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Medications and Pediatric Deterioration
In recent years, many hospitals have implemented rapid response systems (RRSs) in efforts to reduce mortality outside the intensive care unit (ICU). Rapid response systems include 2 clinical components (efferent and afferent limbs) and 2 organizational components (process improvement and administrative limbs).[1, 2] The efferent limb includes medical emergency teams (METs) that can be summoned to hospital wards to rescue deteriorating patients. The afferent limb identifies patients at risk of deterioration using tools such as early warning scores and triggers a MET response when appropriate.[2] The process‐improvement limb evaluates and optimizes the RRS. The administrative limb implements the RRS and supports its ongoing operation. The effectiveness of most RRSs depends upon the ward team making the decision to escalate care by activating the MET. Barriers to activating the MET may include reduced situational awareness,[3, 4] hierarchical barriers to calling for help,[3, 4, 5, 6, 7, 8] fear of criticism,[3, 8, 9] and other hospital safety cultural barriers.[3, 4, 8]
Proactive critical‐care outreach[10, 11, 12, 13] or rover[14] teams seek to reduce barriers to activation and improve outcomes by systematically identifying and evaluating at‐risk patients without relying on requests for assistance from the ward team. Structured similarly to early warning scores, surveillance tools intended for rover teams might improve their ability to rapidly identify at‐risk patients throughout a hospital. They could combine vital signs with other variables, such as diagnostic and therapeutic interventions that reflect the ward team's early, evolving concern. In particular, the incorporation of medications associated with deterioration may enhance the performance of surveillance tools.
Medications may be associated with deterioration in one of several ways. They could play a causal role in deterioration (ie, opioids causing respiratory insufficiency), represent clinical worsening and anticipation of possible deterioration (ie, broad‐spectrum antibiotics for a positive blood culture), or represent rescue therapies for early deterioration (ie, antihistamines for allergic reactions). In each case, the associated therapeutic classes could be considered sentinel markers of clinical deterioration.
Combined with vital signs and other risk factors, therapeutic classes could serve as useful components of surveillance tools to detect signs of early, evolving deterioration and flag at‐risk patients for evaluation. As a first step, we sought to identify therapeutic classes associated with clinical deterioration. This effort to improve existing afferent tools falls within the process‐improvement limb of RRSs.
PATIENTS AND METHODS
Study Design
We performed a case‐crossover study of children who experienced clinical deterioration. An alternative to the matched case‐control design, the case‐crossover design involves longitudinal within‐subject comparisons exclusively of case subjects such that an individual serves as his or her own control. It is most effective when studying intermittent exposures that result in transient changes in the risk of an acute event,[15, 16, 17] making it appropriate for our study.
Using the case‐crossover design, we compared a discrete time period in close proximity to the deterioration event, called the hazard interval, with earlier time periods in the hospitalization, called the control intervals.[15, 16, 17] In our primary analysis (Figure 1B), we defined the durations of these intervals as follows: We first censored the 2 hours immediately preceding the clinical deterioration event (hours 0 to 2). We made this decision a priori to exclude medications used after deterioration was recognized and resuscitation had already begun. The 12‐hour period immediately preceding the censored interval was the hazard interval (hours 2 to 14). Each 12‐hour period immediately preceding the hazard interval was a control interval (hours 14 to 26, 26 to 38, 38 to 50, and 50 to 62). Depending on the child's length of stay prior to the deterioration event, each hazard interval had 14 control intervals for comparison. In sensitivity analysis, we altered the durations of these intervals (see below).
Study Setting and Participants
We performed this study among children age <18 years who experienced clinical deterioration between January 1, 2005, and December 31, 2008, after being hospitalized on a general medical or surgical unit at The Children's Hospital of Philadelphia for 24 hours. Clinical deterioration was a composite outcome defined as cardiopulmonary arrest (CPA), acute respiratory compromise (ARC), or urgent ICU transfer. Cardiopulmonary arrest events required either pulselessness or a pulse with inadequate perfusion treated with chest compressions and/or defibrillation. Acute respiratory compromise events required respiratory insufficiency treated with bag‐valve‐mask or invasive airway interventions. Urgent ICU transfers included 1 of the following outcomes in the 12 hours after transfer: death, CPA, intubation, initiation of noninvasive ventilation, or administration of a vasoactive medication infusion used for the treatment of shock. Time zero was the time of the CPA/ARC, or the time at which the child arrived in the ICU for urgent transfers. These subjects also served as the cases for a previously published case‐control study evaluating different risk factors for deterioration.[18] The institutional review board of The Children's Hospital of Philadelphia approved the study.
At the time of the study, the hospital did not have a formal RRS. An immediate‐response code‐blue team was available throughout the study period for emergencies occurring outside the ICU. Physicians could also page the pediatric ICU fellow to discuss patients who did not require immediate assistance from the code‐blue team but were clinically deteriorating. There were no established triggering criteria.
Medication Exposures
Intravenous (IV) medications administered in the 72 hours prior to clinical deterioration were considered the exposures of interest. Each medication was included in 1 therapeutic classes assigned in the hospital's formulary (Lexicomp, Hudson, OH).[19] In order to determine which therapeutic classes to evaluate, we performed a power calculation using the sampsi_mcc package for Stata 12 (StataCorp, College Station, TX). We estimated that we would have 3 matched control intervals per hazard interval. We found that, in order to detect a minimum odds ratio of 3.0 with 80% power, a therapeutic class had to be administered in 5% of control periods. All therapeutic classes meeting that requirement were included in the analysis and are listed in Table 1. (See lists of the individual medications comprising each class in the Supporting Information, Tables 124, in the online version of this article.)
| Therapeutic Class | No. of Control Intervals | % |
|---|---|---|
| ||
| Sedatives | 107 | 25 |
| Antiemetics | 92 | 22 |
| Third‐ and fourth‐generation cephalosporins | 83 | 20 |
| Antihistamines | 74 | 17 |
| Antidotes to hypersensitivity reactions (diphenhydramine) | 65 | 15 |
| Gastric acid secretion inhibitors | 62 | 15 |
| Loop diuretics | 62 | 15 |
| Anti‐inflammatory agents | 61 | 14 |
| Penicillin antibiotics | 61 | 14 |
| Benzodiazepines | 59 | 14 |
| Hypnotics | 58 | 14 |
| Narcotic analgesics (full opioid agonists) | 54 | 13 |
| Antianxiety agents | 53 | 13 |
| Systemic corticosteroids | 53 | 13 |
| Glycopeptide antibiotics (vancomycin) | 46 | 11 |
| Anaerobic antibiotics | 45 | 11 |
| Histamine H2 antagonists | 41 | 10 |
| Antifungal agents | 37 | 9 |
| Phenothiazine derivatives | 37 | 9 |
| Adrenal corticosteroids | 35 | 8 |
| Antiviral agents | 30 | 7 |
| Aminoglycoside antibiotics | 26 | 6 |
| Narcotic analgesics (partial opioid agonists) | 26 | 6 |
| PPIs | 26 | 6 |
Data Collection
Data were abstracted from the electronic medication administration record (Sunrise Clinical Manager; Allscripts, Chicago, IL) into a database. For each subject, we recorded the name and time of administration of each IV medication given in the 72 hours preceding deterioration, as well as demographic, event, and hospitalization characteristics.
Statistical Analysis
We used univariable conditional logistic regression to evaluate the association between each therapeutic class and the composite outcome of clinical deterioration in the primary analysis. Because cases serve as their own controls in the case‐crossover design, this method inherently adjusts for all subject‐specific time‐invariant confounding variables, such as patient demographics, disease, and hospital‐ward characteristics.[15]
Sensitivity Analysis
Our primary analysis used a 2‐hour censored interval and 12‐hour hazard and control intervals. Excluding the censored interval from analysis was a conservative approach that we chose because our goal was to identify therapeutic classes associated with deterioration during a phase in which adverse outcomes may be prevented with early intervention. In order to test whether our findings were stable across different lengths of censored, hazard, and control intervals, we performed a sensitivity analysis, also using conditional logistic regression, on all therapeutic classes that were significant (P<0.05) in primary analysis. In 6 iterations of the sensitivity analysis, we varied the length of the hazard and control intervals between 8 and 12 hours, and the length of the censored interval between 0 and 4 hours (Figure 1AF). In a seventh iteration, we used a variant of the primary analysis in which we censored the first control interval (Figure 1G).
RESULTS
We identified 12 CPAs, 41 ARCs, and 699 ICU transfers during the study period. Of these 752 events, 141 (19%) were eligible as cases according to our inclusion criteria.[18] (A flowchart demonstrating the identification of eligible cases is provided in Supporting Table 25 in the online version of this article.) Of the 81% excluded, 37% were ICU transfers who did not meet urgent criteria. Another 31% were excluded because they were hospitalized for <24 hours at the time of the event, making their analysis in a case‐crossover design using 12‐hour periods impossible. Event characteristics, demographics, and hospitalization characteristics are shown in Table 2.
| n | % | |
|---|---|---|
| ||
| Type of event | ||
| CPA | 4 | 3 |
| ARC | 29 | 20 |
| Urgent ICU transfer | 108 | 77 |
| Demographics | ||
| Age | ||
| 0<6 months | 17 | 12 |
| 6<12 months | 22 | 16 |
| 1<4 years | 34 | 24 |
| 4<10 years | 26 | 18 |
| 10<18 years | 42 | 30 |
| Sex | ||
| F | 60 | 43 |
| M | 81 | 57 |
| Race | ||
| White | 69 | 49 |
| Black/African American | 49 | 35 |
| Asian/Pacific Islander | 0 | 0 |
| Other | 23 | 16 |
| Ethnicity | ||
| Non‐Hispanic | 127 | 90 |
| Hispanic | 14 | 10 |
| Hospitalization | ||
| Surgical service | 4 | 3 |
| Survived to hospital discharge | 107 | 76 |
Primary Analysis
A total of 141 hazard intervals and 487 control intervals were included in the primary analysis, the results of which are shown in Table 3. Among the antimicrobial therapeutic classes, glycopeptide antibiotics (vancomycin), anaerobic antibiotics, third‐generation and fourth‐generation cephalosporins, and aminoglycoside antibiotics were significant. All of the anti‐inflammatory therapeutic classes, including systemic corticosteroids, anti‐inflammatory agents, and adrenal corticosteroids, were significant. All of the sedatives, hypnotics, and antianxiety therapeutic classes, including sedatives, benzodiazepines, hypnotics, and antianxiety agents, were significant. Among the narcotic analgesic therapeutic classes, only 1 class, narcotic analgesics (full opioid agonists), was significant. None of the gastrointestinal therapeutic classes were significant. Among the classes classified as other, loop diuretics and antidotes to hypersensitivity reactions (diphenhydramine) were significant.
| OR | LCI | UCI | P Value | |
|---|---|---|---|---|
| ||||
| Antimicrobial therapeutic classes | ||||
| Glycopeptide antibiotics (vancomycin) | 5.84 | 2.01 | 16.98 | 0.001 |
| Anaerobic antibiotics | 5.33 | 1.36 | 20.94 | 0.02 |
| Third‐ and fourth‐generation cephalosporins | 2.78 | 1.15 | 6.69 | 0.02 |
| Aminoglycoside antibiotics | 2.90 | 1.11 | 7.56 | 0.03 |
| Penicillin antibiotics | 2.40 | 0.9 | 6.4 | 0.08 |
| Antiviral agents | 1.52 | 0.20 | 11.46 | 0.68 |
| Antifungal agents | 1.06 | 0.44 | 2.58 | 0.89 |
| Corticosteroids and other anti‐inflammatory therapeutic classes* | ||||
| Systemic corticosteroids | 3.69 | 1.09 | 12.55 | 0.04 |
| Anti‐inflammatory agents | 3.69 | 1.09 | 12.55 | 0.04 |
| Adrenal corticosteroids | 3.69 | 1.09 | 12.55 | 0.04 |
| Sedatives, hypnotics, and antianxiety therapeutic classes | ||||
| Sedatives | 3.48 | 1.78 | 6.78 | <0.001 |
| Benzodiazepines | 2.71 | 1.36 | 5.40 | 0.01 |
| Hypnotics | 2.54 | 1.27 | 5.09 | 0.01 |
| Antianxiety agents | 2.28 | 1.06 | 4.91 | 0.04 |
| Narcotic analgesic therapeutic classes | ||||
| Narcotic analgesics (full opioid agonists) | 2.48 | 1.07 | 5.73 | 0.03 |
| Narcotic analgesics (partial opioid agonists) | 1.97 | 0.57 | 6.85 | 0.29 |
| GI therapeutic classes | ||||
| Antiemetics | 0.57 | 0.22 | 1.48 | 0.25 |
| PPIs | 2.05 | 0.58 | 7.25 | 0.26 |
| Phenothiazine derivatives | 0.47 | 0.12 | 1.83 | 0.27 |
| Gastric acid secretion inhibitors | 1.71 | 0.61 | 4.81 | 0.31 |
| Histamine H2 antagonists | 0.95 | 0.17 | 5.19 | 0.95 |
| Other therapeutic classes | ||||
| Loop diuretics | 2.87 | 1.28 | 6.47 | 0.01 |
| Antidotes to hypersensitivity reactions (diphenhydramine) | 2.45 | 1.15 | 5.23 | 0.02 |
| Antihistamines | 2.00 | 0.97 | 4.12 | 0.06 |
Sensitivity Analysis
Of the 14 classes that were significant in primary analysis, we carried 9 forward to sensitivity analysis. The 5 that were not carried forward overlapped substantially with other classes that were carried forward. The decision of which overlapping class to carry forward was based upon (1) parsimony and (2) clinical relevance. This is described briefly in the footnotes to Table 3 (see Supporting information in the online version of this article for a full description of this process). Figure 2 presents the odds ratios and their 95% confidence intervals for the sensitivity analysis of each therapeutic class that was significant in primary analysis. Loop diuretics remained significantly associated with deterioration in all 7 iterations. Glycopeptide antibiotics (vancomycin), third‐generation and fourth‐generation cephalosporins, systemic corticosteroids, and benzodiazepines were significant in 6. Anaerobic antibiotics and narcotic analgesics (full opioid agonists) were significant in 5, and aminoglycoside antibiotics and antidotes to hypersensitivity reactions (diphenhydramine) in 4.
DISCUSSION
We identified 9 therapeutic classes which were associated with a 2.5‐fold to 5.8‐fold increased risk of clinical deterioration. The results were robust to sensitivity analysis. Given their temporal association to the deterioration events, these therapeutic classes may serve as sentinels of early deterioration and are candidate variables to combine with vital signs and other risk factors in a surveillance tool for rover teams or an early warning score.
