Unilateral Eyelid Angioedema With Congestion of the Right Bulbar Conjunctiva Due to Loxoprofen Sodium

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Routine checkups don’t ensure that seniors get preventive services

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Routine checkups don’t ensure that seniors get preventive services

Abstract

Background A small number of preventive services are recommended for all adults ages 65 years and older. It is well established that the combined delivery or being “up to date” on these measures is low. However, the effect of routine checkups on being up to date is not known. We examined the association between routine checkups and the delivery of a group of recommended clinical preventive services for US adults ages 65 and older.

Methods In 2006 the Behavioral Risk Factor Surveillance System conducted telephone surveys. Participants ages 65 years and older were randomly selected in 50 states and the District of Columbia. Sample sizes were 32,243 male respondents and 58,762 female respondents. A composite measure was used that includes screening for colorectal, cervical, and breast cancers, and vaccinations against influenza and pneumococcal disease. The measure quantifies the percentage of adults who are up to date according to recommended schedules.

Results Most adults ages 65 and older were fully insured, had a personal health care provider, reported no cost barrier to seeing a doctor in the past year, and had recently received a routine checkup. Associations between high health care access and checkups and the increased likelihood of being up to date on clinical preventive services were statistically significant. Although a large percentage of the population had high access to care and reported having a recent checkup, the percentage of all those who were up to date was low, and it was only slightly greater for those with high access or a recent checkup (42.6%, 45.1%, and 44.8%, respectively, for men; 35.2%, 37.0%, and 36.8%, respectively for women). For both sexes, the results varied by education, race/ethnicity, marriage, insurance, health, and state.

Conclusions Our study indicates that increasing the use of routine medical checkups will have a negligible impact on the delivery of preventive services.

Just because elderly patients are having regular checkups does not necessarily mean they are receiving needed preventive services. For individuals who are ages 65 and older, such services include vaccinations against influenza and pneumonia, screenings for hypertension and hypercholesterolemia, and screenings for breast, cervical, and colorectal cancers.1

Recently analyzed state and national data for a cluster of 5 of these services indicate that fewer than 41% of men and 32.5% of women ages 65 and older were up to date.2 Time constraints on health care providers and a lack of knowledge about guidelines are perhaps 2 of the biggest barriers to widespread provision of disease prevention services. In this study we extended an earlier analysis and examined, for individuals 65 years of age and older, the association between having a recent checkup and being up to date on a cluster of recommended preventive services. We also propose steps that will likely be needed to increase receipt of preventive services.

Methods

Data source
The Behavioral Risk Factor Surveillance System (BRFSS), coordinated by the Centers for Disease Control and Prevention (CDC), conducts annual state-based telephone surveys of noninstitutionalized US adults ages 18 years or older concerning health practices.3 We used data from 2006 BRFSS participants ages 65 years or older at the time they participated (32,243 male respondents and 58,762 female respondents). All results were based on weighted data that accounted for different probabilities of selection and were adjusted to reflect the population distribution in each state by age and sex, or by age, race, and sex.

Respondents queried about preventive services
We analyzed responses to BRFSS questions about the receipt of clinical preventive services recommended by the US Preventive Services Task Force (USPSTF) or by the Advisory Committee on Immunization Practices for all adults ages 65 or older.* Services included colorectal cancer screening, influenza immunization, pneumococcal immunization, and, for women, mammography and the Papanicolaou (Pap) test. The USPSTF grades these measures* as A or B, meaning it finds “good” or at least “fair” evidence that a service improves important health outcomes and concludes that benefits substantially outweigh harms.4 Questions about these services were asked in all 50 states in 2006.

*The recommendations and grading systems discussed here reflect those that were in place in 2006. There have been changes to both since this study was conducted.

Cardiovascular services excluded. The BRFSS has not asked questions about hypertension screening since 1999, when more than 95% of older adults reported they had their blood pressure checked in the past 2 years.5 Questions about cholesterol screening were not asked in all states in 2006 and were not incorporated into the composite measure. However, analysis from a prior study suggests that including cholesterol screening levels in such a composite measure would not have made a large difference in the percentage of older Americans up to date on all services.2

 

 

Were scheduled intervals for services met? Adults could meet the recommendation for colorectal cancer screening by either having a fecal occult blood test (FOBT) within 1 year or colonoscopy or sigmoidoscopy within 10 years. The USPSTF and other national guidelines recommend a 5-year interval for sigmoidoscopy and a 10-year interval for colonoscopy.6,7 However, no direct evidence has determined the optimal interval for either test,8 and the BRFSS question did not distinguish between the 2 interventions. Because either FOBT or endoscopy satisfies screening recommendations, we did not exclude respondents with missing values for 1 test if they had the other test within the recommended interval.

Other services and recommended intervals were pneumococcal vaccination (ever), influenza vaccination (in past year), and, for women, mammogram (within 2 years) and Pap test (within 3 years).

Assigning Yes or No to responses. If respondents had never received a particular preventive service or had received it outside the interval recommended by the USPSTF,4 we included them in the group answering No. We eliminated 3324 men and 6295 women with missing values for 1 or more measures.

Final determination of being “up to date.” After noting how many of the recommended services each individual had received according to age and sex, we dichotomized the sample according to whether all recommendations had been met—3 clinical preventive services for men 65 years and older (colorectal cancer screening, influenza, and pneumonia vaccination) and 5 for women (adding mammography and Pap test), with a single exception. Because Pap testing is often reported only for women with an intact cervix,9 we excused the lack of a Pap test for women who had undergone hysterectomy (47% of all women ages 65+, or 27,243). We required only that they meet 4 clinical preventive services to be considered up to date. A prior study revealed that excluding the Pap test entirely from the up-to-date measure for women 65 years and older had a minimal effect on up-to-date rates (34.2% when excluding the Pap test vs 32.5% including the Pap test).10

One of the strengths of the up-to-date measure is that it assesses the proportion of those fully up to date and thus allows for variability within subgroups, such as women who have had hysterectomies, without eliminating them arbitrarily from the sample.

Additional participant characteristics. We divided respondents into 4 racial/ethnic categories based on responses to BRFSS questions: White (non-Hispanic); Black (non-Hispanic); Hispanic of any race; or “Other” (American Indians, Asians, Pacific Islanders, and individuals of other or multiple race categories). Age categories were 65 to 69 years, 70 to 74 years, 75 to 79 years, or >80 years. Education categories: less than high school, high school graduate or general equivalency diploma recipient, some college, or college graduate. We further dichotomized the sample according to marital status, having 1 or more personal health care providers (vs none), and health status (fair/poor or good/very good/excellent). Given the amount of missing data (20%), household income was not included in the analysis.

Quantifying health care access. We created a measure of health care access using 3 factors:

  • health insurance (“Do you have any kind of health care coverage, including health insurance, prepaid plans such as HMOs, or government plans such as Medicare?”)
  • one or more personal health care providers (see above)
  • no cost barrier to seeing a doctor (“Was there a time in the past 12 months when you needed to see a doctor but could not because of cost?”).

To measure relative health care access, we scored each of the above items 1 for affirmative or 0 for negative. The sum (0, 1, 2, or 3) represented level of access. Lower numbers indicated more barriers and higher numbers represented greater access. Because only 48 older men and 59 older women had total scores of 0, the lower 2 levels were combined and the resulting 3 levels were termed “low” (0 & 1), “medium” (2) and “high” (3) access. Two of the measures used for health care access were also used to define 3 mutually exclusive health insurance categories: uninsured, fully insured, and underinsured (insured but reporting a cost barrier).2

We determined whether a routine checkup had occurred in the past 2 years by asking, “About how long has it been since you last visited a doctor for a routine checkup? A routine checkup is a general physical exam, not an exam for a specific injury, illness, or condition.”

 

 

Statistical analysis
We conducted statistical analysis using Stata, version 9.0 (Stata Corp; College Station, Tex). We used Pearson chi-square tests to determine whether selected demographic factors were associated with being up to date on all recommended services. We also used Stata in a logistic regression analysis to control simultaneously for age, education, race/ethnicity, marital status, insurance coverage, health care access, having one or more personal health care providers, having a routine checkup within 2 years, current smoking, and health status. We computed odds ratios and 95% confidence intervals for each variable in the model.

Results

Most adults ages 65 years and older were fully insured, had a personal health care provider, and reported no cost barrier to seeing a doctor in the past year (TABLE 1). Breaking out these measures into 3 levels of relative health care access, 88.6% of men and 90.2% of women were at the highest level. More than 90% of respondents reported having a routine checkup in the past 2 years. More than 60% reported receiving each of the separate immunizations and cancer screenings recommended for their age and sex, and almost all had received at least 1 service.

TABLE 2 shows the prevalence of being up to date by demographic group. Only 42.6% of all older men and 35.2% of all older women were up to date, with rates marginally better for those with high access to care (45.1% for men, 37% for women) or those reporting a recent routine checkup (44.8% for men, 36.8% for women). Low access to care yielded dramatically worse up-to-date rates (14.8% for men, 9.1% for women). Similarly, those reporting no recent routine checkup had poor up-to-date rates (20.5% for men, 15.4% for women). The highest rates of being up to date belonged to those with a college degree (49% for men, 42.1% for women). Higher rates were also found among the oldest age groups.

Results of the logistic regression analysis are shown in TABLE 3. Among men and women, being up to date was more likely for those who were older, married, better educated, had high access to health care, and had had a routine checkup in the past 2 years. The latter 2 groups had the highest odds ratios of all groups in the model. Less likely to be up to date were those who were Black, Hispanic, or of a race other than white, those who smoked cigarettes, and (for men) those who were in good or better health. For women, health status had no effect on being up to date.

Table 1
Characteristics of US adults ≥65 years, 2006 Behavioral Risk Factor Surveillance System

 MenWomen
 PercentnPercentn
Total10032,24310058,762
Age (y)
65-6930.710,28627.116,184
70-7425.8841021.214,005
75-7922.9668524.612,562
≥8020.7686227.116,011
Race/ethnicity
White81.727,72081.550,270
Black7.316318.03656
Hispanic6.09316.41824
Other*5.014234.22218
Education
< High school15.0501017.69931
High school29.3990539.022,978
Some college20.9655223.614,372
College grad34.910,66419.711,226
Married74.020,59344.520,551
Insurance
Fully insured94.230,14794.055,066
Underinsured3.511734.32385
Not insured2.37541.7993
Has a personal health care provider93.129,65795.355,586
No cost barrier96.230,83895.456,021
Health care access
Low1.44500.9561
Medium10.035218.85271
High88.627,99690.252,430
Fair/poor health27.6895729.716,727
Clinical preventive services
Flu shot past year68.321,72567.039,205
Pneumococcal polysaccharide vaccine63.619,53166.738,442
Colon cancer screen71.321,39567.937,112
Pap test in 3 years (women with cervix)  70.819,700
Pap test in 3 years (credit for hysterectomy)  84.846,943
Mammogram in 2 years  79.143,874
Number of health care services received
09.629933.01615
119.353355.02659
228.5808510.95679
3§42.612,50619.210,108
4  26.813,935
5§  35.218,471
Total||10028,919100.052,467
Routine checkup91.528,84593.153,037
*Includes American Indian, Asian, Pacific Islander, and individuals of other or multiple race categories.
Underinsurance includes individuals with coverage who indicated there was a time in the past year when they needed to see a doctor but could not due to cost (cost barrier).
Determined from 3 measures: having health insurance, having a personal health care provider, and not reporting a cost barrier. Levels 0 and 1 were combined. Resulting levels were low, medium, and high.

§To be up to date, men required colon cancer screening (fecal occult blood test in past year or endoscopy within 10 years), a flu shot in the past year, and a pneumonia vaccination ever. Women required those same services plus a mammogram within 2 years and Pap test within 3 years (unless prior hysterectomy). ||Total n excludes 3324 men and 6295 women with missing values for one or more tests.
Respondents who indicated they had a routine “checkup” in the past 2 years.

Table 2
Prevalence of being up to date* by demographic characteristics, US adults ≥65 years, 2006 Behavioral Risk Factor Surveillance System

 MenWomen
 Percent95% CIPercent95% CI
Total42.641.6-43.735.234.4-36.0
Age (y)
65-6932.030.3-33.729.828.4-31.3
70-7444.942.7-47.239.137.5-40.8
75-7948.746.2-51.240.238.5-41.9
≥8048.946.5-51.232.931.4-34.5
P value<.0001 <.0001 
Race/ethnicity
White46.245.1-47.337.937.1-38.7
Black27.823.7-32.322.419.7-25.4
Hispanic20.815.7-27.022.117.7-27.2
Other31.025.7-36.924.119.7-29.0
P value<.0001 <.0001 
Married
Yes44.343.0-45.739.338.1-40.6
No37.735.9-39.531.730.7-32.7
P value<.0001 <.0001 
Education
< High school30.928.2-33.825.023.1-26.9
High school39.537.7-41.434.233.0-35.4
Some college44.341.9-46.838.236.6-39.7
College grad49.047.2-50.942.140.2-44.1
P value<.0001 <.0001 
Insurance
Not insured19.915.1-25.817.913.2-23.9
Underinsured29.325.0-34.024.320.7-28.2
Fully insured43.742.5-44.836.035.2-36.8
P value<.0001 <.0001 
Personal health care provider
Has 1 or more44.443.3-45.536.335.5-37.1
None18.816.1-21.911.49.3-14.0
P value<.0001 <.0001 
Health status
Fair/poor health44.342.1-46.533.231.7-34.8
Ex/v good health41.940.7-43.236.035.1-37.0
P value.066 .002 
Health access
“Low”14.89.1-23.19.15.9-13.7
“Medium”24.722.2-27.419.317.1-21.8
“High”45.143.9-46.337.036.2-37.9
P value<.0001 <.0001 
Routine checkup§
Yes44.843.6-45.936.835.9-37.6
No20.517.6-23.715.412.9-18.4
P value<.0001 <.0001 
CI, confidence interval.
*To be up to date, men required colon cancer screening (fecal occult blood test in past year or endoscopy within 10 years), a flu shot in the past year, and a pneumonia vaccination ever. Women required those same services plus a mammogram within 2 years and Pap test within 3 years (unless prior hysterectomy).
Other race includes American Indian, Asian, Pacific Islander, and individuals of other or multiple race categories.
Determined from 3 measures: having health insurance, having a personal health care provider, and not reporting a cost barrier. Levels 0 and 1 were combined. Resulting levels were low, medium, and high.
§Respondents who indicated they had a routine “checkup” in the past 2 years.
 

 

TABLE 3
Results of multiple logistic regression modeling* for being up to datefor cancer screening and adult immunization, by sex and demographic characteristics: 2006 Behavioral Risk Factor Surveillance System, adults ≥65 years

 MenWomen
 OR95% CIP valueOR95% CIP value
Age 65-69 y (referent)
70-741.741.54-1.97<.0011.541.39-1.70<.0001
75-792.041.79-2.32<.0011.561.40-1.74<.0001
≥801.961.72-2.23<.0011.191.06-1.32.002
White (referent)     
Black0.520.41-0.66<.0010.550.46-0.66<.0001
Hispanic0.370.26-0.53<.0010.560.42-0.76<.0001
Other0.530.40-0.71<.0010.550.43-0.72<.0001
Not married (referent)    
Married1.231.12-1.37<.0011.281.18-1.38<.0001
<high></high></high>    
High school1.281.10-1.50.0021.281.14-1.44<.0001
Some college1.541.30-1.83<.0011.501.32-1.69<.0001
College grad1.821.55-2.13<.0011.791.57-2.05<.0001
Health access (“Low” is referent)    
Medium1.320.71-2.45.3781.721.03-2.87.038
High2.411.32-4.41.0043.081.88-5.05<.0001
No checkup§ (referent)
Checkup 2 yr2.532.07-3.10<.0012.722.18-3.40<.0001
Fair/poor health (referent)    
Ex/v good health0.760.68-0.85<.0010.940.87-1.03.167
Nonsmoker (referent)    
Current smoker0.590.48-0.72<.0010.680.58-0.79<.0001
CI, confidence interval; OR, odds ratio.
*N=27,632 for men and 50,024 for women. Includes 50 states plus the District of Columbia and excludes 3324 male respondents and 6295 female respondents with missing values for one or more measures. There were 2 separate models, one for men and one for women.
To be up to date, men required colon cancer screening (fecal occult blood test in past year or endoscopy within 10 years), a flu shot in the past year, and a pneumonia vaccination ever. Women required those same services plus a mammogram within 2 years and Pap test within 3 years (unless prior hysterectomy).
Determined from 3 measures: having health insurance, having a personal health care provider, and not reporting a cost barrier. Levels 0 and 1 were combined. Resulting levels were low, medium, and high.

Discussion

The key finding in this study is that, although most adults ages 65 and older had high access to health care and recent routine checkups, their rates of being up to date with a recommended cluster of preventive services were only about 45% for men and 37% for women.

More than 91% of men and 93% of women reported they had a routine checkup during this timeframe, and 88.6% of men and 90.2% of women also reported they had high access to health care—ie, they had health insurance, at least 1 personal health care provider, and no cost barrier to seeing a doctor. Improving access to health care or increasing the use of routine medical checkups—even to 100%—would likely have a negligible impact on the delivery of recommended services. Despite the very modest composite delivery rates of recommended preventive services in this group, the rates were still 2 to 4 times higher than those of adults with low health care access or no recent routine checkup.

We also found that being up to date generally improves with age. Granted, there is uncertainty as to the appropriate age at which to stop specific screenings. And very elderly Americans may be receiving some services no longer of benefit. But the significance of our finding is that composite delivery rates were lowest among adults at the age for which broad consensus says services are beneficial. For example, the up-to-date rates for men and women ages 65 to 69 were 32% and 29.8%, respectively, compared with 48.7% and 40.2% for adults ages 75 to 79 (TABLE 2).

Our findings are consistent with research documenting inadequate time to incorporate preventive services into the typical office visit.11,12 Similar barriers have been identified by general practitioners in the United Kingdom.13,14 The time constraint is particularly consequential in high-volume primary care practices.15 Some investigations have calculated the actual or necessary time needed to deliver multiple recommended prevention and health promotion services and have found the requirement to be unrealistically high.16-20 Our study suggests that increased access to and use of health care services is a necessary but insufficient condition for achieving high up-to-date levels.

To improve up-to-date rates, likely actions will include more efficient use of office time, increased reliance on nonphysician clinicians, greater use of electronic medical records, and prioritizing services for a routine checkup. External policy changes, such as pay-for-performance, may also enhance preventive service delivery rates. We hope that, in time, the composite measure used in this analysis will be adopted by both primary care clinicians and public health practitioners in the same way that tracking composite children’s vaccination levels are helpful to family practitioners, pediatricians, and local health departments. However, there is probably no easy answer; even the prompts enabled by electronic medical records are useless when ignored by providers.21 Improving delivery of preventive services in office settings will require multiple strategies sustained over many years.22

Community-based efforts. There is a strong rationale for a more determined policy to expand community-based access. Many community-based approaches to individual preventive services have been developed over the last 10 years.23 For example, the CDC’s National Breast and Cervical Cancer Early Detection Program represents one model of a state-based program that can make local assistance available for uninsured women.24 In addition, an evidence-based model developed by the nonprofit agency SPARC (Sickness Prevention Achieved through Regional Collaboration) suggests ways of creating community-based points of access for multiple preventive services.25-27

 

 

Questions still unanswered. Although BRFSS data suggest older adults are regularly receiving “routine checkups,” it is not clear what kind of intervention this refers to beyond its nonacute nature. What characterizes routine checkups in patients’ minds, and how might such visits be limited as venues for providing preventive services? Furthermore, what are the characteristics of providers associated with different types of checkup services? How do primary care providers differ from subspecialists in the kinds of preventive services they provide? Answers to these questions have important implications for physician training and for targeted outreach to subspecialty groups. From a community standpoint, it would be helpful to know if there are specific untapped opportunities for delivering preventive services, particularly in underserved and minority communities where coverage rates are very low.

This study’s limitations. Because the BRFSS relies on self-reports, our findings are subject to various biases, including “telescoping,”28 the tendency of people to remember events as having occurred more recently than they actually did.29 Moreover, because BRFSS surveys exclude people in households without telephones (who are more likely to be poor and thus also less likely to have access to health care and preventive services), our estimates may be slightly higher than the true rates.30 People with cell phone service only were not sampled; however, this had little impact on estimates for older adults, since just an estimated 2.2% use cell phones exclusively.31 People in institutions, such as nursing homes, which account for 3% to 4% of adults 65 and older were also excluded.32

The strength of this study is that, based on a large sample of randomly selected respondents, it is the first report on the adoption of clinical preventive services in all states in relation to the use of routine checkups and a composite measure. However, as noted in the methods, although the interviewer provided a definition for the term routine checkup, the description may have been interpreted differently by survey respondents.

The provider’s office and medical home should remain at the center of a national strategy to increase the delivery of these services, but expanding these efforts to include community access is critical to improving overall rates of preventive services. We need more determined and strategic collaborations between medicine and public health that will facilitate access to, and use of, preventive services for all Americans.

CORRESPONDENCE 
Douglas Shenson, MD, MPH, 76 Prince Street, Newton, MA 02465; [email protected]

References

1. U.S. Preventive Services Task Force. Guide to Clinical Preventive Services: Report of the U.S. Preventive Services Task Force. 3rd ed. Baltimore, Md: Williams and Wilkins; 2004.

2. Shenson D, Bolen J, Adams M. Receipt of preventive services by elders based on composite measures, 1997-2004. Am J Prev Med. 2007;32:11-18.

3. Behavioral Risk Factor Surveillance System operational and users guide version 3.0, March 2005. Available at: http://www.cdc.gov/brfss/pdf/userguide.pdf. Access December 14, 2010.

4. US Preventive Services Task Force The Guide to Clinical Preventive Services, 2007: Recommendations of the US Preventive Services Task Force. Rockville, Md: Agency for Healthcare Research and Quality; September 2007: 23, 26, 32, 204-205, 232. AHRQ publication 07-05100. Available at: https://www.oxhp.com/secure/materials/member/adult_preventive.pdf. Accessed December 21, 2010.

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6. US Preventive Services Task Force The Guide to Clinical Preventive Services 2007: Recommendations of the US Preventive Services Task Force. Rockville, Md: Agency for Healthcare Research and Quality; September 2007: 32-33. AHRQ publication 07-05100. Available at: https://www.oxhp.com/secure/materials/member/adult_preventive.pdf. Accessed December 21, 2010.

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8. U.S. Preventive Services Task Force. Screening for colorectal cancer: recommendation and rational. Ann Intern Med. 2002;137:129-131.

9. US. Preventive Services Task Force. Screening for cervical cancer: recommendations and rationale. January 2003. AHRQ Publication 03-515A. Available at: www.uspreventiveservicestaskforce.org/uspstf/uspscerv.htm. Accessed December 21, 2010.

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17. Carney PA, Dietrich AJ, Freeman DH Jr, et al. The periodic health examination provided to asymptomatic older women: an assessment using standardized patients. Ann Intern Med. 1993;119:129-135.

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21. Schellhase KG, Koepsell TD, Norris TE. Providers’ reactions to an automated health maintenance reminder system incorporated into the patient’s electronic medical record. J Am Board Fam Pract. 2003;16:350-351.

22. Ballard DJ, Nicewander DA, Qin H, et al. Improving delivery of clinical preventive services: a multi-year journey. Am J Prev Med. 2007;33:492-497.

23. Shenson D. Putting prevention in its place: the shift from clinic to community. Health Aff (Millwood). 2006;25:1012-1015.

24. Centers for Disease Control and Prevention. National Breast and Cervical Early Detection Program. Available at: www.cdc.gov/cancer/NBCCEDP/. Accessed: June 20, 2008.

25. Shenson D, Benson W, Harris A. Expanding the delivery of preventive services through community collaboration: the SPARC model. Prev Chronic Dis. 2008;5(1). Available at http://www.cdc.gov/pcd/issues/2008/jan/07_0139.htm. Accessed December 14, 2010.

26. Shenson D, Quinley J, DiMartino D, et al. Pneumococcal immunizations at flu clinics: the impact of community-wide outreach. J Community Health. 2001;26:191-201.

27. Shenson D, Cassarino L, DiMartino D, et al. Improving access to mammography through community-based influenza clinics: a quasi-experimental study. Am J Prev Med. 2001;20:97-102.

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29. Newell SA, Girgis A, Sanson-Fisher RW, et al. The accuracy of self-reported health behaviors and risk factors relating to cancer and cardiovascular disease in the general population: a critical review. Am J Prev Med. 1999;17:211-229.

30. Thornberry OT, Massey JT. Trends in the United States telephone coverage across time and subgroup. In: Groves RM, Biemer PP, Lyberg LR, et al, eds. Telephone Survey Methodology. New York, NY: John Wiley & Sons; 1988:25–49.

31. Blumberg SJ, Luke JV. Wireless substitution: Early release of estimates from the National Health Interview Survey, July-December 2007. National Center for Health Statistics. Available at: http://www.cdc.gov/nchs/data/nhis/earlyrelease/wireless200805.htm. Accessed: May 13, 2008.

32. National Center for Health Statistics. Health, United States, 2002. Special excerpt: trend tables on 65 and older population. Washington, DC: Department of Health and Human Services; 2003. Publication 03-1030. Available at: www.cdc.gov/nchs/data/hushus02.pdf. Accessed December 21, 2010.

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Mary Adams, MPH
On Target Health Data LLC

Julie Bolen, PhD, MPH
Lynda Anderson, PhD
Centers for Disease Control and Prevention (CDC)

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The findings and conclusions in this article are those of the authors and do not necessarily represent the views of the Centers for Disease Control and Prevention (CDC). Funding for this research was provided by the CDC.

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Lynda Anderson, PhD
Centers for Disease Control and Prevention (CDC)

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The findings and conclusions in this article are those of the authors and do not necessarily represent the views of the Centers for Disease Control and Prevention (CDC). Funding for this research was provided by the CDC.

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SPARC, Sickness Prevention Achieved through Regional Collaboration
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On Target Health Data LLC

Julie Bolen, PhD, MPH
Lynda Anderson, PhD
Centers for Disease Control and Prevention (CDC)

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The findings and conclusions in this article are those of the authors and do not necessarily represent the views of the Centers for Disease Control and Prevention (CDC). Funding for this research was provided by the CDC.

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Abstract

Background A small number of preventive services are recommended for all adults ages 65 years and older. It is well established that the combined delivery or being “up to date” on these measures is low. However, the effect of routine checkups on being up to date is not known. We examined the association between routine checkups and the delivery of a group of recommended clinical preventive services for US adults ages 65 and older.

Methods In 2006 the Behavioral Risk Factor Surveillance System conducted telephone surveys. Participants ages 65 years and older were randomly selected in 50 states and the District of Columbia. Sample sizes were 32,243 male respondents and 58,762 female respondents. A composite measure was used that includes screening for colorectal, cervical, and breast cancers, and vaccinations against influenza and pneumococcal disease. The measure quantifies the percentage of adults who are up to date according to recommended schedules.

Results Most adults ages 65 and older were fully insured, had a personal health care provider, reported no cost barrier to seeing a doctor in the past year, and had recently received a routine checkup. Associations between high health care access and checkups and the increased likelihood of being up to date on clinical preventive services were statistically significant. Although a large percentage of the population had high access to care and reported having a recent checkup, the percentage of all those who were up to date was low, and it was only slightly greater for those with high access or a recent checkup (42.6%, 45.1%, and 44.8%, respectively, for men; 35.2%, 37.0%, and 36.8%, respectively for women). For both sexes, the results varied by education, race/ethnicity, marriage, insurance, health, and state.

Conclusions Our study indicates that increasing the use of routine medical checkups will have a negligible impact on the delivery of preventive services.

Just because elderly patients are having regular checkups does not necessarily mean they are receiving needed preventive services. For individuals who are ages 65 and older, such services include vaccinations against influenza and pneumonia, screenings for hypertension and hypercholesterolemia, and screenings for breast, cervical, and colorectal cancers.1

Recently analyzed state and national data for a cluster of 5 of these services indicate that fewer than 41% of men and 32.5% of women ages 65 and older were up to date.2 Time constraints on health care providers and a lack of knowledge about guidelines are perhaps 2 of the biggest barriers to widespread provision of disease prevention services. In this study we extended an earlier analysis and examined, for individuals 65 years of age and older, the association between having a recent checkup and being up to date on a cluster of recommended preventive services. We also propose steps that will likely be needed to increase receipt of preventive services.

Methods

Data source
The Behavioral Risk Factor Surveillance System (BRFSS), coordinated by the Centers for Disease Control and Prevention (CDC), conducts annual state-based telephone surveys of noninstitutionalized US adults ages 18 years or older concerning health practices.3 We used data from 2006 BRFSS participants ages 65 years or older at the time they participated (32,243 male respondents and 58,762 female respondents). All results were based on weighted data that accounted for different probabilities of selection and were adjusted to reflect the population distribution in each state by age and sex, or by age, race, and sex.

Respondents queried about preventive services
We analyzed responses to BRFSS questions about the receipt of clinical preventive services recommended by the US Preventive Services Task Force (USPSTF) or by the Advisory Committee on Immunization Practices for all adults ages 65 or older.* Services included colorectal cancer screening, influenza immunization, pneumococcal immunization, and, for women, mammography and the Papanicolaou (Pap) test. The USPSTF grades these measures* as A or B, meaning it finds “good” or at least “fair” evidence that a service improves important health outcomes and concludes that benefits substantially outweigh harms.4 Questions about these services were asked in all 50 states in 2006.

