam, the Netherlands. E-mail: [email protected].

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Article PDF

 

ABSTRACT

OBJECTIVE: Our goal was to compare the content of family practice in different countries using databases containing information on reasons for encounter, diagnoses, and interventions that are coded with or can be addressed by the International Classification of Primary Care (ICPC).

STUDY DESIGN: In the Netherlands, Japan, and Poland data were collected identically with an electronic patient record (Transhis). For all face-to-face encounters the reasons for encounter, diagnoses, and interventions were coded according to the ICPC within an episode of care structure; prescriptions were coded with the ICPC drug code. Data were collected for research purposes and cannot be considered representative for family practice in these countries. We derived comparable estimates for the United States using visit data from the National Ambulatory Care Survey (NAMCS), with specific emphasis on the contribution of family physicians. NAMCS data were mapped to the ICPC and the ICPC drug code, and Dutch, Polish, and Japanese data were directly standardized for the 1996 US population. Data on utilization, reasons for encounter, encounters per episode of care, new episodes of care, and prescriptions were compared. We also present World Health Organization and Organisation for Economic Co-operation and Development data on health care delivery, efficiency, expenditure, and health status for each country.

POPULATION: We included the following: from the Netherlands: 10 family physicians, 48.640 patient years, 1995-2000; from Japan: 6 family physicians, 17.082 patient years, 1996-1999; from Poland: 22 family physicians, 11.315 patient years, 1997-1999; and from the United States: NAMCS 1995-97 30 991 patient years 91395 visits (26% with a family physician).

RESULTS: We found important differences and striking similarities. Differences in the numbers of episodes and of encounters per patient per year were small compared with differences in utilization per episode of care, including diagnostic and therapeutic interventions. Substantial differences were found in prescribing antibiotics, oral contraceptives, cardiovascular medications, and gastrointestinal therapies. Prescribing behavior in the Netherlands and the United States was similar, while very different patterns were found in Japan and Poland. Similarities were much higher in patients’ reasons for encounter than in diagnoses. Only 35 groups of symptoms/complaints covered the top 30s in all databases, at the same time including 45% to 60% of all symptom/complaint reasons for encounter.

CONCLUSIONS: Even under very different conditions there was substantial overlap in the top 30 symptom/complaint reasons for encounter, incidence rates, and encounters per diagnosis in the 4 countries we studied. This striking resemblance supports the concept of the reason for encounter as a core element of the consultation with a family physician. Similarities between the databases are much better reflected by the way patients formulate their demand for care than in the diagnoses by the family physician. Patients from the US also see providers other than family physicians for common problems; it remains unclear whether a limited group brings most of their health problems to a family physician or whether most people visit a series of primary care physicians. Possibilities to further develop episode-oriented epidemiology in family practice have considerably increased with this study. The potential for comparative studies has also increased with the introduction of complete electronic patient records based on the documentation of episodes of care with the ICPC and with its mapping to International Classification of Diseases-10th revision (or the 9th revision clinical modification).

Internationally, family practice receives increasing emphasis. The World Organization of Family Doctors (WONCA) now has members from more than 80 countries, in several of which family practice has developed into a core element of health care delivery and a well-defined academic discipline. In the United Kingdom, Ireland, Australia, New Zealand, Scandinavia and the Netherlands, the development of family practice has benefited from a health care policy that arranged for direct access for all, and for a gatekeeping function of the family physician that has also resulted in the availability of databases reflecting the distribution of morbidity in family practice populations.1-5 In the US, Japan and many European countries, however, the development of family practice is handicapped by a health care policy less favorable to the discipline. This has resulted in a paucity of information on the distribution of morbidity in the population.6-12

Primary care/family practice is characterized in the 1997 Institute of Medicine (IOM) definition as: “…the provision of integrated accessible health care services by physicians who are accountable for addressing a large majority of personal health care needs, developing a sustained partnership with patients and practicing in the context of family and community.”12 A particularly relevant unit of assessment for this definition is the episode of care, defined as a health problem from its first presentation to a health care provider till the last encounter for it.12-14 This implies that morbidity and mortality rates are insufficient to characterize the content of health care; one must include the patient’s perspective during episodes of illness and episodes of care.7

 

 

Over the years, WONCA has developed the International Classification of Primary Care ICPC as the ordering principle of the family practice domain. The ICPC describes episodes of care by reasons for encounter (reflecting the patient’s perspective), diagnoses (reflecting the physician’s perspective), and interventions.3,15-19 On this basis, family practice databases can be created that allow international comparison.

Unfortunately, national representative databases fulfilling the requirements formulated by White and colleagues in 1961 are still not available.7 The goal of this study is to compare the content of family practice in different countries, using existing databases that (minimally) contain data on reasons for encounter, diagnoses and interventions that are coded with, or can be addressed by ICPC in an episode of care structure.7,18

Family physicians in the Netherlands, Japan, and Poland have been collecting episode of care data over several years in listed populations, with an ICPC -based electronic patient record for all encounters, for research purposes and under controlled conditions. Recently, Green and colleagues noted a serious lack of such data in the US, resulting in problems when estimating essential indicators from available sources.9 Since the publication of the IOM Report on Primary Care in 1996, pointing out that the available information in the US did not allow episode of care analysis, the increasing use of electronic patient records in family practice networks has not yet resulted in databases fulfilling all criteria for this study. However, the National Ambulatory Medical Care Survey (NAMCS) records reasons for visit and diagnoses, allowing an estimation of the family physician’s contribution to ambulatory care; no episodes of care could be identified from NAMCS data.20-23 It was decided to use these four databases in this study.

Obviously, comparative studies must take into account the major differences in the national health care systems; global data from these four countries indicate substantial differences in health care delivery, expenditure and health status Table 1.24-26 Although it is impossible to directly relate these differences to the available databases, and family practice does not have a major impact on all of these outcomes, they can be helpful to better understand the study’s results.

Dutch family physicians are gatekeepers for listed and relatively healthy practice populations with universal access. This contrasts sharply with the US, where far more is spent on health care with disappointing health status indicators, and without a central position for family practice. Dutch family practice data on reasons for encounter, diagnoses and interventions are, by their nature, a close proxy for the population’s demand, clinical need, and supply. Most Dutch family physicians use an electronic patient record, in which the use of ICPC for coding diagnoses is mandatory.27-29

In Poland, little is spent on a health care system with general access; health status is unsatisfactory. Over the past decade, Polish health care policy has strongly supported family practice, deploying a family practice retraining program for general internists, gynecologists, and pediatricians with an often-longstanding experience in hospitals.27-29

In Japan, health care contributes to a relatively long and healthy life at moderate costs; family practice has a weak position, being well developed in rural areas only. Family physician training is much like that in general internal medicine. Although the Japanese have freedom of choice and complete coverage, patients in the participating rural practices bring most health problems to their family physician, with the exception of practically all obstetric/gynecologic and most pediatric and psychiatric care for which they see specialists in the nearest cities.10,11,30-36

Methods

Data from the Netherlands, Japan, and Poland were collected identically with an electronic patient record (“Transhis”) as a part of the Transition Project of the Amsterdam University. For all face-to-face encounters, the reasons for encounter, diagnoses and interventions were coded with ICPC within an episode of care structure. Prescriptions were coded with the ICPC drug-code (derived from the Anatomical Therapeutic Classification [ATC]).29,37

In the Netherlands, 10 family physicians in 6 practices participated from 1995 to 2000. In Japan, 6 family physicians in rural health centers related to Jichi Medical School participated from 1996 to 1999. In Poland, the Family Practice Department of Katowice Medical School organized the study from 1997 to 1999) with 22 family physicians in 2 practices. Their population was assigned to them on the basis of census data. Therefore, families without 1 of its members having had at least 1 encounter with a participating family physician were excluded from the Polish data.

Since no such US data existed, we derived where possible comparable estimates using visit data from the NAMCS database.20 Sample physicians completed forms for a systematic random sample of office visits during a random 1-week period, coding up to 3 reasons for visit and diagnoses using the Reason for Visit Classification for Ambulatory Care (RVC) and the International Classification of Diseases-9th revision (ICD-9-CM).38 Prescribed drugs were classified with the National Drug Code Directory.39 Data included all ages, all races, and both sexes. The 1995-1997 data were used (91,395 visits), with 2955 ambulatory care visits per 1000 US citizens (26% with a family physician).20,22 Data were recoded with ICPC through mappings with RVC and ICD-9-CM, and ICPC drug codes were mapped with the major pharmaceutical groupings in NAMCS.37,39,40

 

 

The content of family practice was established by:

  1. utilization indicators per patient/visit, per patient year, per encounter, per episode of care and per patient per year;
  2. the distribution of reasons for encounter/visit by ICPC-chapter; most frequent (groups of) reasons for encounter expressed as a symptom/complaint; most frequent (groups of) diagnoses in new episodes of care; most frequent (groups of) diagnoses in encounters per episode. While the incidence of chronic health problems is considerably smaller than their prevalence it is more representative considerably smaller than their prevalence, it is more representative for the content of family practice. Therefore, for selected major chronic diseases cumulative prevalences for the complete observation period were calculated;
  3. prescriptions per 1000 direct encounters and per 1000 patients per year.

To improve comparability, all Transhis data were directly standardized for the sex/age distribution of the 1996 US population, in effect using the NAMCS data (that we could not recalculate) as the standard. Utilization indicators and epidemiological rates were calculated using definitions from WONCA’s International Glossary of Primary Care.42

Results

Utilization

Substantial differences and similarities in utilization existed Table 2 often comparable NAMCS data were unavailable to us. Differences in the numbers of episodes of care and of encounters per patient per year were smaller than those in utilization per episode. In Japan, utilization per episode was relatively high, as was the use of physiotherapy and additional testing; in Poland, counseling, electrocardiograms and laboratory tests were rather prominent. Home visits appeared to be common only in the Netherlands; however, the proportion of out-of-hours encounters was quite similar in the 3 Transhis databases. In the Netherlands and Poland, family physicians were actively involved in referring to specialists, as opposed to the situation in Japan.

Reasons for Encounter

The distribution of reasons for encounter by ICPC-chapter illustrates the wide scope of family practice, as well as differences resulting from national health care systems Table 3. Digestive, circulatory, musculoskeletal, respiratory, and skin problems were frequent in all databases. Psychological problems were frequent in Dutch and US primary care, while digestive problems were very prominent in Japan. However, general problems, including prevention, were less frequent. The very limited contribution of Japanese family physicians to gynecologic/obstetric care and psychological and social problems is clear.

The top 30 reasons for an encounter expressed as a symptom/complaint are presented in Table 4. The rank order is derived from the highest frequency per 1000 listed patients (NAMCS: per 1000 US-population). In the US (last column), the relative contribution of family practice to care for common symptoms/complaints appears to be generally high but unevenly distributed; the overall US distribution was rather similar to the Dutch data. Only 35 groups of symptoms/complaints covered the top-thirties in all databases, at the same time including 45% to 60% of all symptom/complaint reasons for encounter.

Diagnoses

Table 5 and Table 6 present the diagnoses in the same format as Table 4; NAMCS-data on new episodes of care per 1000 patients per year were unavailable. The distribution of the incidences of common conditions in Table 5 reflects disease presented to a family physician: respiratory infections, prevention, trauma, gastrointestinal, musculoskeletal and skin problems were frequent in the 3 databases. Approximately 50 diagnoses covered 45% to 60% of all new episodes of care. Large differences, again, existed in the contribution of family practice to gynecology/obstetrics and to psychosocial problems.

Upper respiratory tract infections were far more often diagnosed in Japan and Poland than in the Netherlands, and Polish family physicians diagnosed more tonsillitis and strep throat. In Japan, the family physician’s contribution to prevention was very low, and very high to care for intestinal problems.

Table 6 shows the most frequent face-to-face encounters per episode of care per 1000 patients per year for all four databases, together with the family physician’s contribution to the NAMCS data. Again, data from the Netherlands and NAMCS were relatively similar, and family physicians in the US had a relatively important contribution to care for most common episodes of care. The very high overall number of face-to-face encounters per 1000 patients per year in Japan was rather evenly distributed over the most common episodes of care. The proportion of all encounters per 1000 patients per year covered by the top thirty for each country was 70% to 75%.

Prescribing

Only information on prescriptions by a family physician per 1000 encounters was available for the US. The same rate was calculated for the other 3 database, also, in addition to the number of prescriptions per 1000 patients per year Table 7. Data on prescriptions per 1000 direct encounters in the four countries indicated both similarities and differences. For example, family physicians in the US prescribed more antimicrobial agents than the Dutch, while the choice of antibiotics strongly differed. Dutch physicians prescribed many laxatives, while Polish physicians prescribed many antidiarrheals Cardiovascular treatment in the Netherlands and the US was rather similar, although the choice of drugs differed. Data per 1000 patients per year provided a rather different perspective on prescribing; especially in Japan, and to a lesser extent, in Poland, the large number of encounters per episode resulted in large differences between data per year versus per encounter.

 

 

Discussion

Considerable progress has been made in the methods for the analysis of the content of family practice.42-48 Episodes of care are a critical unit of analysis, and it is timely to recognize the importance and feasibility of using episodes of care prospectively in electronic patient records.12

A major limitation of international comparative studies on the content of family practice is that no nationally representative data on reasons for encounter, diagnoses and episode of care over time are available. The Dutch, Japanese, and Polish data used in this study reflect the contribution of highly motivated, research-oriented family physicians who were not representative for their respective national family practice conditions; rather, they documented in much clinical detail what the content of family practice could be in these countries under optimal conditions. The US NAMCS data were representative for the national health care system, but they lacked data on episodes of care over time. The increasing use of electronic patient records in US practice networks is a very encouraging development, but has not resulted yet in a database that fulfills the criteria for this comparative study.

It is clear that under very different conditions, substantial proportions of all symptom/complaint reasons for encounter, incidence rates, and encounters per diagnosis are covered with the respective top thirty distributions for the four countries studied. Reasons for encounter as a representation of the patient’s demand for care and the diagnoses as the physician’s interpretation of the need for care follow a common pattern. Given the limitations of the study, they allow us to globally characterize the family physician’s contribution to national health care systems in different countries. The striking resemblance in the distribution of common symptoms and complaints supports the concept of the reason for encounter as a core element of the consultation with a family physician. Similarities between the four databases are much better reflected in the manner that patients formulate and express their demand for care than in the diagnoses assigned by family physicians.

Family practice appears to become what the profession, the patients, and the national conditions permit; it is akin to an antibody reacting to the specific antigens of a nation.44-51 Given the substantial variations across countries, several of the “resulting antibodies” appear to be remarkably similar, which suggests a coherence derived from the way people become sick and seek care. The substantial differences in incidence and utilization in episodes of care for common diseases usually allowed an interpretation along these lines. In the discussions with the national project leaders, their interpretations and explanations had high face validity, allowing a better understanding of the data as characteristic for the position of family practice in the four countries. For example, the high utilization for hypertension in Japan can be explained by legal limitations to the amount of medication per prescription, while the high utilization for upper respiratory tract infection and prevention in Poland also reflects formal requirements. Also, the important role of psychological problems in Dutch, and to a lesser extent in US family practice, reflects its importance in training programs, in contrast with the near absence of such problems in Japan and Poland.