Although most early warning scores intended for use at the bedside are based upon vital signs and clinical observations, a few also include medications. Monaghan's Pediatric Early Warning Score, the basis for many modified scores used in children's hospitals throughout the world, assigns points for children requiring frequent doses of nebulized medication.[20, 21, 22] Nebulized epinephrine is a component of the Bristol Paediatric Early Warning Tool.[23] The number of medications administered in the preceding 24 hours was included in an early version of the Bedside Paediatric Early Warning System Score.[24] Adding IV antibiotics to the Maximum Modified Early Warning Score improved prediction of the need for higher care utilization among hospitalized adults.[25]
In order to determine the role of the IV medications we found to be associated with clinical deterioration, the necessary next step is to develop a multivariable predictive model to determine if they improve the performance of existing early warning scores in identifying deteriorating patients. Although simplicity is an important characteristic of hand‐calculated early warning scores, integration of a more complex scoring system with more variables, such as these medications, into the electronic health record would allow for automated scoring, eliminating the need to sacrifice score performance to keep the tool simple. Integration into the electronic health record would have the additional benefit of making the score available to clinicians who are not at the bedside. Such tools would be especially useful for remote surveillance for deterioration by critical‐care outreach or rover teams.
Our study has several limitations. First, the sample size was small, and although we sought to minimize the likelihood of chance associations by performing sensitivity analysis, these findings should be confirmed in a larger study. Second, we only evaluated IV medications. Medications administered by other routes could also be associated with clinical deterioration and should be analyzed in future studies. Third, we excluded children hospitalized for <24 hours, as well as transfers that did not meet urgent criteria. These may be limitations because (1) the first 24 hours of hospitalization may be a high‐risk period, and (2) patients who were on trajectories toward severe deterioration and received interventions that prevented further deterioration but did not meet urgent transfer criteria were excluded. It may be that the children we included as cases were at increased risk of deterioration that is either more difficult to recognize early, or more difficult to treat effectively without ICU interventions. Finally, we acknowledge that in some cases the therapeutic classes were associated with deterioration in a causal fashion, and in others the medications administered did not cause deterioration but were signs of therapeutic interventions that were initiated in response to clinical worsening. Identifying the specific indications for administration of drugs used in response to clinical worsening may have resulted in stronger associations with deterioration. However, these indications are often complex, multifactorial, and poorly documented in real time. This limits the ability to automate their detection using the electronic health record, the ultimate goal of this line of research.
CONCLUSION
We used a case‐crossover approach to identify therapeutic classes that are associated with increased risk of clinical deterioration in hospitalized children on pediatric wards. These sentinel therapeutic classes may serve as useful components of electronic health recordbased surveillance tools to detect signs of early, evolving deterioration and flag at‐risk patients for critical‐care outreach or rover team review. Future research should focus on evaluating whether including these therapeutic classes in early warning scores improves their accuracy in detecting signs of deterioration and determining if providing this information as clinical decision support improves patient outcomes.
Acknowledgments
Disclosures: This study was funded by The Children's Hospital of Philadelphia Center for Pediatric Clinical Effectiveness Pilot Grant and the University of Pennsylvania Provost's Undergraduate Research Mentoring Program. Drs. Bonafide and Keren also receive funding from the Pennsylvania Health Research Formula Fund Award from the Pennsylvania Department of Health for research in pediatric hospital quality, safety, and costs. The authors have no other conflicts of interest to report.
- , , , et al. Findings of the first consensus conference on medical emergency teams. Crit Care Med. 2006;34(9):2463–2478.
- , , , et al. “Identifying the hospitalised patient in crisis”—a consensus conference on the afferent limb of rapid response systems. Resuscitation. 2010;81(4):375–382.
- , , , . Attitudes and barriers to a medical emergency team system at a tertiary paediatric hospital. Resuscitation. 2011;82(2):167–174.
- , , , et al. Why don't hospital staff activate the rapid response system (RRS)? How frequently is it needed and can the process be improved? Implement Sci. 2011;6:39.
- , . Failure of the afferent limb: a persistent problem in rapid response systems. Resuscitation. 2011;82(7):797–798.
- , , . Understanding how rapid response systems may improve safety for the acutely ill patient: learning from the frontline. BMJ Qual Saf. 2012;21(2):135–144.
- , , . How RNs rescue patients: a qualitative study of RNs' perceived involvement in rapid response teams. Qual Saf Health Care. 2010;19(5):1–4.
- , , , et al. A survey of nurses' beliefs about the medical emergency team system in a Canadian tertiary hospital. Am J Crit Care. 2010;19(1):74–83.
- , , , et al. Nurses' attitudes to a medical emergency team service in a teaching hospital. Qual Saf Health Care. 2006;15(6):427–432.
- , , , et al. Introducing critical care outreach: a ward‐randomised trial of phased introduction in a general hospital. Intensive Care Med. 2004;30(7):1398–1404.
- . Out of our reach? Assessing the impact of introducing a critical care outreach service. Anaesthesia. 2003;58(9):882–885.
- , , . Effect of the critical care outreach team on patient survival to discharge from hospital and readmission to critical care: non‐randomised population based study. BMJ. 2003;327(7422):1014.
- , , , et al. Successful implementation of a family and patient activated rapid response team in an adult level 1 trauma center. Resuscitation. 2010;81(12):1676–1681.
- , , , et al. Beyond rapid response teams: instituting a “Rover Team” improves the management of at‐risk patients, facilitates proactive interventions, and improves outcomes. In: Henriksen K, Battles JB, Keyes MA, Grady ML, eds. Advances in Patient Safety: New Directions and Alternative Approaches. Rockville, MD: Agency for Healthcare Research and Quality; 2008.
- , . The case‐crossover study design in pharmacoepidemiology. Stat Methods Med Res. 2009;18(1):53–65.
- , , , et al. Comparison of the statistical efficiency of case‐crossover and case‐control designs: application to severe cutaneous adverse reactions. J Clin Epidemiol. 2001;54(12):1218–1227.
- . The case‐crossover design: a method for studying transient effects on the risk of acute events. Am J Epidemiol. 1991;133(2):144–153.
- , , , , , . Development of a score to predict clinical deterioration in hospitalized children. J Hosp Med. 2012;7(4):345–349.
- Lexicomp. Available at: http://www.lexi.com. Accessed July 26, 2012.
- , , , , , . Sensitivity of the Pediatric Early Warning Score to identify patient deterioration. Pediatrics. 2010;125(4):e763–e769.
- . Detecting and managing deterioration in children. Paediatr Nurs. 2005;17(1):32–35.
- , , , , . Prospective evaluation of a pediatric inpatient early warning scoring system. J Spec Pediatr Nurs. 2009;14(2):79–85.
- , , . Promoting care for acutely ill children—development and evaluation of a Paediatric Early Warning Tool. Intensive Crit Care Nurs. 2006;22(2):73–81.
- , , . The Pediatric Early Warning System Score: a severity of illness score to predict urgent medical need in hospitalized children. J Crit Care. 2006;21(3):271–278.
- , , , , , . Performance of the maximum modified early warning score to predict the need for higher care utilization among admitted emergency department patients. J Hosp Med. 2010;5(1):E46–E52.
In recent years, many hospitals have implemented rapid response systems (RRSs) in efforts to reduce mortality outside the intensive care unit (ICU). Rapid response systems include 2 clinical components (efferent and afferent limbs) and 2 organizational components (process improvement and administrative limbs).[1, 2] The efferent limb includes medical emergency teams (METs) that can be summoned to hospital wards to rescue deteriorating patients. The afferent limb identifies patients at risk of deterioration using tools such as early warning scores and triggers a MET response when appropriate.[2] The process‐improvement limb evaluates and optimizes the RRS. The administrative limb implements the RRS and supports its ongoing operation. The effectiveness of most RRSs depends upon the ward team making the decision to escalate care by activating the MET. Barriers to activating the MET may include reduced situational awareness,[3, 4] hierarchical barriers to calling for help,[3, 4, 5, 6, 7, 8] fear of criticism,[3, 8, 9] and other hospital safety cultural barriers.[3, 4, 8]
Proactive critical‐care outreach[10, 11, 12, 13] or rover[14] teams seek to reduce barriers to activation and improve outcomes by systematically identifying and evaluating at‐risk patients without relying on requests for assistance from the ward team. Structured similarly to early warning scores, surveillance tools intended for rover teams might improve their ability to rapidly identify at‐risk patients throughout a hospital. They could combine vital signs with other variables, such as diagnostic and therapeutic interventions that reflect the ward team's early, evolving concern. In particular, the incorporation of medications associated with deterioration may enhance the performance of surveillance tools.
Medications may be associated with deterioration in one of several ways. They could play a causal role in deterioration (ie, opioids causing respiratory insufficiency), represent clinical worsening and anticipation of possible deterioration (ie, broad‐spectrum antibiotics for a positive blood culture), or represent rescue therapies for early deterioration (ie, antihistamines for allergic reactions). In each case, the associated therapeutic classes could be considered sentinel markers of clinical deterioration.
Combined with vital signs and other risk factors, therapeutic classes could serve as useful components of surveillance tools to detect signs of early, evolving deterioration and flag at‐risk patients for evaluation. As a first step, we sought to identify therapeutic classes associated with clinical deterioration. This effort to improve existing afferent tools falls within the process‐improvement limb of RRSs.
PATIENTS AND METHODS
Study Design
We performed a case‐crossover study of children who experienced clinical deterioration. An alternative to the matched case‐control design, the case‐crossover design involves longitudinal within‐subject comparisons exclusively of case subjects such that an individual serves as his or her own control. It is most effective when studying intermittent exposures that result in transient changes in the risk of an acute event,[15, 16, 17] making it appropriate for our study.
Using the case‐crossover design, we compared a discrete time period in close proximity to the deterioration event, called the hazard interval, with earlier time periods in the hospitalization, called the control intervals.[15, 16, 17] In our primary analysis (Figure 1B), we defined the durations of these intervals as follows: We first censored the 2 hours immediately preceding the clinical deterioration event (hours 0 to 2). We made this decision a priori to exclude medications used after deterioration was recognized and resuscitation had already begun. The 12‐hour period immediately preceding the censored interval was the hazard interval (hours 2 to 14). Each 12‐hour period immediately preceding the hazard interval was a control interval (hours 14 to 26, 26 to 38, 38 to 50, and 50 to 62). Depending on the child's length of stay prior to the deterioration event, each hazard interval had 14 control intervals for comparison. In sensitivity analysis, we altered the durations of these intervals (see below).
Study Setting and Participants
We performed this study among children age <18 years who experienced clinical deterioration between January 1, 2005, and December 31, 2008, after being hospitalized on a general medical or surgical unit at The Children's Hospital of Philadelphia for 24 hours. Clinical deterioration was a composite outcome defined as cardiopulmonary arrest (CPA), acute respiratory compromise (ARC), or urgent ICU transfer. Cardiopulmonary arrest events required either pulselessness or a pulse with inadequate perfusion treated with chest compressions and/or defibrillation. Acute respiratory compromise events required respiratory insufficiency treated with bag‐valve‐mask or invasive airway interventions. Urgent ICU transfers included 1 of the following outcomes in the 12 hours after transfer: death, CPA, intubation, initiation of noninvasive ventilation, or administration of a vasoactive medication infusion used for the treatment of shock. Time zero was the time of the CPA/ARC, or the time at which the child arrived in the ICU for urgent transfers. These subjects also served as the cases for a previously published case‐control study evaluating different risk factors for deterioration.[18] The institutional review board of The Children's Hospital of Philadelphia approved the study.
At the time of the study, the hospital did not have a formal RRS. An immediate‐response code‐blue team was available throughout the study period for emergencies occurring outside the ICU. Physicians could also page the pediatric ICU fellow to discuss patients who did not require immediate assistance from the code‐blue team but were clinically deteriorating. There were no established triggering criteria.
Medication Exposures
Intravenous (IV) medications administered in the 72 hours prior to clinical deterioration were considered the exposures of interest. Each medication was included in 1 therapeutic classes assigned in the hospital's formulary (Lexicomp, Hudson, OH).[19] In order to determine which therapeutic classes to evaluate, we performed a power calculation using the sampsi_mcc package for Stata 12 (StataCorp, College Station, TX). We estimated that we would have 3 matched control intervals per hazard interval. We found that, in order to detect a minimum odds ratio of 3.0 with 80% power, a therapeutic class had to be administered in 5% of control periods. All therapeutic classes meeting that requirement were included in the analysis and are listed in Table 1. (See lists of the individual medications comprising each class in the Supporting Information, Tables 124, in the online version of this article.)
| Therapeutic Class | No. of Control Intervals | % |
|---|---|---|
| ||
| Sedatives | 107 | 25 |
| Antiemetics | 92 | 22 |
| Third‐ and fourth‐generation cephalosporins | 83 | 20 |
| Antihistamines | 74 | 17 |
| Antidotes to hypersensitivity reactions (diphenhydramine) | 65 | 15 |
| Gastric acid secretion inhibitors | 62 | 15 |
| Loop diuretics | 62 | 15 |
| Anti‐inflammatory agents | 61 | 14 |
| Penicillin antibiotics | 61 | 14 |
| Benzodiazepines | 59 | 14 |
| Hypnotics | 58 | 14 |
| Narcotic analgesics (full opioid agonists) | 54 | 13 |
| Antianxiety agents | 53 | 13 |
| Systemic corticosteroids | 53 | 13 |
| Glycopeptide antibiotics (vancomycin) | 46 | 11 |
| Anaerobic antibiotics | 45 | 11 |
| Histamine H2 antagonists | 41 | 10 |
| Antifungal agents | 37 | 9 |
| Phenothiazine derivatives | 37 | 9 |
| Adrenal corticosteroids | 35 | 8 |
| Antiviral agents | 30 | 7 |
| Aminoglycoside antibiotics | 26 | 6 |
| Narcotic analgesics (partial opioid agonists) | 26 | 6 |
| PPIs | 26 | 6 |
Data Collection
Data were abstracted from the electronic medication administration record (Sunrise Clinical Manager; Allscripts, Chicago, IL) into a database. For each subject, we recorded the name and time of administration of each IV medication given in the 72 hours preceding deterioration, as well as demographic, event, and hospitalization characteristics.
Statistical Analysis
We used univariable conditional logistic regression to evaluate the association between each therapeutic class and the composite outcome of clinical deterioration in the primary analysis. Because cases serve as their own controls in the case‐crossover design, this method inherently adjusts for all subject‐specific time‐invariant confounding variables, such as patient demographics, disease, and hospital‐ward characteristics.[15]
Sensitivity Analysis
Our primary analysis used a 2‐hour censored interval and 12‐hour hazard and control intervals. Excluding the censored interval from analysis was a conservative approach that we chose because our goal was to identify therapeutic classes associated with deterioration during a phase in which adverse outcomes may be prevented with early intervention. In order to test whether our findings were stable across different lengths of censored, hazard, and control intervals, we performed a sensitivity analysis, also using conditional logistic regression, on all therapeutic classes that were significant (P<0.05) in primary analysis. In 6 iterations of the sensitivity analysis, we varied the length of the hazard and control intervals between 8 and 12 hours, and the length of the censored interval between 0 and 4 hours (Figure 1AF). In a seventh iteration, we used a variant of the primary analysis in which we censored the first control interval (Figure 1G).