*The recommendations and grading systems discussed here reflect those that were in place in 2006. There have been changes to both since this study was conducted.

Cardiovascular services excluded. The BRFSS has not asked questions about hypertension screening since 1999, when more than 95% of older adults reported they had their blood pressure checked in the past 2 years.5 Questions about cholesterol screening were not asked in all states in 2006 and were not incorporated into the composite measure. However, analysis from a prior study suggests that including cholesterol screening levels in such a composite measure would not have made a large difference in the percentage of older Americans up to date on all services.2

 

 

Were scheduled intervals for services met? Adults could meet the recommendation for colorectal cancer screening by either having a fecal occult blood test (FOBT) within 1 year or colonoscopy or sigmoidoscopy within 10 years. The USPSTF and other national guidelines recommend a 5-year interval for sigmoidoscopy and a 10-year interval for colonoscopy.6,7 However, no direct evidence has determined the optimal interval for either test,8 and the BRFSS question did not distinguish between the 2 interventions. Because either FOBT or endoscopy satisfies screening recommendations, we did not exclude respondents with missing values for 1 test if they had the other test within the recommended interval.

Other services and recommended intervals were pneumococcal vaccination (ever), influenza vaccination (in past year), and, for women, mammogram (within 2 years) and Pap test (within 3 years).

Assigning Yes or No to responses. If respondents had never received a particular preventive service or had received it outside the interval recommended by the USPSTF,4 we included them in the group answering No. We eliminated 3324 men and 6295 women with missing values for 1 or more measures.

Final determination of being “up to date.” After noting how many of the recommended services each individual had received according to age and sex, we dichotomized the sample according to whether all recommendations had been met—3 clinical preventive services for men 65 years and older (colorectal cancer screening, influenza, and pneumonia vaccination) and 5 for women (adding mammography and Pap test), with a single exception. Because Pap testing is often reported only for women with an intact cervix,9 we excused the lack of a Pap test for women who had undergone hysterectomy (47% of all women ages 65+, or 27,243). We required only that they meet 4 clinical preventive services to be considered up to date. A prior study revealed that excluding the Pap test entirely from the up-to-date measure for women 65 years and older had a minimal effect on up-to-date rates (34.2% when excluding the Pap test vs 32.5% including the Pap test).10

One of the strengths of the up-to-date measure is that it assesses the proportion of those fully up to date and thus allows for variability within subgroups, such as women who have had hysterectomies, without eliminating them arbitrarily from the sample.

Additional participant characteristics. We divided respondents into 4 racial/ethnic categories based on responses to BRFSS questions: White (non-Hispanic); Black (non-Hispanic); Hispanic of any race; or “Other” (American Indians, Asians, Pacific Islanders, and individuals of other or multiple race categories). Age categories were 65 to 69 years, 70 to 74 years, 75 to 79 years, or >80 years. Education categories: less than high school, high school graduate or general equivalency diploma recipient, some college, or college graduate. We further dichotomized the sample according to marital status, having 1 or more personal health care providers (vs none), and health status (fair/poor or good/very good/excellent). Given the amount of missing data (20%), household income was not included in the analysis.

Quantifying health care access. We created a measure of health care access using 3 factors:

  • health insurance (“Do you have any kind of health care coverage, including health insurance, prepaid plans such as HMOs, or government plans such as Medicare?”)
  • one or more personal health care providers (see above)
  • no cost barrier to seeing a doctor (“Was there a time in the past 12 months when you needed to see a doctor but could not because of cost?”).

To measure relative health care access, we scored each of the above items 1 for affirmative or 0 for negative. The sum (0, 1, 2, or 3) represented level of access. Lower numbers indicated more barriers and higher numbers represented greater access. Because only 48 older men and 59 older women had total scores of 0, the lower 2 levels were combined and the resulting 3 levels were termed “low” (0 & 1), “medium” (2) and “high” (3) access. Two of the measures used for health care access were also used to define 3 mutually exclusive health insurance categories: uninsured, fully insured, and underinsured (insured but reporting a cost barrier).2

We determined whether a routine checkup had occurred in the past 2 years by asking, “About how long has it been since you last visited a doctor for a routine checkup? A routine checkup is a general physical exam, not an exam for a specific injury, illness, or condition.”

 

 

Statistical analysis
We conducted statistical analysis using Stata, version 9.0 (Stata Corp; College Station, Tex). We used Pearson chi-square tests to determine whether selected demographic factors were associated with being up to date on all recommended services. We also used Stata in a logistic regression analysis to control simultaneously for age, education, race/ethnicity, marital status, insurance coverage, health care access, having one or more personal health care providers, having a routine checkup within 2 years, current smoking, and health status. We computed odds ratios and 95% confidence intervals for each variable in the model.

Results

Most adults ages 65 years and older were fully insured, had a personal health care provider, and reported no cost barrier to seeing a doctor in the past year (TABLE 1). Breaking out these measures into 3 levels of relative health care access, 88.6% of men and 90.2% of women were at the highest level. More than 90% of respondents reported having a routine checkup in the past 2 years. More than 60% reported receiving each of the separate immunizations and cancer screenings recommended for their age and sex, and almost all had received at least 1 service.

TABLE 2 shows the prevalence of being up to date by demographic group. Only 42.6% of all older men and 35.2% of all older women were up to date, with rates marginally better for those with high access to care (45.1% for men, 37% for women) or those reporting a recent routine checkup (44.8% for men, 36.8% for women). Low access to care yielded dramatically worse up-to-date rates (14.8% for men, 9.1% for women). Similarly, those reporting no recent routine checkup had poor up-to-date rates (20.5% for men, 15.4% for women). The highest rates of being up to date belonged to those with a college degree (49% for men, 42.1% for women). Higher rates were also found among the oldest age groups.

Results of the logistic regression analysis are shown in TABLE 3. Among men and women, being up to date was more likely for those who were older, married, better educated, had high access to health care, and had had a routine checkup in the past 2 years. The latter 2 groups had the highest odds ratios of all groups in the model. Less likely to be up to date were those who were Black, Hispanic, or of a race other than white, those who smoked cigarettes, and (for men) those who were in good or better health. For women, health status had no effect on being up to date.

Table 1
Characteristics of US adults ≥65 years, 2006 Behavioral Risk Factor Surveillance System

 MenWomen
 PercentnPercentn
Total10032,24310058,762
Age (y)
65-6930.710,28627.116,184
70-7425.8841021.214,005
75-7922.9668524.612,562
≥8020.7686227.116,011
Race/ethnicity
White81.727,72081.550,270
Black7.316318.03656
Hispanic6.09316.41824
Other*5.014234.22218
Education
< High school15.0501017.69931
High school29.3990539.022,978
Some college20.9655223.614,372
College grad34.910,66419.711,226
Married74.020,59344.520,551
Insurance
Fully insured94.230,14794.055,066
Underinsured3.511734.32385
Not insured2.37541.7993
Has a personal health care provider93.129,65795.355,586
No cost barrier96.230,83895.456,021
Health care access
Low1.44500.9561
Medium10.035218.85271
High88.627,99690.252,430
Fair/poor health27.6895729.716,727
Clinical preventive services
Flu shot past year68.321,72567.039,205
Pneumococcal polysaccharide vaccine63.619,53166.738,442
Colon cancer screen71.321,39567.937,112
Pap test in 3 years (women with cervix)  70.819,700
Pap test in 3 years (credit for hysterectomy)  84.846,943
Mammogram in 2 years  79.143,874
Number of health care services received
09.629933.01615
119.353355.02659
228.5808510.95679
3§42.612,50619.210,108
4  26.813,935
5§  35.218,471
Total||10028,919100.052,467
Routine checkup91.528,84593.153,037
*Includes American Indian, Asian, Pacific Islander, and individuals of other or multiple race categories.
Underinsurance includes individuals with coverage who indicated there was a time in the past year when they needed to see a doctor but could not due to cost (cost barrier).
Determined from 3 measures: having health insurance, having a personal health care provider, and not reporting a cost barrier. Levels 0 and 1 were combined. Resulting levels were low, medium, and high.

§To be up to date, men required colon cancer screening (fecal occult blood test in past year or endoscopy within 10 years), a flu shot in the past year, and a pneumonia vaccination ever. Women required those same services plus a mammogram within 2 years and Pap test within 3 years (unless prior hysterectomy). ||Total n excludes 3324 men and 6295 women with missing values for one or more tests.
Respondents who indicated they had a routine “checkup” in the past 2 years.

Table 2
Prevalence of being up to date* by demographic characteristics, US adults ≥65 years, 2006 Behavioral Risk Factor Surveillance System

 MenWomen
 Percent95% CIPercent95% CI
Total42.641.6-43.735.234.4-36.0
Age (y)
65-6932.030.3-33.729.828.4-31.3
70-7444.942.7-47.239.137.5-40.8
75-7948.746.2-51.240.238.5-41.9
≥8048.946.5-51.232.931.4-34.5
P value<.0001 <.0001 
Race/ethnicity
White46.245.1-47.337.937.1-38.7
Black27.823.7-32.322.419.7-25.4
Hispanic20.815.7-27.022.117.7-27.2
Other31.025.7-36.924.119.7-29.0
P value<.0001 <.0001 
Married
Yes44.343.0-45.739.338.1-40.6
No37.735.9-39.531.730.7-32.7
P value<.0001 <.0001 
Education
< High school30.928.2-33.825.023.1-26.9
High school39.537.7-41.434.233.0-35.4
Some college44.341.9-46.838.236.6-39.7
College grad49.047.2-50.942.140.2-44.1
P value<.0001 <.0001 
Insurance
Not insured19.915.1-25.817.913.2-23.9
Underinsured29.325.0-34.024.320.7-28.2
Fully insured43.742.5-44.836.035.2-36.8
P value<.0001 <.0001 
Personal health care provider
Has 1 or more44.443.3-45.536.335.5-37.1
None18.816.1-21.911.49.3-14.0
P value<.0001 <.0001 
Health status
Fair/poor health44.342.1-46.533.231.7-34.8
Ex/v good health41.940.7-43.236.035.1-37.0
P value.066 .002 
Health access
“Low”14.89.1-23.19.15.9-13.7
“Medium”24.722.2-27.419.317.1-21.8
“High”45.143.9-46.337.036.2-37.9
P value<.0001 <.0001 
Routine checkup§
Yes44.843.6-45.936.835.9-37.6
No20.517.6-23.715.412.9-18.4
P value<.0001 <.0001 
CI, confidence interval.
*To be up to date, men required colon cancer screening (fecal occult blood test in past year or endoscopy within 10 years), a flu shot in the past year, and a pneumonia vaccination ever. Women required those same services plus a mammogram within 2 years and Pap test within 3 years (unless prior hysterectomy).
Other race includes American Indian, Asian, Pacific Islander, and individuals of other or multiple race categories.
Determined from 3 measures: having health insurance, having a personal health care provider, and not reporting a cost barrier. Levels 0 and 1 were combined. Resulting levels were low, medium, and high.
§Respondents who indicated they had a routine “checkup” in the past 2 years.
 

 

TABLE 3
Results of multiple logistic regression modeling* for being up to datefor cancer screening and adult immunization, by sex and demographic characteristics: 2006 Behavioral Risk Factor Surveillance System, adults ≥65 years

 MenWomen
 OR95% CIP valueOR95% CIP value
Age 65-69 y (referent)
70-741.741.54-1.97<.0011.541.39-1.70<.0001
75-792.041.79-2.32<.0011.561.40-1.74<.0001
≥801.961.72-2.23<.0011.191.06-1.32.002
White (referent)     
Black0.520.41-0.66<.0010.550.46-0.66<.0001
Hispanic0.370.26-0.53<.0010.560.42-0.76<.0001
Other0.530.40-0.71<.0010.550.43-0.72<.0001
Not married (referent)    
Married1.231.12-1.37<.0011.281.18-1.38<.0001
<high></high></high>    
High school1.281.10-1.50.0021.281.14-1.44<.0001
Some college1.541.30-1.83<.0011.501.32-1.69<.0001
College grad1.821.55-2.13<.0011.791.57-2.05<.0001
Health access (“Low” is referent)    
Medium1.320.71-2.45.3781.721.03-2.87.038
High2.411.32-4.41.0043.081.88-5.05<.0001
No checkup§ (referent)
Checkup 2 yr2.532.07-3.10<.0012.722.18-3.40<.0001
Fair/poor health (referent)    
Ex/v good health0.760.68-0.85<.0010.940.87-1.03.167
Nonsmoker (referent)    
Current smoker0.590.48-0.72<.0010.680.58-0.79<.0001
CI, confidence interval; OR, odds ratio.
*N=27,632 for men and 50,024 for women. Includes 50 states plus the District of Columbia and excludes 3324 male respondents and 6295 female respondents with missing values for one or more measures. There were 2 separate models, one for men and one for women.
To be up to date, men required colon cancer screening (fecal occult blood test in past year or endoscopy within 10 years), a flu shot in the past year, and a pneumonia vaccination ever. Women required those same services plus a mammogram within 2 years and Pap test within 3 years (unless prior hysterectomy).
Determined from 3 measures: having health insurance, having a personal health care provider, and not reporting a cost barrier. Levels 0 and 1 were combined. Resulting levels were low, medium, and high.

Discussion

The key finding in this study is that, although most adults ages 65 and older had high access to health care and recent routine checkups, their rates of being up to date with a recommended cluster of preventive services were only about 45% for men and 37% for women.

More than 91% of men and 93% of women reported they had a routine checkup during this timeframe, and 88.6% of men and 90.2% of women also reported they had high access to health care—ie, they had health insurance, at least 1 personal health care provider, and no cost barrier to seeing a doctor. Improving access to health care or increasing the use of routine medical checkups—even to 100%—would likely have a negligible impact on the delivery of recommended services. Despite the very modest composite delivery rates of recommended preventive services in this group, the rates were still 2 to 4 times higher than those of adults with low health care access or no recent routine checkup.

We also found that being up to date generally improves with age. Granted, there is uncertainty as to the appropriate age at which to stop specific screenings. And very elderly Americans may be receiving some services no longer of benefit. But the significance of our finding is that composite delivery rates were lowest among adults at the age for which broad consensus says services are beneficial. For example, the up-to-date rates for men and women ages 65 to 69 were 32% and 29.8%, respectively, compared with 48.7% and 40.2% for adults ages 75 to 79 (TABLE 2).

Our findings are consistent with research documenting inadequate time to incorporate preventive services into the typical office visit.11,12 Similar barriers have been identified by general practitioners in the United Kingdom.13,14 The time constraint is particularly consequential in high-volume primary care practices.15 Some investigations have calculated the actual or necessary time needed to deliver multiple recommended prevention and health promotion services and have found the requirement to be unrealistically high.16-20 Our study suggests that increased access to and use of health care services is a necessary but insufficient condition for achieving high up-to-date levels.

To improve up-to-date rates, likely actions will include more efficient use of office time, increased reliance on nonphysician clinicians, greater use of electronic medical records, and prioritizing services for a routine checkup. External policy changes, such as pay-for-performance, may also enhance preventive service delivery rates. We hope that, in time, the composite measure used in this analysis will be adopted by both primary care clinicians and public health practitioners in the same way that tracking composite children’s vaccination levels are helpful to family practitioners, pediatricians, and local health departments. However, there is probably no easy answer; even the prompts enabled by electronic medical records are useless when ignored by providers.21 Improving delivery of preventive services in office settings will require multiple strategies sustained over many years.22

Community-based efforts. There is a strong rationale for a more determined policy to expand community-based access. Many community-based approaches to individual preventive services have been developed over the last 10 years.23 For example, the CDC’s National Breast and Cervical Cancer Early Detection Program represents one model of a state-based program that can make local assistance available for uninsured women.24 In addition, an evidence-based model developed by the nonprofit agency SPARC (Sickness Prevention Achieved through Regional Collaboration) suggests ways of creating community-based points of access for multiple preventive services.25-27

 

 

Questions still unanswered. Although BRFSS data suggest older adults are regularly receiving “routine checkups,” it is not clear what kind of intervention this refers to beyond its nonacute nature. What characterizes routine checkups in patients’ minds, and how might such visits be limited as venues for providing preventive services? Furthermore, what are the characteristics of providers associated with different types of checkup services? How do primary care providers differ from subspecialists in the kinds of preventive services they provide? Answers to these questions have important implications for physician training and for targeted outreach to subspecialty groups. From a community standpoint, it would be helpful to know if there are specific untapped opportunities for delivering preventive services, particularly in underserved and minority communities where coverage rates are very low.

This study’s limitations. Because the BRFSS relies on self-reports, our findings are subject to various biases, including “telescoping,”28 the tendency of people to remember events as having occurred more recently than they actually did.29 Moreover, because BRFSS surveys exclude people in households without telephones (who are more likely to be poor and thus also less likely to have access to health care and preventive services), our estimates may be slightly higher than the true rates.30 People with cell phone service only were not sampled; however, this had little impact on estimates for older adults, since just an estimated 2.2% use cell phones exclusively.31 People in institutions, such as nursing homes, which account for 3% to 4% of adults 65 and older were also excluded.32

The strength of this study is that, based on a large sample of randomly selected respondents, it is the first report on the adoption of clinical preventive services in all states in relation to the use of routine checkups and a composite measure. However, as noted in the methods, although the interviewer provided a definition for the term routine checkup, the description may have been interpreted differently by survey respondents.

The provider’s office and medical home should remain at the center of a national strategy to increase the delivery of these services, but expanding these efforts to include community access is critical to improving overall rates of preventive services. We need more determined and strategic collaborations between medicine and public health that will facilitate access to, and use of, preventive services for all Americans.

CORRESPONDENCE 
Douglas Shenson, MD, MPH, 76 Prince Street, Newton, MA 02465; [email protected]

Abstract

Background A small number of preventive services are recommended for all adults ages 65 years and older. It is well established that the combined delivery or being “up to date” on these measures is low. However, the effect of routine checkups on being up to date is not known. We examined the association between routine checkups and the delivery of a group of recommended clinical preventive services for US adults ages 65 and older.

Methods In 2006 the Behavioral Risk Factor Surveillance System conducted telephone surveys. Participants ages 65 years and older were randomly selected in 50 states and the District of Columbia. Sample sizes were 32,243 male respondents and 58,762 female respondents. A composite measure was used that includes screening for colorectal, cervical, and breast cancers, and vaccinations against influenza and pneumococcal disease. The measure quantifies the percentage of adults who are up to date according to recommended schedules.

Results Most adults ages 65 and older were fully insured, had a personal health care provider, reported no cost barrier to seeing a doctor in the past year, and had recently received a routine checkup. Associations between high health care access and checkups and the increased likelihood of being up to date on clinical preventive services were statistically significant. Although a large percentage of the population had high access to care and reported having a recent checkup, the percentage of all those who were up to date was low, and it was only slightly greater for those with high access or a recent checkup (42.6%, 45.1%, and 44.8%, respectively, for men; 35.2%, 37.0%, and 36.8%, respectively for women). For both sexes, the results varied by education, race/ethnicity, marriage, insurance, health, and state.

Conclusions Our study indicates that increasing the use of routine medical checkups will have a negligible impact on the delivery of preventive services.

Just because elderly patients are having regular checkups does not necessarily mean they are receiving needed preventive services. For individuals who are ages 65 and older, such services include vaccinations against influenza and pneumonia, screenings for hypertension and hypercholesterolemia, and screenings for breast, cervical, and colorectal cancers.1

Recently analyzed state and national data for a cluster of 5 of these services indicate that fewer than 41% of men and 32.5% of women ages 65 and older were up to date.2 Time constraints on health care providers and a lack of knowledge about guidelines are perhaps 2 of the biggest barriers to widespread provision of disease prevention services. In this study we extended an earlier analysis and examined, for individuals 65 years of age and older, the association between having a recent checkup and being up to date on a cluster of recommended preventive services. We also propose steps that will likely be needed to increase receipt of preventive services.

Methods

Data source
The Behavioral Risk Factor Surveillance System (BRFSS), coordinated by the Centers for Disease Control and Prevention (CDC), conducts annual state-based telephone surveys of noninstitutionalized US adults ages 18 years or older concerning health practices.3 We used data from 2006 BRFSS participants ages 65 years or older at the time they participated (32,243 male respondents and 58,762 female respondents). All results were based on weighted data that accounted for different probabilities of selection and were adjusted to reflect the population distribution in each state by age and sex, or by age, race, and sex.

Respondents queried about preventive services
We analyzed responses to BRFSS questions about the receipt of clinical preventive services recommended by the US Preventive Services Task Force (USPSTF) or by the Advisory Committee on Immunization Practices for all adults ages 65 or older.* Services included colorectal cancer screening, influenza immunization, pneumococcal immunization, and, for women, mammography and the Papanicolaou (Pap) test. The USPSTF grades these measures* as A or B, meaning it finds “good” or at least “fair” evidence that a service improves important health outcomes and concludes that benefits substantially outweigh harms.4 Questions about these services were asked in all 50 states in 2006.

*The recommendations and grading systems discussed here reflect those that were in place in 2006. There have been changes to both since this study was conducted.

Cardiovascular services excluded. The BRFSS has not asked questions about hypertension screening since 1999, when more than 95% of older adults reported they had their blood pressure checked in the past 2 years.5 Questions about cholesterol screening were not asked in all states in 2006 and were not incorporated into the composite measure. However, analysis from a prior study suggests that including cholesterol screening levels in such a composite measure would not have made a large difference in the percentage of older Americans up to date on all services.2

 

 

Were scheduled intervals for services met? Adults could meet the recommendation for colorectal cancer screening by either having a fecal occult blood test (FOBT) within 1 year or colonoscopy or sigmoidoscopy within 10 years. The USPSTF and other national guidelines recommend a 5-year interval for sigmoidoscopy and a 10-year interval for colonoscopy.6,7 However, no direct evidence has determined the optimal interval for either test,8 and the BRFSS question did not distinguish between the 2 interventions. Because either FOBT or endoscopy satisfies screening recommendations, we did not exclude respondents with missing values for 1 test if they had the other test within the recommended interval.

Other services and recommended intervals were pneumococcal vaccination (ever), influenza vaccination (in past year), and, for women, mammogram (within 2 years) and Pap test (within 3 years).

Assigning Yes or No to responses. If respondents had never received a particular preventive service or had received it outside the interval recommended by the USPSTF,4 we included them in the group answering No. We eliminated 3324 men and 6295 women with missing values for 1 or more measures.

Final determination of being “up to date.” After noting how many of the recommended services each individual had received according to age and sex, we dichotomized the sample according to whether all recommendations had been met—3 clinical preventive services for men 65 years and older (colorectal cancer screening, influenza, and pneumonia vaccination) and 5 for women (adding mammography and Pap test), with a single exception. Because Pap testing is often reported only for women with an intact cervix,9 we excused the lack of a Pap test for women who had undergone hysterectomy (47% of all women ages 65+, or 27,243). We required only that they meet 4 clinical preventive services to be considered up to date. A prior study revealed that excluding the Pap test entirely from the up-to-date measure for women 65 years and older had a minimal effect on up-to-date rates (34.2% when excluding the Pap test vs 32.5% including the Pap test).10

One of the strengths of the up-to-date measure is that it assesses the proportion of those fully up to date and thus allows for variability within subgroups, such as women who have had hysterectomies, without eliminating them arbitrarily from the sample.

Additional participant characteristics. We divided respondents into 4 racial/ethnic categories based on responses to BRFSS questions: White (non-Hispanic); Black (non-Hispanic); Hispanic of any race; or “Other” (American Indians, Asians, Pacific Islanders, and individuals of other or multiple race categories). Age categories were 65 to 69 years, 70 to 74 years, 75 to 79 years, or >80 years. Education categories: less than high school, high school graduate or general equivalency diploma recipient, some college, or college graduate. We further dichotomized the sample according to marital status, having 1 or more personal health care providers (vs none), and health status (fair/poor or good/very good/excellent). Given the amount of missing data (20%), household income was not included in the analysis.

Quantifying health care access. We created a measure of health care access using 3 factors:

  • health insurance (“Do you have any kind of health care coverage, including health insurance, prepaid plans such as HMOs, or government plans such as Medicare?”)
  • one or more personal health care providers (see above)
  • no cost barrier to seeing a doctor (“Was there a time in the past 12 months when you needed to see a doctor but could not because of cost?”).

To measure relative health care access, we scored each of the above items 1 for affirmative or 0 for negative. The sum (0, 1, 2, or 3) represented level of access. Lower numbers indicated more barriers and higher numbers represented greater access. Because only 48 older men and 59 older women had total scores of 0, the lower 2 levels were combined and the resulting 3 levels were termed “low” (0 & 1), “medium” (2) and “high” (3) access. Two of the measures used for health care access were also used to define 3 mutually exclusive health insurance categories: uninsured, fully insured, and underinsured (insured but reporting a cost barrier).2

We determined whether a routine checkup had occurred in the past 2 years by asking, “About how long has it been since you last visited a doctor for a routine checkup? A routine checkup is a general physical exam, not an exam for a specific injury, illness, or condition.”

 

 

Statistical analysis
We conducted statistical analysis using Stata, version 9.0 (Stata Corp; College Station, Tex). We used Pearson chi-square tests to determine whether selected demographic factors were associated with being up to date on all recommended services. We also used Stata in a logistic regression analysis to control simultaneously for age, education, race/ethnicity, marital status, insurance coverage, health care access, having one or more personal health care providers, having a routine checkup within 2 years, current smoking, and health status. We computed odds ratios and 95% confidence intervals for each variable in the model.

Results

Most adults ages 65 years and older were fully insured, had a personal health care provider, and reported no cost barrier to seeing a doctor in the past year (TABLE 1). Breaking out these measures into 3 levels of relative health care access, 88.6% of men and 90.2% of women were at the highest level. More than 90% of respondents reported having a routine checkup in the past 2 years. More than 60% reported receiving each of the separate immunizations and cancer screenings recommended for their age and sex, and almost all had received at least 1 service.

TABLE 2 shows the prevalence of being up to date by demographic group. Only 42.6% of all older men and 35.2% of all older women were up to date, with rates marginally better for those with high access to care (45.1% for men, 37% for women) or those reporting a recent routine checkup (44.8% for men, 36.8% for women). Low access to care yielded dramatically worse up-to-date rates (14.8% for men, 9.1% for women). Similarly, those reporting no recent routine checkup had poor up-to-date rates (20.5% for men, 15.4% for women). The highest rates of being up to date belonged to those with a college degree (49% for men, 42.1% for women). Higher rates were also found among the oldest age groups.

Results of the logistic regression analysis are shown in TABLE 3. Among men and women, being up to date was more likely for those who were older, married, better educated, had high access to health care, and had had a routine checkup in the past 2 years. The latter 2 groups had the highest odds ratios of all groups in the model. Less likely to be up to date were those who were Black, Hispanic, or of a race other than white, those who smoked cigarettes, and (for men) those who were in good or better health. For women, health status had no effect on being up to date.

Table 1
Characteristics of US adults ≥65 years, 2006 Behavioral Risk Factor Surveillance System

 MenWomen
 PercentnPercentn
Total10032,24310058,762
Age (y)
65-6930.710,28627.116,184
70-7425.8841021.214,005
75-7922.9668524.612,562
≥8020.7686227.116,011
Race/ethnicity
White81.727,72081.550,270
Black7.316318.03656
Hispanic6.09316.41824
Other*5.014234.22218
Education
< High school15.0501017.69931
High school29.3990539.022,978
Some college20.9655223.614,372
College grad34.910,66419.711,226
Married74.020,59344.520,551
Insurance
Fully insured94.230,14794.055,066
Underinsured3.511734.32385
Not insured2.37541.7993
Has a personal health care provider93.129,65795.355,586
No cost barrier96.230,83895.456,021
Health care access
Low1.44500.9561
Medium10.035218.85271
High88.627,99690.252,430
Fair/poor health27.6895729.716,727
Clinical preventive services
Flu shot past year68.321,72567.039,205
Pneumococcal polysaccharide vaccine63.619,53166.738,442
Colon cancer screen71.321,39567.937,112
Pap test in 3 years (women with cervix)  70.819,700
Pap test in 3 years (credit for hysterectomy)  84.846,943
Mammogram in 2 years  79.143,874
Number of health care services received
09.629933.01615
119.353355.02659
228.5808510.95679
3§42.612,50619.210,108
4  26.813,935
5§  35.218,471
Total||10028,919100.052,467
Routine checkup91.528,84593.153,037
*Includes American Indian, Asian, Pacific Islander, and individuals of other or multiple race categories.
Underinsurance includes individuals with coverage who indicated there was a time in the past year when they needed to see a doctor but could not due to cost (cost barrier).
Determined from 3 measures: having health insurance, having a personal health care provider, and not reporting a cost barrier. Levels 0 and 1 were combined. Resulting levels were low, medium, and high.

§To be up to date, men required colon cancer screening (fecal occult blood test in past year or endoscopy within 10 years), a flu shot in the past year, and a pneumonia vaccination ever. Women required those same services plus a mammogram within 2 years and Pap test within 3 years (unless prior hysterectomy). ||Total n excludes 3324 men and 6295 women with missing values for one or more tests.
Respondents who indicated they had a routine “checkup” in the past 2 years.