Gynecology and obstetrics are in the core business of family practice in the Netherlands, but in the US, gynecologists provide a substantial contribution in addition to the role of family physicians. Japanese family physicians play practically no role in this area, while in Poland the role of family physicians is limited to gynecology with only a small contribution to contraception and pregnancy. Although respiratory problems are important for family physicians wherever they work, the high incidence and utilization in Poland can also be explained by the need for sickness certification from the first day of illness.

The Japanese system requires multiple encounters per episode during a short period of time. For example, patients with sinusitis, bronchitis, gastritis, or a self-limiting musculoskeletal problem, are often seen 3 or more times per week. In the Netherlands, the health care system requires a large number of repeat prescriptions or refills by the family physician. A trained medical secretary practically always deals with this, and these encounters are considered as “indirect encounters.” The utilization per episode of care in Poland and the Netherlands is rather similar.

The uneven distribution in the relative contribution of family practice to the care for common conditions indicated that US patients also see other providers for common problems. The NAMCS data cannot tell us whether a limited group of the population brings most of their health problems to a family physician, or whether most people visit a series of physicians (Ob/Gyn, Eye, ENT, Psychiatry) depending on who they consider most fit for each problem.51,52

 

 

The ample use of endoscopy, x-ray, and ultrasound in Japan and of electrocardiograms in Poland contrasts with a relatively modest use of diagnostics in the Netherlands. The referral rate to specialists in the Netherlands is a reliable indicator of the role of secondary care; the very low referral rate in Japan reflects how in a rural area patients seek care either of their family physician or of specialists in a nearby city, to whom they have direct access. The high referral rate in Poland also probably reflects the attitude of former hospital specialists who were practically overnight transformed into family physicians.

It is difficult to interpret all differences in prescribing, because the US data do not include the quantity of medication; in the Transition Project’s data, “Defined Daily Doses” are used to better understand prescribing patterns. This study’s data reflect substantial differences in prescribing antibiotics, oral contraceptives, and cardiovascular and gastrointestinal therapies.53-56 Prescribing behavior in the Netherlands and the US is rather similar, while very different patterns are found in Japan and Poland. Antibiotic use in upper respiratory tract infections differs largely: the Dutch prescribe infrequently and almost always use penicillins, the Japanese rarely prescribe penicillins.

Conclusions

The main conclusion of our study is that family practice varies as a customized service, determined by a combination of factors, including the burden of disease; the habits, customs, and training of physicians; the regulations promulgated by government and guilds; the way people understand their symptoms; and the availability of money, services, tools, and goods. Another important conclusion is that, paradoxically, while the need to document reasons for visit was first acknowledged in the US many years ago, US family practice still has not been in the position to document their contribution to national health care in sufficient clinical detail focussing on episodes of care over time.7,9

The recommendation of the IOM to “foster the development of standards for data collection that will ensure the consistency of data elements and definitions of terms, improve coding, permit analysis of episodes of care, and reflect the content of primary care” has not yet resulted in the availability of such (nationally representative) data to be included in a comparative international study.

The possibilities for international cooperation to further develop episode-oriented epidemiology in family practice have, however, increased considerably over the past decade. Further, the potential for international comparative studies in family practice has increased with the introduction of complete electronic patient records based on a standardized documentation of episodes of care with ICPC together with its mapping to ICD-10 (or, for the time being, ICD-9-CM). Now is the time to make a wide use of the new possibilities in routine documentation of patient-physician encounters in family practice by family physicians, wherever they work.57-60

Acknowledgments

The first and last author gratefully acknowledge the opportunity to work on this paper during their scholarship, in late 1999, at AHCPR (now the Agency for Healthcare and Research Quality) in Washington, DC. The authors do not report any competing interests

 

ABSTRACT

OBJECTIVE: Our goal was to compare the content of family practice in different countries using databases containing information on reasons for encounter, diagnoses, and interventions that are coded with or can be addressed by the International Classification of Primary Care (ICPC).

STUDY DESIGN: In the Netherlands, Japan, and Poland data were collected identically with an electronic patient record (Transhis). For all face-to-face encounters the reasons for encounter, diagnoses, and interventions were coded according to the ICPC within an episode of care structure; prescriptions were coded with the ICPC drug code. Data were collected for research purposes and cannot be considered representative for family practice in these countries. We derived comparable estimates for the United States using visit data from the National Ambulatory Care Survey (NAMCS), with specific emphasis on the contribution of family physicians. NAMCS data were mapped to the ICPC and the ICPC drug code, and Dutch, Polish, and Japanese data were directly standardized for the 1996 US population. Data on utilization, reasons for encounter, encounters per episode of care, new episodes of care, and prescriptions were compared. We also present World Health Organization and Organisation for Economic Co-operation and Development data on health care delivery, efficiency, expenditure, and health status for each country.

POPULATION: We included the following: from the Netherlands: 10 family physicians, 48.640 patient years, 1995-2000; from Japan: 6 family physicians, 17.082 patient years, 1996-1999; from Poland: 22 family physicians, 11.315 patient years, 1997-1999; and from the United States: NAMCS 1995-97 30 991 patient years 91395 visits (26% with a family physician).

RESULTS: We found important differences and striking similarities. Differences in the numbers of episodes and of encounters per patient per year were small compared with differences in utilization per episode of care, including diagnostic and therapeutic interventions. Substantial differences were found in prescribing antibiotics, oral contraceptives, cardiovascular medications, and gastrointestinal therapies. Prescribing behavior in the Netherlands and the United States was similar, while very different patterns were found in Japan and Poland. Similarities were much higher in patients’ reasons for encounter than in diagnoses. Only 35 groups of symptoms/complaints covered the top 30s in all databases, at the same time including 45% to 60% of all symptom/complaint reasons for encounter.

CONCLUSIONS: Even under very different conditions there was substantial overlap in the top 30 symptom/complaint reasons for encounter, incidence rates, and encounters per diagnosis in the 4 countries we studied. This striking resemblance supports the concept of the reason for encounter as a core element of the consultation with a family physician. Similarities between the databases are much better reflected by the way patients formulate their demand for care than in the diagnoses by the family physician. Patients from the US also see providers other than family physicians for common problems; it remains unclear whether a limited group brings most of their health problems to a family physician or whether most people visit a series of primary care physicians. Possibilities to further develop episode-oriented epidemiology in family practice have considerably increased with this study. The potential for comparative studies has also increased with the introduction of complete electronic patient records based on the documentation of episodes of care with the ICPC and with its mapping to International Classification of Diseases-10th revision (or the 9th revision clinical modification).

Internationally, family practice receives increasing emphasis. The World Organization of Family Doctors (WONCA) now has members from more than 80 countries, in several of which family practice has developed into a core element of health care delivery and a well-defined academic discipline. In the United Kingdom, Ireland, Australia, New Zealand, Scandinavia and the Netherlands, the development of family practice has benefited from a health care policy that arranged for direct access for all, and for a gatekeeping function of the family physician that has also resulted in the availability of databases reflecting the distribution of morbidity in family practice populations.1-5 In the US, Japan and many European countries, however, the development of family practice is handicapped by a health care policy less favorable to the discipline. This has resulted in a paucity of information on the distribution of morbidity in the population.6-12

Primary care/family practice is characterized in the 1997 Institute of Medicine (IOM) definition as: “…the provision of integrated accessible health care services by physicians who are accountable for addressing a large majority of personal health care needs, developing a sustained partnership with patients and practicing in the context of family and community.”12 A particularly relevant unit of assessment for this definition is the episode of care, defined as a health problem from its first presentation to a health care provider till the last encounter for it.12-14 This implies that morbidity and mortality rates are insufficient to characterize the content of health care; one must include the patient’s perspective during episodes of illness and episodes of care.7

 

 

Over the years, WONCA has developed the International Classification of Primary Care ICPC as the ordering principle of the family practice domain. The ICPC describes episodes of care by reasons for encounter (reflecting the patient’s perspective), diagnoses (reflecting the physician’s perspective), and interventions.3,15-19 On this basis, family practice databases can be created that allow international comparison.

Unfortunately, national representative databases fulfilling the requirements formulated by White and colleagues in 1961 are still not available.7 The goal of this study is to compare the content of family practice in different countries, using existing databases that (minimally) contain data on reasons for encounter, diagnoses and interventions that are coded with, or can be addressed by ICPC in an episode of care structure.7,18

Family physicians in the Netherlands, Japan, and Poland have been collecting episode of care data over several years in listed populations, with an ICPC -based electronic patient record for all encounters, for research purposes and under controlled conditions. Recently, Green and colleagues noted a serious lack of such data in the US, resulting in problems when estimating essential indicators from available sources.9 Since the publication of the IOM Report on Primary Care in 1996, pointing out that the available information in the US did not allow episode of care analysis, the increasing use of electronic patient records in family practice networks has not yet resulted in databases fulfilling all criteria for this study. However, the National Ambulatory Medical Care Survey (NAMCS) records reasons for visit and diagnoses, allowing an estimation of the family physician’s contribution to ambulatory care; no episodes of care could be identified from NAMCS data.20-23 It was decided to use these four databases in this study.

Obviously, comparative studies must take into account the major differences in the national health care systems; global data from these four countries indicate substantial differences in health care delivery, expenditure and health status Table 1.24-26 Although it is impossible to directly relate these differences to the available databases, and family practice does not have a major impact on all of these outcomes, they can be helpful to better understand the study’s results.

Dutch family physicians are gatekeepers for listed and relatively healthy practice populations with universal access. This contrasts sharply with the US, where far more is spent on health care with disappointing health status indicators, and without a central position for family practice. Dutch family practice data on reasons for encounter, diagnoses and interventions are, by their nature, a close proxy for the population’s demand, clinical need, and supply. Most Dutch family physicians use an electronic patient record, in which the use of ICPC for coding diagnoses is mandatory.27-29

In Poland, little is spent on a health care system with general access; health status is unsatisfactory. Over the past decade, Polish health care policy has strongly supported family practice, deploying a family practice retraining program for general internists, gynecologists, and pediatricians with an often-longstanding experience in hospitals.27-29

In Japan, health care contributes to a relatively long and healthy life at moderate costs; family practice has a weak position, being well developed in rural areas only. Family physician training is much like that in general internal medicine. Although the Japanese have freedom of choice and complete coverage, patients in the participating rural practices bring most health problems to their family physician, with the exception of practically all obstetric/gynecologic and most pediatric and psychiatric care for which they see specialists in the nearest cities.10,11,30-36

Methods

Data from the Netherlands, Japan, and Poland were collected identically with an electronic patient record (“Transhis”) as a part of the Transition Project of the Amsterdam University. For all face-to-face encounters, the reasons for encounter, diagnoses and interventions were coded with ICPC within an episode of care structure. Prescriptions were coded with the ICPC drug-code (derived from the Anatomical Therapeutic Classification [ATC]).29,37

In the Netherlands, 10 family physicians in 6 practices participated from 1995 to 2000. In Japan, 6 family physicians in rural health centers related to Jichi Medical School participated from 1996 to 1999. In Poland, the Family Practice Department of Katowice Medical School organized the study from 1997 to 1999) with 22 family physicians in 2 practices. Their population was assigned to them on the basis of census data. Therefore, families without 1 of its members having had at least 1 encounter with a participating family physician were excluded from the Polish data.

Since no such US data existed, we derived where possible comparable estimates using visit data from the NAMCS database.20 Sample physicians completed forms for a systematic random sample of office visits during a random 1-week period, coding up to 3 reasons for visit and diagnoses using the Reason for Visit Classification for Ambulatory Care (RVC) and the International Classification of Diseases-9th revision (ICD-9-CM).38 Prescribed drugs were classified with the National Drug Code Directory.39 Data included all ages, all races, and both sexes. The 1995-1997 data were used (91,395 visits), with 2955 ambulatory care visits per 1000 US citizens (26% with a family physician).20,22 Data were recoded with ICPC through mappings with RVC and ICD-9-CM, and ICPC drug codes were mapped with the major pharmaceutical groupings in NAMCS.37,39,40

 

 

The content of family practice was established by:

  1. utilization indicators per patient/visit, per patient year, per encounter, per episode of care and per patient per year;
  2. the distribution of reasons for encounter/visit by ICPC-chapter; most frequent (groups of) reasons for encounter expressed as a symptom/complaint; most frequent (groups of) diagnoses in new episodes of care; most frequent (groups of) diagnoses in encounters per episode. While the incidence of chronic health problems is considerably smaller than their prevalence it is more representative considerably smaller than their prevalence, it is more representative for the content of family practice. Therefore, for selected major chronic diseases cumulative prevalences for the complete observation period were calculated;
  3. prescriptions per 1000 direct encounters and per 1000 patients per year.

To improve comparability, all Transhis data were directly standardized for the sex/age distribution of the 1996 US population, in effect using the NAMCS data (that we could not recalculate) as the standard. Utilization indicators and epidemiological rates were calculated using definitions from WONCA’s International Glossary of Primary Care.42

Results

Utilization

Substantial differences and similarities in utilization existed Table 2 often comparable NAMCS data were unavailable to us. Differences in the numbers of episodes of care and of encounters per patient per year were smaller than those in utilization per episode. In Japan, utilization per episode was relatively high, as was the use of physiotherapy and additional testing; in Poland, counseling, electrocardiograms and laboratory tests were rather prominent. Home visits appeared to be common only in the Netherlands; however, the proportion of out-of-hours encounters was quite similar in the 3 Transhis databases. In the Netherlands and Poland, family physicians were actively involved in referring to specialists, as opposed to the situation in Japan.

Reasons for Encounter

The distribution of reasons for encounter by ICPC-chapter illustrates the wide scope of family practice, as well as differences resulting from national health care systems Table 3. Digestive, circulatory, musculoskeletal, respiratory, and skin problems were frequent in all databases. Psychological problems were frequent in Dutch and US primary care, while digestive problems were very prominent in Japan. However, general problems, including prevention, were less frequent. The very limited contribution of Japanese family physicians to gynecologic/obstetric care and psychological and social problems is clear.

The top 30 reasons for an encounter expressed as a symptom/complaint are presented in Table 4. The rank order is derived from the highest frequency per 1000 listed patients (NAMCS: per 1000 US-population). In the US (last column), the relative contribution of family practice to care for common symptoms/complaints appears to be generally high but unevenly distributed; the overall US distribution was rather similar to the Dutch data. Only 35 groups of symptoms/complaints covered the top-thirties in all databases, at the same time including 45% to 60% of all symptom/complaint reasons for encounter.

Diagnoses

Table 5 and Table 6 present the diagnoses in the same format as Table 4; NAMCS-data on new episodes of care per 1000 patients per year were unavailable. The distribution of the incidences of common conditions in Table 5 reflects disease presented to a family physician: respiratory infections, prevention, trauma, gastrointestinal, musculoskeletal and skin problems were frequent in the 3 databases. Approximately 50 diagnoses covered 45% to 60% of all new episodes of care. Large differences, again, existed in the contribution of family practice to gynecology/obstetrics and to psychosocial problems.