RESULTS
We identified 12 CPAs, 41 ARCs, and 699 ICU transfers during the study period. Of these 752 events, 141 (19%) were eligible as cases according to our inclusion criteria.[18] (A flowchart demonstrating the identification of eligible cases is provided in Supporting Table 25 in the online version of this article.) Of the 81% excluded, 37% were ICU transfers who did not meet urgent criteria. Another 31% were excluded because they were hospitalized for <24 hours at the time of the event, making their analysis in a case‐crossover design using 12‐hour periods impossible. Event characteristics, demographics, and hospitalization characteristics are shown in Table 2.
| n | % | |
|---|---|---|
| ||
| Type of event | ||
| CPA | 4 | 3 |
| ARC | 29 | 20 |
| Urgent ICU transfer | 108 | 77 |
| Demographics | ||
| Age | ||
| 0<6 months | 17 | 12 |
| 6<12 months | 22 | 16 |
| 1<4 years | 34 | 24 |
| 4<10 years | 26 | 18 |
| 10<18 years | 42 | 30 |
| Sex | ||
| F | 60 | 43 |
| M | 81 | 57 |
| Race | ||
| White | 69 | 49 |
| Black/African American | 49 | 35 |
| Asian/Pacific Islander | 0 | 0 |
| Other | 23 | 16 |
| Ethnicity | ||
| Non‐Hispanic | 127 | 90 |
| Hispanic | 14 | 10 |
| Hospitalization | ||
| Surgical service | 4 | 3 |
| Survived to hospital discharge | 107 | 76 |
Primary Analysis
A total of 141 hazard intervals and 487 control intervals were included in the primary analysis, the results of which are shown in Table 3. Among the antimicrobial therapeutic classes, glycopeptide antibiotics (vancomycin), anaerobic antibiotics, third‐generation and fourth‐generation cephalosporins, and aminoglycoside antibiotics were significant. All of the anti‐inflammatory therapeutic classes, including systemic corticosteroids, anti‐inflammatory agents, and adrenal corticosteroids, were significant. All of the sedatives, hypnotics, and antianxiety therapeutic classes, including sedatives, benzodiazepines, hypnotics, and antianxiety agents, were significant. Among the narcotic analgesic therapeutic classes, only 1 class, narcotic analgesics (full opioid agonists), was significant. None of the gastrointestinal therapeutic classes were significant. Among the classes classified as other, loop diuretics and antidotes to hypersensitivity reactions (diphenhydramine) were significant.
| OR | LCI | UCI | P Value | |
|---|---|---|---|---|
| ||||
| Antimicrobial therapeutic classes | ||||
| Glycopeptide antibiotics (vancomycin) | 5.84 | 2.01 | 16.98 | 0.001 |
| Anaerobic antibiotics | 5.33 | 1.36 | 20.94 | 0.02 |
| Third‐ and fourth‐generation cephalosporins | 2.78 | 1.15 | 6.69 | 0.02 |
| Aminoglycoside antibiotics | 2.90 | 1.11 | 7.56 | 0.03 |
| Penicillin antibiotics | 2.40 | 0.9 | 6.4 | 0.08 |
| Antiviral agents | 1.52 | 0.20 | 11.46 | 0.68 |
| Antifungal agents | 1.06 | 0.44 | 2.58 | 0.89 |
| Corticosteroids and other anti‐inflammatory therapeutic classes* | ||||
| Systemic corticosteroids | 3.69 | 1.09 | 12.55 | 0.04 |
| Anti‐inflammatory agents | 3.69 | 1.09 | 12.55 | 0.04 |
| Adrenal corticosteroids | 3.69 | 1.09 | 12.55 | 0.04 |
| Sedatives, hypnotics, and antianxiety therapeutic classes | ||||
| Sedatives | 3.48 | 1.78 | 6.78 | <0.001 |
| Benzodiazepines | 2.71 | 1.36 | 5.40 | 0.01 |
| Hypnotics | 2.54 | 1.27 | 5.09 | 0.01 |
| Antianxiety agents | 2.28 | 1.06 | 4.91 | 0.04 |
| Narcotic analgesic therapeutic classes | ||||
| Narcotic analgesics (full opioid agonists) | 2.48 | 1.07 | 5.73 | 0.03 |
| Narcotic analgesics (partial opioid agonists) | 1.97 | 0.57 | 6.85 | 0.29 |
| GI therapeutic classes | ||||
| Antiemetics | 0.57 | 0.22 | 1.48 | 0.25 |
| PPIs | 2.05 | 0.58 | 7.25 | 0.26 |
| Phenothiazine derivatives | 0.47 | 0.12 | 1.83 | 0.27 |
| Gastric acid secretion inhibitors | 1.71 | 0.61 | 4.81 | 0.31 |
| Histamine H2 antagonists | 0.95 | 0.17 | 5.19 | 0.95 |
| Other therapeutic classes | ||||
| Loop diuretics | 2.87 | 1.28 | 6.47 | 0.01 |
| Antidotes to hypersensitivity reactions (diphenhydramine) | 2.45 | 1.15 | 5.23 | 0.02 |
| Antihistamines | 2.00 | 0.97 | 4.12 | 0.06 |
Sensitivity Analysis
Of the 14 classes that were significant in primary analysis, we carried 9 forward to sensitivity analysis. The 5 that were not carried forward overlapped substantially with other classes that were carried forward. The decision of which overlapping class to carry forward was based upon (1) parsimony and (2) clinical relevance. This is described briefly in the footnotes to Table 3 (see Supporting information in the online version of this article for a full description of this process). Figure 2 presents the odds ratios and their 95% confidence intervals for the sensitivity analysis of each therapeutic class that was significant in primary analysis. Loop diuretics remained significantly associated with deterioration in all 7 iterations. Glycopeptide antibiotics (vancomycin), third‐generation and fourth‐generation cephalosporins, systemic corticosteroids, and benzodiazepines were significant in 6. Anaerobic antibiotics and narcotic analgesics (full opioid agonists) were significant in 5, and aminoglycoside antibiotics and antidotes to hypersensitivity reactions (diphenhydramine) in 4.
DISCUSSION
We identified 9 therapeutic classes which were associated with a 2.5‐fold to 5.8‐fold increased risk of clinical deterioration. The results were robust to sensitivity analysis. Given their temporal association to the deterioration events, these therapeutic classes may serve as sentinels of early deterioration and are candidate variables to combine with vital signs and other risk factors in a surveillance tool for rover teams or an early warning score.
Although most early warning scores intended for use at the bedside are based upon vital signs and clinical observations, a few also include medications. Monaghan's Pediatric Early Warning Score, the basis for many modified scores used in children's hospitals throughout the world, assigns points for children requiring frequent doses of nebulized medication.[20, 21, 22] Nebulized epinephrine is a component of the Bristol Paediatric Early Warning Tool.[23] The number of medications administered in the preceding 24 hours was included in an early version of the Bedside Paediatric Early Warning System Score.[24] Adding IV antibiotics to the Maximum Modified Early Warning Score improved prediction of the need for higher care utilization among hospitalized adults.[25]
In order to determine the role of the IV medications we found to be associated with clinical deterioration, the necessary next step is to develop a multivariable predictive model to determine if they improve the performance of existing early warning scores in identifying deteriorating patients. Although simplicity is an important characteristic of hand‐calculated early warning scores, integration of a more complex scoring system with more variables, such as these medications, into the electronic health record would allow for automated scoring, eliminating the need to sacrifice score performance to keep the tool simple. Integration into the electronic health record would have the additional benefit of making the score available to clinicians who are not at the bedside. Such tools would be especially useful for remote surveillance for deterioration by critical‐care outreach or rover teams.
Our study has several limitations. First, the sample size was small, and although we sought to minimize the likelihood of chance associations by performing sensitivity analysis, these findings should be confirmed in a larger study. Second, we only evaluated IV medications. Medications administered by other routes could also be associated with clinical deterioration and should be analyzed in future studies. Third, we excluded children hospitalized for <24 hours, as well as transfers that did not meet urgent criteria. These may be limitations because (1) the first 24 hours of hospitalization may be a high‐risk period, and (2) patients who were on trajectories toward severe deterioration and received interventions that prevented further deterioration but did not meet urgent transfer criteria were excluded. It may be that the children we included as cases were at increased risk of deterioration that is either more difficult to recognize early, or more difficult to treat effectively without ICU interventions. Finally, we acknowledge that in some cases the therapeutic classes were associated with deterioration in a causal fashion, and in others the medications administered did not cause deterioration but were signs of therapeutic interventions that were initiated in response to clinical worsening. Identifying the specific indications for administration of drugs used in response to clinical worsening may have resulted in stronger associations with deterioration. However, these indications are often complex, multifactorial, and poorly documented in real time. This limits the ability to automate their detection using the electronic health record, the ultimate goal of this line of research.
CONCLUSION
We used a case‐crossover approach to identify therapeutic classes that are associated with increased risk of clinical deterioration in hospitalized children on pediatric wards. These sentinel therapeutic classes may serve as useful components of electronic health recordbased surveillance tools to detect signs of early, evolving deterioration and flag at‐risk patients for critical‐care outreach or rover team review. Future research should focus on evaluating whether including these therapeutic classes in early warning scores improves their accuracy in detecting signs of deterioration and determining if providing this information as clinical decision support improves patient outcomes.
Acknowledgments
Disclosures: This study was funded by The Children's Hospital of Philadelphia Center for Pediatric Clinical Effectiveness Pilot Grant and the University of Pennsylvania Provost's Undergraduate Research Mentoring Program. Drs. Bonafide and Keren also receive funding from the Pennsylvania Health Research Formula Fund Award from the Pennsylvania Department of Health for research in pediatric hospital quality, safety, and costs. The authors have no other conflicts of interest to report.
In recent years, many hospitals have implemented rapid response systems (RRSs) in efforts to reduce mortality outside the intensive care unit (ICU). Rapid response systems include 2 clinical components (efferent and afferent limbs) and 2 organizational components (process improvement and administrative limbs).[1, 2] The efferent limb includes medical emergency teams (METs) that can be summoned to hospital wards to rescue deteriorating patients. The afferent limb identifies patients at risk of deterioration using tools such as early warning scores and triggers a MET response when appropriate.[2] The process‐improvement limb evaluates and optimizes the RRS. The administrative limb implements the RRS and supports its ongoing operation. The effectiveness of most RRSs depends upon the ward team making the decision to escalate care by activating the MET. Barriers to activating the MET may include reduced situational awareness,[3, 4] hierarchical barriers to calling for help,[3, 4, 5, 6, 7, 8] fear of criticism,[3, 8, 9] and other hospital safety cultural barriers.[3, 4, 8]
Proactive critical‐care outreach[10, 11, 12, 13] or rover[14] teams seek to reduce barriers to activation and improve outcomes by systematically identifying and evaluating at‐risk patients without relying on requests for assistance from the ward team. Structured similarly to early warning scores, surveillance tools intended for rover teams might improve their ability to rapidly identify at‐risk patients throughout a hospital. They could combine vital signs with other variables, such as diagnostic and therapeutic interventions that reflect the ward team's early, evolving concern. In particular, the incorporation of medications associated with deterioration may enhance the performance of surveillance tools.
Medications may be associated with deterioration in one of several ways. They could play a causal role in deterioration (ie, opioids causing respiratory insufficiency), represent clinical worsening and anticipation of possible deterioration (ie, broad‐spectrum antibiotics for a positive blood culture), or represent rescue therapies for early deterioration (ie, antihistamines for allergic reactions). In each case, the associated therapeutic classes could be considered sentinel markers of clinical deterioration.
Combined with vital signs and other risk factors, therapeutic classes could serve as useful components of surveillance tools to detect signs of early, evolving deterioration and flag at‐risk patients for evaluation. As a first step, we sought to identify therapeutic classes associated with clinical deterioration. This effort to improve existing afferent tools falls within the process‐improvement limb of RRSs.
PATIENTS AND METHODS
Study Design
We performed a case‐crossover study of children who experienced clinical deterioration. An alternative to the matched case‐control design, the case‐crossover design involves longitudinal within‐subject comparisons exclusively of case subjects such that an individual serves as his or her own control. It is most effective when studying intermittent exposures that result in transient changes in the risk of an acute event,[15, 16, 17] making it appropriate for our study.
Using the case‐crossover design, we compared a discrete time period in close proximity to the deterioration event, called the hazard interval, with earlier time periods in the hospitalization, called the control intervals.[15, 16, 17] In our primary analysis (Figure 1B), we defined the durations of these intervals as follows: We first censored the 2 hours immediately preceding the clinical deterioration event (hours 0 to 2). We made this decision a priori to exclude medications used after deterioration was recognized and resuscitation had already begun. The 12‐hour period immediately preceding the censored interval was the hazard interval (hours 2 to 14). Each 12‐hour period immediately preceding the hazard interval was a control interval (hours 14 to 26, 26 to 38, 38 to 50, and 50 to 62). Depending on the child's length of stay prior to the deterioration event, each hazard interval had 14 control intervals for comparison. In sensitivity analysis, we altered the durations of these intervals (see below).
Study Setting and Participants
We performed this study among children age <18 years who experienced clinical deterioration between January 1, 2005, and December 31, 2008, after being hospitalized on a general medical or surgical unit at The Children's Hospital of Philadelphia for 24 hours. Clinical deterioration was a composite outcome defined as cardiopulmonary arrest (CPA), acute respiratory compromise (ARC), or urgent ICU transfer. Cardiopulmonary arrest events required either pulselessness or a pulse with inadequate perfusion treated with chest compressions and/or defibrillation. Acute respiratory compromise events required respiratory insufficiency treated with bag‐valve‐mask or invasive airway interventions. Urgent ICU transfers included 1 of the following outcomes in the 12 hours after transfer: death, CPA, intubation, initiation of noninvasive ventilation, or administration of a vasoactive medication infusion used for the treatment of shock. Time zero was the time of the CPA/ARC, or the time at which the child arrived in the ICU for urgent transfers. These subjects also served as the cases for a previously published case‐control study evaluating different risk factors for deterioration.[18] The institutional review board of The Children's Hospital of Philadelphia approved the study.
At the time of the study, the hospital did not have a formal RRS. An immediate‐response code‐blue team was available throughout the study period for emergencies occurring outside the ICU. Physicians could also page the pediatric ICU fellow to discuss patients who did not require immediate assistance from the code‐blue team but were clinically deteriorating. There were no established triggering criteria.