Table 2
Prevalence of being up to date* by demographic characteristics, US adults ≥65 years, 2006 Behavioral Risk Factor Surveillance System

 MenWomen
 Percent95% CIPercent95% CI
Total42.641.6-43.735.234.4-36.0
Age (y)
65-6932.030.3-33.729.828.4-31.3
70-7444.942.7-47.239.137.5-40.8
75-7948.746.2-51.240.238.5-41.9
≥8048.946.5-51.232.931.4-34.5
P value<.0001 <.0001 
Race/ethnicity
White46.245.1-47.337.937.1-38.7
Black27.823.7-32.322.419.7-25.4
Hispanic20.815.7-27.022.117.7-27.2
Other31.025.7-36.924.119.7-29.0
P value<.0001 <.0001 
Married
Yes44.343.0-45.739.338.1-40.6
No37.735.9-39.531.730.7-32.7
P value<.0001 <.0001 
Education
< High school30.928.2-33.825.023.1-26.9
High school39.537.7-41.434.233.0-35.4
Some college44.341.9-46.838.236.6-39.7
College grad49.047.2-50.942.140.2-44.1
P value<.0001 <.0001 
Insurance
Not insured19.915.1-25.817.913.2-23.9
Underinsured29.325.0-34.024.320.7-28.2
Fully insured43.742.5-44.836.035.2-36.8
P value<.0001 <.0001 
Personal health care provider
Has 1 or more44.443.3-45.536.335.5-37.1
None18.816.1-21.911.49.3-14.0
P value<.0001 <.0001 
Health status
Fair/poor health44.342.1-46.533.231.7-34.8
Ex/v good health41.940.7-43.236.035.1-37.0
P value.066 .002 
Health access
“Low”14.89.1-23.19.15.9-13.7
“Medium”24.722.2-27.419.317.1-21.8
“High”45.143.9-46.337.036.2-37.9
P value<.0001 <.0001 
Routine checkup§
Yes44.843.6-45.936.835.9-37.6
No20.517.6-23.715.412.9-18.4
P value<.0001 <.0001 
CI, confidence interval.
*To be up to date, men required colon cancer screening (fecal occult blood test in past year or endoscopy within 10 years), a flu shot in the past year, and a pneumonia vaccination ever. Women required those same services plus a mammogram within 2 years and Pap test within 3 years (unless prior hysterectomy).
Other race includes American Indian, Asian, Pacific Islander, and individuals of other or multiple race categories.
Determined from 3 measures: having health insurance, having a personal health care provider, and not reporting a cost barrier. Levels 0 and 1 were combined. Resulting levels were low, medium, and high.
§Respondents who indicated they had a routine “checkup” in the past 2 years.
 

 

TABLE 3
Results of multiple logistic regression modeling* for being up to datefor cancer screening and adult immunization, by sex and demographic characteristics: 2006 Behavioral Risk Factor Surveillance System, adults ≥65 years

 MenWomen
 OR95% CIP valueOR95% CIP value
Age 65-69 y (referent)
70-741.741.54-1.97<.0011.541.39-1.70<.0001
75-792.041.79-2.32<.0011.561.40-1.74<.0001
≥801.961.72-2.23<.0011.191.06-1.32.002
White (referent)     
Black0.520.41-0.66<.0010.550.46-0.66<.0001
Hispanic0.370.26-0.53<.0010.560.42-0.76<.0001
Other0.530.40-0.71<.0010.550.43-0.72<.0001
Not married (referent)    
Married1.231.12-1.37<.0011.281.18-1.38<.0001
<high></high></high>    
High school1.281.10-1.50.0021.281.14-1.44<.0001
Some college1.541.30-1.83<.0011.501.32-1.69<.0001
College grad1.821.55-2.13<.0011.791.57-2.05<.0001
Health access (“Low” is referent)    
Medium1.320.71-2.45.3781.721.03-2.87.038
High2.411.32-4.41.0043.081.88-5.05<.0001
No checkup§ (referent)
Checkup 2 yr2.532.07-3.10<.0012.722.18-3.40<.0001
Fair/poor health (referent)    
Ex/v good health0.760.68-0.85<.0010.940.87-1.03.167
Nonsmoker (referent)    
Current smoker0.590.48-0.72<.0010.680.58-0.79<.0001
CI, confidence interval; OR, odds ratio.
*N=27,632 for men and 50,024 for women. Includes 50 states plus the District of Columbia and excludes 3324 male respondents and 6295 female respondents with missing values for one or more measures. There were 2 separate models, one for men and one for women.
To be up to date, men required colon cancer screening (fecal occult blood test in past year or endoscopy within 10 years), a flu shot in the past year, and a pneumonia vaccination ever. Women required those same services plus a mammogram within 2 years and Pap test within 3 years (unless prior hysterectomy).
Determined from 3 measures: having health insurance, having a personal health care provider, and not reporting a cost barrier. Levels 0 and 1 were combined. Resulting levels were low, medium, and high.

Discussion

The key finding in this study is that, although most adults ages 65 and older had high access to health care and recent routine checkups, their rates of being up to date with a recommended cluster of preventive services were only about 45% for men and 37% for women.

More than 91% of men and 93% of women reported they had a routine checkup during this timeframe, and 88.6% of men and 90.2% of women also reported they had high access to health care—ie, they had health insurance, at least 1 personal health care provider, and no cost barrier to seeing a doctor. Improving access to health care or increasing the use of routine medical checkups—even to 100%—would likely have a negligible impact on the delivery of recommended services. Despite the very modest composite delivery rates of recommended preventive services in this group, the rates were still 2 to 4 times higher than those of adults with low health care access or no recent routine checkup.

We also found that being up to date generally improves with age. Granted, there is uncertainty as to the appropriate age at which to stop specific screenings. And very elderly Americans may be receiving some services no longer of benefit. But the significance of our finding is that composite delivery rates were lowest among adults at the age for which broad consensus says services are beneficial. For example, the up-to-date rates for men and women ages 65 to 69 were 32% and 29.8%, respectively, compared with 48.7% and 40.2% for adults ages 75 to 79 (TABLE 2).

Our findings are consistent with research documenting inadequate time to incorporate preventive services into the typical office visit.11,12 Similar barriers have been identified by general practitioners in the United Kingdom.13,14 The time constraint is particularly consequential in high-volume primary care practices.15 Some investigations have calculated the actual or necessary time needed to deliver multiple recommended prevention and health promotion services and have found the requirement to be unrealistically high.16-20 Our study suggests that increased access to and use of health care services is a necessary but insufficient condition for achieving high up-to-date levels.

To improve up-to-date rates, likely actions will include more efficient use of office time, increased reliance on nonphysician clinicians, greater use of electronic medical records, and prioritizing services for a routine checkup. External policy changes, such as pay-for-performance, may also enhance preventive service delivery rates. We hope that, in time, the composite measure used in this analysis will be adopted by both primary care clinicians and public health practitioners in the same way that tracking composite children’s vaccination levels are helpful to family practitioners, pediatricians, and local health departments. However, there is probably no easy answer; even the prompts enabled by electronic medical records are useless when ignored by providers.21 Improving delivery of preventive services in office settings will require multiple strategies sustained over many years.22

Community-based efforts. There is a strong rationale for a more determined policy to expand community-based access. Many community-based approaches to individual preventive services have been developed over the last 10 years.23 For example, the CDC’s National Breast and Cervical Cancer Early Detection Program represents one model of a state-based program that can make local assistance available for uninsured women.24 In addition, an evidence-based model developed by the nonprofit agency SPARC (Sickness Prevention Achieved through Regional Collaboration) suggests ways of creating community-based points of access for multiple preventive services.25-27

 

 

Questions still unanswered. Although BRFSS data suggest older adults are regularly receiving “routine checkups,” it is not clear what kind of intervention this refers to beyond its nonacute nature. What characterizes routine checkups in patients’ minds, and how might such visits be limited as venues for providing preventive services? Furthermore, what are the characteristics of providers associated with different types of checkup services? How do primary care providers differ from subspecialists in the kinds of preventive services they provide? Answers to these questions have important implications for physician training and for targeted outreach to subspecialty groups. From a community standpoint, it would be helpful to know if there are specific untapped opportunities for delivering preventive services, particularly in underserved and minority communities where coverage rates are very low.

This study’s limitations. Because the BRFSS relies on self-reports, our findings are subject to various biases, including “telescoping,”28 the tendency of people to remember events as having occurred more recently than they actually did.29 Moreover, because BRFSS surveys exclude people in households without telephones (who are more likely to be poor and thus also less likely to have access to health care and preventive services), our estimates may be slightly higher than the true rates.30 People with cell phone service only were not sampled; however, this had little impact on estimates for older adults, since just an estimated 2.2% use cell phones exclusively.31 People in institutions, such as nursing homes, which account for 3% to 4% of adults 65 and older were also excluded.32

The strength of this study is that, based on a large sample of randomly selected respondents, it is the first report on the adoption of clinical preventive services in all states in relation to the use of routine checkups and a composite measure. However, as noted in the methods, although the interviewer provided a definition for the term routine checkup, the description may have been interpreted differently by survey respondents.

The provider’s office and medical home should remain at the center of a national strategy to increase the delivery of these services, but expanding these efforts to include community access is critical to improving overall rates of preventive services. We need more determined and strategic collaborations between medicine and public health that will facilitate access to, and use of, preventive services for all Americans.

CORRESPONDENCE 
Douglas Shenson, MD, MPH, 76 Prince Street, Newton, MA 02465; [email protected]

References

1. U.S. Preventive Services Task Force. Guide to Clinical Preventive Services: Report of the U.S. Preventive Services Task Force. 3rd ed. Baltimore, Md: Williams and Wilkins; 2004.

2. Shenson D, Bolen J, Adams M. Receipt of preventive services by elders based on composite measures, 1997-2004. Am J Prev Med. 2007;32:11-18.

3. Behavioral Risk Factor Surveillance System operational and users guide version 3.0, March 2005. Available at: http://www.cdc.gov/brfss/pdf/userguide.pdf. Access December 14, 2010.

4. US Preventive Services Task Force The Guide to Clinical Preventive Services, 2007: Recommendations of the US Preventive Services Task Force. Rockville, Md: Agency for Healthcare Research and Quality; September 2007: 23, 26, 32, 204-205, 232. AHRQ publication 07-05100. Available at: https://www.oxhp.com/secure/materials/member/adult_preventive.pdf. Accessed December 21, 2010.

5. Centers for Disease Control and Prevention State-specific trends in self-reported blood pressure screening and high blood pressure—United States, 1991–1999. MMWR Morb Mortal Wkly Rep. 2002;51(21):456-460.

6. US Preventive Services Task Force The Guide to Clinical Preventive Services 2007: Recommendations of the US Preventive Services Task Force. Rockville, Md: Agency for Healthcare Research and Quality; September 2007: 32-33. AHRQ publication 07-05100. Available at: https://www.oxhp.com/secure/materials/member/adult_preventive.pdf. Accessed December 21, 2010.

7. Byers T, Levin B, Rothenberger D, et al. American Cancer Society guidelines for screening and surveillance for early detection of colorectal polyps and cancer: update 1997. CA Cancer J Clin. 1997;47:154-160.

8. U.S. Preventive Services Task Force. Screening for colorectal cancer: recommendation and rational. Ann Intern Med. 2002;137:129-131.

9. US. Preventive Services Task Force. Screening for cervical cancer: recommendations and rationale. January 2003. AHRQ Publication 03-515A. Available at: www.uspreventiveservicestaskforce.org/uspstf/uspscerv.htm. Accessed December 21, 2010.

10. Shenson D, Bolen J, Adams M. Receipt of preventive services by elders based on composite measures, 1997–2004. Am J Prev Med. 2007;32:11-18.

11. Burack RC. Barriers to clinical preventive medicine. Prim Care. 1989;116:245-250.

12. Kottke TE, Brekke ML, Solberg LI. Making “time” for preventive services. Mayo Clin Proc. 1993;68:786-791.

13. Waller D, Agass M, Mant D, et al. Health checks in general practice: another example of inverse care law? BMJ. 1990;300:1115-1118.

14. Fowler G, Mant D. Health checks for adults. BMJ. 1990;300:1318-1320.

15. Zyzanski SJ, Stange KC, Langa D, et al. Trade-offs in high-volume primary care practices. J Fam Pract. 1998;46:397-402.

16. Yarnall KSH, Pollak KI, Ostbye T, et al. Primary care: is there enough time for prevention? Am J Public Health. 2003;93:635-641.

17. Carney PA, Dietrich AJ, Freeman DH Jr, et al. The periodic health examination provided to asymptomatic older women: an assessment using standardized patients. Ann Intern Med. 1993;119:129-135.

18. Stange KC, Flocke SA, Goodwin MA. Opportunistic preventive services delivery. Are time limitations and patient satisfaction barriers? J Fam Pract. 1998;46:419-424.

19. Russell NK, Roter DL. Health promotion counseling of chronic-disease patients during primary care visits. Am J Public Health. 1993;83:979-982.

20. Rafferty M. Prevention services in primary care: taking time, setting priorities. West J Med. 1998;169:269-275.

21. Schellhase KG, Koepsell TD, Norris TE. Providers’ reactions to an automated health maintenance reminder system incorporated into the patient’s electronic medical record. J Am Board Fam Pract. 2003;16:350-351.

22. Ballard DJ, Nicewander DA, Qin H, et al. Improving delivery of clinical preventive services: a multi-year journey. Am J Prev Med. 2007;33:492-497.

23. Shenson D. Putting prevention in its place: the shift from clinic to community. Health Aff (Millwood). 2006;25:1012-1015.

24. Centers for Disease Control and Prevention. National Breast and Cervical Early Detection Program. Available at: www.cdc.gov/cancer/NBCCEDP/. Accessed: June 20, 2008.

25. Shenson D, Benson W, Harris A. Expanding the delivery of preventive services through community collaboration: the SPARC model. Prev Chronic Dis. 2008;5(1). Available at http://www.cdc.gov/pcd/issues/2008/jan/07_0139.htm. Accessed December 14, 2010.

26. Shenson D, Quinley J, DiMartino D, et al. Pneumococcal immunizations at flu clinics: the impact of community-wide outreach. J Community Health. 2001;26:191-201.

27. Shenson D, Cassarino L, DiMartino D, et al. Improving access to mammography through community-based influenza clinics: a quasi-experimental study. Am J Prev Med. 2001;20:97-102.

28. Sudman SN, Bradburn NM. Effects of time and memory on response in surveys. J Am Stat Assoc. 1973;68:805-815.

29. Newell SA, Girgis A, Sanson-Fisher RW, et al. The accuracy of self-reported health behaviors and risk factors relating to cancer and cardiovascular disease in the general population: a critical review. Am J Prev Med. 1999;17:211-229.

30. Thornberry OT, Massey JT. Trends in the United States telephone coverage across time and subgroup. In: Groves RM, Biemer PP, Lyberg LR, et al, eds. Telephone Survey Methodology. New York, NY: John Wiley & Sons; 1988:25–49.

31. Blumberg SJ, Luke JV. Wireless substitution: Early release of estimates from the National Health Interview Survey, July-December 2007. National Center for Health Statistics. Available at: http://www.cdc.gov/nchs/data/nhis/earlyrelease/wireless200805.htm. Accessed: May 13, 2008.

32. National Center for Health Statistics. Health, United States, 2002. Special excerpt: trend tables on 65 and older population. Washington, DC: Department of Health and Human Services; 2003. Publication 03-1030. Available at: www.cdc.gov/nchs/data/hushus02.pdf. Accessed December 21, 2010.

References

1. U.S. Preventive Services Task Force. Guide to Clinical Preventive Services: Report of the U.S. Preventive Services Task Force. 3rd ed. Baltimore, Md: Williams and Wilkins; 2004.

2. Shenson D, Bolen J, Adams M. Receipt of preventive services by elders based on composite measures, 1997-2004. Am J Prev Med. 2007;32:11-18.

3. Behavioral Risk Factor Surveillance System operational and users guide version 3.0, March 2005. Available at: http://www.cdc.gov/brfss/pdf/userguide.pdf. Access December 14, 2010.

4. US Preventive Services Task Force The Guide to Clinical Preventive Services, 2007: Recommendations of the US Preventive Services Task Force. Rockville, Md: Agency for Healthcare Research and Quality; September 2007: 23, 26, 32, 204-205, 232. AHRQ publication 07-05100. Available at: https://www.oxhp.com/secure/materials/member/adult_preventive.pdf. Accessed December 21, 2010.

5. Centers for Disease Control and Prevention State-specific trends in self-reported blood pressure screening and high blood pressure—United States, 1991–1999. MMWR Morb Mortal Wkly Rep. 2002;51(21):456-460.

6. US Preventive Services Task Force The Guide to Clinical Preventive Services 2007: Recommendations of the US Preventive Services Task Force. Rockville, Md: Agency for Healthcare Research and Quality; September 2007: 32-33. AHRQ publication 07-05100. Available at: https://www.oxhp.com/secure/materials/member/adult_preventive.pdf. Accessed December 21, 2010.

7. Byers T, Levin B, Rothenberger D, et al. American Cancer Society guidelines for screening and surveillance for early detection of colorectal polyps and cancer: update 1997. CA Cancer J Clin. 1997;47:154-160.

8. U.S. Preventive Services Task Force. Screening for colorectal cancer: recommendation and rational. Ann Intern Med. 2002;137:129-131.

9. US. Preventive Services Task Force. Screening for cervical cancer: recommendations and rationale. January 2003. AHRQ Publication 03-515A. Available at: www.uspreventiveservicestaskforce.org/uspstf/uspscerv.htm. Accessed December 21, 2010.

10. Shenson D, Bolen J, Adams M. Receipt of preventive services by elders based on composite measures, 1997–2004. Am J Prev Med. 2007;32:11-18.

11. Burack RC. Barriers to clinical preventive medicine. Prim Care. 1989;116:245-250.

12. Kottke TE, Brekke ML, Solberg LI. Making “time” for preventive services. Mayo Clin Proc. 1993;68:786-791.

13. Waller D, Agass M, Mant D, et al. Health checks in general practice: another example of inverse care law? BMJ. 1990;300:1115-1118.

14. Fowler G, Mant D. Health checks for adults. BMJ. 1990;300:1318-1320.

15. Zyzanski SJ, Stange KC, Langa D, et al. Trade-offs in high-volume primary care practices. J Fam Pract. 1998;46:397-402.

16. Yarnall KSH, Pollak KI, Ostbye T, et al. Primary care: is there enough time for prevention? Am J Public Health. 2003;93:635-641.

17. Carney PA, Dietrich AJ, Freeman DH Jr, et al. The periodic health examination provided to asymptomatic older women: an assessment using standardized patients. Ann Intern Med. 1993;119:129-135.

18. Stange KC, Flocke SA, Goodwin MA. Opportunistic preventive services delivery. Are time limitations and patient satisfaction barriers? J Fam Pract. 1998;46:419-424.

19. Russell NK, Roter DL. Health promotion counseling of chronic-disease patients during primary care visits. Am J Public Health. 1993;83:979-982.

20. Rafferty M. Prevention services in primary care: taking time, setting priorities. West J Med. 1998;169:269-275.

21. Schellhase KG, Koepsell TD, Norris TE. Providers’ reactions to an automated health maintenance reminder system incorporated into the patient’s electronic medical record. J Am Board Fam Pract. 2003;16:350-351.

22. Ballard DJ, Nicewander DA, Qin H, et al. Improving delivery of clinical preventive services: a multi-year journey. Am J Prev Med. 2007;33:492-497.

23. Shenson D. Putting prevention in its place: the shift from clinic to community. Health Aff (Millwood). 2006;25:1012-1015.

24. Centers for Disease Control and Prevention. National Breast and Cervical Early Detection Program. Available at: www.cdc.gov/cancer/NBCCEDP/. Accessed: June 20, 2008.

25. Shenson D, Benson W, Harris A. Expanding the delivery of preventive services through community collaboration: the SPARC model. Prev Chronic Dis. 2008;5(1). Available at http://www.cdc.gov/pcd/issues/2008/jan/07_0139.htm. Accessed December 14, 2010.

26. Shenson D, Quinley J, DiMartino D, et al. Pneumococcal immunizations at flu clinics: the impact of community-wide outreach. J Community Health. 2001;26:191-201.

27. Shenson D, Cassarino L, DiMartino D, et al. Improving access to mammography through community-based influenza clinics: a quasi-experimental study. Am J Prev Med. 2001;20:97-102.

28. Sudman SN, Bradburn NM. Effects of time and memory on response in surveys. J Am Stat Assoc. 1973;68:805-815.

29. Newell SA, Girgis A, Sanson-Fisher RW, et al. The accuracy of self-reported health behaviors and risk factors relating to cancer and cardiovascular disease in the general population: a critical review. Am J Prev Med. 1999;17:211-229.

30. Thornberry OT, Massey JT. Trends in the United States telephone coverage across time and subgroup. In: Groves RM, Biemer PP, Lyberg LR, et al, eds. Telephone Survey Methodology. New York, NY: John Wiley & Sons; 1988:25–49.

31. Blumberg SJ, Luke JV. Wireless substitution: Early release of estimates from the National Health Interview Survey, July-December 2007. National Center for Health Statistics. Available at: http://www.cdc.gov/nchs/data/nhis/earlyrelease/wireless200805.htm. Accessed: May 13, 2008.

32. National Center for Health Statistics. Health, United States, 2002. Special excerpt: trend tables on 65 and older population. Washington, DC: Department of Health and Human Services; 2003. Publication 03-1030. Available at: www.cdc.gov/nchs/data/hushus02.pdf. Accessed December 21, 2010.

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Suicide factors: UNSAFE or SAFER?

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The basic function of a suicide assessment is to identify fixed and modifiable risk factors for suicide and existing or amendable protective factors.1 Epidemiologic studies have defined a range of suicide risk and protective factors for the general population.2 Other research has delineated suicide risk and protective factors for individuals with specific psychiatric disorders.3 The presence of disorder-specific risk and protective factors for suicide must be identified during suicide risk assessment.

Risk factors

Lack of support from family, peers, or the community is a critical concern. Noncompliance with treatment may be associated with onset of suicidality. Help-seeking is impeded by stigma associated with suicide and shame for past attempts. History of physical, sexual, or psychological abuse is tied to subsequent suicidal behavior. Alcohol abuse plays a role in suicide. Many patients who attempt suicide have backgrounds involving suicide loss or attempts by family members. Recurring psychiatric symptoms—particularly depression, anxiety, and panic—can trigger suicidality. Symptom relapse may lead to hospitalization, which is followed by a high-risk period after discharge.

These suicide risk factors can be summarized by the mnemonic UNSAFE:

Unconnected—no support; sense of not belonging or being a burden

Nonadherence—unmanaged mental illness or co-occurring disorders

Stigma/shame related to past attempts or suicidal behavior

Abuse history and/or alcohol misuse; prior attempt

Family history of suicide or suicide attempts

Exacerbations—worsened mental illness, hospitalizations

Protective factors

The presence of a personal crisis or safety self-help plan shows patient insight. Maintaining prescribed treatment indicates a patient’s likelihood of complying with clinical and self-care measures to avert future suicidality. Accessible support from family, peers, and the community demonstrates social integration. The recovery concept promotes these factors as well as wellness and resilience. Awareness of the warning signs of suicide and personal risk factors and precipitants is essential for self-help and help-seeking.

Protective factors for suicide can be summarized by the mnemonic SAFER:

Self-help skills, personal crisis/suicide prevention plan

Adherence to treatment plan

Family and community support

Education about risk factors, warning signs, and triggers for suicide

Recovery and resilience

In our emergency psychiatric facility the UNSAFE and SAFE mnemonics are posted next to the desk of the on-duty psychiatrist. Crisis center staff use these mnemonics to screen patients during psychiatric evaluations. Allied therapists use them during in-patient psychoeducation about suicidality. Peer specialists use them to help patients prepare personal safety plans.

Disclosure

The authors report no financial relationship with any company whose products are mentioned in this article or with manufactures of competing products.

These mnemonics were developed by Tony Salvatore in consultation with Rocio Nell, MD, CPE.

References

1. Simon R, Shuman DW. The standard of care in suicide risk assessment: an elusive concept. CNS Spectr. 2006;11(6):442-445.

2. Goldsmith SK, Pellmar TC, Kleinman AM, et al. eds. Reducing suicide: a national imperative. Washington, DC: The National Academies Press; 2002.

3. Harris EC, Barraclough B. Suicide as an outcome for mental disorders. A meta-analysis. Br J Psychiatry. 1997;170(3):205-228.

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The basic function of a suicide assessment is to identify fixed and modifiable risk factors for suicide and existing or amendable protective factors.1 Epidemiologic studies have defined a range of suicide risk and protective factors for the general population.2 Other research has delineated suicide risk and protective factors for individuals with specific psychiatric disorders.3 The presence of disorder-specific risk and protective factors for suicide must be identified during suicide risk assessment.

Risk factors

Lack of support from family, peers, or the community is a critical concern. Noncompliance with treatment may be associated with onset of suicidality. Help-seeking is impeded by stigma associated with suicide and shame for past attempts. History of physical, sexual, or psychological abuse is tied to subsequent suicidal behavior. Alcohol abuse plays a role in suicide. Many patients who attempt suicide have backgrounds involving suicide loss or attempts by family members. Recurring psychiatric symptoms—particularly depression, anxiety, and panic—can trigger suicidality. Symptom relapse may lead to hospitalization, which is followed by a high-risk period after discharge.

These suicide risk factors can be summarized by the mnemonic UNSAFE:

Unconnected—no support; sense of not belonging or being a burden

Nonadherence—unmanaged mental illness or co-occurring disorders

Stigma/shame related to past attempts or suicidal behavior

Abuse history and/or alcohol misuse; prior attempt

Family history of suicide or suicide attempts

Exacerbations—worsened mental illness, hospitalizations

Protective factors

The presence of a personal crisis or safety self-help plan shows patient insight. Maintaining prescribed treatment indicates a patient’s likelihood of complying with clinical and self-care measures to avert future suicidality. Accessible support from family, peers, and the community demonstrates social integration. The recovery concept promotes these factors as well as wellness and resilience. Awareness of the warning signs of suicide and personal risk factors and precipitants is essential for self-help and help-seeking.

Protective factors for suicide can be summarized by the mnemonic SAFER:

Self-help skills, personal crisis/suicide prevention plan

Adherence to treatment plan

Family and community support

Education about risk factors, warning signs, and triggers for suicide

Recovery and resilience

In our emergency psychiatric facility the UNSAFE and SAFE mnemonics are posted next to the desk of the on-duty psychiatrist. Crisis center staff use these mnemonics to screen patients during psychiatric evaluations. Allied therapists use them during in-patient psychoeducation about suicidality. Peer specialists use them to help patients prepare personal safety plans.

Disclosure

The authors report no financial relationship with any company whose products are mentioned in this article or with manufactures of competing products.

These mnemonics were developed by Tony Salvatore in consultation with Rocio Nell, MD, CPE.

The basic function of a suicide assessment is to identify fixed and modifiable risk factors for suicide and existing or amendable protective factors.1 Epidemiologic studies have defined a range of suicide risk and protective factors for the general population.2 Other research has delineated suicide risk and protective factors for individuals with specific psychiatric disorders.3 The presence of disorder-specific risk and protective factors for suicide must be identified during suicide risk assessment.

Risk factors

Lack of support from family, peers, or the community is a critical concern. Noncompliance with treatment may be associated with onset of suicidality. Help-seeking is impeded by stigma associated with suicide and shame for past attempts. History of physical, sexual, or psychological abuse is tied to subsequent suicidal behavior. Alcohol abuse plays a role in suicide. Many patients who attempt suicide have backgrounds involving suicide loss or attempts by family members. Recurring psychiatric symptoms—particularly depression, anxiety, and panic—can trigger suicidality. Symptom relapse may lead to hospitalization, which is followed by a high-risk period after discharge.

These suicide risk factors can be summarized by the mnemonic UNSAFE:

Unconnected—no support; sense of not belonging or being a burden

Nonadherence—unmanaged mental illness or co-occurring disorders

Stigma/shame related to past attempts or suicidal behavior

Abuse history and/or alcohol misuse; prior attempt

Family history of suicide or suicide attempts

Exacerbations—worsened mental illness, hospitalizations

Protective factors

The presence of a personal crisis or safety self-help plan shows patient insight. Maintaining prescribed treatment indicates a patient’s likelihood of complying with clinical and self-care measures to avert future suicidality. Accessible support from family, peers, and the community demonstrates social integration. The recovery concept promotes these factors as well as wellness and resilience. Awareness of the warning signs of suicide and personal risk factors and precipitants is essential for self-help and help-seeking.

Protective factors for suicide can be summarized by the mnemonic SAFER:

Self-help skills, personal crisis/suicide prevention plan

Adherence to treatment plan

Family and community support

Education about risk factors, warning signs, and triggers for suicide

Recovery and resilience

In our emergency psychiatric facility the UNSAFE and SAFE mnemonics are posted next to the desk of the on-duty psychiatrist. Crisis center staff use these mnemonics to screen patients during psychiatric evaluations. Allied therapists use them during in-patient psychoeducation about suicidality. Peer specialists use them to help patients prepare personal safety plans.

Disclosure

The authors report no financial relationship with any company whose products are mentioned in this article or with manufactures of competing products.

These mnemonics were developed by Tony Salvatore in consultation with Rocio Nell, MD, CPE.

References

1. Simon R, Shuman DW. The standard of care in suicide risk assessment: an elusive concept. CNS Spectr. 2006;11(6):442-445.

2. Goldsmith SK, Pellmar TC, Kleinman AM, et al. eds. Reducing suicide: a national imperative. Washington, DC: The National Academies Press; 2002.

3. Harris EC, Barraclough B. Suicide as an outcome for mental disorders. A meta-analysis. Br J Psychiatry. 1997;170(3):205-228.