Upper respiratory tract infections were far more often diagnosed in Japan and Poland than in the Netherlands, and Polish family physicians diagnosed more tonsillitis and strep throat. In Japan, the family physician’s contribution to prevention was very low, and very high to care for intestinal problems.

Table 6 shows the most frequent face-to-face encounters per episode of care per 1000 patients per year for all four databases, together with the family physician’s contribution to the NAMCS data. Again, data from the Netherlands and NAMCS were relatively similar, and family physicians in the US had a relatively important contribution to care for most common episodes of care. The very high overall number of face-to-face encounters per 1000 patients per year in Japan was rather evenly distributed over the most common episodes of care. The proportion of all encounters per 1000 patients per year covered by the top thirty for each country was 70% to 75%.

Prescribing

Only information on prescriptions by a family physician per 1000 encounters was available for the US. The same rate was calculated for the other 3 database, also, in addition to the number of prescriptions per 1000 patients per year Table 7. Data on prescriptions per 1000 direct encounters in the four countries indicated both similarities and differences. For example, family physicians in the US prescribed more antimicrobial agents than the Dutch, while the choice of antibiotics strongly differed. Dutch physicians prescribed many laxatives, while Polish physicians prescribed many antidiarrheals Cardiovascular treatment in the Netherlands and the US was rather similar, although the choice of drugs differed. Data per 1000 patients per year provided a rather different perspective on prescribing; especially in Japan, and to a lesser extent, in Poland, the large number of encounters per episode resulted in large differences between data per year versus per encounter.

 

 

Discussion

Considerable progress has been made in the methods for the analysis of the content of family practice.42-48 Episodes of care are a critical unit of analysis, and it is timely to recognize the importance and feasibility of using episodes of care prospectively in electronic patient records.12

A major limitation of international comparative studies on the content of family practice is that no nationally representative data on reasons for encounter, diagnoses and episode of care over time are available. The Dutch, Japanese, and Polish data used in this study reflect the contribution of highly motivated, research-oriented family physicians who were not representative for their respective national family practice conditions; rather, they documented in much clinical detail what the content of family practice could be in these countries under optimal conditions. The US NAMCS data were representative for the national health care system, but they lacked data on episodes of care over time. The increasing use of electronic patient records in US practice networks is a very encouraging development, but has not resulted yet in a database that fulfills the criteria for this comparative study.

It is clear that under very different conditions, substantial proportions of all symptom/complaint reasons for encounter, incidence rates, and encounters per diagnosis are covered with the respective top thirty distributions for the four countries studied. Reasons for encounter as a representation of the patient’s demand for care and the diagnoses as the physician’s interpretation of the need for care follow a common pattern. Given the limitations of the study, they allow us to globally characterize the family physician’s contribution to national health care systems in different countries. The striking resemblance in the distribution of common symptoms and complaints supports the concept of the reason for encounter as a core element of the consultation with a family physician. Similarities between the four databases are much better reflected in the manner that patients formulate and express their demand for care than in the diagnoses assigned by family physicians.

Family practice appears to become what the profession, the patients, and the national conditions permit; it is akin to an antibody reacting to the specific antigens of a nation.44-51 Given the substantial variations across countries, several of the “resulting antibodies” appear to be remarkably similar, which suggests a coherence derived from the way people become sick and seek care. The substantial differences in incidence and utilization in episodes of care for common diseases usually allowed an interpretation along these lines. In the discussions with the national project leaders, their interpretations and explanations had high face validity, allowing a better understanding of the data as characteristic for the position of family practice in the four countries. For example, the high utilization for hypertension in Japan can be explained by legal limitations to the amount of medication per prescription, while the high utilization for upper respiratory tract infection and prevention in Poland also reflects formal requirements. Also, the important role of psychological problems in Dutch, and to a lesser extent in US family practice, reflects its importance in training programs, in contrast with the near absence of such problems in Japan and Poland.

Gynecology and obstetrics are in the core business of family practice in the Netherlands, but in the US, gynecologists provide a substantial contribution in addition to the role of family physicians. Japanese family physicians play practically no role in this area, while in Poland the role of family physicians is limited to gynecology with only a small contribution to contraception and pregnancy. Although respiratory problems are important for family physicians wherever they work, the high incidence and utilization in Poland can also be explained by the need for sickness certification from the first day of illness.

The Japanese system requires multiple encounters per episode during a short period of time. For example, patients with sinusitis, bronchitis, gastritis, or a self-limiting musculoskeletal problem, are often seen 3 or more times per week. In the Netherlands, the health care system requires a large number of repeat prescriptions or refills by the family physician. A trained medical secretary practically always deals with this, and these encounters are considered as “indirect encounters.” The utilization per episode of care in Poland and the Netherlands is rather similar.

The uneven distribution in the relative contribution of family practice to the care for common conditions indicated that US patients also see other providers for common problems. The NAMCS data cannot tell us whether a limited group of the population brings most of their health problems to a family physician, or whether most people visit a series of physicians (Ob/Gyn, Eye, ENT, Psychiatry) depending on who they consider most fit for each problem.51,52

 

 

The ample use of endoscopy, x-ray, and ultrasound in Japan and of electrocardiograms in Poland contrasts with a relatively modest use of diagnostics in the Netherlands. The referral rate to specialists in the Netherlands is a reliable indicator of the role of secondary care; the very low referral rate in Japan reflects how in a rural area patients seek care either of their family physician or of specialists in a nearby city, to whom they have direct access. The high referral rate in Poland also probably reflects the attitude of former hospital specialists who were practically overnight transformed into family physicians.

It is difficult to interpret all differences in prescribing, because the US data do not include the quantity of medication; in the Transition Project’s data, “Defined Daily Doses” are used to better understand prescribing patterns. This study’s data reflect substantial differences in prescribing antibiotics, oral contraceptives, and cardiovascular and gastrointestinal therapies.53-56 Prescribing behavior in the Netherlands and the US is rather similar, while very different patterns are found in Japan and Poland. Antibiotic use in upper respiratory tract infections differs largely: the Dutch prescribe infrequently and almost always use penicillins, the Japanese rarely prescribe penicillins.

Conclusions

The main conclusion of our study is that family practice varies as a customized service, determined by a combination of factors, including the burden of disease; the habits, customs, and training of physicians; the regulations promulgated by government and guilds; the way people understand their symptoms; and the availability of money, services, tools, and goods. Another important conclusion is that, paradoxically, while the need to document reasons for visit was first acknowledged in the US many years ago, US family practice still has not been in the position to document their contribution to national health care in sufficient clinical detail focussing on episodes of care over time.7,9

The recommendation of the IOM to “foster the development of standards for data collection that will ensure the consistency of data elements and definitions of terms, improve coding, permit analysis of episodes of care, and reflect the content of primary care” has not yet resulted in the availability of such (nationally representative) data to be included in a comparative international study.

The possibilities for international cooperation to further develop episode-oriented epidemiology in family practice have, however, increased considerably over the past decade. Further, the potential for international comparative studies in family practice has increased with the introduction of complete electronic patient records based on a standardized documentation of episodes of care with ICPC together with its mapping to ICD-10 (or, for the time being, ICD-9-CM). Now is the time to make a wide use of the new possibilities in routine documentation of patient-physician encounters in family practice by family physicians, wherever they work.57-60

Acknowledgments

The first and last author gratefully acknowledge the opportunity to work on this paper during their scholarship, in late 1999, at AHCPR (now the Agency for Healthcare and Research Quality) in Washington, DC. The authors do not report any competing interests

References

 

1. Mainous AG, III, Baker R, Love MM, Pereira Gray D, Gill JM. Continuity of care and trust in one’s physician: evidence from primary care in the United States and the United Kingdom. Fam Med 2001;33:22-7.

2. McCormick A, Fleming D, Charlton J. Morbidity Statistics from General Practice. Fourth National Study 1991-1992. London: HMSO, 1995.

3. Lamberts H, Wood M, Hofmans-Okkes IM, eds. The International Classification of Primary Care in the European Community. With a multi-language layer. Oxford: Oxford University Press, 1993.

4. Bridges-Webb C, Britt H, Miles DA, Neary S, Charles J, Trayner V. Morbidity and treatment in general practice in Australia. Med J Aust 1992;157:Suppl19 Oct:S1-S56.

5. Health Statistics in the Nordic Countries. Copenhagen: NOMESCO, 1998.

6. Marsland D, Wood M, Mayo F. Content of family practice. A statewide study in Virginia with its clinical, educational and research implications. J Fam Pract 1976;3:22-68.

7. White KL, Williams TF, Greenberg BG. The ecology of medical care. N Engl J Med 1961;1961:885-92.

8. Starfield B. Is US health really the best in the world? JAMA 2000;284:483-5.

9. Green LA, Fryer GE, Yawn BP, Lanier D, Dovey SM. The ecology of medical care revisited. N Engl J Med 2001;344:2021-5.

10. Smith BW, Demers R, Garcia-Shelton L. Family medicine in Japan. Arch Fam Med 1997;6:59-62.

11. Tsuda T, Aoyama H, Froom J. Primary health care in Japan and the United States. Soc Sci Med 1994;38:489-95.

12. Donaldson MS, Yordy KD, Lohr KN, Vanselow NA, eds. Primary Care. America’s Health in a new Era. Committee on the Future of Primary Care, Institute of Medicine. Washington DC: National Academy Press 1996 Washington, DC: National Academy Press, 1996.

13. Hornbrook MC, Hurtado RV, Johnson RE. Health care episodes. Definition, measurement and use. Med Care Rev 1985;42:163-218.

14. Lamberts H, Hofmans-Okkes IM. Episode of care: a core concept in family practice. J Fam Pract 1996;42:161-7.

15. Lamberts H, Wood M, eds. ICPC. International Classification of Primary Care. Oxford: Oxford University Press, 1987.

16. ICPC-2. International Classification of Primary Care. Second edition. Oxford: Oxford University Press, 1998.

17. Okkes IM, Jamoulle M, Lamberts H, Bentzen N. ICPC-2-E. The electronic version of ICPC-2. Differences with the printed version and the consequences. Fam Pract 2000;17:101-6.

18. Hofmans-Okkes IM, Lamberts H. The International Classification of Primary Care (ICPC): new applications in research and computer-based patient records in family practice. Fam Pract 1996;13:294-302.

19. Klinkman MS, Green LA. Using ICPC in a computer-based primary care information system. Fam Med 1995;27:449-56.

20. Ambulatory Care Visits to Physician Offices, Hospital Outpatient Departments and Emergency Departments: United States 1995,1996, 1997 [Series 13, No. 129, 134, 143].

21. Franks P, Clancy CM, Nutting PA. Defining primary care. Empirical analysis of the National Ambulatory Medical Care Survey. Med Care 1997;35:655-68.

22. Woodwell DA. National Ambulatory Medical Care Survey: 1997 Summary. Advance Data 1999;305:1-28.

23. Stafford RS, Saglam D, Causino N, Starfield B, Culpepper L, Marder WD, et al. Trends in adult visits to primary care physicians in the United States. Arch Fam Med 1999;8:26-32.

24. OECD Health Data 99. A comparative analysis of 29 countries. Paris: OECD Health Policy Unit, 2000 (CD ROM).

25. WHO. World Health Report 2000. Health Systems: improving performance. Geneva: World Health Organization, 2000.

26. Evans DB, Tandon A, Murray CJL, Lauer JA. Comparative efficiency of national health systems: cross national econometric analysis. BMJ 2001;323:307-10.

27. Okkes IM, Groen A, Oskam SK, Lamberts H. Advantages of long observation in episode oriented electronic patient records in family practice. Meth Inf Med 2001;40:229-35.

28. Van Boven C, Dijksterhuis PH, Lamberts H. Defensive testing in Dutch family practice. Fam Pract 1997;44:468-72.

29. Okkes IM, Oskam SK, Lamberts H. The development of a probability database in family practice. An empirical approach to obtaining reliable prior probabilities in Dutch family to obtaining reliable prior probabilities in Dutch family practice. J Fam Pract 2002;51:31-6.

30. Mierzecki A, Gasiorowski J, Pilawska H. The family doctor and health promotion - Polish experience and perspectives. Eur J Gen Pract 2000;6:57-61.

31. Sabbat J. International assistance and health care reform in Poland: barriers to project development and implementation. Health Policy 1997;41:207-27.

32. Watson P. Health differences in Eastern Europe: preliminary findings from the Nowa Huta study. Soc Sci Med 1998;46:549-58.

33. Froom J, Aoyama H, Hermoni D, Mino Y, Galambos N. Depressive disorders in three primary care populations: United States, Israel, Japan. Fam Pract 1995;12:274-8.

34. Ikegami N, Campbell JC. Medical care in Japan. N Engl J Med 1995;333:1295-9.

35. Yamada T, Yoshimura M, Nago N, Inoue Y, Asai Y, Koga Y, et al. A study on the outcomes of health problems (the concept of ’episode of care‚) based on clinical statistics using the International Classification of Primary Care (ICPC). Jpn J Prim Care 2000;23:213-23.

36. Yamada T, Yoshimura M, Naoki N, Asai Y, Koga Y, Inoue Y, et al. What are common diseases and common health problems? The use of ICPC in the community-based project. Jpn J Prim Care 2000;23:80-9.

37. De Maeseneer J. The ICPC classification of drugs. In: Lamberts H, Wood M, Hofmans-Okkes IM, eds. The International Classification of Primary Care in the European Community. With a multi-language layer. Oxford: Oxford University Press, 1993. pp.163-70.

38. Reason for visit classification for ambulatory care. National Center for Health Statistics, US Public Health Service, Hyattsville MD, USA. Department of Health, Education and Welfare Publication 1979. 74 pp.

39. The collection and processing of drug information. 1980 series 2, no. 90 NCHS Report.

40. Wood M, Lamberts H, Meijer JS, Hofmans-Okkes IM. The conversion between ICPC and ICD-10. Requirements for a family of classification systems in the next decade. J Fam Pract 1992;9:340-8.

41. Bentzen N, ed. An International Glossary for General/Family Practice. Fam Pract 1995;12:341-69.

42. Grumbach K, Selby JV, Damberg C, Bindman AB, Quesenberry C, Jr, Truman A, et al. Resolving the gatekeeper conundrum: what patients value in primary care and referrals to specialists. JAMA 1999;82:261-6.

43. Stange KC, Zyzanski SJ, Jaén CR, Callahan EJ, Kelly RB, Gillanders WR, et al. Illuminating the ‘Black Box’.. A description of 4454 patient visits to 138 family physicians. J Fam Pract 1998;46:377-8

44. Stange KC, Jaén CR, Flocke SA, Miller WL, Crabtree BF, Zyzanski SJ. The value of a family physician. J Fam Pract 1998;46:363-8.

45. Rosenblatt RA, Hart LG, Gamliel S, Goldstein B, McClendon BJ. Identifying primary care disciplines by analyzing the diagnostic content of ambulatory care. J Am Board Fam Pract 1995;8:34-45.