Medication Exposures
Intravenous (IV) medications administered in the 72 hours prior to clinical deterioration were considered the exposures of interest. Each medication was included in 1 therapeutic classes assigned in the hospital's formulary (Lexicomp, Hudson, OH).[19] In order to determine which therapeutic classes to evaluate, we performed a power calculation using the sampsi_mcc package for Stata 12 (StataCorp, College Station, TX). We estimated that we would have 3 matched control intervals per hazard interval. We found that, in order to detect a minimum odds ratio of 3.0 with 80% power, a therapeutic class had to be administered in 5% of control periods. All therapeutic classes meeting that requirement were included in the analysis and are listed in Table 1. (See lists of the individual medications comprising each class in the Supporting Information, Tables 124, in the online version of this article.)
| Therapeutic Class | No. of Control Intervals | % |
|---|---|---|
| ||
| Sedatives | 107 | 25 |
| Antiemetics | 92 | 22 |
| Third‐ and fourth‐generation cephalosporins | 83 | 20 |
| Antihistamines | 74 | 17 |
| Antidotes to hypersensitivity reactions (diphenhydramine) | 65 | 15 |
| Gastric acid secretion inhibitors | 62 | 15 |
| Loop diuretics | 62 | 15 |
| Anti‐inflammatory agents | 61 | 14 |
| Penicillin antibiotics | 61 | 14 |
| Benzodiazepines | 59 | 14 |
| Hypnotics | 58 | 14 |
| Narcotic analgesics (full opioid agonists) | 54 | 13 |
| Antianxiety agents | 53 | 13 |
| Systemic corticosteroids | 53 | 13 |
| Glycopeptide antibiotics (vancomycin) | 46 | 11 |
| Anaerobic antibiotics | 45 | 11 |
| Histamine H2 antagonists | 41 | 10 |
| Antifungal agents | 37 | 9 |
| Phenothiazine derivatives | 37 | 9 |
| Adrenal corticosteroids | 35 | 8 |
| Antiviral agents | 30 | 7 |
| Aminoglycoside antibiotics | 26 | 6 |
| Narcotic analgesics (partial opioid agonists) | 26 | 6 |
| PPIs | 26 | 6 |
Data Collection
Data were abstracted from the electronic medication administration record (Sunrise Clinical Manager; Allscripts, Chicago, IL) into a database. For each subject, we recorded the name and time of administration of each IV medication given in the 72 hours preceding deterioration, as well as demographic, event, and hospitalization characteristics.
Statistical Analysis
We used univariable conditional logistic regression to evaluate the association between each therapeutic class and the composite outcome of clinical deterioration in the primary analysis. Because cases serve as their own controls in the case‐crossover design, this method inherently adjusts for all subject‐specific time‐invariant confounding variables, such as patient demographics, disease, and hospital‐ward characteristics.[15]
Sensitivity Analysis
Our primary analysis used a 2‐hour censored interval and 12‐hour hazard and control intervals. Excluding the censored interval from analysis was a conservative approach that we chose because our goal was to identify therapeutic classes associated with deterioration during a phase in which adverse outcomes may be prevented with early intervention. In order to test whether our findings were stable across different lengths of censored, hazard, and control intervals, we performed a sensitivity analysis, also using conditional logistic regression, on all therapeutic classes that were significant (P<0.05) in primary analysis. In 6 iterations of the sensitivity analysis, we varied the length of the hazard and control intervals between 8 and 12 hours, and the length of the censored interval between 0 and 4 hours (Figure 1AF). In a seventh iteration, we used a variant of the primary analysis in which we censored the first control interval (Figure 1G).
RESULTS
We identified 12 CPAs, 41 ARCs, and 699 ICU transfers during the study period. Of these 752 events, 141 (19%) were eligible as cases according to our inclusion criteria.[18] (A flowchart demonstrating the identification of eligible cases is provided in Supporting Table 25 in the online version of this article.) Of the 81% excluded, 37% were ICU transfers who did not meet urgent criteria. Another 31% were excluded because they were hospitalized for <24 hours at the time of the event, making their analysis in a case‐crossover design using 12‐hour periods impossible. Event characteristics, demographics, and hospitalization characteristics are shown in Table 2.
| n | % | |
|---|---|---|
| ||
| Type of event | ||
| CPA | 4 | 3 |
| ARC | 29 | 20 |
| Urgent ICU transfer | 108 | 77 |
| Demographics | ||
| Age | ||
| 0<6 months | 17 | 12 |
| 6<12 months | 22 | 16 |
| 1<4 years | 34 | 24 |
| 4<10 years | 26 | 18 |
| 10<18 years | 42 | 30 |
| Sex | ||
| F | 60 | 43 |
| M | 81 | 57 |
| Race | ||
| White | 69 | 49 |
| Black/African American | 49 | 35 |
| Asian/Pacific Islander | 0 | 0 |
| Other | 23 | 16 |
| Ethnicity | ||
| Non‐Hispanic | 127 | 90 |
| Hispanic | 14 | 10 |
| Hospitalization | ||
| Surgical service | 4 | 3 |
| Survived to hospital discharge | 107 | 76 |
Primary Analysis
A total of 141 hazard intervals and 487 control intervals were included in the primary analysis, the results of which are shown in Table 3. Among the antimicrobial therapeutic classes, glycopeptide antibiotics (vancomycin), anaerobic antibiotics, third‐generation and fourth‐generation cephalosporins, and aminoglycoside antibiotics were significant. All of the anti‐inflammatory therapeutic classes, including systemic corticosteroids, anti‐inflammatory agents, and adrenal corticosteroids, were significant. All of the sedatives, hypnotics, and antianxiety therapeutic classes, including sedatives, benzodiazepines, hypnotics, and antianxiety agents, were significant. Among the narcotic analgesic therapeutic classes, only 1 class, narcotic analgesics (full opioid agonists), was significant. None of the gastrointestinal therapeutic classes were significant. Among the classes classified as other, loop diuretics and antidotes to hypersensitivity reactions (diphenhydramine) were significant.
| OR | LCI | UCI | P Value | |
|---|---|---|---|---|
| ||||
| Antimicrobial therapeutic classes | ||||
| Glycopeptide antibiotics (vancomycin) | 5.84 | 2.01 | 16.98 | 0.001 |
| Anaerobic antibiotics | 5.33 | 1.36 | 20.94 | 0.02 |
| Third‐ and fourth‐generation cephalosporins | 2.78 | 1.15 | 6.69 | 0.02 |
| Aminoglycoside antibiotics | 2.90 | 1.11 | 7.56 | 0.03 |
| Penicillin antibiotics | 2.40 | 0.9 | 6.4 | 0.08 |
| Antiviral agents | 1.52 | 0.20 | 11.46 | 0.68 |
| Antifungal agents | 1.06 | 0.44 | 2.58 | 0.89 |
| Corticosteroids and other anti‐inflammatory therapeutic classes* | ||||
| Systemic corticosteroids | 3.69 | 1.09 | 12.55 | 0.04 |
| Anti‐inflammatory agents | 3.69 | 1.09 | 12.55 | 0.04 |
| Adrenal corticosteroids | 3.69 | 1.09 | 12.55 | 0.04 |
| Sedatives, hypnotics, and antianxiety therapeutic classes | ||||
| Sedatives | 3.48 | 1.78 | 6.78 | <0.001 |
| Benzodiazepines | 2.71 | 1.36 | 5.40 | 0.01 |
| Hypnotics | 2.54 | 1.27 | 5.09 | 0.01 |
| Antianxiety agents | 2.28 | 1.06 | 4.91 | 0.04 |
| Narcotic analgesic therapeutic classes | ||||
| Narcotic analgesics (full opioid agonists) | 2.48 | 1.07 | 5.73 | 0.03 |
| Narcotic analgesics (partial opioid agonists) | 1.97 | 0.57 | 6.85 | 0.29 |
| GI therapeutic classes | ||||
| Antiemetics | 0.57 | 0.22 | 1.48 | 0.25 |
| PPIs | 2.05 | 0.58 | 7.25 | 0.26 |
| Phenothiazine derivatives | 0.47 | 0.12 | 1.83 | 0.27 |
| Gastric acid secretion inhibitors | 1.71 | 0.61 | 4.81 | 0.31 |
| Histamine H2 antagonists | 0.95 | 0.17 | 5.19 | 0.95 |
| Other therapeutic classes | ||||
| Loop diuretics | 2.87 | 1.28 | 6.47 | 0.01 |
| Antidotes to hypersensitivity reactions (diphenhydramine) | 2.45 | 1.15 | 5.23 | 0.02 |
| Antihistamines | 2.00 | 0.97 | 4.12 | 0.06 |
Sensitivity Analysis
Of the 14 classes that were significant in primary analysis, we carried 9 forward to sensitivity analysis. The 5 that were not carried forward overlapped substantially with other classes that were carried forward. The decision of which overlapping class to carry forward was based upon (1) parsimony and (2) clinical relevance. This is described briefly in the footnotes to Table 3 (see Supporting information in the online version of this article for a full description of this process). Figure 2 presents the odds ratios and their 95% confidence intervals for the sensitivity analysis of each therapeutic class that was significant in primary analysis. Loop diuretics remained significantly associated with deterioration in all 7 iterations. Glycopeptide antibiotics (vancomycin), third‐generation and fourth‐generation cephalosporins, systemic corticosteroids, and benzodiazepines were significant in 6. Anaerobic antibiotics and narcotic analgesics (full opioid agonists) were significant in 5, and aminoglycoside antibiotics and antidotes to hypersensitivity reactions (diphenhydramine) in 4.
DISCUSSION
We identified 9 therapeutic classes which were associated with a 2.5‐fold to 5.8‐fold increased risk of clinical deterioration. The results were robust to sensitivity analysis. Given their temporal association to the deterioration events, these therapeutic classes may serve as sentinels of early deterioration and are candidate variables to combine with vital signs and other risk factors in a surveillance tool for rover teams or an early warning score.
Although most early warning scores intended for use at the bedside are based upon vital signs and clinical observations, a few also include medications. Monaghan's Pediatric Early Warning Score, the basis for many modified scores used in children's hospitals throughout the world, assigns points for children requiring frequent doses of nebulized medication.[20, 21, 22] Nebulized epinephrine is a component of the Bristol Paediatric Early Warning Tool.[23] The number of medications administered in the preceding 24 hours was included in an early version of the Bedside Paediatric Early Warning System Score.[24] Adding IV antibiotics to the Maximum Modified Early Warning Score improved prediction of the need for higher care utilization among hospitalized adults.[25]
In order to determine the role of the IV medications we found to be associated with clinical deterioration, the necessary next step is to develop a multivariable predictive model to determine if they improve the performance of existing early warning scores in identifying deteriorating patients. Although simplicity is an important characteristic of hand‐calculated early warning scores, integration of a more complex scoring system with more variables, such as these medications, into the electronic health record would allow for automated scoring, eliminating the need to sacrifice score performance to keep the tool simple. Integration into the electronic health record would have the additional benefit of making the score available to clinicians who are not at the bedside. Such tools would be especially useful for remote surveillance for deterioration by critical‐care outreach or rover teams.
Our study has several limitations. First, the sample size was small, and although we sought to minimize the likelihood of chance associations by performing sensitivity analysis, these findings should be confirmed in a larger study. Second, we only evaluated IV medications. Medications administered by other routes could also be associated with clinical deterioration and should be analyzed in future studies. Third, we excluded children hospitalized for <24 hours, as well as transfers that did not meet urgent criteria. These may be limitations because (1) the first 24 hours of hospitalization may be a high‐risk period, and (2) patients who were on trajectories toward severe deterioration and received interventions that prevented further deterioration but did not meet urgent transfer criteria were excluded. It may be that the children we included as cases were at increased risk of deterioration that is either more difficult to recognize early, or more difficult to treat effectively without ICU interventions. Finally, we acknowledge that in some cases the therapeutic classes were associated with deterioration in a causal fashion, and in others the medications administered did not cause deterioration but were signs of therapeutic interventions that were initiated in response to clinical worsening. Identifying the specific indications for administration of drugs used in response to clinical worsening may have resulted in stronger associations with deterioration. However, these indications are often complex, multifactorial, and poorly documented in real time. This limits the ability to automate their detection using the electronic health record, the ultimate goal of this line of research.
CONCLUSION
We used a case‐crossover approach to identify therapeutic classes that are associated with increased risk of clinical deterioration in hospitalized children on pediatric wards. These sentinel therapeutic classes may serve as useful components of electronic health recordbased surveillance tools to detect signs of early, evolving deterioration and flag at‐risk patients for critical‐care outreach or rover team review. Future research should focus on evaluating whether including these therapeutic classes in early warning scores improves their accuracy in detecting signs of deterioration and determining if providing this information as clinical decision support improves patient outcomes.
Acknowledgments
Disclosures: This study was funded by The Children's Hospital of Philadelphia Center for Pediatric Clinical Effectiveness Pilot Grant and the University of Pennsylvania Provost's Undergraduate Research Mentoring Program. Drs. Bonafide and Keren also receive funding from the Pennsylvania Health Research Formula Fund Award from the Pennsylvania Department of Health for research in pediatric hospital quality, safety, and costs. The authors have no other conflicts of interest to report.
- , , , et al. Findings of the first consensus conference on medical emergency teams. Crit Care Med. 2006;34(9):2463–2478.
- , , , et al. “Identifying the hospitalised patient in crisis”—a consensus conference on the afferent limb of rapid response systems. Resuscitation. 2010;81(4):375–382.
- , , , . Attitudes and barriers to a medical emergency team system at a tertiary paediatric hospital. Resuscitation. 2011;82(2):167–174.
- , , , et al. Why don't hospital staff activate the rapid response system (RRS)? How frequently is it needed and can the process be improved? Implement Sci. 2011;6:39.
- , . Failure of the afferent limb: a persistent problem in rapid response systems. Resuscitation. 2011;82(7):797–798.
- , , . Understanding how rapid response systems may improve safety for the acutely ill patient: learning from the frontline. BMJ Qual Saf. 2012;21(2):135–144.
- , , . How RNs rescue patients: a qualitative study of RNs' perceived involvement in rapid response teams. Qual Saf Health Care. 2010;19(5):1–4.
- , , , et al. A survey of nurses' beliefs about the medical emergency team system in a Canadian tertiary hospital. Am J Crit Care. 2010;19(1):74–83.
- , , , et al. Nurses' attitudes to a medical emergency team service in a teaching hospital. Qual Saf Health Care. 2006;15(6):427–432.
- , , , et al. Introducing critical care outreach: a ward‐randomised trial of phased introduction in a general hospital. Intensive Care Med. 2004;30(7):1398–1404.