References

1. Simon R, Shuman DW. The standard of care in suicide risk assessment: an elusive concept. CNS Spectr. 2006;11(6):442-445.

2. Goldsmith SK, Pellmar TC, Kleinman AM, et al. eds. Reducing suicide: a national imperative. Washington, DC: The National Academies Press; 2002.

3. Harris EC, Barraclough B. Suicide as an outcome for mental disorders. A meta-analysis. Br J Psychiatry. 1997;170(3):205-228.

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Contraception Counseling for Adolescent Girls

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Adolescents value confidentiality with their health care clinicians very highly. To support the opportunity for confidentiality, you should speak with female adolescents without a parent in the room for at least part of each visit. This fosters an honest conversation about the sensitive issues around contraception, including any intimate relationships, current or planned sexual activity, and the safety and protection afforded by contraception.

Girls are allowed to discuss sexually transmitted infections confidentially with their physicians, and hopefully can be offered a confidential discussion of their sexual activity as well. Ideally, a girl also feels comfortable talking with a parent about her concerns, but this scenario may not be an option for all your patients.

Begin with a discussion about relationships. Avoid preaching to them or asking blunt questions such as: “Hey, are you having sex?” Acknowledge that “sex” can refer to activities beyond sexual intercourse as well.

Ask your patients if they are in a relationship with a girl, a boy, or both. A teenager who is not heterosexual or is unsure will then know you are willing to discuss any specific concerns.

Make sure the teenager knows that abstinence is always the best protection against sexually transmitted infection and/or pregnancy.

Once you ascertain she is heterosexual or bisexual, is sexually active, and needs contraception, focus next on safety. Ask the patient: Are you doing anything to protect yourself against the consequences of sexual activity? Also ensure her participation in the intimate relationship is voluntary and free of any coercion, particularly among younger teenage girls.

There are multiple means of protection against sexually transmitted infections. Educate her that, aside from abstinence, the use of condoms is her best strategy. Make sure the girl understands that she is equally responsible for the proper use of condoms. If you take care of a lot of adolescents, it is reasonable to have a supply of condoms on hand so you can provide them.

Also consider providing a patient who is sexually active (or contemplating sexual activity) with a prescription for emergency, postcoital contraception. She could fill the prescription as needed, within 72 hours of sexual intercourse, to decrease the likelihood of pregnancy considerably. Even if she regularly uses a birth control method, this prescription provides a good backup plan.

Keep the child's developmental level in mind when discussing contraception and sexuality. In general, a 14-year-old girl who is sexually active or considering sex is vastly different from a 17-year-old patient. Also consider the patient and family's culture, ethnic, and/or religious background. For example, there are some religious groups where the kids cannot tell parents they have become sexually active – it could mortify the parents and be dangerous for the teenager.

Title X–funded projects are an option if a girl cannot tell her parent she wants to use contraception and/or if a third party (such as an insurance company) makes confidentiality impossible. Become familiar with the Title X–funded contraception projects in your area, which are frequently run through Planned Parenthood or a university obstetrics and gynecology program (www.hhs.gov/opa/familyplanning/index.html

You really should get to the point where you feel moderately comfortable talking about the basics of contraception and sexuality. There are not enough adolescent medicine specialists in the world to take care of all the teenagers out there, and most ob. gyns. do not see very many teenagers.

Some pediatricians may be comfortable prescribing the birth control pill, but they may not know much about the patch, the contraceptive ring, the implant, or the IUD. If a patient is interested in one of these options, you can refer her to a gynecologist or a family practitioner in your area who is particularly adept at young women's health issues. Planned Parenthood also is a good resource.

The birth control pill and the patch are the two most common birth control methods for first-time users. You do not need to know all the different types of birth control pills; it is sufficient to become comfortable prescribing one or two brands.

Check for any contraindications, such as a history of migraine headache with aura or a clotting abnormality (personal or in a first-degree relative) before prescribing oral contraception. If your patient is having regular, monthly periods, and she's had a period in the last month, some pediatricians still will feel comfortable prescribing only if they get a urine pregnancy test. On the other hand, if you give contraception without the test and the girl does miss her next period, you can always give the pregnancy test then.

 

 

If you prescribe contraception for sexual activity only, keep in mind that most teenagers have a passionate relationship that lasts a few months, followed by an interval without a relationship, followed by involvement with another person. These intermittent relationships mean that they are likely to start and stop contraception. Keep this in mind when discussing contraception options and monitor compliance with less-permanent options over time.

Children's Hospital of Boston produces www.youngwomenshealth.orgwww.acog.org/publications/patient_education/ab020.cfmaappolicy.aappublications.org/cgi/content/full/pediatrics;120/5/1135

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Adolescents value confidentiality with their health care clinicians very highly. To support the opportunity for confidentiality, you should speak with female adolescents without a parent in the room for at least part of each visit. This fosters an honest conversation about the sensitive issues around contraception, including any intimate relationships, current or planned sexual activity, and the safety and protection afforded by contraception.

Girls are allowed to discuss sexually transmitted infections confidentially with their physicians, and hopefully can be offered a confidential discussion of their sexual activity as well. Ideally, a girl also feels comfortable talking with a parent about her concerns, but this scenario may not be an option for all your patients.

Begin with a discussion about relationships. Avoid preaching to them or asking blunt questions such as: “Hey, are you having sex?” Acknowledge that “sex” can refer to activities beyond sexual intercourse as well.

Ask your patients if they are in a relationship with a girl, a boy, or both. A teenager who is not heterosexual or is unsure will then know you are willing to discuss any specific concerns.

Make sure the teenager knows that abstinence is always the best protection against sexually transmitted infection and/or pregnancy.

Once you ascertain she is heterosexual or bisexual, is sexually active, and needs contraception, focus next on safety. Ask the patient: Are you doing anything to protect yourself against the consequences of sexual activity? Also ensure her participation in the intimate relationship is voluntary and free of any coercion, particularly among younger teenage girls.

There are multiple means of protection against sexually transmitted infections. Educate her that, aside from abstinence, the use of condoms is her best strategy. Make sure the girl understands that she is equally responsible for the proper use of condoms. If you take care of a lot of adolescents, it is reasonable to have a supply of condoms on hand so you can provide them.

Also consider providing a patient who is sexually active (or contemplating sexual activity) with a prescription for emergency, postcoital contraception. She could fill the prescription as needed, within 72 hours of sexual intercourse, to decrease the likelihood of pregnancy considerably. Even if she regularly uses a birth control method, this prescription provides a good backup plan.

Keep the child's developmental level in mind when discussing contraception and sexuality. In general, a 14-year-old girl who is sexually active or considering sex is vastly different from a 17-year-old patient. Also consider the patient and family's culture, ethnic, and/or religious background. For example, there are some religious groups where the kids cannot tell parents they have become sexually active – it could mortify the parents and be dangerous for the teenager.

Title X–funded projects are an option if a girl cannot tell her parent she wants to use contraception and/or if a third party (such as an insurance company) makes confidentiality impossible. Become familiar with the Title X–funded contraception projects in your area, which are frequently run through Planned Parenthood or a university obstetrics and gynecology program (www.hhs.gov/opa/familyplanning/index.html

You really should get to the point where you feel moderately comfortable talking about the basics of contraception and sexuality. There are not enough adolescent medicine specialists in the world to take care of all the teenagers out there, and most ob. gyns. do not see very many teenagers.

Some pediatricians may be comfortable prescribing the birth control pill, but they may not know much about the patch, the contraceptive ring, the implant, or the IUD. If a patient is interested in one of these options, you can refer her to a gynecologist or a family practitioner in your area who is particularly adept at young women's health issues. Planned Parenthood also is a good resource.

The birth control pill and the patch are the two most common birth control methods for first-time users. You do not need to know all the different types of birth control pills; it is sufficient to become comfortable prescribing one or two brands.

Check for any contraindications, such as a history of migraine headache with aura or a clotting abnormality (personal or in a first-degree relative) before prescribing oral contraception. If your patient is having regular, monthly periods, and she's had a period in the last month, some pediatricians still will feel comfortable prescribing only if they get a urine pregnancy test. On the other hand, if you give contraception without the test and the girl does miss her next period, you can always give the pregnancy test then.

 

 

If you prescribe contraception for sexual activity only, keep in mind that most teenagers have a passionate relationship that lasts a few months, followed by an interval without a relationship, followed by involvement with another person. These intermittent relationships mean that they are likely to start and stop contraception. Keep this in mind when discussing contraception options and monitor compliance with less-permanent options over time.

Children's Hospital of Boston produces www.youngwomenshealth.orgwww.acog.org/publications/patient_education/ab020.cfmaappolicy.aappublications.org/cgi/content/full/pediatrics;120/5/1135

Adolescents value confidentiality with their health care clinicians very highly. To support the opportunity for confidentiality, you should speak with female adolescents without a parent in the room for at least part of each visit. This fosters an honest conversation about the sensitive issues around contraception, including any intimate relationships, current or planned sexual activity, and the safety and protection afforded by contraception.

Girls are allowed to discuss sexually transmitted infections confidentially with their physicians, and hopefully can be offered a confidential discussion of their sexual activity as well. Ideally, a girl also feels comfortable talking with a parent about her concerns, but this scenario may not be an option for all your patients.

Begin with a discussion about relationships. Avoid preaching to them or asking blunt questions such as: “Hey, are you having sex?” Acknowledge that “sex” can refer to activities beyond sexual intercourse as well.

Ask your patients if they are in a relationship with a girl, a boy, or both. A teenager who is not heterosexual or is unsure will then know you are willing to discuss any specific concerns.

Make sure the teenager knows that abstinence is always the best protection against sexually transmitted infection and/or pregnancy.

Once you ascertain she is heterosexual or bisexual, is sexually active, and needs contraception, focus next on safety. Ask the patient: Are you doing anything to protect yourself against the consequences of sexual activity? Also ensure her participation in the intimate relationship is voluntary and free of any coercion, particularly among younger teenage girls.

There are multiple means of protection against sexually transmitted infections. Educate her that, aside from abstinence, the use of condoms is her best strategy. Make sure the girl understands that she is equally responsible for the proper use of condoms. If you take care of a lot of adolescents, it is reasonable to have a supply of condoms on hand so you can provide them.

Also consider providing a patient who is sexually active (or contemplating sexual activity) with a prescription for emergency, postcoital contraception. She could fill the prescription as needed, within 72 hours of sexual intercourse, to decrease the likelihood of pregnancy considerably. Even if she regularly uses a birth control method, this prescription provides a good backup plan.

Keep the child's developmental level in mind when discussing contraception and sexuality. In general, a 14-year-old girl who is sexually active or considering sex is vastly different from a 17-year-old patient. Also consider the patient and family's culture, ethnic, and/or religious background. For example, there are some religious groups where the kids cannot tell parents they have become sexually active – it could mortify the parents and be dangerous for the teenager.

Title X–funded projects are an option if a girl cannot tell her parent she wants to use contraception and/or if a third party (such as an insurance company) makes confidentiality impossible. Become familiar with the Title X–funded contraception projects in your area, which are frequently run through Planned Parenthood or a university obstetrics and gynecology program (www.hhs.gov/opa/familyplanning/index.html

You really should get to the point where you feel moderately comfortable talking about the basics of contraception and sexuality. There are not enough adolescent medicine specialists in the world to take care of all the teenagers out there, and most ob. gyns. do not see very many teenagers.

Some pediatricians may be comfortable prescribing the birth control pill, but they may not know much about the patch, the contraceptive ring, the implant, or the IUD. If a patient is interested in one of these options, you can refer her to a gynecologist or a family practitioner in your area who is particularly adept at young women's health issues. Planned Parenthood also is a good resource.

The birth control pill and the patch are the two most common birth control methods for first-time users. You do not need to know all the different types of birth control pills; it is sufficient to become comfortable prescribing one or two brands.

Check for any contraindications, such as a history of migraine headache with aura or a clotting abnormality (personal or in a first-degree relative) before prescribing oral contraception. If your patient is having regular, monthly periods, and she's had a period in the last month, some pediatricians still will feel comfortable prescribing only if they get a urine pregnancy test. On the other hand, if you give contraception without the test and the girl does miss her next period, you can always give the pregnancy test then.

 

 

If you prescribe contraception for sexual activity only, keep in mind that most teenagers have a passionate relationship that lasts a few months, followed by an interval without a relationship, followed by involvement with another person. These intermittent relationships mean that they are likely to start and stop contraception. Keep this in mind when discussing contraception options and monitor compliance with less-permanent options over time.

Children's Hospital of Boston produces www.youngwomenshealth.orgwww.acog.org/publications/patient_education/ab020.cfmaappolicy.aappublications.org/cgi/content/full/pediatrics;120/5/1135

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Pneumonia Challenges Hospitalists on Multiple Fronts

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Pneumonia is one of the most common diagnoses encountered by hospitalists, if not the most common, and its presentation continues to become more complicated, says Scott Flanders, MD, SFHM, professor of medicine and director of the hospitalist program at the University of Michigan Health System in Ann Arbor. Dr. Flanders has published on pneumonia (J Hosp Med. 2006;1(3):177-190), and this past fall he gave presentations on the subject at hospitalist conferences in San Francisco and Chicago—with a particular emphasis on how to prevent its recurrence in hospitalized patients at risk.

“The causative agents for community-acquired pneumonia (CAP) evolve over time,” even though the actual source of a hospitalized patient’s pneumonia may never be known, says Dr. Flanders, past president of SHM. The swine flu (H1N1) and community-acquired MRSA “are two examples of etiologic agents that were not even a consideration five years ago—and now are something hospitalists have to be aware of, understand, and recognize that they can cause pneumonia in patients who are admitted to the hospital from the community,” he says. “They need to be considered as potential etiologic agents first and foremost, because the treatments for them differ from usual empiric pneumonia treatments.

There is a subset of patients with bad reflux disease, history of GI bleeds, on anticoagulants, who have more potential benefit than harm from PPIs. Hospitalists should see if their patients fall into these categories and, if they don’t, consider discontinuing these medications.


—Scott Flanders, MD, SFHM, professor of medicine and director of the hospitalist program at the University of Michigan Health System, Ann Arbor, SHM past president

“As hospitalists, we spend a lot of time trying to think what we can do to prevent recurrent pneumonia episodes in our patients and looking for what could have caused the initial incident,” Dr. Flanders says. “Pneumococcal vaccination is not as good as we’d like it to be in preventing recurrent pneumonia. We have to look to see if there’s anything else we can do to help prevent it.”

 

One simple step is to review the patient’s medication list, see if proton pump inhibitors (PPI) for reducing gastric acid or antipsychotic medications are on the list, then ask whether they can be discontinued; both treatments are associated in the medical literature with higher rates of pneumonia recurrence. Patients often receive PPIs for empiric prevention of gastrointestinal bleeding in the ICU, a risk that might have ceased.

“There is a subset of patients with bad reflux disease, history of GI bleeds, on anticoagulants, who have more potential benefit than harm from PPIs,” Dr. Flanders explains. “Hospitalists should see if their patients fall into these categories and, if they don’t, consider discontinuing these medications.”

Dr. Flanders also points out hospitalists should keep an eye out for antipsychotic medications. “Many patients absolutely need these medications and are functional because they are on them,” he says. “We’d never consider stopping them for those patients. But some patients get them started for episodes of delirium in the hospital that have resolved or to enhance their sleep. I’d strongly recommend considering stopping them in that case.”

By contrast, statin use might improve outcomes associated with pneumonia.

Antibiotic selection is another big issue, and Dr. Flanders says hospitalists will be judged by how closely they stick to the recommended treatment guidelines. “They should be familiar with what the guidelines recommend, and recognize the types of variables they need to document if they are going to deviate from the recommendations,” he says. The evidence also says to stop routinely treating pneumonia with antibiotics beyond seven days, he adds.

 

 

Larry Beresford is a freelance writer based in Oakland, Calif. 

Recommended REading

For managing community-acquired pneumonia, Dr. Flanders recommends the Infectious Diseases Society of America and American Thoracic Society Consensus Guidelines, issued in 2007.

 

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Pneumonia is one of the most common diagnoses encountered by hospitalists, if not the most common, and its presentation continues to become more complicated, says Scott Flanders, MD, SFHM, professor of medicine and director of the hospitalist program at the University of Michigan Health System in Ann Arbor. Dr. Flanders has published on pneumonia (J Hosp Med. 2006;1(3):177-190), and this past fall he gave presentations on the subject at hospitalist conferences in San Francisco and Chicago—with a particular emphasis on how to prevent its recurrence in hospitalized patients at risk.

“The causative agents for community-acquired pneumonia (CAP) evolve over time,” even though the actual source of a hospitalized patient’s pneumonia may never be known, says Dr. Flanders, past president of SHM. The swine flu (H1N1) and community-acquired MRSA “are two examples of etiologic agents that were not even a consideration five years ago—and now are something hospitalists have to be aware of, understand, and recognize that they can cause pneumonia in patients who are admitted to the hospital from the community,” he says. “They need to be considered as potential etiologic agents first and foremost, because the treatments for them differ from usual empiric pneumonia treatments.

There is a subset of patients with bad reflux disease, history of GI bleeds, on anticoagulants, who have more potential benefit than harm from PPIs. Hospitalists should see if their patients fall into these categories and, if they don’t, consider discontinuing these medications.


—Scott Flanders, MD, SFHM, professor of medicine and director of the hospitalist program at the University of Michigan Health System, Ann Arbor, SHM past president

“As hospitalists, we spend a lot of time trying to think what we can do to prevent recurrent pneumonia episodes in our patients and looking for what could have caused the initial incident,” Dr. Flanders says. “Pneumococcal vaccination is not as good as we’d like it to be in preventing recurrent pneumonia. We have to look to see if there’s anything else we can do to help prevent it.”

 

One simple step is to review the patient’s medication list, see if proton pump inhibitors (PPI) for reducing gastric acid or antipsychotic medications are on the list, then ask whether they can be discontinued; both treatments are associated in the medical literature with higher rates of pneumonia recurrence. Patients often receive PPIs for empiric prevention of gastrointestinal bleeding in the ICU, a risk that might have ceased.

“There is a subset of patients with bad reflux disease, history of GI bleeds, on anticoagulants, who have more potential benefit than harm from PPIs,” Dr. Flanders explains. “Hospitalists should see if their patients fall into these categories and, if they don’t, consider discontinuing these medications.”

Dr. Flanders also points out hospitalists should keep an eye out for antipsychotic medications. “Many patients absolutely need these medications and are functional because they are on them,” he says. “We’d never consider stopping them for those patients. But some patients get them started for episodes of delirium in the hospital that have resolved or to enhance their sleep. I’d strongly recommend considering stopping them in that case.”

By contrast, statin use might improve outcomes associated with pneumonia.

Antibiotic selection is another big issue, and Dr. Flanders says hospitalists will be judged by how closely they stick to the recommended treatment guidelines. “They should be familiar with what the guidelines recommend, and recognize the types of variables they need to document if they are going to deviate from the recommendations,” he says. The evidence also says to stop routinely treating pneumonia with antibiotics beyond seven days, he adds.

 

 

Larry Beresford is a freelance writer based in Oakland, Calif. 

Recommended REading

For managing community-acquired pneumonia, Dr. Flanders recommends the Infectious Diseases Society of America and American Thoracic Society Consensus Guidelines, issued in 2007.

 

Pneumonia is one of the most common diagnoses encountered by hospitalists, if not the most common, and its presentation continues to become more complicated, says Scott Flanders, MD, SFHM, professor of medicine and director of the hospitalist program at the University of Michigan Health System in Ann Arbor. Dr. Flanders has published on pneumonia (J Hosp Med. 2006;1(3):177-190), and this past fall he gave presentations on the subject at hospitalist conferences in San Francisco and Chicago—with a particular emphasis on how to prevent its recurrence in hospitalized patients at risk.

“The causative agents for community-acquired pneumonia (CAP) evolve over time,” even though the actual source of a hospitalized patient’s pneumonia may never be known, says Dr. Flanders, past president of SHM. The swine flu (H1N1) and community-acquired MRSA “are two examples of etiologic agents that were not even a consideration five years ago—and now are something hospitalists have to be aware of, understand, and recognize that they can cause pneumonia in patients who are admitted to the hospital from the community,” he says. “They need to be considered as potential etiologic agents first and foremost, because the treatments for them differ from usual empiric pneumonia treatments.

There is a subset of patients with bad reflux disease, history of GI bleeds, on anticoagulants, who have more potential benefit than harm from PPIs. Hospitalists should see if their patients fall into these categories and, if they don’t, consider discontinuing these medications.


—Scott Flanders, MD, SFHM, professor of medicine and director of the hospitalist program at the University of Michigan Health System, Ann Arbor, SHM past president

“As hospitalists, we spend a lot of time trying to think what we can do to prevent recurrent pneumonia episodes in our patients and looking for what could have caused the initial incident,” Dr. Flanders says. “Pneumococcal vaccination is not as good as we’d like it to be in preventing recurrent pneumonia. We have to look to see if there’s anything else we can do to help prevent it.”

 

One simple step is to review the patient’s medication list, see if proton pump inhibitors (PPI) for reducing gastric acid or antipsychotic medications are on the list, then ask whether they can be discontinued; both treatments are associated in the medical literature with higher rates of pneumonia recurrence. Patients often receive PPIs for empiric prevention of gastrointestinal bleeding in the ICU, a risk that might have ceased.

“There is a subset of patients with bad reflux disease, history of GI bleeds, on anticoagulants, who have more potential benefit than harm from PPIs,” Dr. Flanders explains. “Hospitalists should see if their patients fall into these categories and, if they don’t, consider discontinuing these medications.”

Dr. Flanders also points out hospitalists should keep an eye out for antipsychotic medications. “Many patients absolutely need these medications and are functional because they are on them,” he says. “We’d never consider stopping them for those patients. But some patients get them started for episodes of delirium in the hospital that have resolved or to enhance their sleep. I’d strongly recommend considering stopping them in that case.”

By contrast, statin use might improve outcomes associated with pneumonia.

Antibiotic selection is another big issue, and Dr. Flanders says hospitalists will be judged by how closely they stick to the recommended treatment guidelines. “They should be familiar with what the guidelines recommend, and recognize the types of variables they need to document if they are going to deviate from the recommendations,” he says. The evidence also says to stop routinely treating pneumonia with antibiotics beyond seven days, he adds.

 

 

Larry Beresford is a freelance writer based in Oakland, Calif. 

Recommended REading

For managing community-acquired pneumonia, Dr. Flanders recommends the Infectious Diseases Society of America and American Thoracic Society Consensus Guidelines, issued in 2007.

 

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Captain of the Ship

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About a year ago, Ira Horowitz, MD, chief medical officer at Emory University Hospital in Atlanta, went to his COO and said, “Something is happening.”

While this would usually be cause for concern, Dr. Horowitz was referring to the landmark changes occurring in the Emory Healthcare System as a result of quality initiatives like the venous thromboembolism (VTE) dashboard, a computerized system that allows hospital staff, in real time, to identify patients on prophylaxis at any of Emory’s three hospitals.

Because VTE is one of the most preventable causes of death in hospitals, the dashboard allows nurses to “take the lead” and build monitoring into their workflow, Dr. Horowitz says. Nurses can begin their shifts by identifying which patients are on prophylaxis. The dashboard also gives them the opportunity, when the physicians are rounding, to discuss whether prophylaxis is appropriate.

Emory hospitalist Jason Stein, MD, SFHM, spearheaded the VTE dashboard team, which included 24 other hospitalists, nurses, pharmacists, and IT personnel. The new system was initiated during Dr. Stein’s research in HM process improvement, at the time Dr. Horowitz and others were doing parallel work in VTE prevention.

“What’s really exciting is having the troops on the ground, so to speak—having the physicians intimately involved with the patients, and the nurses really taking the lead. That’s what this study or this process improvement really illustrates,” says Dr. Horowitz, who cosponsored the research that won SHM’s 2010 Award of Excellence in Teamwork in Quality Improvement. The award was given for both the creation of the dashboard and the monumental changes the new system inspired in Emory’s approach to QI.

Sharlene Toney, RN, PhD, associate chief nursing officer for research and executive director for Emory’s professional nursing practice, says that while the dashboard concept focused on how to prepare physicians and nurses to make a difference in patience outcomes, the QI movement has spread to all parts of the Emory system.

“We actually have housekeeping staff who have had a discussion with patients about the importance of wearing [sequential compression devices],” she says. “This is really about an organizational culture. … You’re in this as a team, and this research is about the synergistic relationship of every employee. You can’t see it as hierarchal; it’s truly partnerships.”

While the dashboard is a start, Drs. Horowitz and Toney say that the positive shift happening at Emory can only be reproduced by first establishing a level of respect among and for all hospital employees, and by breaking down silos.

“I think in medicine, in order for us to provide the best and the safest care for our patients, the physicians have to start realizing they’re not captain of a ship, they’re captain of a team,” Dr. Horowitz says, “and with that comes very different behaviors.”

For more information about SHM's Awards of Excellence winners, visit our website.

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About a year ago, Ira Horowitz, MD, chief medical officer at Emory University Hospital in Atlanta, went to his COO and said, “Something is happening.”

While this would usually be cause for concern, Dr. Horowitz was referring to the landmark changes occurring in the Emory Healthcare System as a result of quality initiatives like the venous thromboembolism (VTE) dashboard, a computerized system that allows hospital staff, in real time, to identify patients on prophylaxis at any of Emory’s three hospitals.

Because VTE is one of the most preventable causes of death in hospitals, the dashboard allows nurses to “take the lead” and build monitoring into their workflow, Dr. Horowitz says. Nurses can begin their shifts by identifying which patients are on prophylaxis. The dashboard also gives them the opportunity, when the physicians are rounding, to discuss whether prophylaxis is appropriate.

Emory hospitalist Jason Stein, MD, SFHM, spearheaded the VTE dashboard team, which included 24 other hospitalists, nurses, pharmacists, and IT personnel. The new system was initiated during Dr. Stein’s research in HM process improvement, at the time Dr. Horowitz and others were doing parallel work in VTE prevention.

“What’s really exciting is having the troops on the ground, so to speak—having the physicians intimately involved with the patients, and the nurses really taking the lead. That’s what this study or this process improvement really illustrates,” says Dr. Horowitz, who cosponsored the research that won SHM’s 2010 Award of Excellence in Teamwork in Quality Improvement. The award was given for both the creation of the dashboard and the monumental changes the new system inspired in Emory’s approach to QI.

Sharlene Toney, RN, PhD, associate chief nursing officer for research and executive director for Emory’s professional nursing practice, says that while the dashboard concept focused on how to prepare physicians and nurses to make a difference in patience outcomes, the QI movement has spread to all parts of the Emory system.

“We actually have housekeeping staff who have had a discussion with patients about the importance of wearing [sequential compression devices],” she says. “This is really about an organizational culture. … You’re in this as a team, and this research is about the synergistic relationship of every employee. You can’t see it as hierarchal; it’s truly partnerships.”

While the dashboard is a start, Drs. Horowitz and Toney say that the positive shift happening at Emory can only be reproduced by first establishing a level of respect among and for all hospital employees, and by breaking down silos.

“I think in medicine, in order for us to provide the best and the safest care for our patients, the physicians have to start realizing they’re not captain of a ship, they’re captain of a team,” Dr. Horowitz says, “and with that comes very different behaviors.”

For more information about SHM's Awards of Excellence winners, visit our website.

About a year ago, Ira Horowitz, MD, chief medical officer at Emory University Hospital in Atlanta, went to his COO and said, “Something is happening.”

While this would usually be cause for concern, Dr. Horowitz was referring to the landmark changes occurring in the Emory Healthcare System as a result of quality initiatives like the venous thromboembolism (VTE) dashboard, a computerized system that allows hospital staff, in real time, to identify patients on prophylaxis at any of Emory’s three hospitals.

Because VTE is one of the most preventable causes of death in hospitals, the dashboard allows nurses to “take the lead” and build monitoring into their workflow, Dr. Horowitz says. Nurses can begin their shifts by identifying which patients are on prophylaxis. The dashboard also gives them the opportunity, when the physicians are rounding, to discuss whether prophylaxis is appropriate.

Emory hospitalist Jason Stein, MD, SFHM, spearheaded the VTE dashboard team, which included 24 other hospitalists, nurses, pharmacists, and IT personnel. The new system was initiated during Dr. Stein’s research in HM process improvement, at the time Dr. Horowitz and others were doing parallel work in VTE prevention.

“What’s really exciting is having the troops on the ground, so to speak—having the physicians intimately involved with the patients, and the nurses really taking the lead. That’s what this study or this process improvement really illustrates,” says Dr. Horowitz, who cosponsored the research that won SHM’s 2010 Award of Excellence in Teamwork in Quality Improvement. The award was given for both the creation of the dashboard and the monumental changes the new system inspired in Emory’s approach to QI.

Sharlene Toney, RN, PhD, associate chief nursing officer for research and executive director for Emory’s professional nursing practice, says that while the dashboard concept focused on how to prepare physicians and nurses to make a difference in patience outcomes, the QI movement has spread to all parts of the Emory system.

“We actually have housekeeping staff who have had a discussion with patients about the importance of wearing [sequential compression devices],” she says. “This is really about an organizational culture. … You’re in this as a team, and this research is about the synergistic relationship of every employee. You can’t see it as hierarchal; it’s truly partnerships.”

While the dashboard is a start, Drs. Horowitz and Toney say that the positive shift happening at Emory can only be reproduced by first establishing a level of respect among and for all hospital employees, and by breaking down silos.

“I think in medicine, in order for us to provide the best and the safest care for our patients, the physicians have to start realizing they’re not captain of a ship, they’re captain of a team,” Dr. Horowitz says, “and with that comes very different behaviors.”