46. Rosenblatt RA, Hart G, Baldwin L, Chan L, Schneeweiss R. The generalist role of specialty physicians: is there a hidden system of primary care? JAMA 1998;279:1364-70.

47. Mold JW, Green LA. Primary care research: revisiting its definition and rationale. J Fam Pract 2000;49:206-8.

48. Perry Dickinson W, Stange KC, Ebell MH, Ewigman BG, Green LA. Involving all family physicians and family medicine faculty members in the use and generation of new knowledge. Fam Med 2000;32:480-90.

49. White KL. Fundamental research at primary care level. Lancet 2000;355:1904-6.

50. Starfield B. Primary Care: balancing health needs, services and technology. New York, NY: Oxford University Press,1998.

51. Greenfield S, Keller A, Kravitz R, Manning W, Nelson E, Rogers W, et al. Variations in resource utilization among medical specialties and systems of care. JAMA 1992;267:1624-30.

52. Green LA, Miller RS, Reed FM, Iverson DC, Barley GE. How representative of typical practice are practice-based research networks? A report from the Ambulatory Sentinel Practice Network Inc (ASPN). Arch Fam Med 1993;152:939-49.

53. Metlay JP, Stafford RD, Singer DE. National trends in the use of antibiotics by primary care physicians for adults patients with cough. Arch Intern Med 1998;158:1813-8.

54. Nyquist A-C, Gonzales R, Steiner JF, Sande MA. Antibiotic prescribing for children with colds, upper respiratory tract infections, and bronchitis. JAMA 1998;279:875-7.

55. Pincus H, Tanielian TL, Marcus SC, Olfson M, Zarin DA, Thompson J, et al. Prescribing trends in psychotropic medications: primary care, psychiatry, and other medical specialties. JAMA 1998;279:526-31.

56. Wang TJ, Stafford RS. National patterns and predictors of beta-blocker use in patients with coronary artery disease beta-blocker use in patients with coronary artery disease. Arch Intern Med 1998;158:1901-6.

57. Ornstein S. Electronic medical records in family practice: the time is now. J Fam Pract 1997;44:45-8.

58. Stephens GG. The intellectual basis of family medicine revisited. Fam Med 1998;9:642-54.

59. Green LA, Nutting PA. Family physicians as researchers in their own practices. J Am Board Fam Pract 1994;7:261-3.

60. Nutting PA, Beasley JW, Werner JJ. Practice-based research networks answer primary care questions. JAMA 1999;281:686-8.

References

 

1. Mainous AG, III, Baker R, Love MM, Pereira Gray D, Gill JM. Continuity of care and trust in one’s physician: evidence from primary care in the United States and the United Kingdom. Fam Med 2001;33:22-7.

2. McCormick A, Fleming D, Charlton J. Morbidity Statistics from General Practice. Fourth National Study 1991-1992. London: HMSO, 1995.

3. Lamberts H, Wood M, Hofmans-Okkes IM, eds. The International Classification of Primary Care in the European Community. With a multi-language layer. Oxford: Oxford University Press, 1993.

4. Bridges-Webb C, Britt H, Miles DA, Neary S, Charles J, Trayner V. Morbidity and treatment in general practice in Australia. Med J Aust 1992;157:Suppl19 Oct:S1-S56.

5. Health Statistics in the Nordic Countries. Copenhagen: NOMESCO, 1998.

6. Marsland D, Wood M, Mayo F. Content of family practice. A statewide study in Virginia with its clinical, educational and research implications. J Fam Pract 1976;3:22-68.

7. White KL, Williams TF, Greenberg BG. The ecology of medical care. N Engl J Med 1961;1961:885-92.

8. Starfield B. Is US health really the best in the world? JAMA 2000;284:483-5.

9. Green LA, Fryer GE, Yawn BP, Lanier D, Dovey SM. The ecology of medical care revisited. N Engl J Med 2001;344:2021-5.

10. Smith BW, Demers R, Garcia-Shelton L. Family medicine in Japan. Arch Fam Med 1997;6:59-62.

11. Tsuda T, Aoyama H, Froom J. Primary health care in Japan and the United States. Soc Sci Med 1994;38:489-95.

12. Donaldson MS, Yordy KD, Lohr KN, Vanselow NA, eds. Primary Care. America’s Health in a new Era. Committee on the Future of Primary Care, Institute of Medicine. Washington DC: National Academy Press 1996 Washington, DC: National Academy Press, 1996.

13. Hornbrook MC, Hurtado RV, Johnson RE. Health care episodes. Definition, measurement and use. Med Care Rev 1985;42:163-218.

14. Lamberts H, Hofmans-Okkes IM. Episode of care: a core concept in family practice. J Fam Pract 1996;42:161-7.

15. Lamberts H, Wood M, eds. ICPC. International Classification of Primary Care. Oxford: Oxford University Press, 1987.

16. ICPC-2. International Classification of Primary Care. Second edition. Oxford: Oxford University Press, 1998.

17. Okkes IM, Jamoulle M, Lamberts H, Bentzen N. ICPC-2-E. The electronic version of ICPC-2. Differences with the printed version and the consequences. Fam Pract 2000;17:101-6.

18. Hofmans-Okkes IM, Lamberts H. The International Classification of Primary Care (ICPC): new applications in research and computer-based patient records in family practice. Fam Pract 1996;13:294-302.

19. Klinkman MS, Green LA. Using ICPC in a computer-based primary care information system. Fam Med 1995;27:449-56.

20. Ambulatory Care Visits to Physician Offices, Hospital Outpatient Departments and Emergency Departments: United States 1995,1996, 1997 [Series 13, No. 129, 134, 143].

21. Franks P, Clancy CM, Nutting PA. Defining primary care. Empirical analysis of the National Ambulatory Medical Care Survey. Med Care 1997;35:655-68.

22. Woodwell DA. National Ambulatory Medical Care Survey: 1997 Summary. Advance Data 1999;305:1-28.

23. Stafford RS, Saglam D, Causino N, Starfield B, Culpepper L, Marder WD, et al. Trends in adult visits to primary care physicians in the United States. Arch Fam Med 1999;8:26-32.

24. OECD Health Data 99. A comparative analysis of 29 countries. Paris: OECD Health Policy Unit, 2000 (CD ROM).

25. WHO. World Health Report 2000. Health Systems: improving performance. Geneva: World Health Organization, 2000.

26. Evans DB, Tandon A, Murray CJL, Lauer JA. Comparative efficiency of national health systems: cross national econometric analysis. BMJ 2001;323:307-10.

27. Okkes IM, Groen A, Oskam SK, Lamberts H. Advantages of long observation in episode oriented electronic patient records in family practice. Meth Inf Med 2001;40:229-35.

28. Van Boven C, Dijksterhuis PH, Lamberts H. Defensive testing in Dutch family practice. Fam Pract 1997;44:468-72.

29. Okkes IM, Oskam SK, Lamberts H. The development of a probability database in family practice. An empirical approach to obtaining reliable prior probabilities in Dutch family to obtaining reliable prior probabilities in Dutch family practice. J Fam Pract 2002;51:31-6.

30. Mierzecki A, Gasiorowski J, Pilawska H. The family doctor and health promotion - Polish experience and perspectives. Eur J Gen Pract 2000;6:57-61.

31. Sabbat J. International assistance and health care reform in Poland: barriers to project development and implementation. Health Policy 1997;41:207-27.

32. Watson P. Health differences in Eastern Europe: preliminary findings from the Nowa Huta study. Soc Sci Med 1998;46:549-58.

33. Froom J, Aoyama H, Hermoni D, Mino Y, Galambos N. Depressive disorders in three primary care populations: United States, Israel, Japan. Fam Pract 1995;12:274-8.

34. Ikegami N, Campbell JC. Medical care in Japan. N Engl J Med 1995;333:1295-9.

35. Yamada T, Yoshimura M, Nago N, Inoue Y, Asai Y, Koga Y, et al. A study on the outcomes of health problems (the concept of ’episode of care‚) based on clinical statistics using the International Classification of Primary Care (ICPC). Jpn J Prim Care 2000;23:213-23.

36. Yamada T, Yoshimura M, Naoki N, Asai Y, Koga Y, Inoue Y, et al. What are common diseases and common health problems? The use of ICPC in the community-based project. Jpn J Prim Care 2000;23:80-9.

37. De Maeseneer J. The ICPC classification of drugs. In: Lamberts H, Wood M, Hofmans-Okkes IM, eds. The International Classification of Primary Care in the European Community. With a multi-language layer. Oxford: Oxford University Press, 1993. pp.163-70.

38. Reason for visit classification for ambulatory care. National Center for Health Statistics, US Public Health Service, Hyattsville MD, USA. Department of Health, Education and Welfare Publication 1979. 74 pp.

39. The collection and processing of drug information. 1980 series 2, no. 90 NCHS Report.

40. Wood M, Lamberts H, Meijer JS, Hofmans-Okkes IM. The conversion between ICPC and ICD-10. Requirements for a family of classification systems in the next decade. J Fam Pract 1992;9:340-8.

41. Bentzen N, ed. An International Glossary for General/Family Practice. Fam Pract 1995;12:341-69.

42. Grumbach K, Selby JV, Damberg C, Bindman AB, Quesenberry C, Jr, Truman A, et al. Resolving the gatekeeper conundrum: what patients value in primary care and referrals to specialists. JAMA 1999;82:261-6.

43. Stange KC, Zyzanski SJ, Jaén CR, Callahan EJ, Kelly RB, Gillanders WR, et al. Illuminating the ‘Black Box’.. A description of 4454 patient visits to 138 family physicians. J Fam Pract 1998;46:377-8

44. Stange KC, Jaén CR, Flocke SA, Miller WL, Crabtree BF, Zyzanski SJ. The value of a family physician. J Fam Pract 1998;46:363-8.

45. Rosenblatt RA, Hart LG, Gamliel S, Goldstein B, McClendon BJ. Identifying primary care disciplines by analyzing the diagnostic content of ambulatory care. J Am Board Fam Pract 1995;8:34-45.

46. Rosenblatt RA, Hart G, Baldwin L, Chan L, Schneeweiss R. The generalist role of specialty physicians: is there a hidden system of primary care? JAMA 1998;279:1364-70.

47. Mold JW, Green LA. Primary care research: revisiting its definition and rationale. J Fam Pract 2000;49:206-8.

48. Perry Dickinson W, Stange KC, Ebell MH, Ewigman BG, Green LA. Involving all family physicians and family medicine faculty members in the use and generation of new knowledge. Fam Med 2000;32:480-90.

49. White KL. Fundamental research at primary care level. Lancet 2000;355:1904-6.

50. Starfield B. Primary Care: balancing health needs, services and technology. New York, NY: Oxford University Press,1998.

51. Greenfield S, Keller A, Kravitz R, Manning W, Nelson E, Rogers W, et al. Variations in resource utilization among medical specialties and systems of care. JAMA 1992;267:1624-30.

52. Green LA, Miller RS, Reed FM, Iverson DC, Barley GE. How representative of typical practice are practice-based research networks? A report from the Ambulatory Sentinel Practice Network Inc (ASPN). Arch Fam Med 1993;152:939-49.

53. Metlay JP, Stafford RD, Singer DE. National trends in the use of antibiotics by primary care physicians for adults patients with cough. Arch Intern Med 1998;158:1813-8.

54. Nyquist A-C, Gonzales R, Steiner JF, Sande MA. Antibiotic prescribing for children with colds, upper respiratory tract infections, and bronchitis. JAMA 1998;279:875-7.

55. Pincus H, Tanielian TL, Marcus SC, Olfson M, Zarin DA, Thompson J, et al. Prescribing trends in psychotropic medications: primary care, psychiatry, and other medical specialties. JAMA 1998;279:526-31.

56. Wang TJ, Stafford RS. National patterns and predictors of beta-blocker use in patients with coronary artery disease beta-blocker use in patients with coronary artery disease. Arch Intern Med 1998;158:1901-6.

57. Ornstein S. Electronic medical records in family practice: the time is now. J Fam Pract 1997;44:45-8.

58. Stephens GG. The intellectual basis of family medicine revisited. Fam Med 1998;9:642-54.

59. Green LA, Nutting PA. Family physicians as researchers in their own practices. J Am Board Fam Pract 1994;7:261-3.

60. Nutting PA, Beasley JW, Werner JJ. Practice-based research networks answer primary care questions. JAMA 1999;281:686-8.

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Physician Recruitment for a Community-Based Smoking Cessation Intervention

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Physician Recruitment for a Community-Based Smoking Cessation Intervention

ABSTRACT

OBJECTIVE: Our goal was to describe a strategy to recruit a population-based sample of physicians to test an approach to disseminate physician-delivered smoking cessation interventions.

STUDY DESIGN: The 3-phase population-based recruitment trial included: (1) a print-based promotional appeal; (2) in-person presentations by the principal investigator (PI); and (3) follow-up calls by the PI and paid physician recruiters. Participation requirements were kept minimal to facilitate recruitment.

POPULATION: All primary care physicians statewide were targeted; 3 counties were chosen as intervention areas and 2 counties as control areas. A subsample of physicians was targeted in the larger control areas through a matching process.

OUTCOME MEASURED: We measured physician recruitment rate.

RESULTS: Eighty-one percent (n=259) of all eligible physicians were successfully recruited into our study.

CONCLUSIONS: The full multistep process was important in getting participation agreement. By using an intensive recruitment strategy and minimizing research demands, it is possible to recruit community-based primary care physicians for research projects that will help them enhance the preventive services they provide to their patients.

KEY POINTS FOR CLINICIANS

  1. We describe a successful method of recruiting physicians into a community-based trial.
  2. Our multiphase multifaceted recruitment design included an initial mailing, presentations at local hospitals, clinic and office visits, and follow-up phone calls.
  3. Successful recruitment rate was attributed to the enhanced involvement by a physicians, and the minimized research demands in return for participation.

Controlled research trials have demonstrated that physician-delivered smoking interventions are an effective means of increasing quit rates among patients who smoke.1-4 However, these trials involved samples of physicians-in-training or volunteer physicians that were not representative of the general primary care physician population. Furthermore, the percentage of eligible physicians who participated in community-based trials ranged from 5% to 50%,V5-9 and obstacles to practicing physician’ participation in research are now greater than ever.10

Physicians Counseling Smokers (PCS) is a National Cancer Institute-funded phase IV trial that tested the effectiveness of an approach to disseminate physician-delivered smoking cessation interventions.11,12 Because this was a dissemination trial that would assess smoking outcomes at a population level, it was necessary to recruit a very high percentage of practicing primary care providers within the discreet geographic areas. The objective of this paper is to describe the process of recruiting a population-based sample of physicians.

Methods

Identification of intervention and control areas

The entire state population of primary care physicians was targeted for recruitment. Three counties served as intervention areas; the 2 smaller counties (Newport and Washington) were combined to approximately match the size of the third (Kent). These 2 geographic areas were chosen for intervention because it was feasible to intervene with the total estimated number of physicians (n=100) in each area. Saturation of intervention areas was important because patient level outcomes were being assessed with random digit dial techniques.15 The largest and smallest counties (Providence and Bristol) were combined to serve as the control area.