- . Out of our reach? Assessing the impact of introducing a critical care outreach service. Anaesthesia. 2003;58(9):882–885.
- , , . Effect of the critical care outreach team on patient survival to discharge from hospital and readmission to critical care: non‐randomised population based study. BMJ. 2003;327(7422):1014.
- , , , et al. Successful implementation of a family and patient activated rapid response team in an adult level 1 trauma center. Resuscitation. 2010;81(12):1676–1681.
- , , , et al. Beyond rapid response teams: instituting a “Rover Team” improves the management of at‐risk patients, facilitates proactive interventions, and improves outcomes. In: Henriksen K, Battles JB, Keyes MA, Grady ML, eds. Advances in Patient Safety: New Directions and Alternative Approaches. Rockville, MD: Agency for Healthcare Research and Quality; 2008.
- , . The case‐crossover study design in pharmacoepidemiology. Stat Methods Med Res. 2009;18(1):53–65.
- , , , et al. Comparison of the statistical efficiency of case‐crossover and case‐control designs: application to severe cutaneous adverse reactions. J Clin Epidemiol. 2001;54(12):1218–1227.
- . The case‐crossover design: a method for studying transient effects on the risk of acute events. Am J Epidemiol. 1991;133(2):144–153.
- , , , , , . Development of a score to predict clinical deterioration in hospitalized children. J Hosp Med. 2012;7(4):345–349.
- Lexicomp. Available at: http://www.lexi.com. Accessed July 26, 2012.
- , , , , , . Sensitivity of the Pediatric Early Warning Score to identify patient deterioration. Pediatrics. 2010;125(4):e763–e769.
- . Detecting and managing deterioration in children. Paediatr Nurs. 2005;17(1):32–35.
- , , , , . Prospective evaluation of a pediatric inpatient early warning scoring system. J Spec Pediatr Nurs. 2009;14(2):79–85.
- , , . Promoting care for acutely ill children—development and evaluation of a Paediatric Early Warning Tool. Intensive Crit Care Nurs. 2006;22(2):73–81.
- , , . The Pediatric Early Warning System Score: a severity of illness score to predict urgent medical need in hospitalized children. J Crit Care. 2006;21(3):271–278.
- , , , , , . Performance of the maximum modified early warning score to predict the need for higher care utilization among admitted emergency department patients. J Hosp Med. 2010;5(1):E46–E52.
- , , , et al. Findings of the first consensus conference on medical emergency teams. Crit Care Med. 2006;34(9):2463–2478.
- , , , et al. “Identifying the hospitalised patient in crisis”—a consensus conference on the afferent limb of rapid response systems. Resuscitation. 2010;81(4):375–382.
- , , , . Attitudes and barriers to a medical emergency team system at a tertiary paediatric hospital. Resuscitation. 2011;82(2):167–174.
- , , , et al. Why don't hospital staff activate the rapid response system (RRS)? How frequently is it needed and can the process be improved? Implement Sci. 2011;6:39.
- , . Failure of the afferent limb: a persistent problem in rapid response systems. Resuscitation. 2011;82(7):797–798.
- , , . Understanding how rapid response systems may improve safety for the acutely ill patient: learning from the frontline. BMJ Qual Saf. 2012;21(2):135–144.
- , , . How RNs rescue patients: a qualitative study of RNs' perceived involvement in rapid response teams. Qual Saf Health Care. 2010;19(5):1–4.
- , , , et al. A survey of nurses' beliefs about the medical emergency team system in a Canadian tertiary hospital. Am J Crit Care. 2010;19(1):74–83.
- , , , et al. Nurses' attitudes to a medical emergency team service in a teaching hospital. Qual Saf Health Care. 2006;15(6):427–432.
- , , , et al. Introducing critical care outreach: a ward‐randomised trial of phased introduction in a general hospital. Intensive Care Med. 2004;30(7):1398–1404.
- . Out of our reach? Assessing the impact of introducing a critical care outreach service. Anaesthesia. 2003;58(9):882–885.
- , , . Effect of the critical care outreach team on patient survival to discharge from hospital and readmission to critical care: non‐randomised population based study. BMJ. 2003;327(7422):1014.
- , , , et al. Successful implementation of a family and patient activated rapid response team in an adult level 1 trauma center. Resuscitation. 2010;81(12):1676–1681.
- , , , et al. Beyond rapid response teams: instituting a “Rover Team” improves the management of at‐risk patients, facilitates proactive interventions, and improves outcomes. In: Henriksen K, Battles JB, Keyes MA, Grady ML, eds. Advances in Patient Safety: New Directions and Alternative Approaches. Rockville, MD: Agency for Healthcare Research and Quality; 2008.
- , . The case‐crossover study design in pharmacoepidemiology. Stat Methods Med Res. 2009;18(1):53–65.
- , , , et al. Comparison of the statistical efficiency of case‐crossover and case‐control designs: application to severe cutaneous adverse reactions. J Clin Epidemiol. 2001;54(12):1218–1227.
- . The case‐crossover design: a method for studying transient effects on the risk of acute events. Am J Epidemiol. 1991;133(2):144–153.
- , , , , , . Development of a score to predict clinical deterioration in hospitalized children. J Hosp Med. 2012;7(4):345–349.
- Lexicomp. Available at: http://www.lexi.com. Accessed July 26, 2012.
- , , , , , . Sensitivity of the Pediatric Early Warning Score to identify patient deterioration. Pediatrics. 2010;125(4):e763–e769.
- . Detecting and managing deterioration in children. Paediatr Nurs. 2005;17(1):32–35.
- , , , , . Prospective evaluation of a pediatric inpatient early warning scoring system. J Spec Pediatr Nurs. 2009;14(2):79–85.
- , , . Promoting care for acutely ill children—development and evaluation of a Paediatric Early Warning Tool. Intensive Crit Care Nurs. 2006;22(2):73–81.
- , , . The Pediatric Early Warning System Score: a severity of illness score to predict urgent medical need in hospitalized children. J Crit Care. 2006;21(3):271–278.
- , , , , , . Performance of the maximum modified early warning score to predict the need for higher care utilization among admitted emergency department patients. J Hosp Med. 2010;5(1):E46–E52.
Copyright © 2013 Society of Hospital Medicine
Patients at Risk for 30‐Day Readmission
Readmissions to the hospital are common and costly.[1] However, identifying patients prospectively who are likely to be readmitted and who may benefit from interventions to reduce readmission risk has proven challenging, with published risk scores having only moderate ability to discriminate between patients likely and unlikely to be readmitted.[2] One reason for this may be that published studies have not typically focused on patients who are cognitively impaired, psychiatrically ill, have low health or English literacy, or have poor social supports, all of whom may represent a substantial fraction of readmitted patients.[2, 3, 4, 5]
Psychiatric disease, in particular, may contribute to increased readmission risk for nonpsychiatric (medical) illness, and is associated with increased utilization of healthcare resources.[6, 7, 8, 9, 10, 11] For example, patients with mental illness who were discharged from New York hospitals were more likely to be rehospitalized and had more costly readmissions than patients without these comorbidities, including a length of stay nearly 1 day longer on average.[7] An unmet need for treatment of substance abuse was projected to cost Tennessee $772 million of excess healthcare costs in 2000, mostly incurred through repeat hospitalizations and emergency department (ED) visits.[10]
Despite this, few investigators have considered the role of psychiatric disease and/or substance abuse in medical readmission risk. The purpose of the current study was to evaluate the role of psychiatric illness and substance abuse in unselected medical patients to determine their relative contributions to 30‐day all‐cause readmissions (ACR) and potentially avoidable readmissions (PAR).
METHODS
Patients and Setting
We conducted a retrospective cohort study of consecutive adult patients discharged from medicine services at Brigham and Women's Hospital (BWH), a 747‐bed tertiary referral center and teaching hospital, between July 1, 2009 and June 30, 2010. Most patients are cared for by resident housestaff teams at BWH (approximately 25% are cared for by physician assistants working directly with attending physicians), and approximately half receive primary care in the Partners system, which has a shared electronic medical record (EMR). Outpatient mental health services are provided by Partners‐associated mental health professionals including those at McLean Hospital and MassHealth (Medicaid)‐associated sites through the Massachusetts Behavioral Health Partnership. Exclusion criteria were death in the hospital or discharge to another acute care facility. We also excluded patients who left against medical advice (AMA). The study protocol was approved by the Partners Institutional Review Board.
Outcome
The primary outcomes were ACR and PAR within 30 days of discharge. First, we identified all 30‐day readmissions to BWH or to 2 other hospitals in the Partners Healthcare Network (previous studies have shown that 80% of all readmitted patients are readmitted to 1 of these 3 hospitals).[12] For patients with multiple readmissions, only the first readmission was included in the dataset.
To find potentially avoidable readmissions, administrative and billing data for these patients were processed using the SQLape (SQLape s.a.r.l., Corseaux, Switzerland) algorithm, which identifies PAR by excluding patients who undergo planned follow‐up treatment (such as a cycle of planned chemotherapy) or are readmitted for conditions unrelated in any way to the index hospitalization.[13, 14] Common complications of treatment are categorized as potentially avoidable, such as development of a deep venous thrombosis, a decubitus ulcer after prolonged bed rest, or bleeding complications after starting anticoagulation. Although the algorithm identifies theoretically preventable readmissions, the algorithm does not quantify how preventable they are, and these are thus referred to as potentially avoidable. This is similar to other admission metrics, such as the Agency for Healthcare Research and Quality's prevention quality indicators, which are created from a list of ambulatory care‐sensitive conditions.[15] SQLape has the advantage of being a specific tool for readmissions. Patients with 30‐day readmissions identified by SQLape as planned or unlikely to be avoidable were excluded in the PAR analysis, although still included in ACR analysis. In each case, the comparison group is patients without any readmission.
Predictors
Our predictors of interest included the overall prevalence of a psychiatric diagnosis or diagnosis of substance abuse, the presence of specific psychiatric diagnoses, and prescription of psychiatric medications to help assess the independent contribution of these comorbidities to readmission risk.
We used a combination of easily obtainable inpatient and outpatient clinical and administrative data to identify relevant patients. Patients were considered likely to be psychiatrically ill if they: (1) had a psychiatric diagnosis on their Partners outpatient EMR problem list and were prescribed a medication to treat that condition as an outpatient, or (2) had an International Classification of Diseases, 9th Revision diagnosis of a psychiatric illness at hospital discharge. Patients were considered to have moderate probability of disease if they: (1) had a psychiatric diagnosis on their outpatient problem list, or (2) were prescribed a medication intended to treat a psychiatric condition as an outpatient. Patients were considered unlikely to have psychiatric disease if none of these criteria were met. Patients were considered likely to have a substance abuse disorder if they had this diagnosis on their outpatient EMR, or were prescribed a medication to treat this condition (eg, buprenorphine/naloxone), or received inpatient consultation from a substance abuse treatment team during their inpatient hospitalization, and were considered unlikely if none of these were true. We also evaluated individual categories of psychiatric illness (schizophrenia, depression, anxiety, bipolar disorder) and of psychotropic medications (antidepressants, antipsychotics, anxiolytics).
Potential Confounders
Data on potential confounders, based on prior literature,[16, 17] collected at the index admission were derived from electronic administrative, clinical, and billing sources, including the Brigham Integrated Computer System and the Partners Clinical Data Repository. They included patient age, gender, ethnicity, primary language, marital status, insurance status, living situation prior to admission, discharge location, length of stay, Elixhauser comorbidity index,[18] total number of medications prescribed, and number of prior admissions and ED visits in the prior year.
Statistical Analysis
Bivariate comparisons of each of the predictors of ACR and PAR risk (ie, patients with a 30‐day ACR or PAR vs those not readmitted within 30 days) were conducted using 2 trend tests for ordinal predictors (eg, likelihood of psychiatric disease), and 2 or Fisher exact test for dichotomous predictors (eg, receipt of inpatient substance abuse counseling).
We then used multivariate logistic regression analysis to adjust for all of the potential confounders noted above, entering each variable related to psychiatric illness into the model separately (eg, likely psychiatric illness, number of psychiatric medications). In a secondary analysis, we removed potentially collinear variables from the final model; as this did not alter the results, the full model is presented. We also conducted a secondary analysis where we included patients who left against medical advice (AMA), which also did not alter the results. Two‐sided P values <0.05 were considered significant, and all analyses were performed using the SAS version 9.2 (SAS Institute, Inc., Cary, NC).
RESULTS
There were 7984 unique patients discharged during the study period. Patients were generally white and English speaking; just over half of admissions came from the ED (Table 1). Of note, nearly all patients were insured, as are almost all patients in Massachusetts. They had high degrees of comorbid illness and large numbers of prescribed medications. Nearly 30% had at least 1 hospital admission within the prior year.