For more information about SHM's Awards of Excellence winners, visit our website.

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In the Literature: Research You Need to Know

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Clinical question: In the era of combination antiretroviral therapy, what is the incidence of PcP among patients with CD4 counts less than 200 cells/µL across a spectrum of virologic suppression?

Background: The incidence of Pneumocystis jiroveci pneumonia (PcP) has decreased significantly with the advent of effective combination antiretroviral therapy (cART). Guidelines have historically recommended PcP prophylaxis for HIV patients once the CD4 cell count drops below 200 cells/µL. The incidence of PcP in the era of cART, and by extension the ongoing applicability of this traditional prophylaxis guideline, is uncertain.

Study design: Prospective observational cohort.

Setting: Twelve European HIV cohorts.

Synopsis: The investigators combined data from 12 prospective HIV cohorts representing 107,016 patient-years of follow-up (PYFU), with a median of 4.7 years. There were 11,932 PYFU for those with CD4 cell counts below 200 cells/µL. Across all CD4 cell counts, 76% of PcP infections occurred among patients with viral loads >10,000 copies/mL. Among all patients with CD4 cell counts from 101 cells/µL and 200 cells/µL, there was a nonsignificant trend towards lower PcP rates with prophylaxis. For the subset of patients with CD4 cell counts from 101 cells/µL and 200 cells/µL and viral load <400 copies/mL, PcP rates were quite low and no different among those taking and not taking prophylaxis.

Bottom line: PcP is uncommon in HIV patients taking cART with CD4 cell counts from 101 cells/µL and 200 cells/µL and viral load <400 copies/mL. Discontinuing prophylaxis might be safe in these well-controlled patients.

Citation: The Opportunistic Infections Project Team of the Collaboration of Observational HIV Epidemiological Research in Europe (COHERE), Mocroft A, Reiss P, Kirk O, et al. Is it safe to discontinue primary Pneumocystis jiroveci prophylaxis in patients with virologically suppressed HIV infection and a CD4 cell count <200 cells/microL? Clin Infect Dis. 2010;51(5):611-619.

Reviewed for TH eWire by Alexis E. Shanahan, MD, Chad R. Stickrath, MD, Mel L. Anderson, MD, Section of Hospital Medicine, Denver VA Medical Center, Division of General Internal Medicine, University of Colorado

For more physician reviews of HM-related research, visit our website.

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Clinical question: In the era of combination antiretroviral therapy, what is the incidence of PcP among patients with CD4 counts less than 200 cells/µL across a spectrum of virologic suppression?

Background: The incidence of Pneumocystis jiroveci pneumonia (PcP) has decreased significantly with the advent of effective combination antiretroviral therapy (cART). Guidelines have historically recommended PcP prophylaxis for HIV patients once the CD4 cell count drops below 200 cells/µL. The incidence of PcP in the era of cART, and by extension the ongoing applicability of this traditional prophylaxis guideline, is uncertain.

Study design: Prospective observational cohort.

Setting: Twelve European HIV cohorts.

Synopsis: The investigators combined data from 12 prospective HIV cohorts representing 107,016 patient-years of follow-up (PYFU), with a median of 4.7 years. There were 11,932 PYFU for those with CD4 cell counts below 200 cells/µL. Across all CD4 cell counts, 76% of PcP infections occurred among patients with viral loads >10,000 copies/mL. Among all patients with CD4 cell counts from 101 cells/µL and 200 cells/µL, there was a nonsignificant trend towards lower PcP rates with prophylaxis. For the subset of patients with CD4 cell counts from 101 cells/µL and 200 cells/µL and viral load <400 copies/mL, PcP rates were quite low and no different among those taking and not taking prophylaxis.

Bottom line: PcP is uncommon in HIV patients taking cART with CD4 cell counts from 101 cells/µL and 200 cells/µL and viral load <400 copies/mL. Discontinuing prophylaxis might be safe in these well-controlled patients.

Citation: The Opportunistic Infections Project Team of the Collaboration of Observational HIV Epidemiological Research in Europe (COHERE), Mocroft A, Reiss P, Kirk O, et al. Is it safe to discontinue primary Pneumocystis jiroveci prophylaxis in patients with virologically suppressed HIV infection and a CD4 cell count <200 cells/microL? Clin Infect Dis. 2010;51(5):611-619.

Reviewed for TH eWire by Alexis E. Shanahan, MD, Chad R. Stickrath, MD, Mel L. Anderson, MD, Section of Hospital Medicine, Denver VA Medical Center, Division of General Internal Medicine, University of Colorado

For more physician reviews of HM-related research, visit our website.

Clinical question: In the era of combination antiretroviral therapy, what is the incidence of PcP among patients with CD4 counts less than 200 cells/µL across a spectrum of virologic suppression?

Background: The incidence of Pneumocystis jiroveci pneumonia (PcP) has decreased significantly with the advent of effective combination antiretroviral therapy (cART). Guidelines have historically recommended PcP prophylaxis for HIV patients once the CD4 cell count drops below 200 cells/µL. The incidence of PcP in the era of cART, and by extension the ongoing applicability of this traditional prophylaxis guideline, is uncertain.

Study design: Prospective observational cohort.

Setting: Twelve European HIV cohorts.

Synopsis: The investigators combined data from 12 prospective HIV cohorts representing 107,016 patient-years of follow-up (PYFU), with a median of 4.7 years. There were 11,932 PYFU for those with CD4 cell counts below 200 cells/µL. Across all CD4 cell counts, 76% of PcP infections occurred among patients with viral loads >10,000 copies/mL. Among all patients with CD4 cell counts from 101 cells/µL and 200 cells/µL, there was a nonsignificant trend towards lower PcP rates with prophylaxis. For the subset of patients with CD4 cell counts from 101 cells/µL and 200 cells/µL and viral load <400 copies/mL, PcP rates were quite low and no different among those taking and not taking prophylaxis.

Bottom line: PcP is uncommon in HIV patients taking cART with CD4 cell counts from 101 cells/µL and 200 cells/µL and viral load <400 copies/mL. Discontinuing prophylaxis might be safe in these well-controlled patients.

Citation: The Opportunistic Infections Project Team of the Collaboration of Observational HIV Epidemiological Research in Europe (COHERE), Mocroft A, Reiss P, Kirk O, et al. Is it safe to discontinue primary Pneumocystis jiroveci prophylaxis in patients with virologically suppressed HIV infection and a CD4 cell count <200 cells/microL? Clin Infect Dis. 2010;51(5):611-619.

Reviewed for TH eWire by Alexis E. Shanahan, MD, Chad R. Stickrath, MD, Mel L. Anderson, MD, Section of Hospital Medicine, Denver VA Medical Center, Division of General Internal Medicine, University of Colorado

For more physician reviews of HM-related research, visit our website.

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Gainsharing: A Bigger Piece of the Pie

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HM leaders are in a position to advocate for the potential cost savings and care efficiencies associated with gainsharing, according to a hospitalist who coauthored a study on the topic in this month’s Journal of Hospital Medicine. Gainsharing is a pay-for-performance model that tabulates the cost savings achieved via the adoption of best practices, then pays physicians bonuses with a portion of the savings.

The study found that in a three-year period ending June 2009, Beth Israel Medical Center in New York City reported a $25.1 million reduction in hospital costs, $16 million of which was attributed to physicians participating in the gainsharing program and $9.1 million from nonparticipating doctors (P<0.01) (DOI: 10.1002/jhm.788). In the same time frame, delinquent medical records dropped an average of 43% (P<0.0001).

Latha Sivaprasad, MD, FACP, FHM, medical director of quality management and patient safety and an internal-medicine attending at Beth Israel, says the data shows the viability of pay-for-performance programs.

“Gainsharing essentially aligns the incentives of physicians and hospitals to provide cost-efficient care without compromising patient safety,” says Dr. Sivaprasad. “Who better in the hospital to understand those principles than the hospitalist?”

Dr. Sivaprasad, who has been a hospitalist for eight years and is also an assistant professor at Albert Einstein College of Medicine in New York, says the majority of eligible physicians are now participating in Beth Israel’s gainsharing program, which started in 2006. She says that the validation by the Centers for Medicare & Medicaid Services (CMS)—evidenced by the Medicare demonstration project, which started in 2008—counters arguments about ethical concerns over pay for performance, as does the level of buy-in by physicians.

As it relates to HM groups, she adds, most already have some level of pay-for-performance budgeting in place.

“Pieces of it are there, even though they don’t call it gainsharing,” Dr. Sivaprasad says. “If hospitalists are incentivized for appropriate testing or streamlining throughput, pieces of this program are in place because efficient utilization of healthcare dollars is the heart of gainsharing. … Don’t excessively use precious resources you don’t need to in order to deliver quality medical care.”

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HM leaders are in a position to advocate for the potential cost savings and care efficiencies associated with gainsharing, according to a hospitalist who coauthored a study on the topic in this month’s Journal of Hospital Medicine. Gainsharing is a pay-for-performance model that tabulates the cost savings achieved via the adoption of best practices, then pays physicians bonuses with a portion of the savings.

The study found that in a three-year period ending June 2009, Beth Israel Medical Center in New York City reported a $25.1 million reduction in hospital costs, $16 million of which was attributed to physicians participating in the gainsharing program and $9.1 million from nonparticipating doctors (P<0.01) (DOI: 10.1002/jhm.788). In the same time frame, delinquent medical records dropped an average of 43% (P<0.0001).

Latha Sivaprasad, MD, FACP, FHM, medical director of quality management and patient safety and an internal-medicine attending at Beth Israel, says the data shows the viability of pay-for-performance programs.

“Gainsharing essentially aligns the incentives of physicians and hospitals to provide cost-efficient care without compromising patient safety,” says Dr. Sivaprasad. “Who better in the hospital to understand those principles than the hospitalist?”

Dr. Sivaprasad, who has been a hospitalist for eight years and is also an assistant professor at Albert Einstein College of Medicine in New York, says the majority of eligible physicians are now participating in Beth Israel’s gainsharing program, which started in 2006. She says that the validation by the Centers for Medicare & Medicaid Services (CMS)—evidenced by the Medicare demonstration project, which started in 2008—counters arguments about ethical concerns over pay for performance, as does the level of buy-in by physicians.

As it relates to HM groups, she adds, most already have some level of pay-for-performance budgeting in place.

“Pieces of it are there, even though they don’t call it gainsharing,” Dr. Sivaprasad says. “If hospitalists are incentivized for appropriate testing or streamlining throughput, pieces of this program are in place because efficient utilization of healthcare dollars is the heart of gainsharing. … Don’t excessively use precious resources you don’t need to in order to deliver quality medical care.”

HM leaders are in a position to advocate for the potential cost savings and care efficiencies associated with gainsharing, according to a hospitalist who coauthored a study on the topic in this month’s Journal of Hospital Medicine. Gainsharing is a pay-for-performance model that tabulates the cost savings achieved via the adoption of best practices, then pays physicians bonuses with a portion of the savings.

The study found that in a three-year period ending June 2009, Beth Israel Medical Center in New York City reported a $25.1 million reduction in hospital costs, $16 million of which was attributed to physicians participating in the gainsharing program and $9.1 million from nonparticipating doctors (P<0.01) (DOI: 10.1002/jhm.788). In the same time frame, delinquent medical records dropped an average of 43% (P<0.0001).

Latha Sivaprasad, MD, FACP, FHM, medical director of quality management and patient safety and an internal-medicine attending at Beth Israel, says the data shows the viability of pay-for-performance programs.

“Gainsharing essentially aligns the incentives of physicians and hospitals to provide cost-efficient care without compromising patient safety,” says Dr. Sivaprasad. “Who better in the hospital to understand those principles than the hospitalist?”

Dr. Sivaprasad, who has been a hospitalist for eight years and is also an assistant professor at Albert Einstein College of Medicine in New York, says the majority of eligible physicians are now participating in Beth Israel’s gainsharing program, which started in 2006. She says that the validation by the Centers for Medicare & Medicaid Services (CMS)—evidenced by the Medicare demonstration project, which started in 2008—counters arguments about ethical concerns over pay for performance, as does the level of buy-in by physicians.

As it relates to HM groups, she adds, most already have some level of pay-for-performance budgeting in place.

“Pieces of it are there, even though they don’t call it gainsharing,” Dr. Sivaprasad says. “If hospitalists are incentivized for appropriate testing or streamlining throughput, pieces of this program are in place because efficient utilization of healthcare dollars is the heart of gainsharing. … Don’t excessively use precious resources you don’t need to in order to deliver quality medical care.”

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Sobering News on Quality Front

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Last month, the Office of Inspector General (OIG) issued a report (PDF) that estimates 15,000 Medicare patient deaths each month are attributable at least in part to the care they received in hospitals.

The federal watchdog agency tallied adverse events from the National Quality Forum’s list of serious reportable events and other hospital-acquired conditions in the charts of 780 Medicare patients from 2008, then extrapolated the proportions harmed through hospital care (13.5%) or who die as a result of that care (1.5%).

“Because many adverse events we identified were preventable, our study confirms the need and opportunity for hospitals to significantly reduce the incidence of events,” the report concludes. It recommends that the Agency for Healthcare Research and Quality (AHRQ) broadens patient-safety efforts and that the Centers for Medicaid & Medicare Services (CMS) provides further incentives for hospitals to reduce their incidences through its payment and oversight functions.

Confirmation of hospital safety concerns comes from a study published in the New England Journal of Medicine (2010;2363:2124-2134) that found harm to patients in North Carolina hospitals was common and did not decrease from 2002 to 2007.

Christopher Landrigan, MD, of Harvard Medical School and coauthors concluded that 18% of hospitalized patients were harmed through their medical care and, for 2.4%, it caused or contributed to their deaths.

The results of the OIG study are not surprising and might even underestimate the extent of the problem, says Gregory Seymann, MD, a hospitalist at the University of California at San Diego and a member of the Society of Hospital Medicine’s Performance and Standards Committee. The report doesn’t address what proportion of the harmed patients was on a service managed by hospitalists, “but we are in the best position to impact quality and safety—to go to our hospital administrators and get resources earmarked for quality,” he says.

Such results also mirror findings from the Institute of Medicine’s landmark 1999 report To Err is Human, adds Andrew Dunn, MD, a hospitalist at Mount Sinai Medical Center in New York City. “They suggest that medical errors are rampant in hospitals,” he says. “Because the incidence of harm is so broad across the elderly population, quality-improvement efforts in hospitals need to be across the board.”

Every hospitalist should be involved with these efforts, Dr. Dunn says. “There’s no putting your feet up. There’s always room to improve quality,” he adds. He predicts that safety outcomes will increasingly be tied to hospital reimbursement, “which is a good thing. It’s very motivational.”

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Last month, the Office of Inspector General (OIG) issued a report (PDF) that estimates 15,000 Medicare patient deaths each month are attributable at least in part to the care they received in hospitals.

The federal watchdog agency tallied adverse events from the National Quality Forum’s list of serious reportable events and other hospital-acquired conditions in the charts of 780 Medicare patients from 2008, then extrapolated the proportions harmed through hospital care (13.5%) or who die as a result of that care (1.5%).

“Because many adverse events we identified were preventable, our study confirms the need and opportunity for hospitals to significantly reduce the incidence of events,” the report concludes. It recommends that the Agency for Healthcare Research and Quality (AHRQ) broadens patient-safety efforts and that the Centers for Medicaid & Medicare Services (CMS) provides further incentives for hospitals to reduce their incidences through its payment and oversight functions.

Confirmation of hospital safety concerns comes from a study published in the New England Journal of Medicine (2010;2363:2124-2134) that found harm to patients in North Carolina hospitals was common and did not decrease from 2002 to 2007.

Christopher Landrigan, MD, of Harvard Medical School and coauthors concluded that 18% of hospitalized patients were harmed through their medical care and, for 2.4%, it caused or contributed to their deaths.

The results of the OIG study are not surprising and might even underestimate the extent of the problem, says Gregory Seymann, MD, a hospitalist at the University of California at San Diego and a member of the Society of Hospital Medicine’s Performance and Standards Committee. The report doesn’t address what proportion of the harmed patients was on a service managed by hospitalists, “but we are in the best position to impact quality and safety—to go to our hospital administrators and get resources earmarked for quality,” he says.

Such results also mirror findings from the Institute of Medicine’s landmark 1999 report To Err is Human, adds Andrew Dunn, MD, a hospitalist at Mount Sinai Medical Center in New York City. “They suggest that medical errors are rampant in hospitals,” he says. “Because the incidence of harm is so broad across the elderly population, quality-improvement efforts in hospitals need to be across the board.”

Every hospitalist should be involved with these efforts, Dr. Dunn says. “There’s no putting your feet up. There’s always room to improve quality,” he adds. He predicts that safety outcomes will increasingly be tied to hospital reimbursement, “which is a good thing. It’s very motivational.”

Last month, the Office of Inspector General (OIG) issued a report (PDF) that estimates 15,000 Medicare patient deaths each month are attributable at least in part to the care they received in hospitals.

The federal watchdog agency tallied adverse events from the National Quality Forum’s list of serious reportable events and other hospital-acquired conditions in the charts of 780 Medicare patients from 2008, then extrapolated the proportions harmed through hospital care (13.5%) or who die as a result of that care (1.5%).

“Because many adverse events we identified were preventable, our study confirms the need and opportunity for hospitals to significantly reduce the incidence of events,” the report concludes. It recommends that the Agency for Healthcare Research and Quality (AHRQ) broadens patient-safety efforts and that the Centers for Medicaid & Medicare Services (CMS) provides further incentives for hospitals to reduce their incidences through its payment and oversight functions.

Confirmation of hospital safety concerns comes from a study published in the New England Journal of Medicine (2010;2363:2124-2134) that found harm to patients in North Carolina hospitals was common and did not decrease from 2002 to 2007.

Christopher Landrigan, MD, of Harvard Medical School and coauthors concluded that 18% of hospitalized patients were harmed through their medical care and, for 2.4%, it caused or contributed to their deaths.

The results of the OIG study are not surprising and might even underestimate the extent of the problem, says Gregory Seymann, MD, a hospitalist at the University of California at San Diego and a member of the Society of Hospital Medicine’s Performance and Standards Committee. The report doesn’t address what proportion of the harmed patients was on a service managed by hospitalists, “but we are in the best position to impact quality and safety—to go to our hospital administrators and get resources earmarked for quality,” he says.

Such results also mirror findings from the Institute of Medicine’s landmark 1999 report To Err is Human, adds Andrew Dunn, MD, a hospitalist at Mount Sinai Medical Center in New York City. “They suggest that medical errors are rampant in hospitals,” he says. “Because the incidence of harm is so broad across the elderly population, quality-improvement efforts in hospitals need to be across the board.”

Every hospitalist should be involved with these efforts, Dr. Dunn says. “There’s no putting your feet up. There’s always room to improve quality,” he adds. He predicts that safety outcomes will increasingly be tied to hospital reimbursement, “which is a good thing. It’s very motivational.”

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Intra‐Hospital Transfer to a Higher Level of Care

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Intra‐hospital transfers to a higher level of care: Contribution to total hospital and intensive care unit (ICU) mortality and length of stay (LOS)

Considerable research and public attention is being paid to the quantification, risk adjustment, and reporting of inpatient mortality.15 Inpatient mortality is reported as aggregate mortality (for all hospitalized patients or those with a specific diagnosis3, 6) or intensive care unit (ICU) mortality.7, 8 While reporting aggregate hospital or aggregate ICU mortality rates is useful, it is also important to develop reporting strategies that go beyond simply using data elements found in administrative databases (eg, diagnosis and procedure codes) to quantify practice variation. Ideally, such strategies would permit delineating processes of careparticularly those potentially under the control of hospitalists, not only intensiviststo identify improvement opportunities. One such process, which can be tracked using the bed history component of a patient's electronic medical record, is the transfer of patients between different units within the same hospital.

Several studies have documented that risk of ICU death is highest among patients transferred from general medical‐surgical wards, intermediate among direct admissions from the emergency department, and lowest among surgical admissions.911 Opportunities to reduce subsequent ICU mortality have been studied among ward patients who develop sepsis and are then transferred to the ICU,12 among patients who experience cardiac arrest,13, 14 as well as among patients with any physiological deterioration (eg, through the use of rapid response teams).1517 Most of these studies have been single‐center studies and/or studies reporting only an ICU denominator. While useful in some respects, such studies are less helpful to hospitalists, who would benefit from better understanding of the types of patients transferred and the total impact that transfers to a higher level of care make on general medical‐surgical wards. In addition, entities such as the Institute for Healthcare Improvement recommend the manual review of records of patients who were transferred from the ward to the ICU18 to identify performance improvement opportunities. While laudable, such approaches do not lend themselves to automated reporting strategies.

We recently described a new risk adjustment methodology for inpatient mortality based entirely on automated data preceding hospital admission and not restricted to ICU patients. This methodology, which has been externally validated in Ottawa, Canada, after development in the Kaiser Permanente Medical Care Program (KPMCP), permits quantification of a patient's pre‐existing comorbidity burden, physiologic derangement at the time of admission, and overall inpatient mortality risk.19, 20 The primary purpose of this study was to combine this methodology with bed history analysis to quantify the in‐hospital mortality and length of stay (LOS) of patients who experienced intra‐hospital transfers in a large, multihospital system. As a secondary goal, we also wanted to assess the degree to which these transfers could be predicted based on information available prior to a patient's admission.

ABBREVIATIONS AND TERMS USED IN TEXT

COPS: COmorbidity Point Score. Point score based on a patient's health care utilization diagnoses (during the year preceding admission to the hospital. Analogous to POA (present on admission) coding. Scores can range from 0 to a theoretical maximum of 701 but scores >200 are rare. With respect to a patient's pre‐existing comorbidity burden, the unadjusted relationship of COPS and inpatient mortality is as follows: a COPS <50 is associated with a mortality risk of <1%, <100 with a mortality risk of <5%, 100 to 145 with a mortality risk of 5% to 10%, and >145 with a mortality risk of 10% or more.

ICU: Intensive Care Unit. In this study, all ICUs have a minimum registered nurse to patient ratio of 1:2.

LAPS: Laboratory Acute Physiology Score. Point score based on 14 laboratory test results obtained in the 72 hours preceding hospitalization. With respect to a patient's physiologic derangement, the unadjusted relationship of LAPS and inpatient mortality is as follows: a LAPS <7 is associated with a mortality risk of <1%, <7 to 30 with a mortality risk of <5%, 30 to 60 with a mortality risk of 5% to 9%, and >60 with a mortality risk of 10% or more.

LOS: Exact hospital Length Of Stay. LOS is calculated from admission until first discharge home (i.e., it may span more than one hospital stay if a patient experienced inter‐hospital transport).

Predicted (expected) mortality risk: the % risk of death for a given patient based on his/her age, sex, admission diagnosis, COPS, and LAPS.

OEMR: Observed to Expected Mortality Ratio. For a given patient subset, the ratio of the actual mortality experienced by the subset to the expected (predicted) mortality for the subset. Predicted mortality is based on patients' age, sex, admission diagnosis, COPS, and LAPS.

OMELOS: Observed Minus Expected LOS. For a given patient subset, the difference between the actual number of hospital days experienced by the subset and the expected (predicted) number of hospital days for the subset. Predicted LOS is based on patients' age, sex, admission diagnosis, COPS, and LAPS.

TCU: Transitional Care Unit (also called intermediate care unit or stepdown unit). In this study, TCUs have variable nurse to patient ratios ranging from 1:2.5 to 1:3 and did not provide assisted ventilation, continuous pressor infusions, or invasive monitoring.

Materials and Methods

This project was approved by the Northern California KPMCP Institutional Review Board for the Protection of Human Subjects.

The Northern California KPMCP serves a total population of approximately 3.3 million members. Under a mutual exclusivity arrangement, physicians of The Permanente Medical Group, Inc., care for Kaiser Foundation Health Plan, Inc. members at facilities owned by Kaiser Foundation Hospitals, Inc. All Northern California KPMCP hospitals and clinics employ the same information systems with a common medical record number and can track care covered by the plan but delivered elsewhere. Databases maintained by the KPMCP capture admission and discharge times, admission and discharge diagnoses and procedures (assigned by professional coders), bed histories, inter‐hospital transfers, as well as the results of all inpatient and outpatient laboratory tests. The use of these databases for research has been described in multiple reports.2124

Our setting consisted of all 19 hospitals owned and operated by the KPMCP, whose characteristics are summarized in the Supporting Information Appendix available to interested readers. These include the 17 described in our previous report19 as well as 2 new hospitals (Antioch and Manteca) which are similar in size and type of population served. Our study population consisted of all patients admitted to these 19 hospitals who met these criteria: 1) hospitalization began from November 1st, 2006 through January 31st, 2008; 2) initial hospitalization occurred at a Northern California KPMCP hospital (ie, for inter‐hospital transfers, the first hospital stay occurred within the KPMCP); 3) age 15 years; and 4) hospitalization was not for childbirth.

We defined a linked hospitalization as the time period that began with a patient's admission to the hospital and ended with the patient's discharge (home, to a nursing home, or death). Linked hospitalizations can thus involve more than 1 hospital stay and could include a patient transfer from one hospital to another prior to definitive discharge. For linked hospitalizations, mortality was attributed to the admitting KPMCP hospital (ie, if a patient was admitted to hospital A, transferred to B, and died at hospital B, mortality was attributed to hospital A). We defined total LOS as the exact time in hours from when a patient was first admitted to the hospital until death or final discharge home or to a nursing home, while total ICU or transitional care unit (TCU, referred to as stepdown unit in some hospitals) LOS was calculated for all individual ICU or TCU stays during the hospital stay.

Intra‐Hospital Transfers

We grouped all possible hospital units into four types: general medical‐surgical ward (henceforth, ward); operating room (OR)/post‐anesthesia recovery (PAR); TCU; and ICU. In 2003, the KPMCP implemented a mandatory minimum staffing ratio of one registered nurse for every four patients in all its hospital units; in addition, staffing levels for designated ICUs adhered to the previously mandated minimum of one nurse for every 2 patients. So long as they adhere to these minimum ratios, individual hospitals have considerable autonomy with respect to how they staff or designate individual hospital units. Registered nurse‐to‐patient ratios during the time of this study were as follows: ward patients, 1:3.5 to 1:4; TCU patients, 1:2.5 to 1:3; and ICU patients, 1:1 to 1:2. Staffing ratios for the OR and PAR are more variable, depending on the surgical procedures involved. Current KPMCP databases do not permit accurate quantification of physician staffing. All 19 study hospitals had designated ICUs, 6 were teaching hospitals, and 11 had designated TCUs. None of the study hospitals had closed ICUs (units where only intensivists admit patients) and none had continuous coverage of the ICU by intensivists. While we were not able to employ electronic data to determine who made the decision to transfer, we did find considerable variation with respect to how intensivists covered the ICUs and how they interfaced with hospitalists. Staffing levels for specialized coronary care units and non‐ICU monitored beds were not standardized. All study hospitals had rapid response teams as well as code blue teams during the time period covered by this report. Respiratory care practitioners were available to patients in all hospital units, but considerable variation existed with respect to other services available (eg, cardiac catheterization units, provision of noninvasive positive pressure ventilation outside the ICU, etc.).

This report focuses on intra‐hospital transfers to the ICU and TCU, with special emphasis on nonsurgical transfers (due to space limitations, we are not reporting on the outcomes of patients whose first hospital unit was the OR; additional details on these patients are provided in the Supporting Information Appendix). For the purposes of this report, we defined the following admission types: direct admits (patients admitted to the ICU or TCU whose first hospital unit on admission was the ICU or TCU); and nonsurgical transfers to a higher level of care. These latter transfers could be of 3 types: ward to ICU, ward to TCU, and TCU to ICU. We also quantified the effect of inter‐hospital transfers.

Independent Variables

In addition to patients' age and sex, we employed the following independent variables to predict transfer to a higher level of care. These variables are part of the risk adjustment model described in greater detail in our previous report19 and were available electronically for all patients in the cohort. We grouped admission diagnoses into 44 broad diagnostic categories (Primary Conditions), and admission types into 4 groups (emergency medical, emergency surgical, elective medical, and elective surgical). We quantified patients' degree of physiologic derangement using a Laboratory‐based Acute Physiology Score (LAPS) using laboratory test results prior to hospitalization. We quantified patients' comorbid illness burden using a Comorbidity Point Score (COPS) based on patients' pre‐existing diagnoses over the 12‐month period preceding hospitalization. Lastly, we assigned each patient a predicted mortality risk (%) and LOS based on the above predictors,19 permitting calculation of observed to expected mortality ratios (OEMRs) and observed minus expected LOS (OMELOS).

Statistical Methods

All analyses were performed in SAS.25 We calculated standard descriptive statistics (medians, means, standard deviations) and compared different patient groupings using t and chi‐square tests. We employed a similar approach to that reported by Render et al.7 to calculate OEMR and OMELOS.

To determine the degree to which transfers to a higher level of care from the ward or TCU would be predictable using information available at the time of admission, we performed 4 sets of logistic regression analyses using the above‐mentioned predictors in which the outcome variables were as follows: 1) transfer occurring in the first 48 hours after admission (time frame by which point approximately half of the transferred patients experienced a transfer) among ward or TCU patients and 2) transfer occurring after 48 hours among ward or TCU patients. We evaluated the discrimination and calibration of these models using the same methods described in our original report (measuring the area under the receiver operator characteristic curve, or c statistic, and visually examining observed and expected mortality rates among predicted risk bands as well as risk deciles) as well as additional statistical tests recommended by Cook.19, 26

Results

During the study period, a total of 249,129 individual hospital stays involving 170,151 patients occurred at these 19 hospitals. After concatenation of inter‐hospital transfers, we were left with 237,208 linked hospitalizations. We excluded 26,738 linked hospitalizations that began at a non‐KPMCP hospital (ie, they were transported in), leaving a total of 210,470 linked hospitalizations involving 150,495 patients. The overall linked hospitalization mortality rate was 3.30%.