Given the comparatively large number of physicians in the control area, a subsample of these physicians were targeted for recruitment. This subsample was created by matching the intervention sample on gender, practice specialty, and years since graduation from medical school. In addition, the control sample was matched at a rate of 1.5 times the mean of the number of eligible physicians in the 2 intervention areas. Eligible physicians in the intervention and control areas practiced in community health centers, colleges or universities, private practices, and a state-wide staff model health maintenance organization. Five large urban public health clinics in Providence, Rhode Island, were not targeted because there was no comparable group of urban public health clinics in the intervention areas. We targeted physicians for enrollment because they were most often the key decision makers, both for consent for the office to participate and for their influence over change in practice patterns. Further, the NCI model being disseminated had been previously tested on physicians. However, once the office was enrolled, all clinicians were invited to participate in sessions with our practice consultants.

We did not include practice-based midlevel providers since the purpose of the study design was to assess the NCI smoking cessation recommendations for physicians, which was a model that had previously been tested specifically on physicians. Also, to conduct a practice-based study, the consent of the physicians is necessary. Our emphasis was to establish physician buy-in so that we could enhance our ability to conduct the intervention and involve all practice-based clinicians.

Identification of the physician sample

To be eligible for participation in PCS, physicians had to (1) have a primary care specialty; (2) provide regular, ongoing care to at least 25% of their patients; (3) practice in a nonhospital-based location; and (4) intend on being available during the 3-year evaluation period.

 

 

A Health Department list of all physicians licensed in the state was obtained to identify potentially eligible physicians, and the list was supplemented with a private directory of physicians who practice in the state.16 Medical staff lists from local hospitals, health maintenance organizations, and community health centers were used. Office addresses were further verified by phone.

Recruitment strategy

The multi-stepped recruitment process included 3 phases. Phase 1 of recruitment included widespread promotion through various forms of written communication in which the important role of physician involvement in smoking cessation efforts was emphasized.1,7,17,18 based on the work of Rogers19 and Lomas and coworkers,20 an advisory board made up of local, influential physicians was formed to assist with recruitment efforts during the Phase 1. Board members were selected from various physician and health-related organizations throughout the state. The advisory board was designed to serve as a linkage system21 between research intervention offerings and community physicians.

A mailed invitation to participate was sent on advisory board letterhead, under the signature of the medical director of the state Health Department and the president of the state medical society. The mailing included information about the study, an enrollment form that assessed eligibility, and a consent form for participation along with a postage-paid return envelope. Mailed postcards and telephone calls from research staff were used as follow-up for those who did not respond to the initial mailing. In addition, public relations departments at the State Health Department, state medical society, 3 community hospitals, and a regional metropolitan newspaper were asked to include a brief article about the study in their newsletter or newspaper, which they all did.

Phase 2 of the recruitment process involved making appearances at local hospitals and visits to practices and clinics. Physicians had an opportunity to enroll at department staff meetings following presentations that were made by the principal investigator and staff. In addition, individual meetings were scheduled with a small number of physicians who requested this. Also, members of the physician advisory board were asked to make brief phone calls to a small number of their physician colleagues to solicit their participation in the study. Physicians who enrolled during these 2 phases were also encouraged to talk to their colleagues informally about participating in the project. Phase 3 of the recruitment process focused on enrolling remaining eligible physicians. Paid physician recruiters were hired to assist the principal investigator in making telephone contact. Physicians who could not be reached by the paid recruiters also received a phone call from the principal investigator. Early outreach required participant initiative for enrollment, therefore all refusals occurred during telephone contacts in Phase 3.

Participation requirements

Participation requirements were kept minimal to facilitate and encourage enrollment of all eligible physicians, regardless of their readiness to adopt smoking interventions.12,19,22,23 To enroll, physicians had to agree to complete 3 annual, 20-minute surveys and allow 20-minute assessments of the office environment to determine smoking cessation tools and resources available to patients and providers. This latter assessment was conducted with one of the office staff in order to minimize time demands of the physician. The intervention was designed to test an approach to gain enhanced access to physicians in their offices. Acceptance of intervention visits from research staff was optional in order to encourage participation of physicians with a broad range of interest and readiness to adopt smoking cessation interventions.

Upon return of their completed baseline survey, physicians in the intervention were offered information based on their readiness to enhance their cessation efforts, samples of patient education materials, a poster listing local smoking cessation programs, and the NCI physician manual, “How to Help Your Patients Stop Smoking.”24 Physicians in the intervention area were offered various resources and training opportunities to enhance smoking cessation interventions in their office. Research staff, trained as consultants to deliver tailored interventions based on an academic detailing approach, scheduled intervention meetings to be most convenient for the physician and office staff. While physician attendance at intervention meetings was encouraged, physicians were offered the option of designating office staff to meet with the research consultants. The goal was to meet with physicians or designated staff roughly 4 to 5 times during the intervention year. No adoption of cessation efforts were required. Physicians in the control area were offered the same manual after completing their baseline survey and the opportunity to receive the other resources and participate in counseling skills training at the end of the intervention period.

Results

Of 2316 licensed physicians in Rhode Island in 1989, 822 were identified as meeting the primary care specialty criteria, based on information provided in the listings used: 557 from the control area and 265 from the 2 intervention areas. Of the physicians from the control area, 202 were matched to the physicians from the intervention areas and became part of the sampling frame. Initial contacts to physicians in the sampling frame determined that an additional 148 were not eligible. The majority of these did not meet the requirements for primary care due to not practicing in a primary specialty or not providing regular, ongoing care to at least 25% of their patients.25 Others had moved from the state, retired, or died. After elimination of physicians who did not meet eligibility criteria, 187 intervention area physicians and 132 control area physicians remained in the final pool of physicians eligible for recruitment. Less than 10% of recruitments responded to the initial mailing, and another 10% were recruited directly from the in-person presentations at department meetings.

 

 

Eighty percent of recruitment came from Phase 3, from phone calls by the physician recruiters. Approximately two thirds of study participants were recruited by the principal investigator. However, the ground work from publicity, endorsements from physician leadership, and familiarity with the aims of the trial were clearly important in getting agreement during the recruitment phone call.

Among all eligible physicians, 81% (N=259) were successfully recruited into the study: 80% (n=106) of targeted control area physicians (Providence/Bristol); 85% (n=88) of physicians in the first intervention area (Newport/Washington); and 77% (n=65) of physicians in the second intervention area (Kent) were enrolled. Characteristics of the sample are displayed in Table 1.

The 18% of physicians who refused to take part cited the following reasons for not participating: (1)they preferred not to participate in studies or fill out surveys; (2) they had a shortage of resources and did not have the time; (3) they were undergoing significant staff turnover or felt that their office staff were already overburdened; (4) they felt they were already providing effective smoking cessation interventions to their patients; or (5) they did not accept smokers into their practice. Chi square tests indicated that refusers were significantly more likely to be male (F=6.5, P < .05) and to have been out of medical school for more than 25 years (F=20.7, P < .001). Less than 5% of eligible female physicians refused to participate as compared with 21% of men. Medical specialty did not have a significant impact participation in this study.

Discussion

Results of the multi-faceted recruitment approach used in the Physicians Counseling Smokers project demonstrate that it is feasible to enroll a population-based sample of primary care physicians into a dissemination trial. We were successful at recruiting a representative sample of community-based physicians. It was our goal to saturate our target geographic area to obtain a truly population based sample. We succeeded in achieving this, recruiting 81% of eligible physicians. It is noteworthy that we were able to retain 88% of enrolled physicians at the end of the 3-year study period. This reinforces that physicians were willing and able to keep their minimal commitment to complete the annual assessments. The most common reason for drop out was leaving the practice/moving out of state.

Recruiting physicians and practices into community-based trials is a challenging process, and several investigators have examined the effectiveness of different recruitment strategies. Recruitment efforts have evolved from a single mailing method to a multi-stepped process. Kottke and colleagues13 assessed and compared mailed recruitment methods for primary care physicians in Minnesota for a 1-month office-based smoking intervention. Eligible family medicine physicians (n=1100) were mailed a brochure alone or a brochure with an explanatory letter signed by one of the investigators on university letterhead or by an investigator on a state Academy of Family Physicians letterhead. Ten percent of eligible physicians responded and no difference between brochure alone or brochure plus letter groups. In a second study, the brochure only mailing strategy was used again to recruit 1108 general internists and cardiologists on the mailing list of a state Medical Association into a one-year trial. Five percent responded and 2.7% participated. Dietrich and colleagues14 used a multi-faceted approach to recruit community-based physicians into a randomized trial to increase cancer prevention practices. Of 628 eligible family physicians and internists in Vermont and New Hampshire, 234 physicians (37%) agreed to participate. Physicians with name recognition in their communities assisted with recruitment Table 2.

Since PCS was conducted, recruitment strategies targeting community-based individual physicians and practices for cancer prevention studies have evolved from single mailing techniques to more common use of multi-step approaches, including face-to-face visits, advisory boards, and physician phone calls Table 3. Participation incentives including honorarium, office staff trainings, and patient materials are often included to enhance recruitment rates,25,26 but even substantial physician incentives do not guaranteed high participation rates.10

In reviewing these studies, it is difficult to assess the impact of each specific recruitment strategy used. However, the in-person appearance of the principal investigator, a physician, appeared to have a major impact on physician enrollment. Earlier studies7,13 producing lower recruitment rates did not involve this in-person meeting component, and Asch’s review of physician recruitment studies supports the importance of personal contact. Two recent community-based physician office recruitment trials included in-person office visits.25,26 In addition to office and clinic visits, in PCS the principal investigator was also present at hospital departmental meetings and gave grand rounds at these hospitals.

Another successful strategy demonstrated in PCS was involvement of the principal investigator in calling physicians who were difficult to recruit. Although nonphysician PCS research staff made concerted efforts to assist with recruitment, their access to the physician by phone was often limited by gatekeepers within the office. PCS demonstrated that, although time intensive and costly, the use of a physician recruiter may be necessary to recruit a representative sample, for example, with at least 75% of eligible physicians, into a dissemination trial. Although difficult to assess the impact of the impact of these preliminary phases, it was also evident that the work completed in Phases 1 and 2 created familiarity and laid the groundwork for the Phase 3 calls.

 

 

Obtaining support of prominent local physicians, and involving many in our advisory board, contributed to our success. The “RAND” method, which involves influential physicians recruiting community-based physicians,10 was deemed useful in this study. Similarly, a study which investigated the relationship between medical malpractice claims and physician patient communication, also utilized prominent members of the local physician community as advisory board members who made recruitment calls and endorsed the study introductory letter.27 In PCS these physicians not only participated in recruitment calls and endorsed the study introductory letter, but also allowed access to hospitals and physicians so that in-person visits and presentations could occur.

Finally, minimizing research demands, maintaining flexibility in scheduling interventions, and offering tailored interventions to meet physician’s needs all appeared to enhance recruitment rates. In particular, emphasizing that a low burden will be caused by study participation seems key, as lack of time was cited in our study as a reason for nonparticipation and has been the most common reason given for nonparticipation in other studies.10 Initial contacts with physicians focused on the individual benefits that each physician would gain from participation. An emphasis was placed on acknowledgment of physicians’ time constraints; we emphasized our intent to share resources and tools that would help physicians be more effective with the existent time constraints. Additionally, our study did not require medical office staff to be involved in recruiting patients, nor did it require access to patients’ medical records.

Several recruitment strategies used in PCS appeared to be less effective. Use of physician graduate fellows, physicians who were awarded a fellowship for postgraduate study, as a final step to contact and enroll eligible physicians did not appear to contribute to our success. Also, recruitment rates were lower among physicians who were at least 25 years out of medical school. This is consistent with Dietrich and colleagues’14 finding that nonparticipants were significantly older than participants. The reasons for this result are unclear. Perhaps more recently trained physicians are more receptive to participation in a study that targets prevention, or more receptive to participating in research. Another potential reason is that an age-based sampling bias occurred. We know that the majority of our sample were generalists, but we did not measure other variables, such as race or practice setting, that may have also influenced the formation of the sample. It was a limitation that we did not gather information on background variables that may have influenced the sample makeup, and, in using physician recruiters, there is a potential that a sampling bias will occur.

Conclusions

There is a growing need to disseminate effective strategies to assist physicians with the delivery of preventive services. We were successful in recruiting more than 80% of community-based physicians, saturating a discreet geographic area, into a dissemination trial. The enhanced involvement by a physician investigator and endorsement and efforts by local influential physicians contributed to our success. Additionally, we minimized research demands in return for participation. Studies that have required more physician involvement have not been as successful and may need more intensive recruitment strategies. The relatively low refusal rate in this study suggests that community-based, primary care physicians are interested and willing to participate in research that will help them enhance the preventive services they provide to their patients.

Acknowledgments

This study was supported by grant PO1CA50087 (James Prochaska, Principal Investigator) from the National Cancer Institute, Washington, DC. The authors wish to acknowledge David Abrams, PhD, James Prochaska, PhD, Wayne Velicer, PhD, and Joseph Rossi, PhD who contributed to the measure development process and provided support and guidance as senior scientists in the Rhode Island Cancer Prevention Consortium. We also wish to acknowledge Alexander Prokhorov, MD, PhD, Alicia Monroe, MD, William Rakowski, PhD, and Lisa Harlow, PhD, as investigators on the PCS research team. We wish to also acknowledge Allen Dietrich, MD, Judith Ockene, PhD, Jean Kristeller, PhD, and Thomas Kottke, MD, for valuable scientific consultation. We especially wish to thank and acknowledge Linda Moreau and Barbara Doll who provided essential secretarial support and Elena Morgans for her help in coding and entering data.

References

1. Kottke T.E., R. N. Battista, et al. Attributes of successful smoking cessation interventions in medical practice. A meta-analysis of 39 controlled trials. JAMA 1988;259(19):2883-9.

2. Ockene J. K., J. Kristeller, et al. Increasing the efficacy of physician-delivered smoking interventions: a randomized clinical trial [see comments]. J Gen Intern Med 1991;6(1):1-8.

3. Schwartz J. L. Methods of smoking cessation. Med Clin North Am 1992;76 (2):451-76.

4. Fiore M., W. Bailey, et al. (1996). Smoking Cessation: Clinical Practice Guideline No. 18, Agency for Health Care Policy and Research, Public Health Service, U.S. Department of Health And Human Services.

5. Wilson D. M., D. W. Taylor, et al. A randomized trial of a family physician intervention for smoking cessation. JAMA 1988;260(11):1570-4.

6. Cummings S. R., R. J. Richard, et al. Training physicians about smoking cessation: a controlled trial in private practice. J Gen Intern Med 1989;4(6):482-9.

7. Kottke T. E., L. I. Solberg, et al. A comparison of two methods to recruit physicians to deliver smoking cessation interventions. Arch Intern Med 1990;150(7):1477-81.