| Characteristic | All Patients, N (%) | Not Readmitted, N (%) | ACR, N (%) | PAR N (%)a |
|---|---|---|---|---|
| ||||
| Study cohort | 6987 (100) | 5727 (72) | 1260 (18) | 388 (5.6) |
| Age, y | ||||
| <50 | 1663 (23.8) | 1343 (23.5) | 320 (25.4) | 85 (21.9) |
| 5165 | 2273 (32.5) | 1859 (32.5) | 414 (32.9) | 136 (35.1) |
| 6679 | 1444 (20.7) | 1176 (20.5) | 268 (18.6) | 80 (20.6) |
| >80 | 1607 (23.0) | 1349 (23.6) | 258 (16.1) | 87 (22.4) |
| Female | 3604 (51.6) | 2967 (51.8) | 637 (50.6) | 206 (53.1) |
| Race | ||||
| White | 5126 (73.4) | 4153 (72.5) | 973 (77.2) | 300 (77.3) |
| Black | 1075 (15.4) | 899 (15.7) | 176 (14.0) | 53 (13.7) |
| Hispanic | 562 (8.0) | 477 (8.3) | 85 (6.8) | 28 (7.2) |
| Other | 224 (3.2) | 198 (3.5) | 26 (2.1) | 7 (1.8) |
| Primary language | ||||
| English | 6345 (90.8) | 5180 (90.5) | 1165 (92.5) | 356 (91.8) |
| Marital status | ||||
| Married | 3642 (52.1) | 2942 (51.4) | 702 (55.7) | 214 (55.2) |
| Single, never married | 1662 (23.8) | 1393 (24.3) | 269 (21.4) | 73 (18.8) |
| Previously married | 1683 (24.1) | 1386 (24.2) | 289 (22.9) | 101 (26.0) |
| Insurance | ||||
| Medicare | 3550 (50.8) | 2949 (51.5) | 601 (47.7) | 188 (48.5) |
| Medicaid | 539 (7.7) | 430 (7.5) | 109 (8.7) | 33 (8.5) |
| Private | 2892 (41.4) | 2344 (40.9) | 548 (43.5) | 167 (43.0) |
| Uninsured | 6 (0.1) | 4 (0.1) | 2 (0.1) | 0 (0) |
| Source of index admission | ||||
| Clinic or home | 2136 (30.6) | 1711 (29.9) | 425 (33.7) | 117 (30.2) |
| Emergency department | 3592 (51.4) | 2999 (52.4) | 593 (47.1) | 181 (46.7) |
| Nursing facility | 1204 (17.2) | 977 (17.1) | 227 (18.0) | 84 (21.7) |
| Other | 55 (0.1) | 40 (0.7) | 15 (1.1) | 6 (1.6) |
| Length of stay, d | ||||
| 02 | 1757 (25.2) | 1556 (27.2) | 201 (16.0) | 55 (14.2) |
| 34 | 2200 (31.5) | 1842 (32.2) | 358 (28.4) | 105 (27.1) |
| 57 | 1521 (21.8) | 1214 (21.2) | 307 (24.4) | 101 (26.0) |
| >7 | 1509 (21.6) | 1115 (19.5) | 394 (31.3) | 127 (32.7) |
| Elixhauser comorbidity index score | ||||
| 01 | 1987 (28.4) | 1729 (30.2) | 258 (20.5) | 66 (17.0) |
| 27 | 1773 (25.4) | 1541 (26.9) | 232 (18.4) | 67 (17.3) |
| 813 | 1535 (22.0) | 1212 (21.2) | 323 (25.6) | 86 (22.2) |
| >13 | 1692 (24.2) | 1245 (21.7) | 447 (35.5) | 169 (43.6) |
| Medications prescribed as outpatient | ||||
| 06 | 1684 (24.1) | 1410 (24.6) | 274 (21.8) | 72 (18.6) |
| 79 | 1601 (22.9) | 1349 (23.6) | 252 (20.0) | 77 (19.9) |
| 1013 | 1836 (26.3) | 1508 (26.3) | 328 (26.0) | 107 (27.6) |
| >13 | 1866 (26.7) | 1460 (25.5) | 406 (32.2) | 132 (34.0) |
| Number of admissions in past year | ||||
| 0 | 4816 (68.9) | 4032 (70.4) | 784 (62.2) | 279 (71.9) |
| 15 | 2075 (29.7) | 1640 (28.6) | 435 (34.5) | 107 (27.6) |
| >5 | 96 (1.4) | 55 (1.0) | 41 (3.3) | 2 (0.5) |
| Number of ED visits in past year | ||||
| 0 | 4661 (66.7) | 3862 (67.4) | 799 (63.4) | 261 (67.3) |
| 15 | 2326 (33.3) | 1865 (32.6) | 461 (36.6) | 127 (32.7) |
All‐Cause Readmissions
After exclusion of 997 patients who died, were discharged to skilled nursing or rehabilitation facilities, or left AMA, 6987 patients were included (Figure 1). Of these, 1260 had a readmission (18%). Approximately half were considered unlikely to be psychiatrically ill, 22% were considered moderately likely, and 29% likely (Table 2).
| All‐Cause Readmission Analysis | Potentially Avoidable Readmission Analysis | |||||
|---|---|---|---|---|---|---|
| No. in Cohort (%) | % of Patients With ACR | P Valuea | No. in Cohort (%) | % of Patients With PAR | P Valuea | |
| ||||||
| Entire cohort | 6987 | 18.0 | 6115 | 6.3 | ||
| Likelihood of psychiatric illness | ||||||
| Unlikely | 3424 (49) | 16.5 | 3026 (49) | 5.6 | ||
| Moderate | 1564 (22) | 23.5 | 1302 (21) | 7.1 | ||
| Likely | 1999 (29) | 16.4 | 1787 (29) | 6.4 | ||
| Likely versus unlikely | 0.87 | 0.20 | ||||
| Moderate+likely versus unlikely | 0.001 | 0.02 | ||||
| Likelihood of substance abuse | 0.01 | 0.20 | ||||
| Unlikely | 5804 (83) | 18.7 | 5104 (83) | 6.5 | ||
| Likely | 1183 (17) | 14.8 | 1011 (17) | 5.4 | 0.14 | |
| Number of prescribed outpatient psychotropic medications | <0.001 | 0.04 | ||||
| 0 | 4420 (63) | 16.3 | 3931 (64) | 5.9 | ||
| 1 | 1725 (25) | 20.4 | 1481 (24) | 7.2 | ||
| 2 | 781 (11) | 22.3 | 653 (11) | 7.0 | ||
| >2 | 61 (1) | 23.0 | 50 (1) | 6.0 | ||
| Prescribed antidepressant | 1474 (21) | 20.6 | 0.005 | 1248 (20) | 6.2 | 0.77 |
| Prescribed antipsychotic | 375 (5) | 22.4 | 0.02 | 315 (5) | 7.6 | 0.34 |
| Prescribed mood stabilizer | 81 (1) | 18.5 | 0.91 | 69 (1) | 4.4 | 0.49 |
| Prescribed anxiolytic | 1814 (26) | 21.8 | <0.001 | 1537 (25) | 7.7 | 0.01 |
| Prescribed stimulant | 101 (2) | 26.7 | 0.02 | 83 (1) | 10.8 | 0.09 |
| Prescribed pharmacologic treatment for substance abuse | 79 (1) | 25.3 | 0.09 | 60 (1) | 1.7 | 0.14 |
| Number of psychiatric diagnoses on outpatient problem list | 0.31 | 0.74 | ||||
| 0 | 6405 (92) | 18.2 | 5509 (90) | 6.3 | ||
| 1 or more | 582 (8) | 16.5 | 474 (8) | 7.0 | ||
| Outpatient diagnosis of substance abuse | 159 (2) | 13.2 | 0.11 | 144 (2) | 4.2 | 0.28 |
| Outpatient diagnosis of any psychiatric illness | 582 (8) | 16.5 | 0.31 | 517 (8) | 8.0 | 0.73 |
| Discharge diagnosis of depression | 774 (11) | 17.7 | 0.80 | 690 (11) | 7.7 | 0.13 |
| Discharge diagnosis of schizophrenia | 56 (1) | 23.2 | 0.31 | 50 (1) | 14 | 0.03 |
| Discharge diagnosis of bipolar disorder | 101 (1) | 10.9 | 0.06 | 92 (2) | 2.2 | 0.10 |
| Discharge diagnosis of anxiety | 1192 (17) | 15.0 | 0.003 | 1080 (18) | 6.2 | 0.83 |
| Discharge diagnosis of substance abuse | 885 (13) | 14.8 | 0.008 | 803 (13) | 6.1 | 0.76 |
| Discharge diagnosis of any psychiatric illness | 1839 (26) | 16.0 | 0.008 | 1654 (27) | 6.6 | 0.63 |
| Substance abuse consultation as inpatient | 284 (4) | 14.4 | 0.11 | 252 (4) | 3.6 | 0.07 |
In bivariate analysis (Table 2), likelihood of psychiatric illness (P<0.01) and increasing numbers of prescribed outpatient psychiatric medications (P<0.01) were significantly associated with ACR. In multivariate analysis, each additional prescribed outpatient psychiatric medication increased ACR risk (odds ratio [OR]: 1.10, 95% confidence interval [CI]: 1.01‐1.20) or any prescription of an anxiolytic in particular (OR: 1.16, 95% CI: 1.001.35) was associated with increased risk of ACR, whereas discharge diagnoses of anxiety (OR: 0.82, 95% CI: 0.68‐0.99) and substance abuse (OR: 0.80, 95% CI: 0.65‐0.99) were associated with lower risk of ACR (Table 3).
| ACR, OR (95% CI) | PAR, OR (95% CI)a | |
|---|---|---|
| ||
| Likely psychiatric disease | 0.97 (0.82‐1.14) | 1.20 (0.92‐1.56) |
| Likely and possible psychiatric disease | 1.07 (0.94‐1.22) | 1.18 (0.94‐1.47) |
| Likely substance abuse | 0.83 (0.69‐0.99) | 0.85 (0.63‐1.16) |
| Psychiatric diagnosis on outpatient problem list | 0.97 (0.76‐1.23) | 1.04 (0.70‐1.55) |
| Substance abuse diagnosis on outpatient problem list | 0.63 (0.39‐1.02) | 0.65 (0.28‐1.52) |
| Increasing number of prescribed psychiatric medications | 1.10 (1.01‐1.20) | 1.00 (0.86‐1.16) |
| Outpatient prescription for antidepressant | 1.10 (0.94‐1.29) | 0.86 (0.66‐1.13) |
| Outpatient prescription for antipsychotic | 1.03 (0.79‐1.34) | 0.93 (0.59‐1.45) |
| Outpatient prescription for anxiolytic | 1.16 (1.001.35) | 1.13 (0.88‐1.44) |
| Outpatient prescription for methadone or buprenorphine | 1.15 (0.67‐1.98) | 0.18 (0.03‐1.36) |
| Discharge diagnosis of depression | 1.06 (0.86‐1.30) | 1.49 (1.09‐2.04) |
| Discharge diagnosis of schizophrenia | 1.43 (0.75‐2.74) | 2.63 (1.13‐6.13) |
| Discharge diagnosis of bipolar disorder | 0.53 (0.28‐1.02) | 0.35 (0.09‐1.45) |
| Discharge diagnosis of anxiety | 0.82 (0.68‐0.99) | 1.11 (0.83‐1.49) |
| Discharge diagnosis of substance abuse | 0.80 (0.65‐0.99) | 1.05 (0.75‐1.46) |
| Discharge diagnosis of any psychiatric illness | 0.88 (0.75‐1.02) | 1.22 (0.96‐1.56) |
| Addiction team consult while inpatient | 0.82 (0.58‐1.17) | 0.58 (0.29‐1.17) |
Potentially Avoidable Readmissions
After further exclusion of 872 patients who had unavoidable readmissions according to the SQLape algorithm, 6115 patients remained. Of these, 388 had a PAR within 30 days (6.3%, Table 1).
In bivariate analysis (Table 2), the likelihood of psychiatric illness (P=0.02), number of outpatient psychiatric medications (P=0.04), and prescription of anxiolytics (P=0.01) were significantly associated with PAR, as they were with ACR. A discharge diagnosis of schizophrenia was also associated with PAR (P=0.03).
In multivariate analysis, only discharge diagnoses of depression (OR: 1.49, 95% CI: 1.09‐2.04) and schizophrenia (OR: 2.63, 95% CI: 1.13‐6.13) were associated with PAR.
DISCUSSION
Comorbid psychiatric illness was common among patients admitted to the medicine wards. Patients with documented discharge diagnoses of depression or schizophrenia were at highest risk for a potentially avoidable 30‐day readmission, whereas those prescribed more psychiatric medications were at increased risk for ACR. These findings were independent of a comprehensive set of risk factors among medicine inpatients in this retrospective cohort study.
This study extends prior work indicating patients with psychiatric disease have increased healthcare utilization,[6, 7, 8, 9, 10, 11] by identifying at least 2 subpopulations of the psychiatrically ill (those with depression and schizophrenia) at particularly high risk for 30‐day PAR. To our knowledge, this is the first study to identify schizophrenia as a predictor of hospital readmission for medical illnesses. One prior study prospectively identified depression as increasing the 90‐day risk of readmission 3‐fold, although medication usage was not assessed,[6] and our report strengthens this association.
There are several possible explanations why these two subpopulations in particular would be more predisposed to readmissions that are potentially avoidable. It is known that patients with schizophrenia, for example, live on average 20 years less than the general population, and most of this excess mortality is due to medical illnesses.[20, 21] Reasons for this may include poor healthcare access, adverse effects of medication, and socioeconomic factors among others.[21, 22] All of these reasons may contribute to the increased PAR risk in this population, mediated, for example, by decreased ability to adhere to postdischarge care plans. Successful community‐based interventions to decrease these inequities have been described and could serve as a model for addressing the increased readmission risk in this population.[23]
Our finding that patients with a greater number of prescribed psychiatric medications are at increased risk for ACR may be expected, given other studies that have highlighted the crucial importance of medications in postdischarge adverse events, including readmissions.[24] Indeed, medication‐related errors and toxicities are the most common postdischarge adverse events experienced by patients.[25] Whether psychiatric medications are particularly prone to causing postdischarge adverse events or whether these medications represent greater psychiatric comorbidity cannot be answered by this study.
It was surprising but reassuring that substance abuse was not a predictor of short‐term readmissions as identified using our measures; in fact, a discharge diagnosis of substance abuse was associated with lower risk of ACR than comparator patients. It seems unlikely that we would have inadequate power to find such a result, as we found a statistically significant negative association in the ACR population, and 17% of our population overall was considered likely to have a substance abuse comorbidity. However, it is likely the burden of disease was underestimated given that we did not try to determine the contribution of long‐term substance abuse to medical diseases that may increase readmission risk (eg, liver cirrhosis from alcohol use). Unlike other conditions in our study, patients with substance abuse diagnoses at BWH can be seen by a dedicated multidisciplinary team while an inpatient to start treatment and plan for postdischarge follow‐up; this may have played a role in our findings.
A discharge diagnosis of anxiety was also somewhat protective against readmission, whereas a prescription of an anxiolytic (predominantly benzodiazepines) increased risk; many patients prescribed a benzodiazepine do not have a Diagnostic and Statistical Manual of Mental Disorders4th Edition (DSM‐IV) diagnosis of anxiety disorder, and thus these findings may reflect different patient populations. Discharging physicians may have used anxiety as a discharge diagnosis in patients in whom they suspected somatic complaints without organic basis; these patients may be at lower risk of readmission.
Discharge diagnoses of psychiatric illnesses were associated with ACR and PAR in our study, but outpatient diagnoses were not. This likely reflects greater severity of illness (documentation as a treated diagnosis on discharge indicates the illness was relevant during the hospitalization), but may also reflect inaccuracies of diagnosis and lack of assessment of severity in outpatient coding, which would bias toward null findings. Although many of the patients in our study were seen by primary care doctors within the Partners system, some patients had outside primary care physicians and we did not have access to these records. This may also have decreased our ability to find associations.
The findings of our study should be interpreted in the context of the study design. Our study was retrospective, which limited our ability to conclusively diagnose psychiatric disease presence or severity (as is true of most institutions, validated psychiatric screening was not routinely used at our institutions on hospital admission or discharge). However, we used a conservative scale to classify the likelihood of patients having psychiatric or substance abuse disorders, and we used other metrics to establish the presence of illness, such as the number of prescribed medications, inpatient consultation with a substance abuse service, and hospital discharge diagnoses. This approach also allowed us to quickly identify a large cohort unaffected by selection bias. Our study was single center, potentially limiting generalizability. Although we capture at least 80% of readmissions, we were not able to capture all readmissions, and we cannot rule out that patients readmitted elsewhere are different than those readmitted within the Partners system. Last, the SQLape algorithm is not perfectly sensitive or specific in identifying avoidable readmissions,[13] but it does eliminate many readmissions that are clearly unavoidable, creating an enriched cohort of patients whose readmissions are more likely to be avoidable and therefore potentially actionable.