Table 1 summarizes cohort characteristics based on initial hospital location. On admission, ICU patients had the highest degree of physiologic derangement as well as the highest predicted mortality. Considerable inter‐hospital variation was present in both predictors and outcomes; details on these variations are provided in the Supporting Information Appendix.

Characteristics of Study Cohort Based on Patients' Admission Hospital Unit
 WardTCUICUAll*
  • NOTE: See text for description of unit characteristics and staffing.

  • Abbreviations: COPS, Comorbidity Point Score; ICU, Intensive Care Unit; LAPS, Laboratory Acute Physiology Score; LOS, length of stay; SD, standard deviation; TCU, Transitional Care Unit.

  • Number includes 52,676 excluded surgical patients described in the Supporting Information Appendix.

  • See Supporting Information Appendix for details on inter‐hospital variation.

  • Numbers in parentheses are 95% confidence intervals. Total ratio for cohort is <1.0 because risk adjustment is based on an earlier calibration dataset (the 2002‐2005 Kaiser Permanente hospital cohort described in citation 19).

n121,23720,55616,001210,470
Admitted via emergency department, n (%)99,909 (82.4)18,612 (90.5)13,847 (86.5)139,036 (66.1)
% range across hospitals55.0‐94.264.7‐97.649.5‐97.453.6‐76.9
Male, n (%)53,744 (44.3)10,362 (50.4)8,378 (52.4)94,451 (44.9)
Age in years (mean SD)64.5 19.269.0 15.663.7 17.863.2 18.6
LAPS (mean SD)19.2 18.023.3 19.531.7 25.716.7 19.0
COPS (mean SD)90.4 64.099.2 65.994.5 67.584.7 61.8
% predicted mortality (mean SD)4.0 7.14.6 7.38.7 12.83.6 7.3
Observed in‐hospital deaths (n, %)3,793 (3.1)907 (4.4)1,995 (12.5)6,952 (3.3)
Observed to expected mortality ratio0.79 (0.77‐0.82)0.95 (0.89‐1.02)1.43 (1.36‐1.49)0.92 (0.89‐0.94)
Total hospital LOS, days (mean SD)4.6 7.55.3 10.07.8 14.04.6 8.1

Table 2 summarizes data from 3 groups of patients: patients initially admitted to the ward, or TCU, who did not experience a transfer to a higher level of care and patients admitted to these 2 units who did experience such a transfer. Patients who experienced a transfer constituted 5.3% (6,484/121,237) of ward patients and 6.7% (1,384/20,556) of TCU patients. Transferred patients tended to be older, have more acute physiologic derangement (higher LAPS), a greater pre‐existing illness burden (higher COPS), and a higher predicted mortality risk. Among ward patients, those with the following admission diagnoses were most likely to experience a transfer to a higher level of care: gastrointestinal bleeding (10.8% of all transfers), pneumonia (8.7%), and other infections (8.2%). The diagnoses most likely to be associated with death following transfer were cancer (death rate among transferred patients, 48%), renal disease (death rate, 36%), and liver disease (33%). Similar distributions were observed for TCU patients.

Characteristics of Ward and Transitional Care Unit (TCU) Patients Who Did and Did Not Experience Transfer to a Higher Level of Care
 Patients Initially Admitted to Ward, Remained TherePatients Initially Admitted to TCU, Remained TherePatients Transferred to Higher Level of CareAll
  • Abbreviations: COPS, Comorbidity Point Score; GI, Gastrointestinal; LAPS, Laboratory Acute Physiology Score; SD, Standard Deviation.

n114,75319,1727,868141,793
Male, n (%)50,586 (44.1)9,626 (50.2)3,894 (49.5)64,106 (45.2)
Age (mean SD)64.3 19.469.0 15.768.1 16.165.2 18.8
LAPS (mean SD)18.9 17.822.7 19.126.7 21.019.8 18.3
COPS (mean SD)89.4 63.798.3 65.5107.9 67.691.7 64.4
% predicted mortality risk (mean SD)3.8 7.04.4 7.06.5 8.84.1 7.1
Admission diagnosis of pneumonia, n (%)5,624 (4.9)865 (4.5)684 (8.7)7,173 (5.1)
Admission diagnosis of sepsis, n (%)1,181 (1.0)227 (1.2)168 (2.1)1,576 (1.1)
Admission diagnosis of GI bleed, n (%)13,615 (11.9)1,448 (7.6)851 (10.8)15,914 (11.2)
Admission diagnosis of cancer, n (%)2,406 (2.1)80 (0.4)186 (2.4)2,672 (1.9)

Table 3 compares outcomes among ward and TCU patients who did and did not experience a transfer to a higher level of care. The table shows that transferred patients were almost 3 times as likely to die, even after controlling for severity of illness, and that their hospital LOS was 9 days higher than expected. This increased risk was seen in all hospitals and among all transfer types (ward to ICU, ward to TCU, and TCU to ICU).

Outcomes of Ward and Transitional Care Unit (TCU) Patients Who Did and Did Not Experience Transfer to a Higher Level of Care
 Patients Initially Admitted to Ward, Remained TherePatients Initially Admitted to TCU, Remained TherePatients Transferred to Higher Level of Care
  • Abbreviations: CI, confidence interval; ICU, intensive care unit; SD, standard deviation.

n114,75319,1727,868
Admitted to ICU, n (%)0 (0.0)0 (0.0)5,245 (66.7)
Ventilated, n (%)0 (0.0)0 (0.0)1,346 (17.1)
Died in the hospital, n (%)2,619 (2.3)572 (3.0)1,509 (19.2)
Length of stay, in days, at time of death (mean SD)7.0 11.98.3 12.416.2 23.7
Observed to expected mortality ratio (95% CI)0.60 (0.57‐0.62)0.68 (0.63‐0.74)2.93 (2.79‐3.09)
Total hospital length of stay, days (mean SD)4.0 5.74.4 6.914.3 21.3
Observed minus expected length of stay (95% CI)0.4 (0.3‐0.4)0.8 (0.7‐0.9)9.1 (8.6‐9.5)
Length of stay, in hours, at time of transfer (mean SD)  80.8 167.2

Table 3 also shows that, among decedent patients, those who never left the ward or TCU died much sooner than those who died following transfer. Among direct admits to the ICU, the median LOS at time of death was 3.9 days, with a mean of 9.4 standard deviation of 19.9 days, while the corresponding times for TCU direct admits were a median and mean LOS of 6.5 and 11.7 19.5 days.

Table 4 summarizes outcomes among different patient subgroups that did and did not experience a transfer to a higher level of care. Based on location, patients who experienced a transfer from the TCU to the ICU had the highest crude death rate, but patients transferred from the ward to the ICU had the highest OEMR. On the other hand, if one divides patients by the degree of physiologic derangement, patients with low LAPS who experienced a transfer had the highest OEMR. With respect to LOS, patients transferred from the TCU to the ICU had the highest OMELOS (13.4 extra days).

Death Rates and Hospital Length of Stay Among Ward and Transitional Care Unit (TCU) Patients
 n (%)*Death Rate (%)OEMRLOS (mean SD)OMELOS
  • Abbreviations: COPS, COmorbidity Point Score; ICU, intensive care unit; LAPS, Laboratory Acute Physiology Score; LOS, length of stay; OEMR, Observed to expected mortality ratio; OMELOS, Observed minus expected length of stay; SD, standard deviation.

  • Percentage refers to % among all hospital admissions.

  • Numbers in parentheses are the 95% confidence intervals.

  • Numbers in parentheses are the 95% confidence intervals.

Never admitted to TCU or ICU157,632 (74.9)1.60.55 (0.53‐0.57)3.6 4.60.04 (0.02‐0.07)
Direct admit to TCU18,464 (8.8)2.90.66 (0.61‐0.72)4.2 5.80.60 (0.52‐0.68)
Direct admit to ICU14,655 (7.0)11.91.38 (1.32‐1.45)6.4 9.42.28 (2.14‐2.43)
Transferred from ward to ICU5,145 (2.4)21.53.23 (3.04‐3.42)15.7 21.610.33 (9.70‐10.96)
Transferred from ward to TCU3,144 (1.5)11.91.99 (1.79‐2.20)13.6 23.28.02 (7.23‐8.82)
Transferred from TCU to ICU1,107 (0.5)25.72.94 (2.61‐3.31)18.0 28.213.35 (11.49‐15.21)
Admitted to ward, COPS 80, no transfer to ICU or TCU55,405 (26.3)3.40.59 (0.56‐0.62)4.5 5.90.29 (0.24‐0.34)
Admitted to ward, COPS 80, did experience transfer to ICU or TCU4,851 (2.3)19.32.72 (2.55‐2.90)14.2 20.08.14 (7.56‐8.71)
Admitted to ward, COPS <80, no transfer to ICU or TCU57,421 (27.3)1.10.55 (0.51‐0.59)3.4 4.20.23 (0.19‐0.26)
Admitted to ward, COPS <80, did experience transfer to ICU or TCU3,560 (1.7)9.82.93 (2.63‐3.26)12.0 19.07.52 (6.89‐8.15)
Admitted to ward, LAPS 20, no transfer to ICU or TCU46,492 (22.1)4.20.59 (0.56‐0.61)4.6 5.40.16 (0.12‐0.21)
Admitted to ward, LAPS 20, did experience transfer to ICU or TCU4,070 (1.9)21.42.37 (2.22‐2.54)14.8 21.08.76 (8.06‐9.47)
Admitted to ward, LAPS <20, no transfer to ICU or TCU66,334 (31.5)0.90.55 (0.51‐0.60)3.5 4.90.32 (0.28‐0.36)
Admitted to ward, LAPS <20, did experience transfer to ICU or TCU4,341 (2.1)9.54.31 (3.90‐4.74)11.8 18.17.12 (6.61‐7.64)

Transfers to a higher level of care at a different hospital, which in the KPMCP are usually planned, experienced lower mortality than transfers within the same hospital. For ward to TCU transfers, intra‐hospital transfers had a mortality of 12.1% while inter‐hospital transfers had a mortality of 5.7%. Corresponding rates for ward to ICU transfers were 21.7% and 11.2%, and for TCU to ICU transfers the rates were 25.9% and 12.5%, respectively.

Among patients initially admitted to the ward, a model to predict the occurrence of a transfer to a higher level of care (within 48 hours after admission) that included age, sex, admission type, primary condition, LAPS, COPS, and interaction terms had poor discrimination, with an area under the receiver operator characteristic (c statistic) of only 0.64. The c statistic for a model to predict transfer after 48 hours was 0.66. The corresponding models for TCU admits had c statistics of 0.67 and 0.68. All four models had poor calibration.

Discussion

Using automated bed history data permits characterizing a patient population with disproportionate mortality and LOS: intra‐hospital transfers to special care units (ICUs or TCUs). Indeed, the largest subset of these patients (those initially admitted to the ward or TCU) constituted only 3.7% of all admissions, but accounted for 24.2% of all ICU admissions, 21.7% of all hospital deaths, and 13.2% of all hospital days. These patients also had very elevated OEMRs and OMELOS. Models based on age, sex, preadmission laboratory test results, and comorbidities did not predict the occurrence of these transfers.

We performed multivariate analyses to explore the degree to which electronically assigned preadmission severity scores could predict these transfers. These analyses found that, compared to our ability to predict inpatient or 30‐day mortality at the time of admission, which is excellent, our ability to predict the occurrence of transfer after admission is much more limited. These results highlight the limitations of severity scores that rely on automated data, which may not have adequate discrimination when it comes to determining the risk of an adverse outcome within a narrow time frame. For example, among the 121,237 patients initially admitted to the ward who did not experience an intra‐hospital transfer, the mean LAPS was 18.9, while the mean LAPS among the 6,484 ward patients who did experience a transfer was 25.5. Differences between the mean and median LAPS, COPS, and predicted mortality risk among transferred and non‐transferred patients were significant (P < 0.0001 for all comparisons). However, examination of the distribution of LAPS, COPS, and predicted mortality risk between these two groups of patients showed considerable overlap.

Our methodology resembles Silber et al.'s27, 28 concept of failure to rescue in that it focuses on events occurring after hospitalization. Silber et al. argue that a hospital's quality can be measured by quantifying the degree to which patients who experience new problems are successfully rescued. Furthermore, quantification of those situations where rescue attempts are unsuccessful is felt to be superior to simply comparing raw or adjusted mortality rates because these are primarily determined by underlying case mix. The primary difference between Silber et al.'s approach and ours is at the level of detailthey specified a specific set of complications, whereas our measure is more generic and would include patients with many of the complications specified by Silber et al.27, 28

Most of the patients transferred to a higher level of care in our cohort survived (ie, were rescued), indicating that intensive care is beneficial. However, the fact that these patients had elevated OEMRs and OMELOS indicates that the real challenge facing hospitalists involves the timing of provision of a beneficial intervention. In theory, improved timing could result from earlier detection of problems, which is the underlying rationale for employing rapid response teams. However, the fact that our electronic tools (LAPS, COPS) cannot predict patient deteriorations within a narrow time frame suggests that early detection will remain a major challenge. Manually assigned vital signs scores designed for this purpose do not have good discrimination either.29, 30 This raises the possibility that, though patient groups may differ in terms of overall illness severity and mortality risk, differences at the individual patient level may be too subtle for clinicians to detect. Future research may thus need to focus on scores that combine laboratory data, vital signs, trends in data,31, 32 and newer proteomic markers (eg, procalcitonin).33 We also found that most transfers occurred early (within <72 hours), raising the possibility that at least some of these transfers may involve issues around triage rather than sudden deterioration.

Our study has important limitations. Due to resource constraints and limited data availability, we could not characterize the patients as well as might be desirable; in particular, we could not make full determinations of the actual reasons for patients' transfer for all patients. Broadly speaking, transfer to a higher level of care could be due to inappropriate triage, appropriate (preventive) transfer (which could include transfer to a more richly staffed unit for a specific procedure), relentless progression of disease despite maximal therapy, the occurrence of management errors, patient and family uncertainty about goals of care or inadequate understanding of treatment options and prognoses, or a combination of these factors. We could not make these distinctions with currently available electronic data. This is also true of postsurgical patients, in whom it is difficult to determine which transfers to intensive care might be planned (eg, in the case of surgical procedures where ICU care is anticipated) as opposed to the occurrence of a deterioration during or following surgery. Another major limitation of this study is our inability to identify code or no code status electronically. The elapsed LOS at time of death among patients who experienced a transfer to a higher level of care (as compared to patients who died in the ward without ever experiencing intra‐hospital transfer) suggests, but does not prove, that prolonged efforts were being made to keep them alive. We were also limited in terms of having access to other process data (eg, physician staffing levels, provision and timing of palliative care). Having ICU severity of illness scores would have permitted us to compare our cohort to those of other recent studies showing elevated mortality rates among transfer patients,911 but we have not yet developed that capability.

Consideration of our study findings suggests a possible research agenda that could be implemented by hospitalist researchers. This agenda should emphasize three areas: detection, intervention, and reflection.

With respect to detection, attention needs to be paid to better tools for quantifying patient risk at the time a decision to admit to the ward is made. It is likely that such tools will need to combine the attributes of our severity score (LAPS) with those of the manually assigned scores.30, 34 In some cases, use of these tools could lead a physician to change the locus of admission from the ward to the TCU or ICU, which could improve outcomes by ensuring more timely provision of intensive care. Since problems with initial triage could be due to factors other than the failure to suspect or anticipate impending instability, future research should also include a cognitive component (eg, quantifying what proportion of subsequent patient deteriorations could be ascribed to missed diagnoses35). Additional work also needs to be done on developing mathematical models that can inform electronic monitoring of ward (not just ICU) patients.

Research on interventions that hospitalists can use to prevent the need for intensive care or to improve the rescue rate should take two routes. The first is a disease‐specific route, which builds on the fact that a relatively small set of conditions (pneumonia, sepsis, gastrointestinal bleeding) account for most transfers to a higher level of care. Condition‐specific protocols, checklists, and bundles36 tailored to a ward environment (as opposed to the ICU or to the entire hospital) might prevent deteriorations in these patients, as has been reported for sepsis.37 The second route is to improve the overall capabilities of rapid response and code blue teams. Such research would need to include a more careful assessment of what commonalities exist among patients who were and were not successfully rescued by these teams. This approach would probably yield more insights than the current literature, which focuses on whether rapid response teams are a good thing or not.

Finally, research also needs to be performed on how hospitalists reflect on adverse outcomes among ward patients. Greater emphasis needs to be placed on moving beyond trigger tool approaches that rely on manual chart review. In an era of expanding use of electronic medical record systems, more work needs to be done on how to harness these to provide hospitalists with better quantitative and risk‐adjusted information. This information should not be limited to simply reporting rates of transfers and deaths. Rather, finer distinctions must be provided with respect of the type of patients (ie, more diagnostic detail), the clinical status of patients (ie, more physiologic detail), as well as the effects of including or excluding patients in whom therapeutic options may be limited (ie, do not resuscitate and comfort care patients) on reported rates. Ideally, researchers should develop better process and outcomes measures that could be tested in collaborative networks that include multiple nonacademic general medical‐surgical wards.

Acknowledgements

The authors thank Drs. Paul Feigenbaum, Alan Whippy, Joseph V. Selby, and Philip Madvig for reviewing the manuscript and Ms. Jennifer Calhoun for formatting the manuscript.

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References
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Journal of Hospital Medicine - 6(2)
Page Number
74-80
Legacy Keywords
failure to rescue, hospital mortality, intensive care unit, intra‐hospital transfer, patient outcomes, transitional care unit
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Considerable research and public attention is being paid to the quantification, risk adjustment, and reporting of inpatient mortality.15 Inpatient mortality is reported as aggregate mortality (for all hospitalized patients or those with a specific diagnosis3, 6) or intensive care unit (ICU) mortality.7, 8 While reporting aggregate hospital or aggregate ICU mortality rates is useful, it is also important to develop reporting strategies that go beyond simply using data elements found in administrative databases (eg, diagnosis and procedure codes) to quantify practice variation. Ideally, such strategies would permit delineating processes of careparticularly those potentially under the control of hospitalists, not only intensiviststo identify improvement opportunities. One such process, which can be tracked using the bed history component of a patient's electronic medical record, is the transfer of patients between different units within the same hospital.

Several studies have documented that risk of ICU death is highest among patients transferred from general medical‐surgical wards, intermediate among direct admissions from the emergency department, and lowest among surgical admissions.911 Opportunities to reduce subsequent ICU mortality have been studied among ward patients who develop sepsis and are then transferred to the ICU,12 among patients who experience cardiac arrest,13, 14 as well as among patients with any physiological deterioration (eg, through the use of rapid response teams).1517 Most of these studies have been single‐center studies and/or studies reporting only an ICU denominator. While useful in some respects, such studies are less helpful to hospitalists, who would benefit from better understanding of the types of patients transferred and the total impact that transfers to a higher level of care make on general medical‐surgical wards. In addition, entities such as the Institute for Healthcare Improvement recommend the manual review of records of patients who were transferred from the ward to the ICU18 to identify performance improvement opportunities. While laudable, such approaches do not lend themselves to automated reporting strategies.

We recently described a new risk adjustment methodology for inpatient mortality based entirely on automated data preceding hospital admission and not restricted to ICU patients. This methodology, which has been externally validated in Ottawa, Canada, after development in the Kaiser Permanente Medical Care Program (KPMCP), permits quantification of a patient's pre‐existing comorbidity burden, physiologic derangement at the time of admission, and overall inpatient mortality risk.19, 20 The primary purpose of this study was to combine this methodology with bed history analysis to quantify the in‐hospital mortality and length of stay (LOS) of patients who experienced intra‐hospital transfers in a large, multihospital system. As a secondary goal, we also wanted to assess the degree to which these transfers could be predicted based on information available prior to a patient's admission.

ABBREVIATIONS AND TERMS USED IN TEXT

COPS: COmorbidity Point Score. Point score based on a patient's health care utilization diagnoses (during the year preceding admission to the hospital. Analogous to POA (present on admission) coding. Scores can range from 0 to a theoretical maximum of 701 but scores >200 are rare. With respect to a patient's pre‐existing comorbidity burden, the unadjusted relationship of COPS and inpatient mortality is as follows: a COPS <50 is associated with a mortality risk of <1%, <100 with a mortality risk of <5%, 100 to 145 with a mortality risk of 5% to 10%, and >145 with a mortality risk of 10% or more.

ICU: Intensive Care Unit. In this study, all ICUs have a minimum registered nurse to patient ratio of 1:2.

LAPS: Laboratory Acute Physiology Score. Point score based on 14 laboratory test results obtained in the 72 hours preceding hospitalization. With respect to a patient's physiologic derangement, the unadjusted relationship of LAPS and inpatient mortality is as follows: a LAPS <7 is associated with a mortality risk of <1%, <7 to 30 with a mortality risk of <5%, 30 to 60 with a mortality risk of 5% to 9%, and >60 with a mortality risk of 10% or more.

LOS: Exact hospital Length Of Stay. LOS is calculated from admission until first discharge home (i.e., it may span more than one hospital stay if a patient experienced inter‐hospital transport).

Predicted (expected) mortality risk: the % risk of death for a given patient based on his/her age, sex, admission diagnosis, COPS, and LAPS.

OEMR: Observed to Expected Mortality Ratio. For a given patient subset, the ratio of the actual mortality experienced by the subset to the expected (predicted) mortality for the subset. Predicted mortality is based on patients' age, sex, admission diagnosis, COPS, and LAPS.

OMELOS: Observed Minus Expected LOS. For a given patient subset, the difference between the actual number of hospital days experienced by the subset and the expected (predicted) number of hospital days for the subset. Predicted LOS is based on patients' age, sex, admission diagnosis, COPS, and LAPS.

TCU: Transitional Care Unit (also called intermediate care unit or stepdown unit). In this study, TCUs have variable nurse to patient ratios ranging from 1:2.5 to 1:3 and did not provide assisted ventilation, continuous pressor infusions, or invasive monitoring.

Materials and Methods

This project was approved by the Northern California KPMCP Institutional Review Board for the Protection of Human Subjects.

The Northern California KPMCP serves a total population of approximately 3.3 million members. Under a mutual exclusivity arrangement, physicians of The Permanente Medical Group, Inc., care for Kaiser Foundation Health Plan, Inc. members at facilities owned by Kaiser Foundation Hospitals, Inc. All Northern California KPMCP hospitals and clinics employ the same information systems with a common medical record number and can track care covered by the plan but delivered elsewhere. Databases maintained by the KPMCP capture admission and discharge times, admission and discharge diagnoses and procedures (assigned by professional coders), bed histories, inter‐hospital transfers, as well as the results of all inpatient and outpatient laboratory tests. The use of these databases for research has been described in multiple reports.2124

Our setting consisted of all 19 hospitals owned and operated by the KPMCP, whose characteristics are summarized in the Supporting Information Appendix available to interested readers. These include the 17 described in our previous report19 as well as 2 new hospitals (Antioch and Manteca) which are similar in size and type of population served. Our study population consisted of all patients admitted to these 19 hospitals who met these criteria: 1) hospitalization began from November 1st, 2006 through January 31st, 2008; 2) initial hospitalization occurred at a Northern California KPMCP hospital (ie, for inter‐hospital transfers, the first hospital stay occurred within the KPMCP); 3) age 15 years; and 4) hospitalization was not for childbirth.

We defined a linked hospitalization as the time period that began with a patient's admission to the hospital and ended with the patient's discharge (home, to a nursing home, or death). Linked hospitalizations can thus involve more than 1 hospital stay and could include a patient transfer from one hospital to another prior to definitive discharge. For linked hospitalizations, mortality was attributed to the admitting KPMCP hospital (ie, if a patient was admitted to hospital A, transferred to B, and died at hospital B, mortality was attributed to hospital A). We defined total LOS as the exact time in hours from when a patient was first admitted to the hospital until death or final discharge home or to a nursing home, while total ICU or transitional care unit (TCU, referred to as stepdown unit in some hospitals) LOS was calculated for all individual ICU or TCU stays during the hospital stay.

Intra‐Hospital Transfers

We grouped all possible hospital units into four types: general medical‐surgical ward (henceforth, ward); operating room (OR)/post‐anesthesia recovery (PAR); TCU; and ICU. In 2003, the KPMCP implemented a mandatory minimum staffing ratio of one registered nurse for every four patients in all its hospital units; in addition, staffing levels for designated ICUs adhered to the previously mandated minimum of one nurse for every 2 patients. So long as they adhere to these minimum ratios, individual hospitals have considerable autonomy with respect to how they staff or designate individual hospital units. Registered nurse‐to‐patient ratios during the time of this study were as follows: ward patients, 1:3.5 to 1:4; TCU patients, 1:2.5 to 1:3; and ICU patients, 1:1 to 1:2. Staffing ratios for the OR and PAR are more variable, depending on the surgical procedures involved. Current KPMCP databases do not permit accurate quantification of physician staffing. All 19 study hospitals had designated ICUs, 6 were teaching hospitals, and 11 had designated TCUs. None of the study hospitals had closed ICUs (units where only intensivists admit patients) and none had continuous coverage of the ICU by intensivists. While we were not able to employ electronic data to determine who made the decision to transfer, we did find considerable variation with respect to how intensivists covered the ICUs and how they interfaced with hospitalists. Staffing levels for specialized coronary care units and non‐ICU monitored beds were not standardized. All study hospitals had rapid response teams as well as code blue teams during the time period covered by this report. Respiratory care practitioners were available to patients in all hospital units, but considerable variation existed with respect to other services available (eg, cardiac catheterization units, provision of noninvasive positive pressure ventilation outside the ICU, etc.).

This report focuses on intra‐hospital transfers to the ICU and TCU, with special emphasis on nonsurgical transfers (due to space limitations, we are not reporting on the outcomes of patients whose first hospital unit was the OR; additional details on these patients are provided in the Supporting Information Appendix). For the purposes of this report, we defined the following admission types: direct admits (patients admitted to the ICU or TCU whose first hospital unit on admission was the ICU or TCU); and nonsurgical transfers to a higher level of care. These latter transfers could be of 3 types: ward to ICU, ward to TCU, and TCU to ICU. We also quantified the effect of inter‐hospital transfers.

Independent Variables

In addition to patients' age and sex, we employed the following independent variables to predict transfer to a higher level of care. These variables are part of the risk adjustment model described in greater detail in our previous report19 and were available electronically for all patients in the cohort. We grouped admission diagnoses into 44 broad diagnostic categories (Primary Conditions), and admission types into 4 groups (emergency medical, emergency surgical, elective medical, and elective surgical). We quantified patients' degree of physiologic derangement using a Laboratory‐based Acute Physiology Score (LAPS) using laboratory test results prior to hospitalization. We quantified patients' comorbid illness burden using a Comorbidity Point Score (COPS) based on patients' pre‐existing diagnoses over the 12‐month period preceding hospitalization. Lastly, we assigned each patient a predicted mortality risk (%) and LOS based on the above predictors,19 permitting calculation of observed to expected mortality ratios (OEMRs) and observed minus expected LOS (OMELOS).

Statistical Methods

All analyses were performed in SAS.25 We calculated standard descriptive statistics (medians, means, standard deviations) and compared different patient groupings using t and chi‐square tests. We employed a similar approach to that reported by Render et al.7 to calculate OEMR and OMELOS.

To determine the degree to which transfers to a higher level of care from the ward or TCU would be predictable using information available at the time of admission, we performed 4 sets of logistic regression analyses using the above‐mentioned predictors in which the outcome variables were as follows: 1) transfer occurring in the first 48 hours after admission (time frame by which point approximately half of the transferred patients experienced a transfer) among ward or TCU patients and 2) transfer occurring after 48 hours among ward or TCU patients. We evaluated the discrimination and calibration of these models using the same methods described in our original report (measuring the area under the receiver operator characteristic curve, or c statistic, and visually examining observed and expected mortality rates among predicted risk bands as well as risk deciles) as well as additional statistical tests recommended by Cook.19, 26

Results

During the study period, a total of 249,129 individual hospital stays involving 170,151 patients occurred at these 19 hospitals. After concatenation of inter‐hospital transfers, we were left with 237,208 linked hospitalizations. We excluded 26,738 linked hospitalizations that began at a non‐KPMCP hospital (ie, they were transported in), leaving a total of 210,470 linked hospitalizations involving 150,495 patients. The overall linked hospitalization mortality rate was 3.30%.

Table 1 summarizes cohort characteristics based on initial hospital location. On admission, ICU patients had the highest degree of physiologic derangement as well as the highest predicted mortality. Considerable inter‐hospital variation was present in both predictors and outcomes; details on these variations are provided in the Supporting Information Appendix.

Characteristics of Study Cohort Based on Patients' Admission Hospital Unit
 WardTCUICUAll*
  • NOTE: See text for description of unit characteristics and staffing.

  • Abbreviations: COPS, Comorbidity Point Score; ICU, Intensive Care Unit; LAPS, Laboratory Acute Physiology Score; LOS, length of stay; SD, standard deviation; TCU, Transitional Care Unit.

  • Number includes 52,676 excluded surgical patients described in the Supporting Information Appendix.