8. Haug K., P. Fugelli, et al. Recruitment and Participation of General Practitioners in a Multipractice Study of Smoking Cessation. Scandanavian Journal of Primary Health Care 1992;10:206-210.

9. Richmond R, C. Mendelsohn, et al. Family physicians’ utilization of a brief smoking cessation program following reinforcement contact after training: a randomized trial. Prev Med 1998;27(1):77-83.

10. Asch S, S. Connor, et al. Problems in Recruiting Community-Based Physicians for Health Services Research. J Gen Internal Med 2000;15:591-599.

11. Goldstein M. G., DePue J.D., Monroe AD, Lessne CW, Rakowski W, Prokhorov A, Niaura R, Dube CE. A Population-Based Survey of Physicians’ Smoking Counseling Behavior. Preventive Medicine 1998;7(5 Pt 1):720-9.

12. Goldstein M. G, N. A. MacDonald, et al. (1993). Dissemination of physician-based smoking cessation interventions. Tobacco and the Clinician; Interventions For Medical and Dental Practice. S. Burns, S. Cohen, E. Gritz and T. Kottke. Bethesda, MD, USDHHS, PHS, NIH, NCI.

13. Kottke T. E., L. I. Solberg, et al. Initiation and maintenance of patient behavioral change: what is the role of the physician? J Gen Intern Med 1990;5 (5 Suppl):S62-7.

14. Dietrich A. J., G. O’Connor, et al. (1990). Will community physicians participate in rigorous studies of cancer control? The methodology and recruitment of a randomized trial of physician practices. Advances in Cancer Control: Screening and Prevention Research, Wiley-Liss, Inc.: 373-381.

15. Goldstein M. G., R. Niaura, et al. Physicians counseling smokers. A population-based survey of patients’ perceptions of health care provider-delivered smoking cessation interventions. Arch Intern Med 1997;157(12):1313-9.

16. Folio Associates (1990). Folio’s Medical Directory of Connecticut and Rhode Island, Folio Associates, Inc. Tobacco and Cancer Program, Division of Cancer Prefvention and Control, National Cancer Institute.

17. Battista R. N., J. I. Williams, et al. Determinants of primary medical practice in adult cancer prevention. Med Care 1986;24(3):216-24.

18. Ockene J. K. Smoking intervention: the expanding role of the physician [editorial]. Am J Public Health 1987;77(7):782-3.

19. Rogers E. M. (1983). Diffusion of Innovations. New York, The Free Press.

20. Lomas J, Enkin M., et al. Opinion Leaders vs audit and feedback to implement practice guidelines. JAMA 1991;266 (9):1217.-

21. Orlandi M. A. Promoting health and preventing disease in health care settings: An analysis of barriers. Preventive Medicine 1987;16:119-130.

22. Prochaska J. O., & DiClemente C. C. Toward a comprehensive model of change. Treating Addictive Disorders: Processes of Change. W. R. Miller and N. Heather. New York, Plenum Press, 1986.

23. Prochaska J., W. Velicer, et al. Measuring processes of change: Applications to the cessation of smoking. J Consult Clin Psychology 1988;56:520-528.

24. Glynn T. J., M. W. Manley (1989). andHow to Help Your Patients Stop Smoking. A National Cancer Institute Manual for Physicians. Bethesda, Maryland, Smoking,

25. Carey T., L. Kinsinger, et al. Research in the Community: Recruiting and Retaining Practices. Journal of Community Health 1996;21:315-327.

26. McBride P., K. Massoth, et al. Recruitment of Private Practices for Primary Care Research: Experience in a Preventive Services Clinical Trial. J Fam Pract 1996;4:389-395.

27. Levinson W., V. Dull, et al. Recruiting Physicians for Office-Based Research. Medical Care 1998;36(6):934-937.

Reprint requests should be addressed to Elyse Park, PhD, MGH, 50 Staniford Street, 904A, Boston, MA 02115. E-mail: [email protected].

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Elyse R. Park, PhD
Nancy A. MacDonald Gross, MPH, CHES
Michael G. Goldstein, MD
Judith D. DePue, EdD, MPH
Jacklyn P. Hecht, MSN, RN
Cheryl A. Eaton, MA
Raymond Niaura, PhD
Catherine E. Dubé
EdD Providence, Rhode Island
Submitted, revised, October 28, 2001.
From the Centers for Behavioral and Preventive Medicine, Brown Medical School, and the Miriam Hospital (E.R.P., N.A.M.G., M.G.G., J.D.D., J.P.H., R.N.), and Brown University School of Medicine (C.A.E., C.E.D.). This study was supported by grant PO1CA50087 from the National Cancer Institute, Washington, DC. Dr Goldstein is currently at the Bayer Institute for Health Care Communication in West Haven, Connecticut, and Dr Park is currently at Massachusetts General Hospital in Boston, Massachusetts. The authors do not report any competing interests.

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The Journal of Family Practice - 51(1)
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,Smoking cessationphysician recruitment methods [non-MESH]health services research. (J Fam Pract 2002; 51:70)
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Author and Disclosure Information

Elyse R. Park, PhD
Nancy A. MacDonald Gross, MPH, CHES
Michael G. Goldstein, MD
Judith D. DePue, EdD, MPH
Jacklyn P. Hecht, MSN, RN
Cheryl A. Eaton, MA
Raymond Niaura, PhD
Catherine E. Dubé
EdD Providence, Rhode Island
Submitted, revised, October 28, 2001.
From the Centers for Behavioral and Preventive Medicine, Brown Medical School, and the Miriam Hospital (E.R.P., N.A.M.G., M.G.G., J.D.D., J.P.H., R.N.), and Brown University School of Medicine (C.A.E., C.E.D.). This study was supported by grant PO1CA50087 from the National Cancer Institute, Washington, DC. Dr Goldstein is currently at the Bayer Institute for Health Care Communication in West Haven, Connecticut, and Dr Park is currently at Massachusetts General Hospital in Boston, Massachusetts. The authors do not report any competing interests.

Author and Disclosure Information

Elyse R. Park, PhD
Nancy A. MacDonald Gross, MPH, CHES
Michael G. Goldstein, MD
Judith D. DePue, EdD, MPH
Jacklyn P. Hecht, MSN, RN
Cheryl A. Eaton, MA
Raymond Niaura, PhD
Catherine E. Dubé
EdD Providence, Rhode Island
Submitted, revised, October 28, 2001.
From the Centers for Behavioral and Preventive Medicine, Brown Medical School, and the Miriam Hospital (E.R.P., N.A.M.G., M.G.G., J.D.D., J.P.H., R.N.), and Brown University School of Medicine (C.A.E., C.E.D.). This study was supported by grant PO1CA50087 from the National Cancer Institute, Washington, DC. Dr Goldstein is currently at the Bayer Institute for Health Care Communication in West Haven, Connecticut, and Dr Park is currently at Massachusetts General Hospital in Boston, Massachusetts. The authors do not report any competing interests.

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Article PDF

ABSTRACT

OBJECTIVE: Our goal was to describe a strategy to recruit a population-based sample of physicians to test an approach to disseminate physician-delivered smoking cessation interventions.

STUDY DESIGN: The 3-phase population-based recruitment trial included: (1) a print-based promotional appeal; (2) in-person presentations by the principal investigator (PI); and (3) follow-up calls by the PI and paid physician recruiters. Participation requirements were kept minimal to facilitate recruitment.

POPULATION: All primary care physicians statewide were targeted; 3 counties were chosen as intervention areas and 2 counties as control areas. A subsample of physicians was targeted in the larger control areas through a matching process.

OUTCOME MEASURED: We measured physician recruitment rate.

RESULTS: Eighty-one percent (n=259) of all eligible physicians were successfully recruited into our study.

CONCLUSIONS: The full multistep process was important in getting participation agreement. By using an intensive recruitment strategy and minimizing research demands, it is possible to recruit community-based primary care physicians for research projects that will help them enhance the preventive services they provide to their patients.

KEY POINTS FOR CLINICIANS

  1. We describe a successful method of recruiting physicians into a community-based trial.
  2. Our multiphase multifaceted recruitment design included an initial mailing, presentations at local hospitals, clinic and office visits, and follow-up phone calls.
  3. Successful recruitment rate was attributed to the enhanced involvement by a physicians, and the minimized research demands in return for participation.

Controlled research trials have demonstrated that physician-delivered smoking interventions are an effective means of increasing quit rates among patients who smoke.1-4 However, these trials involved samples of physicians-in-training or volunteer physicians that were not representative of the general primary care physician population. Furthermore, the percentage of eligible physicians who participated in community-based trials ranged from 5% to 50%,V5-9 and obstacles to practicing physician’ participation in research are now greater than ever.10

Physicians Counseling Smokers (PCS) is a National Cancer Institute-funded phase IV trial that tested the effectiveness of an approach to disseminate physician-delivered smoking cessation interventions.11,12 Because this was a dissemination trial that would assess smoking outcomes at a population level, it was necessary to recruit a very high percentage of practicing primary care providers within the discreet geographic areas. The objective of this paper is to describe the process of recruiting a population-based sample of physicians.

Methods

Identification of intervention and control areas

The entire state population of primary care physicians was targeted for recruitment. Three counties served as intervention areas; the 2 smaller counties (Newport and Washington) were combined to approximately match the size of the third (Kent). These 2 geographic areas were chosen for intervention because it was feasible to intervene with the total estimated number of physicians (n=100) in each area. Saturation of intervention areas was important because patient level outcomes were being assessed with random digit dial techniques.15 The largest and smallest counties (Providence and Bristol) were combined to serve as the control area.

Given the comparatively large number of physicians in the control area, a subsample of these physicians were targeted for recruitment. This subsample was created by matching the intervention sample on gender, practice specialty, and years since graduation from medical school. In addition, the control sample was matched at a rate of 1.5 times the mean of the number of eligible physicians in the 2 intervention areas. Eligible physicians in the intervention and control areas practiced in community health centers, colleges or universities, private practices, and a state-wide staff model health maintenance organization. Five large urban public health clinics in Providence, Rhode Island, were not targeted because there was no comparable group of urban public health clinics in the intervention areas. We targeted physicians for enrollment because they were most often the key decision makers, both for consent for the office to participate and for their influence over change in practice patterns. Further, the NCI model being disseminated had been previously tested on physicians. However, once the office was enrolled, all clinicians were invited to participate in sessions with our practice consultants.

We did not include practice-based midlevel providers since the purpose of the study design was to assess the NCI smoking cessation recommendations for physicians, which was a model that had previously been tested specifically on physicians. Also, to conduct a practice-based study, the consent of the physicians is necessary. Our emphasis was to establish physician buy-in so that we could enhance our ability to conduct the intervention and involve all practice-based clinicians.

Identification of the physician sample

To be eligible for participation in PCS, physicians had to (1) have a primary care specialty; (2) provide regular, ongoing care to at least 25% of their patients; (3) practice in a nonhospital-based location; and (4) intend on being available during the 3-year evaluation period.

 

 

A Health Department list of all physicians licensed in the state was obtained to identify potentially eligible physicians, and the list was supplemented with a private directory of physicians who practice in the state.16 Medical staff lists from local hospitals, health maintenance organizations, and community health centers were used. Office addresses were further verified by phone.

Recruitment strategy

The multi-stepped recruitment process included 3 phases. Phase 1 of recruitment included widespread promotion through various forms of written communication in which the important role of physician involvement in smoking cessation efforts was emphasized.1,7,17,18 based on the work of Rogers19 and Lomas and coworkers,20 an advisory board made up of local, influential physicians was formed to assist with recruitment efforts during the Phase 1. Board members were selected from various physician and health-related organizations throughout the state. The advisory board was designed to serve as a linkage system21 between research intervention offerings and community physicians.

A mailed invitation to participate was sent on advisory board letterhead, under the signature of the medical director of the state Health Department and the president of the state medical society. The mailing included information about the study, an enrollment form that assessed eligibility, and a consent form for participation along with a postage-paid return envelope. Mailed postcards and telephone calls from research staff were used as follow-up for those who did not respond to the initial mailing. In addition, public relations departments at the State Health Department, state medical society, 3 community hospitals, and a regional metropolitan newspaper were asked to include a brief article about the study in their newsletter or newspaper, which they all did.

Phase 2 of the recruitment process involved making appearances at local hospitals and visits to practices and clinics. Physicians had an opportunity to enroll at department staff meetings following presentations that were made by the principal investigator and staff. In addition, individual meetings were scheduled with a small number of physicians who requested this. Also, members of the physician advisory board were asked to make brief phone calls to a small number of their physician colleagues to solicit their participation in the study. Physicians who enrolled during these 2 phases were also encouraged to talk to their colleagues informally about participating in the project. Phase 3 of the recruitment process focused on enrolling remaining eligible physicians. Paid physician recruiters were hired to assist the principal investigator in making telephone contact. Physicians who could not be reached by the paid recruiters also received a phone call from the principal investigator. Early outreach required participant initiative for enrollment, therefore all refusals occurred during telephone contacts in Phase 3.

Participation requirements

Participation requirements were kept minimal to facilitate and encourage enrollment of all eligible physicians, regardless of their readiness to adopt smoking interventions.12,19,22,23 To enroll, physicians had to agree to complete 3 annual, 20-minute surveys and allow 20-minute assessments of the office environment to determine smoking cessation tools and resources available to patients and providers. This latter assessment was conducted with one of the office staff in order to minimize time demands of the physician. The intervention was designed to test an approach to gain enhanced access to physicians in their offices. Acceptance of intervention visits from research staff was optional in order to encourage participation of physicians with a broad range of interest and readiness to adopt smoking cessation interventions.

Upon return of their completed baseline survey, physicians in the intervention were offered information based on their readiness to enhance their cessation efforts, samples of patient education materials, a poster listing local smoking cessation programs, and the NCI physician manual, “How to Help Your Patients Stop Smoking.”24 Physicians in the intervention area were offered various resources and training opportunities to enhance smoking cessation interventions in their office. Research staff, trained as consultants to deliver tailored interventions based on an academic detailing approach, scheduled intervention meetings to be most convenient for the physician and office staff. While physician attendance at intervention meetings was encouraged, physicians were offered the option of designating office staff to meet with the research consultants. The goal was to meet with physicians or designated staff roughly 4 to 5 times during the intervention year. No adoption of cessation efforts were required. Physicians in the control area were offered the same manual after completing their baseline survey and the opportunity to receive the other resources and participate in counseling skills training at the end of the intervention period.

Results

Of 2316 licensed physicians in Rhode Island in 1989, 822 were identified as meeting the primary care specialty criteria, based on information provided in the listings used: 557 from the control area and 265 from the 2 intervention areas. Of the physicians from the control area, 202 were matched to the physicians from the intervention areas and became part of the sampling frame. Initial contacts to physicians in the sampling frame determined that an additional 148 were not eligible. The majority of these did not meet the requirements for primary care due to not practicing in a primary specialty or not providing regular, ongoing care to at least 25% of their patients.25 Others had moved from the state, retired, or died. After elimination of physicians who did not meet eligibility criteria, 187 intervention area physicians and 132 control area physicians remained in the final pool of physicians eligible for recruitment. Less than 10% of recruitments responded to the initial mailing, and another 10% were recruited directly from the in-person presentations at department meetings.