We suggest that our study findings first be considered when risk stratifying patients before hospital discharge in terms of readmission risk. Patients with depression and schizophrenia would seem to merit postdischarge interventions to decrease their potentially avoidable readmissions. Compulsory community treatment (a feature of treatment in Canada and Australia that is ordered by clinicians) has been shown to decrease mortality due to medical illness in patients who have been hospitalized and are psychiatrically ill, and addition of these services to postdischarge care may be useful.[23] Inpatient physicians could work to ensure follow‐up not just with medical providers but with robust outpatient mental health programs to decrease potentially avoidable readmission risk, and administrators could work to ensure close linkages with these community resources. Studies evaluating the impact of these types of interventions would need to be conducted. Patients with polypharmacy, including psychiatric medications, may benefit from interventions to improve medication safety, such as enhanced medication reconciliation and pharmacist counseling.[26]
Our study suggests that patients with depression, those with schizophrenia, and those who have increased numbers of prescribed psychiatric medications should be considered at high risk for readmission for medical illnesses. Targeting interventions to these patients may be fruitful in preventing avoidable readmissions.
Acknowledgements
The authors thank Dr. Yves Eggli for screening the database for potentially avoidable readmissions using the SQLape algorithm.
Disclosures
Dr. Donz was supported by the Swiss National Science Foundation and the Swiss Foundation for MedicalBiological Scholarships. The authors otherwise have no conflicts of interest to disclose. The content is solely the responsibility of the authors and does not necessarily represent the official views of the US Department of Veterans Affairs.
- , , . Rehospitalizations among patients in the Medicare fee‐for‐service program. N Engl J Med. 2009;360(14):1418–1428.
- , , , et al. Risk prediction models for hospital readmission: a systematic review. JAMA. 2011;306(15):1688–1698.
- , . Hospital readmission as an accountability measure. JAMA. 2011;305(5):504–505.
- , , , , , . Postdischarge environmental and socioeconomic factors and the likelihood of early hospital readmission among community‐dwelling Medicare beneficiaries. Gerontologist. 2008;48(4):495–504.
- , , , , . Low health literacy and health outcomes: an updated systematic review. Ann Intern Med. 2011;155(2):97–107.
- , , , et al. Depression is a risk factor for rehospitalization in medical inpatients. Prim Care Companion J Clin Psychiatry. 2007;9(4):256–262.
- , , , . Mental illness and hospitalization for ambulatory care sensitive medical conditions. Med Care. 2008;46(12):1249–1256.
- , , , , , . Substance use treatment barriers for patients with frequent hospital admissions. J Subst Abuse Treat. 2010;38(1):22–30.
- , , , , . Managed care and the quality of substance abuse treatment. J Ment Health Policy Econ. 2002;5(4):163–174.
- , , , , . Unmet substance abuse treatment need, health services utilization, and cost: a population‐based emergency department study. Ann Emerg Med. 2005;45(2):118–127.
- , , , . Elderly Medicare inpatients with substance use disorders: characteristics and predictors of hospital readmissions over a four‐year interval. J Stud Alcohol. 2000;61(6):891–895.
- , , , et al. Rationale and design of the Pharmacist Intervention for Low Literacy in Cardiovascular Disease (PILL‐CVD) study. Circ Cardiovasc Qual Outcomes. 2010;3(2):212–219.
- , , , , , . Validation of the potentially avoidable hospital readmission rate as a routine indicator of the quality of hospital care. Med Care. 2006;44(11);972–981.
- , , , , , . Measuring potentially avoidable hospital readmissions. J Clin Epidemiol. 2002;55:573–587.
- Agency for Healthcare Research and Quality Quality Indicators. (April 7, 2006). Prevention Quality Indicators (PQI) Composite Measure Workgroup Final Report. Available at: http://www.qualityindicators.ahrq.gov/modules/pqi_resources.aspx. Accessed June 1, 2012.
- , , , et al. Hospital readmission in general medicine patients: a prediction model. J Gen Intern Med. 2010;25(3):211–219.
- , , , et al. Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community. CMAJ. 2010;182(6):551–557.
- , , , . Comorbidity measures for use with administrative data. Med Care. Jan 1998;36(1):8–27.
- , , , , eds. Morbidity and mortality in people with serious mental illness. October 2006. National Association of State Mental Health Directors, Medical Directors Council. Available at: http://www.nasmhpd.org/docs/publications/MDCdocs/Mortality%20and%20 Mo rbidity%20Final%20Report%208.18.08.pdf. Accessed January 13, 2013.
- , , , . Mortality in individuals who have had psychiatric treatment: population‐based study in Nova Scotia. Br J Psychiat. 2005;187:552–558.
- , , , , , . Inequitable access for mentally ill patients to some medically necessary procedures. CMAJ. 2007;176(6):779–784.
- , , . Quality of medical care for people with and without comorbid mental illness and substance misuse: systematic review of comparative studies. Br J Psychiat. 2009;194:491–499.
- , , , , , . Reducing all‐cause mortality among patients with psychiatric disorders: a population‐based study. CMAJ. 2013;185(1):E50–E56.
- , , , et al. Incidence of potentially avoidable urgent readmissions and their relation to all‐cause urgent readmissions. CMAJ. 2011;183(14):E1067–E1072.
- , , , , . The incidence and severity of adverse events affecting patients after discharge from the hospital. Ann Intern Med. 2003;138(3):161–167.
- , , , . Hospital‐based medication reconciliation practices: a systematic review. Arch Intern Med. 2012;172(14):1057–1069.
Readmissions to the hospital are common and costly.[1] However, identifying patients prospectively who are likely to be readmitted and who may benefit from interventions to reduce readmission risk has proven challenging, with published risk scores having only moderate ability to discriminate between patients likely and unlikely to be readmitted.[2] One reason for this may be that published studies have not typically focused on patients who are cognitively impaired, psychiatrically ill, have low health or English literacy, or have poor social supports, all of whom may represent a substantial fraction of readmitted patients.[2, 3, 4, 5]
Psychiatric disease, in particular, may contribute to increased readmission risk for nonpsychiatric (medical) illness, and is associated with increased utilization of healthcare resources.[6, 7, 8, 9, 10, 11] For example, patients with mental illness who were discharged from New York hospitals were more likely to be rehospitalized and had more costly readmissions than patients without these comorbidities, including a length of stay nearly 1 day longer on average.[7] An unmet need for treatment of substance abuse was projected to cost Tennessee $772 million of excess healthcare costs in 2000, mostly incurred through repeat hospitalizations and emergency department (ED) visits.[10]
Despite this, few investigators have considered the role of psychiatric disease and/or substance abuse in medical readmission risk. The purpose of the current study was to evaluate the role of psychiatric illness and substance abuse in unselected medical patients to determine their relative contributions to 30‐day all‐cause readmissions (ACR) and potentially avoidable readmissions (PAR).
METHODS
Patients and Setting
We conducted a retrospective cohort study of consecutive adult patients discharged from medicine services at Brigham and Women's Hospital (BWH), a 747‐bed tertiary referral center and teaching hospital, between July 1, 2009 and June 30, 2010. Most patients are cared for by resident housestaff teams at BWH (approximately 25% are cared for by physician assistants working directly with attending physicians), and approximately half receive primary care in the Partners system, which has a shared electronic medical record (EMR). Outpatient mental health services are provided by Partners‐associated mental health professionals including those at McLean Hospital and MassHealth (Medicaid)‐associated sites through the Massachusetts Behavioral Health Partnership. Exclusion criteria were death in the hospital or discharge to another acute care facility. We also excluded patients who left against medical advice (AMA). The study protocol was approved by the Partners Institutional Review Board.
Outcome
The primary outcomes were ACR and PAR within 30 days of discharge. First, we identified all 30‐day readmissions to BWH or to 2 other hospitals in the Partners Healthcare Network (previous studies have shown that 80% of all readmitted patients are readmitted to 1 of these 3 hospitals).[12] For patients with multiple readmissions, only the first readmission was included in the dataset.
To find potentially avoidable readmissions, administrative and billing data for these patients were processed using the SQLape (SQLape s.a.r.l., Corseaux, Switzerland) algorithm, which identifies PAR by excluding patients who undergo planned follow‐up treatment (such as a cycle of planned chemotherapy) or are readmitted for conditions unrelated in any way to the index hospitalization.[13, 14] Common complications of treatment are categorized as potentially avoidable, such as development of a deep venous thrombosis, a decubitus ulcer after prolonged bed rest, or bleeding complications after starting anticoagulation. Although the algorithm identifies theoretically preventable readmissions, the algorithm does not quantify how preventable they are, and these are thus referred to as potentially avoidable. This is similar to other admission metrics, such as the Agency for Healthcare Research and Quality's prevention quality indicators, which are created from a list of ambulatory care‐sensitive conditions.[15] SQLape has the advantage of being a specific tool for readmissions. Patients with 30‐day readmissions identified by SQLape as planned or unlikely to be avoidable were excluded in the PAR analysis, although still included in ACR analysis. In each case, the comparison group is patients without any readmission.
Predictors
Our predictors of interest included the overall prevalence of a psychiatric diagnosis or diagnosis of substance abuse, the presence of specific psychiatric diagnoses, and prescription of psychiatric medications to help assess the independent contribution of these comorbidities to readmission risk.
We used a combination of easily obtainable inpatient and outpatient clinical and administrative data to identify relevant patients. Patients were considered likely to be psychiatrically ill if they: (1) had a psychiatric diagnosis on their Partners outpatient EMR problem list and were prescribed a medication to treat that condition as an outpatient, or (2) had an International Classification of Diseases, 9th Revision diagnosis of a psychiatric illness at hospital discharge. Patients were considered to have moderate probability of disease if they: (1) had a psychiatric diagnosis on their outpatient problem list, or (2) were prescribed a medication intended to treat a psychiatric condition as an outpatient. Patients were considered unlikely to have psychiatric disease if none of these criteria were met. Patients were considered likely to have a substance abuse disorder if they had this diagnosis on their outpatient EMR, or were prescribed a medication to treat this condition (eg, buprenorphine/naloxone), or received inpatient consultation from a substance abuse treatment team during their inpatient hospitalization, and were considered unlikely if none of these were true. We also evaluated individual categories of psychiatric illness (schizophrenia, depression, anxiety, bipolar disorder) and of psychotropic medications (antidepressants, antipsychotics, anxiolytics).
Potential Confounders
Data on potential confounders, based on prior literature,[16, 17] collected at the index admission were derived from electronic administrative, clinical, and billing sources, including the Brigham Integrated Computer System and the Partners Clinical Data Repository. They included patient age, gender, ethnicity, primary language, marital status, insurance status, living situation prior to admission, discharge location, length of stay, Elixhauser comorbidity index,[18] total number of medications prescribed, and number of prior admissions and ED visits in the prior year.
Statistical Analysis
Bivariate comparisons of each of the predictors of ACR and PAR risk (ie, patients with a 30‐day ACR or PAR vs those not readmitted within 30 days) were conducted using 2 trend tests for ordinal predictors (eg, likelihood of psychiatric disease), and 2 or Fisher exact test for dichotomous predictors (eg, receipt of inpatient substance abuse counseling).
We then used multivariate logistic regression analysis to adjust for all of the potential confounders noted above, entering each variable related to psychiatric illness into the model separately (eg, likely psychiatric illness, number of psychiatric medications). In a secondary analysis, we removed potentially collinear variables from the final model; as this did not alter the results, the full model is presented. We also conducted a secondary analysis where we included patients who left against medical advice (AMA), which also did not alter the results. Two‐sided P values <0.05 were considered significant, and all analyses were performed using the SAS version 9.2 (SAS Institute, Inc., Cary, NC).
RESULTS
There were 7984 unique patients discharged during the study period. Patients were generally white and English speaking; just over half of admissions came from the ED (Table 1). Of note, nearly all patients were insured, as are almost all patients in Massachusetts. They had high degrees of comorbid illness and large numbers of prescribed medications. Nearly 30% had at least 1 hospital admission within the prior year.