  • See Supporting Information Appendix for details on inter‐hospital variation.

  • Numbers in parentheses are 95% confidence intervals. Total ratio for cohort is <1.0 because risk adjustment is based on an earlier calibration dataset (the 2002‐2005 Kaiser Permanente hospital cohort described in citation 19).

n121,23720,55616,001210,470
Admitted via emergency department, n (%)99,909 (82.4)18,612 (90.5)13,847 (86.5)139,036 (66.1)
% range across hospitals55.0‐94.264.7‐97.649.5‐97.453.6‐76.9
Male, n (%)53,744 (44.3)10,362 (50.4)8,378 (52.4)94,451 (44.9)
Age in years (mean SD)64.5 19.269.0 15.663.7 17.863.2 18.6
LAPS (mean SD)19.2 18.023.3 19.531.7 25.716.7 19.0
COPS (mean SD)90.4 64.099.2 65.994.5 67.584.7 61.8
% predicted mortality (mean SD)4.0 7.14.6 7.38.7 12.83.6 7.3
Observed in‐hospital deaths (n, %)3,793 (3.1)907 (4.4)1,995 (12.5)6,952 (3.3)
Observed to expected mortality ratio0.79 (0.77‐0.82)0.95 (0.89‐1.02)1.43 (1.36‐1.49)0.92 (0.89‐0.94)
Total hospital LOS, days (mean SD)4.6 7.55.3 10.07.8 14.04.6 8.1

Table 2 summarizes data from 3 groups of patients: patients initially admitted to the ward, or TCU, who did not experience a transfer to a higher level of care and patients admitted to these 2 units who did experience such a transfer. Patients who experienced a transfer constituted 5.3% (6,484/121,237) of ward patients and 6.7% (1,384/20,556) of TCU patients. Transferred patients tended to be older, have more acute physiologic derangement (higher LAPS), a greater pre‐existing illness burden (higher COPS), and a higher predicted mortality risk. Among ward patients, those with the following admission diagnoses were most likely to experience a transfer to a higher level of care: gastrointestinal bleeding (10.8% of all transfers), pneumonia (8.7%), and other infections (8.2%). The diagnoses most likely to be associated with death following transfer were cancer (death rate among transferred patients, 48%), renal disease (death rate, 36%), and liver disease (33%). Similar distributions were observed for TCU patients.

Characteristics of Ward and Transitional Care Unit (TCU) Patients Who Did and Did Not Experience Transfer to a Higher Level of Care
 Patients Initially Admitted to Ward, Remained TherePatients Initially Admitted to TCU, Remained TherePatients Transferred to Higher Level of CareAll
  • Abbreviations: COPS, Comorbidity Point Score; GI, Gastrointestinal; LAPS, Laboratory Acute Physiology Score; SD, Standard Deviation.

n114,75319,1727,868141,793
Male, n (%)50,586 (44.1)9,626 (50.2)3,894 (49.5)64,106 (45.2)
Age (mean SD)64.3 19.469.0 15.768.1 16.165.2 18.8
LAPS (mean SD)18.9 17.822.7 19.126.7 21.019.8 18.3
COPS (mean SD)89.4 63.798.3 65.5107.9 67.691.7 64.4
% predicted mortality risk (mean SD)3.8 7.04.4 7.06.5 8.84.1 7.1
Admission diagnosis of pneumonia, n (%)5,624 (4.9)865 (4.5)684 (8.7)7,173 (5.1)
Admission diagnosis of sepsis, n (%)1,181 (1.0)227 (1.2)168 (2.1)1,576 (1.1)
Admission diagnosis of GI bleed, n (%)13,615 (11.9)1,448 (7.6)851 (10.8)15,914 (11.2)
Admission diagnosis of cancer, n (%)2,406 (2.1)80 (0.4)186 (2.4)2,672 (1.9)

Table 3 compares outcomes among ward and TCU patients who did and did not experience a transfer to a higher level of care. The table shows that transferred patients were almost 3 times as likely to die, even after controlling for severity of illness, and that their hospital LOS was 9 days higher than expected. This increased risk was seen in all hospitals and among all transfer types (ward to ICU, ward to TCU, and TCU to ICU).

Outcomes of Ward and Transitional Care Unit (TCU) Patients Who Did and Did Not Experience Transfer to a Higher Level of Care
 Patients Initially Admitted to Ward, Remained TherePatients Initially Admitted to TCU, Remained TherePatients Transferred to Higher Level of Care
  • Abbreviations: CI, confidence interval; ICU, intensive care unit; SD, standard deviation.

n114,75319,1727,868
Admitted to ICU, n (%)0 (0.0)0 (0.0)5,245 (66.7)
Ventilated, n (%)0 (0.0)0 (0.0)1,346 (17.1)
Died in the hospital, n (%)2,619 (2.3)572 (3.0)1,509 (19.2)
Length of stay, in days, at time of death (mean SD)7.0 11.98.3 12.416.2 23.7
Observed to expected mortality ratio (95% CI)0.60 (0.57‐0.62)0.68 (0.63‐0.74)2.93 (2.79‐3.09)
Total hospital length of stay, days (mean SD)4.0 5.74.4 6.914.3 21.3
Observed minus expected length of stay (95% CI)0.4 (0.3‐0.4)0.8 (0.7‐0.9)9.1 (8.6‐9.5)
Length of stay, in hours, at time of transfer (mean SD)  80.8 167.2

Table 3 also shows that, among decedent patients, those who never left the ward or TCU died much sooner than those who died following transfer. Among direct admits to the ICU, the median LOS at time of death was 3.9 days, with a mean of 9.4 standard deviation of 19.9 days, while the corresponding times for TCU direct admits were a median and mean LOS of 6.5 and 11.7 19.5 days.

Table 4 summarizes outcomes among different patient subgroups that did and did not experience a transfer to a higher level of care. Based on location, patients who experienced a transfer from the TCU to the ICU had the highest crude death rate, but patients transferred from the ward to the ICU had the highest OEMR. On the other hand, if one divides patients by the degree of physiologic derangement, patients with low LAPS who experienced a transfer had the highest OEMR. With respect to LOS, patients transferred from the TCU to the ICU had the highest OMELOS (13.4 extra days).

Death Rates and Hospital Length of Stay Among Ward and Transitional Care Unit (TCU) Patients
 n (%)*Death Rate (%)OEMRLOS (mean SD)OMELOS
  • Abbreviations: COPS, COmorbidity Point Score; ICU, intensive care unit; LAPS, Laboratory Acute Physiology Score; LOS, length of stay; OEMR, Observed to expected mortality ratio; OMELOS, Observed minus expected length of stay; SD, standard deviation.

  • Percentage refers to % among all hospital admissions.

  • Numbers in parentheses are the 95% confidence intervals.

  • Numbers in parentheses are the 95% confidence intervals.

Never admitted to TCU or ICU157,632 (74.9)1.60.55 (0.53‐0.57)3.6 4.60.04 (0.02‐0.07)
Direct admit to TCU18,464 (8.8)2.90.66 (0.61‐0.72)4.2 5.80.60 (0.52‐0.68)
Direct admit to ICU14,655 (7.0)11.91.38 (1.32‐1.45)6.4 9.42.28 (2.14‐2.43)
Transferred from ward to ICU5,145 (2.4)21.53.23 (3.04‐3.42)15.7 21.610.33 (9.70‐10.96)
Transferred from ward to TCU3,144 (1.5)11.91.99 (1.79‐2.20)13.6 23.28.02 (7.23‐8.82)
Transferred from TCU to ICU1,107 (0.5)25.72.94 (2.61‐3.31)18.0 28.213.35 (11.49‐15.21)
Admitted to ward, COPS 80, no transfer to ICU or TCU55,405 (26.3)3.40.59 (0.56‐0.62)4.5 5.90.29 (0.24‐0.34)
Admitted to ward, COPS 80, did experience transfer to ICU or TCU4,851 (2.3)19.32.72 (2.55‐2.90)14.2 20.08.14 (7.56‐8.71)
Admitted to ward, COPS <80, no transfer to ICU or TCU57,421 (27.3)1.10.55 (0.51‐0.59)3.4 4.20.23 (0.19‐0.26)
Admitted to ward, COPS <80, did experience transfer to ICU or TCU3,560 (1.7)9.82.93 (2.63‐3.26)12.0 19.07.52 (6.89‐8.15)
Admitted to ward, LAPS 20, no transfer to ICU or TCU46,492 (22.1)4.20.59 (0.56‐0.61)4.6 5.40.16 (0.12‐0.21)
Admitted to ward, LAPS 20, did experience transfer to ICU or TCU4,070 (1.9)21.42.37 (2.22‐2.54)14.8 21.08.76 (8.06‐9.47)
Admitted to ward, LAPS <20, no transfer to ICU or TCU66,334 (31.5)0.90.55 (0.51‐0.60)3.5 4.90.32 (0.28‐0.36)
Admitted to ward, LAPS <20, did experience transfer to ICU or TCU4,341 (2.1)9.54.31 (3.90‐4.74)11.8 18.17.12 (6.61‐7.64)

Transfers to a higher level of care at a different hospital, which in the KPMCP are usually planned, experienced lower mortality than transfers within the same hospital. For ward to TCU transfers, intra‐hospital transfers had a mortality of 12.1% while inter‐hospital transfers had a mortality of 5.7%. Corresponding rates for ward to ICU transfers were 21.7% and 11.2%, and for TCU to ICU transfers the rates were 25.9% and 12.5%, respectively.

Among patients initially admitted to the ward, a model to predict the occurrence of a transfer to a higher level of care (within 48 hours after admission) that included age, sex, admission type, primary condition, LAPS, COPS, and interaction terms had poor discrimination, with an area under the receiver operator characteristic (c statistic) of only 0.64. The c statistic for a model to predict transfer after 48 hours was 0.66. The corresponding models for TCU admits had c statistics of 0.67 and 0.68. All four models had poor calibration.

Discussion

Using automated bed history data permits characterizing a patient population with disproportionate mortality and LOS: intra‐hospital transfers to special care units (ICUs or TCUs). Indeed, the largest subset of these patients (those initially admitted to the ward or TCU) constituted only 3.7% of all admissions, but accounted for 24.2% of all ICU admissions, 21.7% of all hospital deaths, and 13.2% of all hospital days. These patients also had very elevated OEMRs and OMELOS. Models based on age, sex, preadmission laboratory test results, and comorbidities did not predict the occurrence of these transfers.

We performed multivariate analyses to explore the degree to which electronically assigned preadmission severity scores could predict these transfers. These analyses found that, compared to our ability to predict inpatient or 30‐day mortality at the time of admission, which is excellent, our ability to predict the occurrence of transfer after admission is much more limited. These results highlight the limitations of severity scores that rely on automated data, which may not have adequate discrimination when it comes to determining the risk of an adverse outcome within a narrow time frame. For example, among the 121,237 patients initially admitted to the ward who did not experience an intra‐hospital transfer, the mean LAPS was 18.9, while the mean LAPS among the 6,484 ward patients who did experience a transfer was 25.5. Differences between the mean and median LAPS, COPS, and predicted mortality risk among transferred and non‐transferred patients were significant (P < 0.0001 for all comparisons). However, examination of the distribution of LAPS, COPS, and predicted mortality risk between these two groups of patients showed considerable overlap.

Our methodology resembles Silber et al.'s27, 28 concept of failure to rescue in that it focuses on events occurring after hospitalization. Silber et al. argue that a hospital's quality can be measured by quantifying the degree to which patients who experience new problems are successfully rescued. Furthermore, quantification of those situations where rescue attempts are unsuccessful is felt to be superior to simply comparing raw or adjusted mortality rates because these are primarily determined by underlying case mix. The primary difference between Silber et al.'s approach and ours is at the level of detailthey specified a specific set of complications, whereas our measure is more generic and would include patients with many of the complications specified by Silber et al.27, 28

Most of the patients transferred to a higher level of care in our cohort survived (ie, were rescued), indicating that intensive care is beneficial. However, the fact that these patients had elevated OEMRs and OMELOS indicates that the real challenge facing hospitalists involves the timing of provision of a beneficial intervention. In theory, improved timing could result from earlier detection of problems, which is the underlying rationale for employing rapid response teams. However, the fact that our electronic tools (LAPS, COPS) cannot predict patient deteriorations within a narrow time frame suggests that early detection will remain a major challenge. Manually assigned vital signs scores designed for this purpose do not have good discrimination either.29, 30 This raises the possibility that, though patient groups may differ in terms of overall illness severity and mortality risk, differences at the individual patient level may be too subtle for clinicians to detect. Future research may thus need to focus on scores that combine laboratory data, vital signs, trends in data,31, 32 and newer proteomic markers (eg, procalcitonin).33 We also found that most transfers occurred early (within <72 hours), raising the possibility that at least some of these transfers may involve issues around triage rather than sudden deterioration.

Our study has important limitations. Due to resource constraints and limited data availability, we could not characterize the patients as well as might be desirable; in particular, we could not make full determinations of the actual reasons for patients' transfer for all patients. Broadly speaking, transfer to a higher level of care could be due to inappropriate triage, appropriate (preventive) transfer (which could include transfer to a more richly staffed unit for a specific procedure), relentless progression of disease despite maximal therapy, the occurrence of management errors, patient and family uncertainty about goals of care or inadequate understanding of treatment options and prognoses, or a combination of these factors. We could not make these distinctions with currently available electronic data. This is also true of postsurgical patients, in whom it is difficult to determine which transfers to intensive care might be planned (eg, in the case of surgical procedures where ICU care is anticipated) as opposed to the occurrence of a deterioration during or following surgery. Another major limitation of this study is our inability to identify code or no code status electronically. The elapsed LOS at time of death among patients who experienced a transfer to a higher level of care (as compared to patients who died in the ward without ever experiencing intra‐hospital transfer) suggests, but does not prove, that prolonged efforts were being made to keep them alive. We were also limited in terms of having access to other process data (eg, physician staffing levels, provision and timing of palliative care). Having ICU severity of illness scores would have permitted us to compare our cohort to those of other recent studies showing elevated mortality rates among transfer patients,911 but we have not yet developed that capability.

Consideration of our study findings suggests a possible research agenda that could be implemented by hospitalist researchers. This agenda should emphasize three areas: detection, intervention, and reflection.

With respect to detection, attention needs to be paid to better tools for quantifying patient risk at the time a decision to admit to the ward is made. It is likely that such tools will need to combine the attributes of our severity score (LAPS) with those of the manually assigned scores.30, 34 In some cases, use of these tools could lead a physician to change the locus of admission from the ward to the TCU or ICU, which could improve outcomes by ensuring more timely provision of intensive care. Since problems with initial triage could be due to factors other than the failure to suspect or anticipate impending instability, future research should also include a cognitive component (eg, quantifying what proportion of subsequent patient deteriorations could be ascribed to missed diagnoses35). Additional work also needs to be done on developing mathematical models that can inform electronic monitoring of ward (not just ICU) patients.

Research on interventions that hospitalists can use to prevent the need for intensive care or to improve the rescue rate should take two routes. The first is a disease‐specific route, which builds on the fact that a relatively small set of conditions (pneumonia, sepsis, gastrointestinal bleeding) account for most transfers to a higher level of care. Condition‐specific protocols, checklists, and bundles36 tailored to a ward environment (as opposed to the ICU or to the entire hospital) might prevent deteriorations in these patients, as has been reported for sepsis.37 The second route is to improve the overall capabilities of rapid response and code blue teams. Such research would need to include a more careful assessment of what commonalities exist among patients who were and were not successfully rescued by these teams. This approach would probably yield more insights than the current literature, which focuses on whether rapid response teams are a good thing or not.

Finally, research also needs to be performed on how hospitalists reflect on adverse outcomes among ward patients. Greater emphasis needs to be placed on moving beyond trigger tool approaches that rely on manual chart review. In an era of expanding use of electronic medical record systems, more work needs to be done on how to harness these to provide hospitalists with better quantitative and risk‐adjusted information. This information should not be limited to simply reporting rates of transfers and deaths. Rather, finer distinctions must be provided with respect of the type of patients (ie, more diagnostic detail), the clinical status of patients (ie, more physiologic detail), as well as the effects of including or excluding patients in whom therapeutic options may be limited (ie, do not resuscitate and comfort care patients) on reported rates. Ideally, researchers should develop better process and outcomes measures that could be tested in collaborative networks that include multiple nonacademic general medical‐surgical wards.

Acknowledgements

The authors thank Drs. Paul Feigenbaum, Alan Whippy, Joseph V. Selby, and Philip Madvig for reviewing the manuscript and Ms. Jennifer Calhoun for formatting the manuscript.

Considerable research and public attention is being paid to the quantification, risk adjustment, and reporting of inpatient mortality.15 Inpatient mortality is reported as aggregate mortality (for all hospitalized patients or those with a specific diagnosis3, 6) or intensive care unit (ICU) mortality.7, 8 While reporting aggregate hospital or aggregate ICU mortality rates is useful, it is also important to develop reporting strategies that go beyond simply using data elements found in administrative databases (eg, diagnosis and procedure codes) to quantify practice variation. Ideally, such strategies would permit delineating processes of careparticularly those potentially under the control of hospitalists, not only intensiviststo identify improvement opportunities. One such process, which can be tracked using the bed history component of a patient's electronic medical record, is the transfer of patients between different units within the same hospital.

Several studies have documented that risk of ICU death is highest among patients transferred from general medical‐surgical wards, intermediate among direct admissions from the emergency department, and lowest among surgical admissions.911 Opportunities to reduce subsequent ICU mortality have been studied among ward patients who develop sepsis and are then transferred to the ICU,12 among patients who experience cardiac arrest,13, 14 as well as among patients with any physiological deterioration (eg, through the use of rapid response teams).1517 Most of these studies have been single‐center studies and/or studies reporting only an ICU denominator. While useful in some respects, such studies are less helpful to hospitalists, who would benefit from better understanding of the types of patients transferred and the total impact that transfers to a higher level of care make on general medical‐surgical wards. In addition, entities such as the Institute for Healthcare Improvement recommend the manual review of records of patients who were transferred from the ward to the ICU18 to identify performance improvement opportunities. While laudable, such approaches do not lend themselves to automated reporting strategies.

We recently described a new risk adjustment methodology for inpatient mortality based entirely on automated data preceding hospital admission and not restricted to ICU patients. This methodology, which has been externally validated in Ottawa, Canada, after development in the Kaiser Permanente Medical Care Program (KPMCP), permits quantification of a patient's pre‐existing comorbidity burden, physiologic derangement at the time of admission, and overall inpatient mortality risk.19, 20 The primary purpose of this study was to combine this methodology with bed history analysis to quantify the in‐hospital mortality and length of stay (LOS) of patients who experienced intra‐hospital transfers in a large, multihospital system. As a secondary goal, we also wanted to assess the degree to which these transfers could be predicted based on information available prior to a patient's admission.

ABBREVIATIONS AND TERMS USED IN TEXT

COPS: COmorbidity Point Score. Point score based on a patient's health care utilization diagnoses (during the year preceding admission to the hospital. Analogous to POA (present on admission) coding. Scores can range from 0 to a theoretical maximum of 701 but scores >200 are rare. With respect to a patient's pre‐existing comorbidity burden, the unadjusted relationship of COPS and inpatient mortality is as follows: a COPS <50 is associated with a mortality risk of <1%, <100 with a mortality risk of <5%, 100 to 145 with a mortality risk of 5% to 10%, and >145 with a mortality risk of 10% or more.

ICU: Intensive Care Unit. In this study, all ICUs have a minimum registered nurse to patient ratio of 1:2.

LAPS: Laboratory Acute Physiology Score. Point score based on 14 laboratory test results obtained in the 72 hours preceding hospitalization. With respect to a patient's physiologic derangement, the unadjusted relationship of LAPS and inpatient mortality is as follows: a LAPS <7 is associated with a mortality risk of <1%, <7 to 30 with a mortality risk of <5%, 30 to 60 with a mortality risk of 5% to 9%, and >60 with a mortality risk of 10% or more.

LOS: Exact hospital Length Of Stay. LOS is calculated from admission until first discharge home (i.e., it may span more than one hospital stay if a patient experienced inter‐hospital transport).

Predicted (expected) mortality risk: the % risk of death for a given patient based on his/her age, sex, admission diagnosis, COPS, and LAPS.

OEMR: Observed to Expected Mortality Ratio. For a given patient subset, the ratio of the actual mortality experienced by the subset to the expected (predicted) mortality for the subset. Predicted mortality is based on patients' age, sex, admission diagnosis, COPS, and LAPS.

OMELOS: Observed Minus Expected LOS. For a given patient subset, the difference between the actual number of hospital days experienced by the subset and the expected (predicted) number of hospital days for the subset. Predicted LOS is based on patients' age, sex, admission diagnosis, COPS, and LAPS.

TCU: Transitional Care Unit (also called intermediate care unit or stepdown unit). In this study, TCUs have variable nurse to patient ratios ranging from 1:2.5 to 1:3 and did not provide assisted ventilation, continuous pressor infusions, or invasive monitoring.

Materials and Methods

This project was approved by the Northern California KPMCP Institutional Review Board for the Protection of Human Subjects.

The Northern California KPMCP serves a total population of approximately 3.3 million members. Under a mutual exclusivity arrangement, physicians of The Permanente Medical Group, Inc., care for Kaiser Foundation Health Plan, Inc. members at facilities owned by Kaiser Foundation Hospitals, Inc. All Northern California KPMCP hospitals and clinics employ the same information systems with a common medical record number and can track care covered by the plan but delivered elsewhere. Databases maintained by the KPMCP capture admission and discharge times, admission and discharge diagnoses and procedures (assigned by professional coders), bed histories, inter‐hospital transfers, as well as the results of all inpatient and outpatient laboratory tests. The use of these databases for research has been described in multiple reports.2124

Our setting consisted of all 19 hospitals owned and operated by the KPMCP, whose characteristics are summarized in the Supporting Information Appendix available to interested readers. These include the 17 described in our previous report19 as well as 2 new hospitals (Antioch and Manteca) which are similar in size and type of population served. Our study population consisted of all patients admitted to these 19 hospitals who met these criteria: 1) hospitalization began from November 1st, 2006 through January 31st, 2008; 2) initial hospitalization occurred at a Northern California KPMCP hospital (ie, for inter‐hospital transfers, the first hospital stay occurred within the KPMCP); 3) age 15 years; and 4) hospitalization was not for childbirth.

We defined a linked hospitalization as the time period that began with a patient's admission to the hospital and ended with the patient's discharge (home, to a nursing home, or death). Linked hospitalizations can thus involve more than 1 hospital stay and could include a patient transfer from one hospital to another prior to definitive discharge. For linked hospitalizations, mortality was attributed to the admitting KPMCP hospital (ie, if a patient was admitted to hospital A, transferred to B, and died at hospital B, mortality was attributed to hospital A). We defined total LOS as the exact time in hours from when a patient was first admitted to the hospital until death or final discharge home or to a nursing home, while total ICU or transitional care unit (TCU, referred to as stepdown unit in some hospitals) LOS was calculated for all individual ICU or TCU stays during the hospital stay.

Intra‐Hospital Transfers

We grouped all possible hospital units into four types: general medical‐surgical ward (henceforth, ward); operating room (OR)/post‐anesthesia recovery (PAR); TCU; and ICU. In 2003, the KPMCP implemented a mandatory minimum staffing ratio of one registered nurse for every four patients in all its hospital units; in addition, staffing levels for designated ICUs adhered to the previously mandated minimum of one nurse for every 2 patients. So long as they adhere to these minimum ratios, individual hospitals have considerable autonomy with respect to how they staff or designate individual hospital units. Registered nurse‐to‐patient ratios during the time of this study were as follows: ward patients, 1:3.5 to 1:4; TCU patients, 1:2.5 to 1:3; and ICU patients, 1:1 to 1:2. Staffing ratios for the OR and PAR are more variable, depending on the surgical procedures involved. Current KPMCP databases do not permit accurate quantification of physician staffing. All 19 study hospitals had designated ICUs, 6 were teaching hospitals, and 11 had designated TCUs. None of the study hospitals had closed ICUs (units where only intensivists admit patients) and none had continuous coverage of the ICU by intensivists. While we were not able to employ electronic data to determine who made the decision to transfer, we did find considerable variation with respect to how intensivists covered the ICUs and how they interfaced with hospitalists. Staffing levels for specialized coronary care units and non‐ICU monitored beds were not standardized. All study hospitals had rapid response teams as well as code blue teams during the time period covered by this report. Respiratory care practitioners were available to patients in all hospital units, but considerable variation existed with respect to other services available (eg, cardiac catheterization units, provision of noninvasive positive pressure ventilation outside the ICU, etc.).

This report focuses on intra‐hospital transfers to the ICU and TCU, with special emphasis on nonsurgical transfers (due to space limitations, we are not reporting on the outcomes of patients whose first hospital unit was the OR; additional details on these patients are provided in the Supporting Information Appendix). For the purposes of this report, we defined the following admission types: direct admits (patients admitted to the ICU or TCU whose first hospital unit on admission was the ICU or TCU); and nonsurgical transfers to a higher level of care. These latter transfers could be of 3 types: ward to ICU, ward to TCU, and TCU to ICU. We also quantified the effect of inter‐hospital transfers.

Independent Variables

In addition to patients' age and sex, we employed the following independent variables to predict transfer to a higher level of care. These variables are part of the risk adjustment model described in greater detail in our previous report19 and were available electronically for all patients in the cohort. We grouped admission diagnoses into 44 broad diagnostic categories (Primary Conditions), and admission types into 4 groups (emergency medical, emergency surgical, elective medical, and elective surgical). We quantified patients' degree of physiologic derangement using a Laboratory‐based Acute Physiology Score (LAPS) using laboratory test results prior to hospitalization. We quantified patients' comorbid illness burden using a Comorbidity Point Score (COPS) based on patients' pre‐existing diagnoses over the 12‐month period preceding hospitalization. Lastly, we assigned each patient a predicted mortality risk (%) and LOS based on the above predictors,19 permitting calculation of observed to expected mortality ratios (OEMRs) and observed minus expected LOS (OMELOS).

Statistical Methods

All analyses were performed in SAS.25 We calculated standard descriptive statistics (medians, means, standard deviations) and compared different patient groupings using t and chi‐square tests. We employed a similar approach to that reported by Render et al.7 to calculate OEMR and OMELOS.

To determine the degree to which transfers to a higher level of care from the ward or TCU would be predictable using information available at the time of admission, we performed 4 sets of logistic regression analyses using the above‐mentioned predictors in which the outcome variables were as follows: 1) transfer occurring in the first 48 hours after admission (time frame by which point approximately half of the transferred patients experienced a transfer) among ward or TCU patients and 2) transfer occurring after 48 hours among ward or TCU patients. We evaluated the discrimination and calibration of these models using the same methods described in our original report (measuring the area under the receiver operator characteristic curve, or c statistic, and visually examining observed and expected mortality rates among predicted risk bands as well as risk deciles) as well as additional statistical tests recommended by Cook.19, 26

Results

During the study period, a total of 249,129 individual hospital stays involving 170,151 patients occurred at these 19 hospitals. After concatenation of inter‐hospital transfers, we were left with 237,208 linked hospitalizations. We excluded 26,738 linked hospitalizations that began at a non‐KPMCP hospital (ie, they were transported in), leaving a total of 210,470 linked hospitalizations involving 150,495 patients. The overall linked hospitalization mortality rate was 3.30%.

Table 1 summarizes cohort characteristics based on initial hospital location. On admission, ICU patients had the highest degree of physiologic derangement as well as the highest predicted mortality. Considerable inter‐hospital variation was present in both predictors and outcomes; details on these variations are provided in the Supporting Information Appendix.

Characteristics of Study Cohort Based on Patients' Admission Hospital Unit
 WardTCUICUAll*
  • NOTE: See text for description of unit characteristics and staffing.

  • Abbreviations: COPS, Comorbidity Point Score; ICU, Intensive Care Unit; LAPS, Laboratory Acute Physiology Score; LOS, length of stay; SD, standard deviation; TCU, Transitional Care Unit.

  • Number includes 52,676 excluded surgical patients described in the Supporting Information Appendix.

  • See Supporting Information Appendix for details on inter‐hospital variation.

  • Numbers in parentheses are 95% confidence intervals. Total ratio for cohort is <1.0 because risk adjustment is based on an earlier calibration dataset (the 2002‐2005 Kaiser Permanente hospital cohort described in citation 19).

n121,23720,55616,001210,470
Admitted via emergency department, n (%)99,909 (82.4)18,612 (90.5)13,847 (86.5)139,036 (66.1)
% range across hospitals55.0‐94.264.7‐97.649.5‐97.453.6‐76.9
Male, n (%)53,744 (44.3)10,362 (50.4)8,378 (52.4)94,451 (44.9)
Age in years (mean SD)64.5 19.269.0 15.663.7 17.863.2 18.6
LAPS (mean SD)19.2 18.023.3 19.531.7 25.716.7 19.0
COPS (mean SD)90.4 64.099.2 65.994.5 67.584.7 61.8
% predicted mortality (mean SD)4.0 7.14.6 7.38.7 12.83.6 7.3
Observed in‐hospital deaths (n, %)3,793 (3.1)907 (4.4)1,995 (12.5)6,952 (3.3)
Observed to expected mortality ratio0.79 (0.77‐0.82)0.95 (0.89‐1.02)1.43 (1.36‐1.49)0.92 (0.89‐0.94)
Total hospital LOS, days (mean SD)4.6 7.55.3 10.07.8 14.04.6 8.1

Table 2 summarizes data from 3 groups of patients: patients initially admitted to the ward, or TCU, who did not experience a transfer to a higher level of care and patients admitted to these 2 units who did experience such a transfer. Patients who experienced a transfer constituted 5.3% (6,484/121,237) of ward patients and 6.7% (1,384/20,556) of TCU patients. Transferred patients tended to be older, have more acute physiologic derangement (higher LAPS), a greater pre‐existing illness burden (higher COPS), and a higher predicted mortality risk. Among ward patients, those with the following admission diagnoses were most likely to experience a transfer to a higher level of care: gastrointestinal bleeding (10.8% of all transfers), pneumonia (8.7%), and other infections (8.2%). The diagnoses most likely to be associated with death following transfer were cancer (death rate among transferred patients, 48%), renal disease (death rate, 36%), and liver disease (33%). Similar distributions were observed for TCU patients.