 

 

Eighty percent of recruitment came from Phase 3, from phone calls by the physician recruiters. Approximately two thirds of study participants were recruited by the principal investigator. However, the ground work from publicity, endorsements from physician leadership, and familiarity with the aims of the trial were clearly important in getting agreement during the recruitment phone call.

Among all eligible physicians, 81% (N=259) were successfully recruited into the study: 80% (n=106) of targeted control area physicians (Providence/Bristol); 85% (n=88) of physicians in the first intervention area (Newport/Washington); and 77% (n=65) of physicians in the second intervention area (Kent) were enrolled. Characteristics of the sample are displayed in Table 1.

The 18% of physicians who refused to take part cited the following reasons for not participating: (1)they preferred not to participate in studies or fill out surveys; (2) they had a shortage of resources and did not have the time; (3) they were undergoing significant staff turnover or felt that their office staff were already overburdened; (4) they felt they were already providing effective smoking cessation interventions to their patients; or (5) they did not accept smokers into their practice. Chi square tests indicated that refusers were significantly more likely to be male (F=6.5, P < .05) and to have been out of medical school for more than 25 years (F=20.7, P < .001). Less than 5% of eligible female physicians refused to participate as compared with 21% of men. Medical specialty did not have a significant impact participation in this study.

Discussion

Results of the multi-faceted recruitment approach used in the Physicians Counseling Smokers project demonstrate that it is feasible to enroll a population-based sample of primary care physicians into a dissemination trial. We were successful at recruiting a representative sample of community-based physicians. It was our goal to saturate our target geographic area to obtain a truly population based sample. We succeeded in achieving this, recruiting 81% of eligible physicians. It is noteworthy that we were able to retain 88% of enrolled physicians at the end of the 3-year study period. This reinforces that physicians were willing and able to keep their minimal commitment to complete the annual assessments. The most common reason for drop out was leaving the practice/moving out of state.

Recruiting physicians and practices into community-based trials is a challenging process, and several investigators have examined the effectiveness of different recruitment strategies. Recruitment efforts have evolved from a single mailing method to a multi-stepped process. Kottke and colleagues13 assessed and compared mailed recruitment methods for primary care physicians in Minnesota for a 1-month office-based smoking intervention. Eligible family medicine physicians (n=1100) were mailed a brochure alone or a brochure with an explanatory letter signed by one of the investigators on university letterhead or by an investigator on a state Academy of Family Physicians letterhead. Ten percent of eligible physicians responded and no difference between brochure alone or brochure plus letter groups. In a second study, the brochure only mailing strategy was used again to recruit 1108 general internists and cardiologists on the mailing list of a state Medical Association into a one-year trial. Five percent responded and 2.7% participated. Dietrich and colleagues14 used a multi-faceted approach to recruit community-based physicians into a randomized trial to increase cancer prevention practices. Of 628 eligible family physicians and internists in Vermont and New Hampshire, 234 physicians (37%) agreed to participate. Physicians with name recognition in their communities assisted with recruitment Table 2.

Since PCS was conducted, recruitment strategies targeting community-based individual physicians and practices for cancer prevention studies have evolved from single mailing techniques to more common use of multi-step approaches, including face-to-face visits, advisory boards, and physician phone calls Table 3. Participation incentives including honorarium, office staff trainings, and patient materials are often included to enhance recruitment rates,25,26 but even substantial physician incentives do not guaranteed high participation rates.10

In reviewing these studies, it is difficult to assess the impact of each specific recruitment strategy used. However, the in-person appearance of the principal investigator, a physician, appeared to have a major impact on physician enrollment. Earlier studies7,13 producing lower recruitment rates did not involve this in-person meeting component, and Asch’s review of physician recruitment studies supports the importance of personal contact. Two recent community-based physician office recruitment trials included in-person office visits.25,26 In addition to office and clinic visits, in PCS the principal investigator was also present at hospital departmental meetings and gave grand rounds at these hospitals.

Another successful strategy demonstrated in PCS was involvement of the principal investigator in calling physicians who were difficult to recruit. Although nonphysician PCS research staff made concerted efforts to assist with recruitment, their access to the physician by phone was often limited by gatekeepers within the office. PCS demonstrated that, although time intensive and costly, the use of a physician recruiter may be necessary to recruit a representative sample, for example, with at least 75% of eligible physicians, into a dissemination trial. Although difficult to assess the impact of the impact of these preliminary phases, it was also evident that the work completed in Phases 1 and 2 created familiarity and laid the groundwork for the Phase 3 calls.

 

 

Obtaining support of prominent local physicians, and involving many in our advisory board, contributed to our success. The “RAND” method, which involves influential physicians recruiting community-based physicians,10 was deemed useful in this study. Similarly, a study which investigated the relationship between medical malpractice claims and physician patient communication, also utilized prominent members of the local physician community as advisory board members who made recruitment calls and endorsed the study introductory letter.27 In PCS these physicians not only participated in recruitment calls and endorsed the study introductory letter, but also allowed access to hospitals and physicians so that in-person visits and presentations could occur.

Finally, minimizing research demands, maintaining flexibility in scheduling interventions, and offering tailored interventions to meet physician’s needs all appeared to enhance recruitment rates. In particular, emphasizing that a low burden will be caused by study participation seems key, as lack of time was cited in our study as a reason for nonparticipation and has been the most common reason given for nonparticipation in other studies.10 Initial contacts with physicians focused on the individual benefits that each physician would gain from participation. An emphasis was placed on acknowledgment of physicians’ time constraints; we emphasized our intent to share resources and tools that would help physicians be more effective with the existent time constraints. Additionally, our study did not require medical office staff to be involved in recruiting patients, nor did it require access to patients’ medical records.

Several recruitment strategies used in PCS appeared to be less effective. Use of physician graduate fellows, physicians who were awarded a fellowship for postgraduate study, as a final step to contact and enroll eligible physicians did not appear to contribute to our success. Also, recruitment rates were lower among physicians who were at least 25 years out of medical school. This is consistent with Dietrich and colleagues’14 finding that nonparticipants were significantly older than participants. The reasons for this result are unclear. Perhaps more recently trained physicians are more receptive to participation in a study that targets prevention, or more receptive to participating in research. Another potential reason is that an age-based sampling bias occurred. We know that the majority of our sample were generalists, but we did not measure other variables, such as race or practice setting, that may have also influenced the formation of the sample. It was a limitation that we did not gather information on background variables that may have influenced the sample makeup, and, in using physician recruiters, there is a potential that a sampling bias will occur.

Conclusions

There is a growing need to disseminate effective strategies to assist physicians with the delivery of preventive services. We were successful in recruiting more than 80% of community-based physicians, saturating a discreet geographic area, into a dissemination trial. The enhanced involvement by a physician investigator and endorsement and efforts by local influential physicians contributed to our success. Additionally, we minimized research demands in return for participation. Studies that have required more physician involvement have not been as successful and may need more intensive recruitment strategies. The relatively low refusal rate in this study suggests that community-based, primary care physicians are interested and willing to participate in research that will help them enhance the preventive services they provide to their patients.

Acknowledgments

This study was supported by grant PO1CA50087 (James Prochaska, Principal Investigator) from the National Cancer Institute, Washington, DC. The authors wish to acknowledge David Abrams, PhD, James Prochaska, PhD, Wayne Velicer, PhD, and Joseph Rossi, PhD who contributed to the measure development process and provided support and guidance as senior scientists in the Rhode Island Cancer Prevention Consortium. We also wish to acknowledge Alexander Prokhorov, MD, PhD, Alicia Monroe, MD, William Rakowski, PhD, and Lisa Harlow, PhD, as investigators on the PCS research team. We wish to also acknowledge Allen Dietrich, MD, Judith Ockene, PhD, Jean Kristeller, PhD, and Thomas Kottke, MD, for valuable scientific consultation. We especially wish to thank and acknowledge Linda Moreau and Barbara Doll who provided essential secretarial support and Elena Morgans for her help in coding and entering data.

ABSTRACT

OBJECTIVE: Our goal was to describe a strategy to recruit a population-based sample of physicians to test an approach to disseminate physician-delivered smoking cessation interventions.

STUDY DESIGN: The 3-phase population-based recruitment trial included: (1) a print-based promotional appeal; (2) in-person presentations by the principal investigator (PI); and (3) follow-up calls by the PI and paid physician recruiters. Participation requirements were kept minimal to facilitate recruitment.

POPULATION: All primary care physicians statewide were targeted; 3 counties were chosen as intervention areas and 2 counties as control areas. A subsample of physicians was targeted in the larger control areas through a matching process.

OUTCOME MEASURED: We measured physician recruitment rate.

RESULTS: Eighty-one percent (n=259) of all eligible physicians were successfully recruited into our study.

CONCLUSIONS: The full multistep process was important in getting participation agreement. By using an intensive recruitment strategy and minimizing research demands, it is possible to recruit community-based primary care physicians for research projects that will help them enhance the preventive services they provide to their patients.

KEY POINTS FOR CLINICIANS

  1. We describe a successful method of recruiting physicians into a community-based trial.
  2. Our multiphase multifaceted recruitment design included an initial mailing, presentations at local hospitals, clinic and office visits, and follow-up phone calls.
  3. Successful recruitment rate was attributed to the enhanced involvement by a physicians, and the minimized research demands in return for participation.

Controlled research trials have demonstrated that physician-delivered smoking interventions are an effective means of increasing quit rates among patients who smoke.1-4 However, these trials involved samples of physicians-in-training or volunteer physicians that were not representative of the general primary care physician population. Furthermore, the percentage of eligible physicians who participated in community-based trials ranged from 5% to 50%,V5-9 and obstacles to practicing physician’ participation in research are now greater than ever.10

Physicians Counseling Smokers (PCS) is a National Cancer Institute-funded phase IV trial that tested the effectiveness of an approach to disseminate physician-delivered smoking cessation interventions.11,12 Because this was a dissemination trial that would assess smoking outcomes at a population level, it was necessary to recruit a very high percentage of practicing primary care providers within the discreet geographic areas. The objective of this paper is to describe the process of recruiting a population-based sample of physicians.

Methods

Identification of intervention and control areas

The entire state population of primary care physicians was targeted for recruitment. Three counties served as intervention areas; the 2 smaller counties (Newport and Washington) were combined to approximately match the size of the third (Kent). These 2 geographic areas were chosen for intervention because it was feasible to intervene with the total estimated number of physicians (n=100) in each area. Saturation of intervention areas was important because patient level outcomes were being assessed with random digit dial techniques.15 The largest and smallest counties (Providence and Bristol) were combined to serve as the control area.

Given the comparatively large number of physicians in the control area, a subsample of these physicians were targeted for recruitment. This subsample was created by matching the intervention sample on gender, practice specialty, and years since graduation from medical school. In addition, the control sample was matched at a rate of 1.5 times the mean of the number of eligible physicians in the 2 intervention areas. Eligible physicians in the intervention and control areas practiced in community health centers, colleges or universities, private practices, and a state-wide staff model health maintenance organization. Five large urban public health clinics in Providence, Rhode Island, were not targeted because there was no comparable group of urban public health clinics in the intervention areas. We targeted physicians for enrollment because they were most often the key decision makers, both for consent for the office to participate and for their influence over change in practice patterns. Further, the NCI model being disseminated had been previously tested on physicians. However, once the office was enrolled, all clinicians were invited to participate in sessions with our practice consultants.

We did not include practice-based midlevel providers since the purpose of the study design was to assess the NCI smoking cessation recommendations for physicians, which was a model that had previously been tested specifically on physicians. Also, to conduct a practice-based study, the consent of the physicians is necessary. Our emphasis was to establish physician buy-in so that we could enhance our ability to conduct the intervention and involve all practice-based clinicians.

Identification of the physician sample

To be eligible for participation in PCS, physicians had to (1) have a primary care specialty; (2) provide regular, ongoing care to at least 25% of their patients; (3) practice in a nonhospital-based location; and (4) intend on being available during the 3-year evaluation period.

 

 

A Health Department list of all physicians licensed in the state was obtained to identify potentially eligible physicians, and the list was supplemented with a private directory of physicians who practice in the state.16 Medical staff lists from local hospitals, health maintenance organizations, and community health centers were used. Office addresses were further verified by phone.

Recruitment strategy

The multi-stepped recruitment process included 3 phases. Phase 1 of recruitment included widespread promotion through various forms of written communication in which the important role of physician involvement in smoking cessation efforts was emphasized.1,7,17,18 based on the work of Rogers19 and Lomas and coworkers,20 an advisory board made up of local, influential physicians was formed to assist with recruitment efforts during the Phase 1. Board members were selected from various physician and health-related organizations throughout the state. The advisory board was designed to serve as a linkage system21 between research intervention offerings and community physicians.

A mailed invitation to participate was sent on advisory board letterhead, under the signature of the medical director of the state Health Department and the president of the state medical society. The mailing included information about the study, an enrollment form that assessed eligibility, and a consent form for participation along with a postage-paid return envelope. Mailed postcards and telephone calls from research staff were used as follow-up for those who did not respond to the initial mailing. In addition, public relations departments at the State Health Department, state medical society, 3 community hospitals, and a regional metropolitan newspaper were asked to include a brief article about the study in their newsletter or newspaper, which they all did.

Phase 2 of the recruitment process involved making appearances at local hospitals and visits to practices and clinics. Physicians had an opportunity to enroll at department staff meetings following presentations that were made by the principal investigator and staff. In addition, individual meetings were scheduled with a small number of physicians who requested this. Also, members of the physician advisory board were asked to make brief phone calls to a small number of their physician colleagues to solicit their participation in the study. Physicians who enrolled during these 2 phases were also encouraged to talk to their colleagues informally about participating in the project. Phase 3 of the recruitment process focused on enrolling remaining eligible physicians. Paid physician recruiters were hired to assist the principal investigator in making telephone contact. Physicians who could not be reached by the paid recruiters also received a phone call from the principal investigator. Early outreach required participant initiative for enrollment, therefore all refusals occurred during telephone contacts in Phase 3.

Participation requirements

Participation requirements were kept minimal to facilitate and encourage enrollment of all eligible physicians, regardless of their readiness to adopt smoking interventions.12,19,22,23 To enroll, physicians had to agree to complete 3 annual, 20-minute surveys and allow 20-minute assessments of the office environment to determine smoking cessation tools and resources available to patients and providers. This latter assessment was conducted with one of the office staff in order to minimize time demands of the physician. The intervention was designed to test an approach to gain enhanced access to physicians in their offices. Acceptance of intervention visits from research staff was optional in order to encourage participation of physicians with a broad range of interest and readiness to adopt smoking cessation interventions.

Upon return of their completed baseline survey, physicians in the intervention were offered information based on their readiness to enhance their cessation efforts, samples of patient education materials, a poster listing local smoking cessation programs, and the NCI physician manual, “How to Help Your Patients Stop Smoking.”24 Physicians in the intervention area were offered various resources and training opportunities to enhance smoking cessation interventions in their office. Research staff, trained as consultants to deliver tailored interventions based on an academic detailing approach, scheduled intervention meetings to be most convenient for the physician and office staff. While physician attendance at intervention meetings was encouraged, physicians were offered the option of designating office staff to meet with the research consultants. The goal was to meet with physicians or designated staff roughly 4 to 5 times during the intervention year. No adoption of cessation efforts were required. Physicians in the control area were offered the same manual after completing their baseline survey and the opportunity to receive the other resources and participate in counseling skills training at the end of the intervention period.

Results

Of 2316 licensed physicians in Rhode Island in 1989, 822 were identified as meeting the primary care specialty criteria, based on information provided in the listings used: 557 from the control area and 265 from the 2 intervention areas. Of the physicians from the control area, 202 were matched to the physicians from the intervention areas and became part of the sampling frame. Initial contacts to physicians in the sampling frame determined that an additional 148 were not eligible. The majority of these did not meet the requirements for primary care due to not practicing in a primary specialty or not providing regular, ongoing care to at least 25% of their patients.25 Others had moved from the state, retired, or died. After elimination of physicians who did not meet eligibility criteria, 187 intervention area physicians and 132 control area physicians remained in the final pool of physicians eligible for recruitment. Less than 10% of recruitments responded to the initial mailing, and another 10% were recruited directly from the in-person presentations at department meetings.

 

 

Eighty percent of recruitment came from Phase 3, from phone calls by the physician recruiters. Approximately two thirds of study participants were recruited by the principal investigator. However, the ground work from publicity, endorsements from physician leadership, and familiarity with the aims of the trial were clearly important in getting agreement during the recruitment phone call.

Among all eligible physicians, 81% (N=259) were successfully recruited into the study: 80% (n=106) of targeted control area physicians (Providence/Bristol); 85% (n=88) of physicians in the first intervention area (Newport/Washington); and 77% (n=65) of physicians in the second intervention area (Kent) were enrolled. Characteristics of the sample are displayed in Table 1.

The 18% of physicians who refused to take part cited the following reasons for not participating: (1)they preferred not to participate in studies or fill out surveys; (2) they had a shortage of resources and did not have the time; (3) they were undergoing significant staff turnover or felt that their office staff were already overburdened; (4) they felt they were already providing effective smoking cessation interventions to their patients; or (5) they did not accept smokers into their practice. Chi square tests indicated that refusers were significantly more likely to be male (F=6.5, P < .05) and to have been out of medical school for more than 25 years (F=20.7, P < .001). Less than 5% of eligible female physicians refused to participate as compared with 21% of men. Medical specialty did not have a significant impact participation in this study.

Discussion

Results of the multi-faceted recruitment approach used in the Physicians Counseling Smokers project demonstrate that it is feasible to enroll a population-based sample of primary care physicians into a dissemination trial. We were successful at recruiting a representative sample of community-based physicians. It was our goal to saturate our target geographic area to obtain a truly population based sample. We succeeded in achieving this, recruiting 81% of eligible physicians. It is noteworthy that we were able to retain 88% of enrolled physicians at the end of the 3-year study period. This reinforces that physicians were willing and able to keep their minimal commitment to complete the annual assessments. The most common reason for drop out was leaving the practice/moving out of state.

Recruiting physicians and practices into community-based trials is a challenging process, and several investigators have examined the effectiveness of different recruitment strategies. Recruitment efforts have evolved from a single mailing method to a multi-stepped process. Kottke and colleagues13 assessed and compared mailed recruitment methods for primary care physicians in Minnesota for a 1-month office-based smoking intervention. Eligible family medicine physicians (n=1100) were mailed a brochure alone or a brochure with an explanatory letter signed by one of the investigators on university letterhead or by an investigator on a state Academy of Family Physicians letterhead. Ten percent of eligible physicians responded and no difference between brochure alone or brochure plus letter groups. In a second study, the brochure only mailing strategy was used again to recruit 1108 general internists and cardiologists on the mailing list of a state Medical Association into a one-year trial. Five percent responded and 2.7% participated. Dietrich and colleagues14 used a multi-faceted approach to recruit community-based physicians into a randomized trial to increase cancer prevention practices. Of 628 eligible family physicians and internists in Vermont and New Hampshire, 234 physicians (37%) agreed to participate. Physicians with name recognition in their communities assisted with recruitment Table 2.

Since PCS was conducted, recruitment strategies targeting community-based individual physicians and practices for cancer prevention studies have evolved from single mailing techniques to more common use of multi-step approaches, including face-to-face visits, advisory boards, and physician phone calls Table 3. Participation incentives including honorarium, office staff trainings, and patient materials are often included to enhance recruitment rates,25,26 but even substantial physician incentives do not guaranteed high participation rates.10

In reviewing these studies, it is difficult to assess the impact of each specific recruitment strategy used. However, the in-person appearance of the principal investigator, a physician, appeared to have a major impact on physician enrollment. Earlier studies7,13 producing lower recruitment rates did not involve this in-person meeting component, and Asch’s review of physician recruitment studies supports the importance of personal contact. Two recent community-based physician office recruitment trials included in-person office visits.25,26 In addition to office and clinic visits, in PCS the principal investigator was also present at hospital departmental meetings and gave grand rounds at these hospitals.

Another successful strategy demonstrated in PCS was involvement of the principal investigator in calling physicians who were difficult to recruit. Although nonphysician PCS research staff made concerted efforts to assist with recruitment, their access to the physician by phone was often limited by gatekeepers within the office. PCS demonstrated that, although time intensive and costly, the use of a physician recruiter may be necessary to recruit a representative sample, for example, with at least 75% of eligible physicians, into a dissemination trial. Although difficult to assess the impact of the impact of these preliminary phases, it was also evident that the work completed in Phases 1 and 2 created familiarity and laid the groundwork for the Phase 3 calls.

 

 

Obtaining support of prominent local physicians, and involving many in our advisory board, contributed to our success. The “RAND” method, which involves influential physicians recruiting community-based physicians,10 was deemed useful in this study. Similarly, a study which investigated the relationship between medical malpractice claims and physician patient communication, also utilized prominent members of the local physician community as advisory board members who made recruitment calls and endorsed the study introductory letter.27 In PCS these physicians not only participated in recruitment calls and endorsed the study introductory letter, but also allowed access to hospitals and physicians so that in-person visits and presentations could occur.

Finally, minimizing research demands, maintaining flexibility in scheduling interventions, and offering tailored interventions to meet physician’s needs all appeared to enhance recruitment rates. In particular, emphasizing that a low burden will be caused by study participation seems key, as lack of time was cited in our study as a reason for nonparticipation and has been the most common reason given for nonparticipation in other studies.10 Initial contacts with physicians focused on the individual benefits that each physician would gain from participation. An emphasis was placed on acknowledgment of physicians’ time constraints; we emphasized our intent to share resources and tools that would help physicians be more effective with the existent time constraints. Additionally, our study did not require medical office staff to be involved in recruiting patients, nor did it require access to patients’ medical records.

Several recruitment strategies used in PCS appeared to be less effective. Use of physician graduate fellows, physicians who were awarded a fellowship for postgraduate study, as a final step to contact and enroll eligible physicians did not appear to contribute to our success. Also, recruitment rates were lower among physicians who were at least 25 years out of medical school. This is consistent with Dietrich and colleagues’14 finding that nonparticipants were significantly older than participants. The reasons for this result are unclear. Perhaps more recently trained physicians are more receptive to participation in a study that targets prevention, or more receptive to participating in research. Another potential reason is that an age-based sampling bias occurred. We know that the majority of our sample were generalists, but we did not measure other variables, such as race or practice setting, that may have also influenced the formation of the sample. It was a limitation that we did not gather information on background variables that may have influenced the sample makeup, and, in using physician recruiters, there is a potential that a sampling bias will occur.

Conclusions

There is a growing need to disseminate effective strategies to assist physicians with the delivery of preventive services. We were successful in recruiting more than 80% of community-based physicians, saturating a discreet geographic area, into a dissemination trial. The enhanced involvement by a physician investigator and endorsement and efforts by local influential physicians contributed to our success. Additionally, we minimized research demands in return for participation. Studies that have required more physician involvement have not been as successful and may need more intensive recruitment strategies. The relatively low refusal rate in this study suggests that community-based, primary care physicians are interested and willing to participate in research that will help them enhance the preventive services they provide to their patients.

Acknowledgments

This study was supported by grant PO1CA50087 (James Prochaska, Principal Investigator) from the National Cancer Institute, Washington, DC. The authors wish to acknowledge David Abrams, PhD, James Prochaska, PhD, Wayne Velicer, PhD, and Joseph Rossi, PhD who contributed to the measure development process and provided support and guidance as senior scientists in the Rhode Island Cancer Prevention Consortium. We also wish to acknowledge Alexander Prokhorov, MD, PhD, Alicia Monroe, MD, William Rakowski, PhD, and Lisa Harlow, PhD, as investigators on the PCS research team. We wish to also acknowledge Allen Dietrich, MD, Judith Ockene, PhD, Jean Kristeller, PhD, and Thomas Kottke, MD, for valuable scientific consultation. We especially wish to thank and acknowledge Linda Moreau and Barbara Doll who provided essential secretarial support and Elena Morgans for her help in coding and entering data.

References

1. Kottke T.E., R. N. Battista, et al. Attributes of successful smoking cessation interventions in medical practice. A meta-analysis of 39 controlled trials. JAMA 1988;259(19):2883-9.

2. Ockene J. K., J. Kristeller, et al. Increasing the efficacy of physician-delivered smoking interventions: a randomized clinical trial [see comments]. J Gen Intern Med 1991;6(1):1-8.

3. Schwartz J. L. Methods of smoking cessation. Med Clin North Am 1992;76 (2):451-76.

4. Fiore M., W. Bailey, et al. (1996). Smoking Cessation: Clinical Practice Guideline No. 18, Agency for Health Care Policy and Research, Public Health Service, U.S. Department of Health And Human Services.

5. Wilson D. M., D. W. Taylor, et al. A randomized trial of a family physician intervention for smoking cessation. JAMA 1988;260(11):1570-4.

6. Cummings S. R., R. J. Richard, et al. Training physicians about smoking cessation: a controlled trial in private practice. J Gen Intern Med 1989;4(6):482-9.

7. Kottke T. E., L. I. Solberg, et al. A comparison of two methods to recruit physicians to deliver smoking cessation interventions. Arch Intern Med 1990;150(7):1477-81.

8. Haug K., P. Fugelli, et al. Recruitment and Participation of General Practitioners in a Multipractice Study of Smoking Cessation. Scandanavian Journal of Primary Health Care 1992;10:206-210.

9. Richmond R, C. Mendelsohn, et al. Family physicians’ utilization of a brief smoking cessation program following reinforcement contact after training: a randomized trial. Prev Med 1998;27(1):77-83.

10. Asch S, S. Connor, et al. Problems in Recruiting Community-Based Physicians for Health Services Research. J Gen Internal Med 2000;15:591-599.

11. Goldstein M. G., DePue J.D., Monroe AD, Lessne CW, Rakowski W, Prokhorov A, Niaura R, Dube CE. A Population-Based Survey of Physicians’ Smoking Counseling Behavior. Preventive Medicine 1998;7(5 Pt 1):720-9.

12. Goldstein M. G, N. A. MacDonald, et al. (1993). Dissemination of physician-based smoking cessation interventions. Tobacco and the Clinician; Interventions For Medical and Dental Practice. S. Burns, S. Cohen, E. Gritz and T. Kottke. Bethesda, MD, USDHHS, PHS, NIH, NCI.

13. Kottke T. E., L. I. Solberg, et al. Initiation and maintenance of patient behavioral change: what is the role of the physician? J Gen Intern Med 1990;5 (5 Suppl):S62-7.

14. Dietrich A. J., G. O’Connor, et al. (1990). Will community physicians participate in rigorous studies of cancer control? The methodology and recruitment of a randomized trial of physician practices. Advances in Cancer Control: Screening and Prevention Research, Wiley-Liss, Inc.: 373-381.

15. Goldstein M. G., R. Niaura, et al. Physicians counseling smokers. A population-based survey of patients’ perceptions of health care provider-delivered smoking cessation interventions. Arch Intern Med 1997;157(12):1313-9.

16. Folio Associates (1990). Folio’s Medical Directory of Connecticut and Rhode Island, Folio Associates, Inc. Tobacco and Cancer Program, Division of Cancer Prefvention and Control, National Cancer Institute.

17. Battista R. N., J. I. Williams, et al. Determinants of primary medical practice in adult cancer prevention. Med Care 1986;24(3):216-24.

18. Ockene J. K. Smoking intervention: the expanding role of the physician [editorial]. Am J Public Health 1987;77(7):782-3.

19. Rogers E. M. (1983). Diffusion of Innovations. New York, The Free Press.

20. Lomas J, Enkin M., et al. Opinion Leaders vs audit and feedback to implement practice guidelines. JAMA 1991;266 (9):1217.-

21. Orlandi M. A. Promoting health and preventing disease in health care settings: An analysis of barriers. Preventive Medicine 1987;16:119-130.

22. Prochaska J. O., & DiClemente C. C. Toward a comprehensive model of change. Treating Addictive Disorders: Processes of Change. W. R. Miller and N. Heather. New York, Plenum Press, 1986.

23. Prochaska J., W. Velicer, et al. Measuring processes of change: Applications to the cessation of smoking. J Consult Clin Psychology 1988;56:520-528.

24. Glynn T. J., M. W. Manley (1989). andHow to Help Your Patients Stop Smoking. A National Cancer Institute Manual for Physicians. Bethesda, Maryland, Smoking,

25. Carey T., L. Kinsinger, et al. Research in the Community: Recruiting and Retaining Practices. Journal of Community Health 1996;21:315-327.

26. McBride P., K. Massoth, et al. Recruitment of Private Practices for Primary Care Research: Experience in a Preventive Services Clinical Trial. J Fam Pract 1996;4:389-395.

27. Levinson W., V. Dull, et al. Recruiting Physicians for Office-Based Research. Medical Care 1998;36(6):934-937.

Reprint requests should be addressed to Elyse Park, PhD, MGH, 50 Staniford Street, 904A, Boston, MA 02115. E-mail: [email protected].

References

1. Kottke T.E., R. N. Battista, et al. Attributes of successful smoking cessation interventions in medical practice. A meta-analysis of 39 controlled trials. JAMA 1988;259(19):2883-9.

2. Ockene J. K., J. Kristeller, et al. Increasing the efficacy of physician-delivered smoking interventions: a randomized clinical trial [see comments]. J Gen Intern Med 1991;6(1):1-8.

3. Schwartz J. L. Methods of smoking cessation. Med Clin North Am 1992;76 (2):451-76.

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Reprint requests should be addressed to Elyse Park, PhD, MGH, 50 Staniford Street, 904A, Boston, MA 02115. E-mail: [email protected].

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Physician Recruitment for a Community-Based Smoking Cessation Intervention
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