Characteristics of Ward and Transitional Care Unit (TCU) Patients Who Did and Did Not Experience Transfer to a Higher Level of Care
 Patients Initially Admitted to Ward, Remained TherePatients Initially Admitted to TCU, Remained TherePatients Transferred to Higher Level of CareAll
  • Abbreviations: COPS, Comorbidity Point Score; GI, Gastrointestinal; LAPS, Laboratory Acute Physiology Score; SD, Standard Deviation.

n114,75319,1727,868141,793
Male, n (%)50,586 (44.1)9,626 (50.2)3,894 (49.5)64,106 (45.2)
Age (mean SD)64.3 19.469.0 15.768.1 16.165.2 18.8
LAPS (mean SD)18.9 17.822.7 19.126.7 21.019.8 18.3
COPS (mean SD)89.4 63.798.3 65.5107.9 67.691.7 64.4
% predicted mortality risk (mean SD)3.8 7.04.4 7.06.5 8.84.1 7.1
Admission diagnosis of pneumonia, n (%)5,624 (4.9)865 (4.5)684 (8.7)7,173 (5.1)
Admission diagnosis of sepsis, n (%)1,181 (1.0)227 (1.2)168 (2.1)1,576 (1.1)
Admission diagnosis of GI bleed, n (%)13,615 (11.9)1,448 (7.6)851 (10.8)15,914 (11.2)
Admission diagnosis of cancer, n (%)2,406 (2.1)80 (0.4)186 (2.4)2,672 (1.9)

Table 3 compares outcomes among ward and TCU patients who did and did not experience a transfer to a higher level of care. The table shows that transferred patients were almost 3 times as likely to die, even after controlling for severity of illness, and that their hospital LOS was 9 days higher than expected. This increased risk was seen in all hospitals and among all transfer types (ward to ICU, ward to TCU, and TCU to ICU).

Outcomes of Ward and Transitional Care Unit (TCU) Patients Who Did and Did Not Experience Transfer to a Higher Level of Care
 Patients Initially Admitted to Ward, Remained TherePatients Initially Admitted to TCU, Remained TherePatients Transferred to Higher Level of Care
  • Abbreviations: CI, confidence interval; ICU, intensive care unit; SD, standard deviation.

n114,75319,1727,868
Admitted to ICU, n (%)0 (0.0)0 (0.0)5,245 (66.7)
Ventilated, n (%)0 (0.0)0 (0.0)1,346 (17.1)
Died in the hospital, n (%)2,619 (2.3)572 (3.0)1,509 (19.2)
Length of stay, in days, at time of death (mean SD)7.0 11.98.3 12.416.2 23.7
Observed to expected mortality ratio (95% CI)0.60 (0.57‐0.62)0.68 (0.63‐0.74)2.93 (2.79‐3.09)
Total hospital length of stay, days (mean SD)4.0 5.74.4 6.914.3 21.3
Observed minus expected length of stay (95% CI)0.4 (0.3‐0.4)0.8 (0.7‐0.9)9.1 (8.6‐9.5)
Length of stay, in hours, at time of transfer (mean SD)  80.8 167.2

Table 3 also shows that, among decedent patients, those who never left the ward or TCU died much sooner than those who died following transfer. Among direct admits to the ICU, the median LOS at time of death was 3.9 days, with a mean of 9.4 standard deviation of 19.9 days, while the corresponding times for TCU direct admits were a median and mean LOS of 6.5 and 11.7 19.5 days.

Table 4 summarizes outcomes among different patient subgroups that did and did not experience a transfer to a higher level of care. Based on location, patients who experienced a transfer from the TCU to the ICU had the highest crude death rate, but patients transferred from the ward to the ICU had the highest OEMR. On the other hand, if one divides patients by the degree of physiologic derangement, patients with low LAPS who experienced a transfer had the highest OEMR. With respect to LOS, patients transferred from the TCU to the ICU had the highest OMELOS (13.4 extra days).

Death Rates and Hospital Length of Stay Among Ward and Transitional Care Unit (TCU) Patients
 n (%)*Death Rate (%)OEMRLOS (mean SD)OMELOS
  • Abbreviations: COPS, COmorbidity Point Score; ICU, intensive care unit; LAPS, Laboratory Acute Physiology Score; LOS, length of stay; OEMR, Observed to expected mortality ratio; OMELOS, Observed minus expected length of stay; SD, standard deviation.

  • Percentage refers to % among all hospital admissions.

  • Numbers in parentheses are the 95% confidence intervals.

  • Numbers in parentheses are the 95% confidence intervals.

Never admitted to TCU or ICU157,632 (74.9)1.60.55 (0.53‐0.57)3.6 4.60.04 (0.02‐0.07)
Direct admit to TCU18,464 (8.8)2.90.66 (0.61‐0.72)4.2 5.80.60 (0.52‐0.68)
Direct admit to ICU14,655 (7.0)11.91.38 (1.32‐1.45)6.4 9.42.28 (2.14‐2.43)
Transferred from ward to ICU5,145 (2.4)21.53.23 (3.04‐3.42)15.7 21.610.33 (9.70‐10.96)
Transferred from ward to TCU3,144 (1.5)11.91.99 (1.79‐2.20)13.6 23.28.02 (7.23‐8.82)
Transferred from TCU to ICU1,107 (0.5)25.72.94 (2.61‐3.31)18.0 28.213.35 (11.49‐15.21)
Admitted to ward, COPS 80, no transfer to ICU or TCU55,405 (26.3)3.40.59 (0.56‐0.62)4.5 5.90.29 (0.24‐0.34)
Admitted to ward, COPS 80, did experience transfer to ICU or TCU4,851 (2.3)19.32.72 (2.55‐2.90)14.2 20.08.14 (7.56‐8.71)
Admitted to ward, COPS <80, no transfer to ICU or TCU57,421 (27.3)1.10.55 (0.51‐0.59)3.4 4.20.23 (0.19‐0.26)
Admitted to ward, COPS <80, did experience transfer to ICU or TCU3,560 (1.7)9.82.93 (2.63‐3.26)12.0 19.07.52 (6.89‐8.15)
Admitted to ward, LAPS 20, no transfer to ICU or TCU46,492 (22.1)4.20.59 (0.56‐0.61)4.6 5.40.16 (0.12‐0.21)
Admitted to ward, LAPS 20, did experience transfer to ICU or TCU4,070 (1.9)21.42.37 (2.22‐2.54)14.8 21.08.76 (8.06‐9.47)
Admitted to ward, LAPS <20, no transfer to ICU or TCU66,334 (31.5)0.90.55 (0.51‐0.60)3.5 4.90.32 (0.28‐0.36)
Admitted to ward, LAPS <20, did experience transfer to ICU or TCU4,341 (2.1)9.54.31 (3.90‐4.74)11.8 18.17.12 (6.61‐7.64)

Transfers to a higher level of care at a different hospital, which in the KPMCP are usually planned, experienced lower mortality than transfers within the same hospital. For ward to TCU transfers, intra‐hospital transfers had a mortality of 12.1% while inter‐hospital transfers had a mortality of 5.7%. Corresponding rates for ward to ICU transfers were 21.7% and 11.2%, and for TCU to ICU transfers the rates were 25.9% and 12.5%, respectively.

Among patients initially admitted to the ward, a model to predict the occurrence of a transfer to a higher level of care (within 48 hours after admission) that included age, sex, admission type, primary condition, LAPS, COPS, and interaction terms had poor discrimination, with an area under the receiver operator characteristic (c statistic) of only 0.64. The c statistic for a model to predict transfer after 48 hours was 0.66. The corresponding models for TCU admits had c statistics of 0.67 and 0.68. All four models had poor calibration.

Discussion

Using automated bed history data permits characterizing a patient population with disproportionate mortality and LOS: intra‐hospital transfers to special care units (ICUs or TCUs). Indeed, the largest subset of these patients (those initially admitted to the ward or TCU) constituted only 3.7% of all admissions, but accounted for 24.2% of all ICU admissions, 21.7% of all hospital deaths, and 13.2% of all hospital days. These patients also had very elevated OEMRs and OMELOS. Models based on age, sex, preadmission laboratory test results, and comorbidities did not predict the occurrence of these transfers.

We performed multivariate analyses to explore the degree to which electronically assigned preadmission severity scores could predict these transfers. These analyses found that, compared to our ability to predict inpatient or 30‐day mortality at the time of admission, which is excellent, our ability to predict the occurrence of transfer after admission is much more limited. These results highlight the limitations of severity scores that rely on automated data, which may not have adequate discrimination when it comes to determining the risk of an adverse outcome within a narrow time frame. For example, among the 121,237 patients initially admitted to the ward who did not experience an intra‐hospital transfer, the mean LAPS was 18.9, while the mean LAPS among the 6,484 ward patients who did experience a transfer was 25.5. Differences between the mean and median LAPS, COPS, and predicted mortality risk among transferred and non‐transferred patients were significant (P < 0.0001 for all comparisons). However, examination of the distribution of LAPS, COPS, and predicted mortality risk between these two groups of patients showed considerable overlap.

Our methodology resembles Silber et al.'s27, 28 concept of failure to rescue in that it focuses on events occurring after hospitalization. Silber et al. argue that a hospital's quality can be measured by quantifying the degree to which patients who experience new problems are successfully rescued. Furthermore, quantification of those situations where rescue attempts are unsuccessful is felt to be superior to simply comparing raw or adjusted mortality rates because these are primarily determined by underlying case mix. The primary difference between Silber et al.'s approach and ours is at the level of detailthey specified a specific set of complications, whereas our measure is more generic and would include patients with many of the complications specified by Silber et al.27, 28

Most of the patients transferred to a higher level of care in our cohort survived (ie, were rescued), indicating that intensive care is beneficial. However, the fact that these patients had elevated OEMRs and OMELOS indicates that the real challenge facing hospitalists involves the timing of provision of a beneficial intervention. In theory, improved timing could result from earlier detection of problems, which is the underlying rationale for employing rapid response teams. However, the fact that our electronic tools (LAPS, COPS) cannot predict patient deteriorations within a narrow time frame suggests that early detection will remain a major challenge. Manually assigned vital signs scores designed for this purpose do not have good discrimination either.29, 30 This raises the possibility that, though patient groups may differ in terms of overall illness severity and mortality risk, differences at the individual patient level may be too subtle for clinicians to detect. Future research may thus need to focus on scores that combine laboratory data, vital signs, trends in data,31, 32 and newer proteomic markers (eg, procalcitonin).33 We also found that most transfers occurred early (within <72 hours), raising the possibility that at least some of these transfers may involve issues around triage rather than sudden deterioration.

Our study has important limitations. Due to resource constraints and limited data availability, we could not characterize the patients as well as might be desirable; in particular, we could not make full determinations of the actual reasons for patients' transfer for all patients. Broadly speaking, transfer to a higher level of care could be due to inappropriate triage, appropriate (preventive) transfer (which could include transfer to a more richly staffed unit for a specific procedure), relentless progression of disease despite maximal therapy, the occurrence of management errors, patient and family uncertainty about goals of care or inadequate understanding of treatment options and prognoses, or a combination of these factors. We could not make these distinctions with currently available electronic data. This is also true of postsurgical patients, in whom it is difficult to determine which transfers to intensive care might be planned (eg, in the case of surgical procedures where ICU care is anticipated) as opposed to the occurrence of a deterioration during or following surgery. Another major limitation of this study is our inability to identify code or no code status electronically. The elapsed LOS at time of death among patients who experienced a transfer to a higher level of care (as compared to patients who died in the ward without ever experiencing intra‐hospital transfer) suggests, but does not prove, that prolonged efforts were being made to keep them alive. We were also limited in terms of having access to other process data (eg, physician staffing levels, provision and timing of palliative care). Having ICU severity of illness scores would have permitted us to compare our cohort to those of other recent studies showing elevated mortality rates among transfer patients,911 but we have not yet developed that capability.

Consideration of our study findings suggests a possible research agenda that could be implemented by hospitalist researchers. This agenda should emphasize three areas: detection, intervention, and reflection.

With respect to detection, attention needs to be paid to better tools for quantifying patient risk at the time a decision to admit to the ward is made. It is likely that such tools will need to combine the attributes of our severity score (LAPS) with those of the manually assigned scores.30, 34 In some cases, use of these tools could lead a physician to change the locus of admission from the ward to the TCU or ICU, which could improve outcomes by ensuring more timely provision of intensive care. Since problems with initial triage could be due to factors other than the failure to suspect or anticipate impending instability, future research should also include a cognitive component (eg, quantifying what proportion of subsequent patient deteriorations could be ascribed to missed diagnoses35). Additional work also needs to be done on developing mathematical models that can inform electronic monitoring of ward (not just ICU) patients.

Research on interventions that hospitalists can use to prevent the need for intensive care or to improve the rescue rate should take two routes. The first is a disease‐specific route, which builds on the fact that a relatively small set of conditions (pneumonia, sepsis, gastrointestinal bleeding) account for most transfers to a higher level of care. Condition‐specific protocols, checklists, and bundles36 tailored to a ward environment (as opposed to the ICU or to the entire hospital) might prevent deteriorations in these patients, as has been reported for sepsis.37 The second route is to improve the overall capabilities of rapid response and code blue teams. Such research would need to include a more careful assessment of what commonalities exist among patients who were and were not successfully rescued by these teams. This approach would probably yield more insights than the current literature, which focuses on whether rapid response teams are a good thing or not.

Finally, research also needs to be performed on how hospitalists reflect on adverse outcomes among ward patients. Greater emphasis needs to be placed on moving beyond trigger tool approaches that rely on manual chart review. In an era of expanding use of electronic medical record systems, more work needs to be done on how to harness these to provide hospitalists with better quantitative and risk‐adjusted information. This information should not be limited to simply reporting rates of transfers and deaths. Rather, finer distinctions must be provided with respect of the type of patients (ie, more diagnostic detail), the clinical status of patients (ie, more physiologic detail), as well as the effects of including or excluding patients in whom therapeutic options may be limited (ie, do not resuscitate and comfort care patients) on reported rates. Ideally, researchers should develop better process and outcomes measures that could be tested in collaborative networks that include multiple nonacademic general medical‐surgical wards.

Acknowledgements

The authors thank Drs. Paul Feigenbaum, Alan Whippy, Joseph V. Selby, and Philip Madvig for reviewing the manuscript and Ms. Jennifer Calhoun for formatting the manuscript.

References
  1. Kohn LT,Corrigan JM,Donaldson MS.To Err is Human: Building a Safer Health System.Washington, D. C.:National Academy Press;2000.
  2. Institute for Healthcare Improvement. Protecting 5 million lives from harm. Available at: http://www.ihi.org/IHI/Programs/Campaign. Accessed June2010.
  3. Hofer TP,Hayward RA.Identifying poor‐quality hospitals. Can hospital mortality rates detect quality problems for medical diagnoses?Med Care.1996;34(8):737753.
  4. Dimick JB,Welch HG,Birkmeyer JD.Surgical mortality as an indicator of hospital quality: the problem with small sample size.JAMA.2004;292(7):847851.
  5. State of California Office of Statewide Health Planning and Development. AHRQ ‐ Inpatient quality indicators (IQIs) hospital inpatient mortality indicators for California. Available at: http://www.oshpd.ca.gov/HID/Products/PatDischargeData/AHRQ/iqi‐imi_overview.html. Accessed June2010.
  6. Pine M,Jordan HS,Elixhauser A, et al.Enhancement of claims data to improve risk adjustment of hospital mortality.JAMA.2007;297(1):7176.
  7. Render ML,Kim HM,Deddens J, et al.Variation in outcomes in Veterans Affairs intensive care units with a computerized severity measure.Crit Care Med.2005;33(5):930939.
  8. Zimmerman JE,Kramer AA,McNair DS,Malila FM.Acute Physiology and Chronic Health Evaluation (APACHE) IV: hospital mortality assessment for today's critically ill patients.Crit Care Med.2006;34(5):12971310.
  9. Barnett MJ,Kaboli PJ,Sirio CA,Rosenthal GE.Day of the week of intensive care admission and patient outcomes: a multisite regional evaluation.Med Care.2002;40(6):530539.
  10. Ensminger SA,Morales IJ,Peters SG, et al.The hospital mortality of patients admitted to the ICU on weekends.Chest.2004;126(4):12921298.
  11. Luyt CE,Combes A,Aegerter P, et al.Mortality among patients admitted to intensive care units during weekday day shifts compared with “off” hours.Crit Care Med.2007;35(1):311.
  12. Lundberg JS,Perl TM,Wiblin T, et al.Septic shock: an analysis of outcomes for patients with onset on hospital wards versus intensive care units.Crit Care Med.1998;26(6):10201024.
  13. Schein RM,Hazday N,Pena M,Ruben BH,Sprung CL.Clinical antecedents to in‐hospital cardiopulmonary arrest.Chest.1990;98(6):13881392.
  14. Franklin C,Mathew J.Developing strategies to prevent inhospital cardiac arrest: analyzing responses of physicians and nurses in the hours before the event.Crit Care Med.1994;22(2):244247.
  15. MERIT Study Investigators.Introduction of the medical emergency team (MET) system: a cluster‐randomized controlled trial.Lancet.2005;365(9477):20912097.
  16. Institute for Healthcare Improvement.The “MERIT” Trial of Medical Emergency Teams in Australia: An Analysis of Findings and Implications.Boston, MA:2005. Available on www.ihi.org
  17. Winters BD,Pham J,Pronovost PJ.Rapid response teams‐‐walk, don't run.JAMA.2006;296(13):16451647.
  18. Griffin F,Resar R.IHI Global Trigger Tool for Measuring Adverse Events.2nd ed.Cambridge, Massachusetts:Institute for Healthcare Improvement;2009.
  19. Escobar G,Greene J,Scheirer P,Gardner M,Draper D,Kipnis P.Risk adjusting hospital inpatient mortality using automated inpatient, outpatient, and laboratory databases.Medical Care.2008;46(3):232239.
  20. van Walraven C,Escobar GJ,Greene JD,Forster AJ.The Kaiser Permanente inpatient risk adjustment methodology was valid in an external patient population.J Clin Epidemiol.2010;63(7):798803.
  21. Selby JV.Linking automated databases for research in managed care settings.Ann Intern Med.1997;127(8 Pt 2):719724.
  22. Go AS,Hylek EM,Chang Y, et al.Anticoagulation therapy for stroke prevention in atrial fibrillation: how well do randomized trials translate into clinical practice?JAMA.2003;290(20):26852692.
  23. Escobar G,Shaheen S,Breed E, et al.Richardson score predicts short‐term adverse respiratory outcomes in newborns >/=34 weeks gestation.J Pediatr.2004;145(6):754760.
  24. Escobar GJ,Fireman BH,Palen TE, et al.Risk adjusting community‐acquired pneumonia hospital outcomes using automated databases.Am J Manag Care.2008;14(3):158166.
  25. Statistical Analysis Software [computer program]. Version 8.Cary, NC:SAS Institute, Inc.;2000.
  26. Cook NR.Use and misuse of the receiver operating characteristic curve in risk prediction.Circulation.2007;115(7):928935.
  27. Silber JH,Williams SV,Krakauer H,Schwartz JS.Hospital and patient characteristics associated with death after surgery. A study of adverse occurrence and failure to rescue.Med Care.1992;30(7):615629.
  28. Silber JH,Rosenbaum PR,Ross RN.Comparing the contributions of groups of predictors: which outcomes vary with hospital rather than patient characteristics?J Am Stat Assoc.1995;90(429):718.
  29. Naeem N,Montenegro H.Beyond the intensive care unit: A review of interventions aimed at anticipating and preventing in‐hospital cardiopulmonary arrest.Resuscitation.2005;67(1):1323.
  30. Subbe CP,Gao H,Harrison DA.Reproducibility of physiological track‐and‐trigger warning systems for identifying at‐risk patients on the ward.Intensive Care Med.2007;33(4):619624.
  31. Ferreira FL,Bota DP,Bross A,Melot C,Vincent JL.Serial evaluation of the SOFA score to predict outcome in critically ill patients.JAMA.2001;286(14):17541758.
  32. Kuzniewicz M,Draper D,Escobar GJ.Incorporation of Physiologic Trend and Interaction Effects in Neonatal Severity of Illness Scores: An Experiment Using a Variant of the Richardson Score.Intensive Care Med.2007;33(9):16021608.
  33. Clec'h C,Ferriere F,Karoubi P, et al.Diagnostic and prognostic value of procalcitonin in patients with septic shock.Crit Care Med.2004;32(5):11661169.
  34. Hucker TR,Mitchell GP,Blake LD, et al.Identifying the sick: can biochemical measurements be used to aid decision making on presentation to the accident and emergency department.Br J Anaesth.2005;94(6):735741.
  35. Redelmeier DA.Improving patient care. The cognitive psychology of missed diagnoses.Ann Intern Med.2005;142(2):115120.
  36. Robb E,Jarman B,Suntharalingam G,Higgens C,Tennant R,Elcock K.Using care bundles to reduce in‐hospital mortality: quantitative survey.BMJ.2010;340:c1234.
  37. Sebat F,Musthafa AA,Johnson D, et al.Effect of a rapid response system for patients in shock on time to treatment and mortality during 5 years.Crit Care Med.2007;35(11):25682575.
References
  1. Kohn LT,Corrigan JM,Donaldson MS.To Err is Human: Building a Safer Health System.Washington, D. C.:National Academy Press;2000.
  2. Institute for Healthcare Improvement. Protecting 5 million lives from harm. Available at: http://www.ihi.org/IHI/Programs/Campaign. Accessed June2010.
  3. Hofer TP,Hayward RA.Identifying poor‐quality hospitals. Can hospital mortality rates detect quality problems for medical diagnoses?Med Care.1996;34(8):737753.
  4. Dimick JB,Welch HG,Birkmeyer JD.Surgical mortality as an indicator of hospital quality: the problem with small sample size.JAMA.2004;292(7):847851.
  5. State of California Office of Statewide Health Planning and Development. AHRQ ‐ Inpatient quality indicators (IQIs) hospital inpatient mortality indicators for California. Available at: http://www.oshpd.ca.gov/HID/Products/PatDischargeData/AHRQ/iqi‐imi_overview.html. Accessed June2010.
  6. Pine M,Jordan HS,Elixhauser A, et al.Enhancement of claims data to improve risk adjustment of hospital mortality.JAMA.2007;297(1):7176.
  7. Render ML,Kim HM,Deddens J, et al.Variation in outcomes in Veterans Affairs intensive care units with a computerized severity measure.Crit Care Med.2005;33(5):930939.
  8. Zimmerman JE,Kramer AA,McNair DS,Malila FM.Acute Physiology and Chronic Health Evaluation (APACHE) IV: hospital mortality assessment for today's critically ill patients.Crit Care Med.2006;34(5):12971310.
  9. Barnett MJ,Kaboli PJ,Sirio CA,Rosenthal GE.Day of the week of intensive care admission and patient outcomes: a multisite regional evaluation.Med Care.2002;40(6):530539.
  10. Ensminger SA,Morales IJ,Peters SG, et al.The hospital mortality of patients admitted to the ICU on weekends.Chest.2004;126(4):12921298.
  11. Luyt CE,Combes A,Aegerter P, et al.Mortality among patients admitted to intensive care units during weekday day shifts compared with “off” hours.Crit Care Med.2007;35(1):311.
  12. Lundberg JS,Perl TM,Wiblin T, et al.Septic shock: an analysis of outcomes for patients with onset on hospital wards versus intensive care units.Crit Care Med.1998;26(6):10201024.
  13. Schein RM,Hazday N,Pena M,Ruben BH,Sprung CL.Clinical antecedents to in‐hospital cardiopulmonary arrest.Chest.1990;98(6):13881392.
  14. Franklin C,Mathew J.Developing strategies to prevent inhospital cardiac arrest: analyzing responses of physicians and nurses in the hours before the event.Crit Care Med.1994;22(2):244247.
  15. MERIT Study Investigators.Introduction of the medical emergency team (MET) system: a cluster‐randomized controlled trial.Lancet.2005;365(9477):20912097.
  16. Institute for Healthcare Improvement.The “MERIT” Trial of Medical Emergency Teams in Australia: An Analysis of Findings and Implications.Boston, MA:2005. Available on www.ihi.org
  17. Winters BD,Pham J,Pronovost PJ.Rapid response teams‐‐walk, don't run.JAMA.2006;296(13):16451647.
  18. Griffin F,Resar R.IHI Global Trigger Tool for Measuring Adverse Events.2nd ed.Cambridge, Massachusetts:Institute for Healthcare Improvement;2009.
  19. Escobar G,Greene J,Scheirer P,Gardner M,Draper D,Kipnis P.Risk adjusting hospital inpatient mortality using automated inpatient, outpatient, and laboratory databases.Medical Care.2008;46(3):232239.
  20. van Walraven C,Escobar GJ,Greene JD,Forster AJ.The Kaiser Permanente inpatient risk adjustment methodology was valid in an external patient population.J Clin Epidemiol.2010;63(7):798803.
  21. Selby JV.Linking automated databases for research in managed care settings.Ann Intern Med.1997;127(8 Pt 2):719724.
  22. Go AS,Hylek EM,Chang Y, et al.Anticoagulation therapy for stroke prevention in atrial fibrillation: how well do randomized trials translate into clinical practice?JAMA.2003;290(20):26852692.
  23. Escobar G,Shaheen S,Breed E, et al.Richardson score predicts short‐term adverse respiratory outcomes in newborns >/=34 weeks gestation.J Pediatr.2004;145(6):754760.
  24. Escobar GJ,Fireman BH,Palen TE, et al.Risk adjusting community‐acquired pneumonia hospital outcomes using automated databases.Am J Manag Care.2008;14(3):158166.
  25. Statistical Analysis Software [computer program]. Version 8.Cary, NC:SAS Institute, Inc.;2000.
  26. Cook NR.Use and misuse of the receiver operating characteristic curve in risk prediction.Circulation.2007;115(7):928935.
  27. Silber JH,Williams SV,Krakauer H,Schwartz JS.Hospital and patient characteristics associated with death after surgery. A study of adverse occurrence and failure to rescue.Med Care.1992;30(7):615629.
  28. Silber JH,Rosenbaum PR,Ross RN.Comparing the contributions of groups of predictors: which outcomes vary with hospital rather than patient characteristics?J Am Stat Assoc.1995;90(429):718.
  29. Naeem N,Montenegro H.Beyond the intensive care unit: A review of interventions aimed at anticipating and preventing in‐hospital cardiopulmonary arrest.Resuscitation.2005;67(1):1323.
  30. Subbe CP,Gao H,Harrison DA.Reproducibility of physiological track‐and‐trigger warning systems for identifying at‐risk patients on the ward.Intensive Care Med.2007;33(4):619624.
  31. Ferreira FL,Bota DP,Bross A,Melot C,Vincent JL.Serial evaluation of the SOFA score to predict outcome in critically ill patients.JAMA.2001;286(14):17541758.
  32. Kuzniewicz M,Draper D,Escobar GJ.Incorporation of Physiologic Trend and Interaction Effects in Neonatal Severity of Illness Scores: An Experiment Using a Variant of the Richardson Score.Intensive Care Med.2007;33(9):16021608.
  33. Clec'h C,Ferriere F,Karoubi P, et al.Diagnostic and prognostic value of procalcitonin in patients with septic shock.Crit Care Med.2004;32(5):11661169.
  34. Hucker TR,Mitchell GP,Blake LD, et al.Identifying the sick: can biochemical measurements be used to aid decision making on presentation to the accident and emergency department.Br J Anaesth.2005;94(6):735741.
  35. Redelmeier DA.Improving patient care. The cognitive psychology of missed diagnoses.Ann Intern Med.2005;142(2):115120.
  36. Robb E,Jarman B,Suntharalingam G,Higgens C,Tennant R,Elcock K.Using care bundles to reduce in‐hospital mortality: quantitative survey.BMJ.2010;340:c1234.
  37. Sebat F,Musthafa AA,Johnson D, et al.Effect of a rapid response system for patients in shock on time to treatment and mortality during 5 years.Crit Care Med.2007;35(11):25682575.
Issue
Journal of Hospital Medicine - 6(2)
Issue
Journal of Hospital Medicine - 6(2)
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74-80
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74-80
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Intra‐hospital transfers to a higher level of care: Contribution to total hospital and intensive care unit (ICU) mortality and length of stay (LOS)
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Intra‐hospital transfers to a higher level of care: Contribution to total hospital and intensive care unit (ICU) mortality and length of stay (LOS)
Legacy Keywords
failure to rescue, hospital mortality, intensive care unit, intra‐hospital transfer, patient outcomes, transitional care unit
Legacy Keywords
failure to rescue, hospital mortality, intensive care unit, intra‐hospital transfer, patient outcomes, transitional care unit
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