| Characteristic | All Patients, N (%) | Not Readmitted, N (%) | ACR, N (%) | PAR N (%)a |
|---|---|---|---|---|
| ||||
| Study cohort | 6987 (100) | 5727 (72) | 1260 (18) | 388 (5.6) |
| Age, y | ||||
| <50 | 1663 (23.8) | 1343 (23.5) | 320 (25.4) | 85 (21.9) |
| 5165 | 2273 (32.5) | 1859 (32.5) | 414 (32.9) | 136 (35.1) |
| 6679 | 1444 (20.7) | 1176 (20.5) | 268 (18.6) | 80 (20.6) |
| >80 | 1607 (23.0) | 1349 (23.6) | 258 (16.1) | 87 (22.4) |
| Female | 3604 (51.6) | 2967 (51.8) | 637 (50.6) | 206 (53.1) |
| Race | ||||
| White | 5126 (73.4) | 4153 (72.5) | 973 (77.2) | 300 (77.3) |
| Black | 1075 (15.4) | 899 (15.7) | 176 (14.0) | 53 (13.7) |
| Hispanic | 562 (8.0) | 477 (8.3) | 85 (6.8) | 28 (7.2) |
| Other | 224 (3.2) | 198 (3.5) | 26 (2.1) | 7 (1.8) |
| Primary language | ||||
| English | 6345 (90.8) | 5180 (90.5) | 1165 (92.5) | 356 (91.8) |
| Marital status | ||||
| Married | 3642 (52.1) | 2942 (51.4) | 702 (55.7) | 214 (55.2) |
| Single, never married | 1662 (23.8) | 1393 (24.3) | 269 (21.4) | 73 (18.8) |
| Previously married | 1683 (24.1) | 1386 (24.2) | 289 (22.9) | 101 (26.0) |
| Insurance | ||||
| Medicare | 3550 (50.8) | 2949 (51.5) | 601 (47.7) | 188 (48.5) |
| Medicaid | 539 (7.7) | 430 (7.5) | 109 (8.7) | 33 (8.5) |
| Private | 2892 (41.4) | 2344 (40.9) | 548 (43.5) | 167 (43.0) |
| Uninsured | 6 (0.1) | 4 (0.1) | 2 (0.1) | 0 (0) |
| Source of index admission | ||||
| Clinic or home | 2136 (30.6) | 1711 (29.9) | 425 (33.7) | 117 (30.2) |
| Emergency department | 3592 (51.4) | 2999 (52.4) | 593 (47.1) | 181 (46.7) |
| Nursing facility | 1204 (17.2) | 977 (17.1) | 227 (18.0) | 84 (21.7) |
| Other | 55 (0.1) | 40 (0.7) | 15 (1.1) | 6 (1.6) |
| Length of stay, d | ||||
| 02 | 1757 (25.2) | 1556 (27.2) | 201 (16.0) | 55 (14.2) |
| 34 | 2200 (31.5) | 1842 (32.2) | 358 (28.4) | 105 (27.1) |
| 57 | 1521 (21.8) | 1214 (21.2) | 307 (24.4) | 101 (26.0) |
| >7 | 1509 (21.6) | 1115 (19.5) | 394 (31.3) | 127 (32.7) |
| Elixhauser comorbidity index score | ||||
| 01 | 1987 (28.4) | 1729 (30.2) | 258 (20.5) | 66 (17.0) |
| 27 | 1773 (25.4) | 1541 (26.9) | 232 (18.4) | 67 (17.3) |
| 813 | 1535 (22.0) | 1212 (21.2) | 323 (25.6) | 86 (22.2) |
| >13 | 1692 (24.2) | 1245 (21.7) | 447 (35.5) | 169 (43.6) |
| Medications prescribed as outpatient | ||||
| 06 | 1684 (24.1) | 1410 (24.6) | 274 (21.8) | 72 (18.6) |
| 79 | 1601 (22.9) | 1349 (23.6) | 252 (20.0) | 77 (19.9) |
| 1013 | 1836 (26.3) | 1508 (26.3) | 328 (26.0) | 107 (27.6) |
| >13 | 1866 (26.7) | 1460 (25.5) | 406 (32.2) | 132 (34.0) |
| Number of admissions in past year | ||||
| 0 | 4816 (68.9) | 4032 (70.4) | 784 (62.2) | 279 (71.9) |
| 15 | 2075 (29.7) | 1640 (28.6) | 435 (34.5) | 107 (27.6) |
| >5 | 96 (1.4) | 55 (1.0) | 41 (3.3) | 2 (0.5) |
| Number of ED visits in past year | ||||
| 0 | 4661 (66.7) | 3862 (67.4) | 799 (63.4) | 261 (67.3) |
| 15 | 2326 (33.3) | 1865 (32.6) | 461 (36.6) | 127 (32.7) |
All‐Cause Readmissions
After exclusion of 997 patients who died, were discharged to skilled nursing or rehabilitation facilities, or left AMA, 6987 patients were included (Figure 1). Of these, 1260 had a readmission (18%). Approximately half were considered unlikely to be psychiatrically ill, 22% were considered moderately likely, and 29% likely (Table 2).
| All‐Cause Readmission Analysis | Potentially Avoidable Readmission Analysis | |||||
|---|---|---|---|---|---|---|
| No. in Cohort (%) | % of Patients With ACR | P Valuea | No. in Cohort (%) | % of Patients With PAR | P Valuea | |
| ||||||
| Entire cohort | 6987 | 18.0 | 6115 | 6.3 | ||
| Likelihood of psychiatric illness | ||||||
| Unlikely | 3424 (49) | 16.5 | 3026 (49) | 5.6 | ||
| Moderate | 1564 (22) | 23.5 | 1302 (21) | 7.1 | ||
| Likely | 1999 (29) | 16.4 | 1787 (29) | 6.4 | ||
| Likely versus unlikely | 0.87 | 0.20 | ||||
| Moderate+likely versus unlikely | 0.001 | 0.02 | ||||
| Likelihood of substance abuse | 0.01 | 0.20 | ||||
| Unlikely | 5804 (83) | 18.7 | 5104 (83) | 6.5 | ||
| Likely | 1183 (17) | 14.8 | 1011 (17) | 5.4 | 0.14 | |
| Number of prescribed outpatient psychotropic medications | <0.001 | 0.04 | ||||
| 0 | 4420 (63) | 16.3 | 3931 (64) | 5.9 | ||
| 1 | 1725 (25) | 20.4 | 1481 (24) | 7.2 | ||
| 2 | 781 (11) | 22.3 | 653 (11) | 7.0 | ||
| >2 | 61 (1) | 23.0 | 50 (1) | 6.0 | ||
| Prescribed antidepressant | 1474 (21) | 20.6 | 0.005 | 1248 (20) | 6.2 | 0.77 |
| Prescribed antipsychotic | 375 (5) | 22.4 | 0.02 | 315 (5) | 7.6 | 0.34 |
| Prescribed mood stabilizer | 81 (1) | 18.5 | 0.91 | 69 (1) | 4.4 | 0.49 |
| Prescribed anxiolytic | 1814 (26) | 21.8 | <0.001 | 1537 (25) | 7.7 | 0.01 |
| Prescribed stimulant | 101 (2) | 26.7 | 0.02 | 83 (1) | 10.8 | 0.09 |
| Prescribed pharmacologic treatment for substance abuse | 79 (1) | 25.3 | 0.09 | 60 (1) | 1.7 | 0.14 |
| Number of psychiatric diagnoses on outpatient problem list | 0.31 | 0.74 | ||||
| 0 | 6405 (92) | 18.2 | 5509 (90) | 6.3 | ||
| 1 or more | 582 (8) | 16.5 | 474 (8) | 7.0 | ||
| Outpatient diagnosis of substance abuse | 159 (2) | 13.2 | 0.11 | 144 (2) | 4.2 | 0.28 |
| Outpatient diagnosis of any psychiatric illness | 582 (8) | 16.5 | 0.31 | 517 (8) | 8.0 | 0.73 |
| Discharge diagnosis of depression | 774 (11) | 17.7 | 0.80 | 690 (11) | 7.7 | 0.13 |
| Discharge diagnosis of schizophrenia | 56 (1) | 23.2 | 0.31 | 50 (1) | 14 | 0.03 |
| Discharge diagnosis of bipolar disorder | 101 (1) | 10.9 | 0.06 | 92 (2) | 2.2 | 0.10 |
| Discharge diagnosis of anxiety | 1192 (17) | 15.0 | 0.003 | 1080 (18) | 6.2 | 0.83 |
| Discharge diagnosis of substance abuse | 885 (13) | 14.8 | 0.008 | 803 (13) | 6.1 | 0.76 |
| Discharge diagnosis of any psychiatric illness | 1839 (26) | 16.0 | 0.008 | 1654 (27) | 6.6 | 0.63 |
| Substance abuse consultation as inpatient | 284 (4) | 14.4 | 0.11 | 252 (4) | 3.6 | 0.07 |
In bivariate analysis (Table 2), likelihood of psychiatric illness (P<0.01) and increasing numbers of prescribed outpatient psychiatric medications (P<0.01) were significantly associated with ACR. In multivariate analysis, each additional prescribed outpatient psychiatric medication increased ACR risk (odds ratio [OR]: 1.10, 95% confidence interval [CI]: 1.01‐1.20) or any prescription of an anxiolytic in particular (OR: 1.16, 95% CI: 1.001.35) was associated with increased risk of ACR, whereas discharge diagnoses of anxiety (OR: 0.82, 95% CI: 0.68‐0.99) and substance abuse (OR: 0.80, 95% CI: 0.65‐0.99) were associated with lower risk of ACR (Table 3).
| ACR, OR (95% CI) | PAR, OR (95% CI)a | |
|---|---|---|
| ||
| Likely psychiatric disease | 0.97 (0.82‐1.14) | 1.20 (0.92‐1.56) |
| Likely and possible psychiatric disease | 1.07 (0.94‐1.22) | 1.18 (0.94‐1.47) |
| Likely substance abuse | 0.83 (0.69‐0.99) | 0.85 (0.63‐1.16) |
| Psychiatric diagnosis on outpatient problem list | 0.97 (0.76‐1.23) | 1.04 (0.70‐1.55) |
| Substance abuse diagnosis on outpatient problem list | 0.63 (0.39‐1.02) | 0.65 (0.28‐1.52) |
| Increasing number of prescribed psychiatric medications | 1.10 (1.01‐1.20) | 1.00 (0.86‐1.16) |
| Outpatient prescription for antidepressant | 1.10 (0.94‐1.29) | 0.86 (0.66‐1.13) |
| Outpatient prescription for antipsychotic | 1.03 (0.79‐1.34) | 0.93 (0.59‐1.45) |
| Outpatient prescription for anxiolytic | 1.16 (1.001.35) | 1.13 (0.88‐1.44) |
| Outpatient prescription for methadone or buprenorphine | 1.15 (0.67‐1.98) | 0.18 (0.03‐1.36) |
| Discharge diagnosis of depression | 1.06 (0.86‐1.30) | 1.49 (1.09‐2.04) |
| Discharge diagnosis of schizophrenia | 1.43 (0.75‐2.74) | 2.63 (1.13‐6.13) |
| Discharge diagnosis of bipolar disorder | 0.53 (0.28‐1.02) | 0.35 (0.09‐1.45) |
| Discharge diagnosis of anxiety | 0.82 (0.68‐0.99) | 1.11 (0.83‐1.49) |
| Discharge diagnosis of substance abuse | 0.80 (0.65‐0.99) | 1.05 (0.75‐1.46) |
| Discharge diagnosis of any psychiatric illness | 0.88 (0.75‐1.02) | 1.22 (0.96‐1.56) |
| Addiction team consult while inpatient | 0.82 (0.58‐1.17) | 0.58 (0.29‐1.17) |
Potentially Avoidable Readmissions
After further exclusion of 872 patients who had unavoidable readmissions according to the SQLape algorithm, 6115 patients remained. Of these, 388 had a PAR within 30 days (6.3%, Table 1).
In bivariate analysis (Table 2), the likelihood of psychiatric illness (P=0.02), number of outpatient psychiatric medications (P=0.04), and prescription of anxiolytics (P=0.01) were significantly associated with PAR, as they were with ACR. A discharge diagnosis of schizophrenia was also associated with PAR (P=0.03).
In multivariate analysis, only discharge diagnoses of depression (OR: 1.49, 95% CI: 1.09‐2.04) and schizophrenia (OR: 2.63, 95% CI: 1.13‐6.13) were associated with PAR.
DISCUSSION
Comorbid psychiatric illness was common among patients admitted to the medicine wards. Patients with documented discharge diagnoses of depression or schizophrenia were at highest risk for a potentially avoidable 30‐day readmission, whereas those prescribed more psychiatric medications were at increased risk for ACR. These findings were independent of a comprehensive set of risk factors among medicine inpatients in this retrospective cohort study.
This study extends prior work indicating patients with psychiatric disease have increased healthcare utilization,[6, 7, 8, 9, 10, 11] by identifying at least 2 subpopulations of the psychiatrically ill (those with depression and schizophrenia) at particularly high risk for 30‐day PAR. To our knowledge, this is the first study to identify schizophrenia as a predictor of hospital readmission for medical illnesses. One prior study prospectively identified depression as increasing the 90‐day risk of readmission 3‐fold, although medication usage was not assessed,[6] and our report strengthens this association.
There are several possible explanations why these two subpopulations in particular would be more predisposed to readmissions that are potentially avoidable. It is known that patients with schizophrenia, for example, live on average 20 years less than the general population, and most of this excess mortality is due to medical illnesses.[20, 21] Reasons for this may include poor healthcare access, adverse effects of medication, and socioeconomic factors among others.[21, 22] All of these reasons may contribute to the increased PAR risk in this population, mediated, for example, by decreased ability to adhere to postdischarge care plans. Successful community‐based interventions to decrease these inequities have been described and could serve as a model for addressing the increased readmission risk in this population.[23]
Our finding that patients with a greater number of prescribed psychiatric medications are at increased risk for ACR may be expected, given other studies that have highlighted the crucial importance of medications in postdischarge adverse events, including readmissions.[24] Indeed, medication‐related errors and toxicities are the most common postdischarge adverse events experienced by patients.[25] Whether psychiatric medications are particularly prone to causing postdischarge adverse events or whether these medications represent greater psychiatric comorbidity cannot be answered by this study.
It was surprising but reassuring that substance abuse was not a predictor of short‐term readmissions as identified using our measures; in fact, a discharge diagnosis of substance abuse was associated with lower risk of ACR than comparator patients. It seems unlikely that we would have inadequate power to find such a result, as we found a statistically significant negative association in the ACR population, and 17% of our population overall was considered likely to have a substance abuse comorbidity. However, it is likely the burden of disease was underestimated given that we did not try to determine the contribution of long‐term substance abuse to medical diseases that may increase readmission risk (eg, liver cirrhosis from alcohol use). Unlike other conditions in our study, patients with substance abuse diagnoses at BWH can be seen by a dedicated multidisciplinary team while an inpatient to start treatment and plan for postdischarge follow‐up; this may have played a role in our findings.
A discharge diagnosis of anxiety was also somewhat protective against readmission, whereas a prescription of an anxiolytic (predominantly benzodiazepines) increased risk; many patients prescribed a benzodiazepine do not have a Diagnostic and Statistical Manual of Mental Disorders4th Edition (DSM‐IV) diagnosis of anxiety disorder, and thus these findings may reflect different patient populations. Discharging physicians may have used anxiety as a discharge diagnosis in patients in whom they suspected somatic complaints without organic basis; these patients may be at lower risk of readmission.
Discharge diagnoses of psychiatric illnesses were associated with ACR and PAR in our study, but outpatient diagnoses were not. This likely reflects greater severity of illness (documentation as a treated diagnosis on discharge indicates the illness was relevant during the hospitalization), but may also reflect inaccuracies of diagnosis and lack of assessment of severity in outpatient coding, which would bias toward null findings. Although many of the patients in our study were seen by primary care doctors within the Partners system, some patients had outside primary care physicians and we did not have access to these records. This may also have decreased our ability to find associations.
The findings of our study should be interpreted in the context of the study design. Our study was retrospective, which limited our ability to conclusively diagnose psychiatric disease presence or severity (as is true of most institutions, validated psychiatric screening was not routinely used at our institutions on hospital admission or discharge). However, we used a conservative scale to classify the likelihood of patients having psychiatric or substance abuse disorders, and we used other metrics to establish the presence of illness, such as the number of prescribed medications, inpatient consultation with a substance abuse service, and hospital discharge diagnoses. This approach also allowed us to quickly identify a large cohort unaffected by selection bias. Our study was single center, potentially limiting generalizability. Although we capture at least 80% of readmissions, we were not able to capture all readmissions, and we cannot rule out that patients readmitted elsewhere are different than those readmitted within the Partners system. Last, the SQLape algorithm is not perfectly sensitive or specific in identifying avoidable readmissions,[13] but it does eliminate many readmissions that are clearly unavoidable, creating an enriched cohort of patients whose readmissions are more likely to be avoidable and therefore potentially actionable.
We suggest that our study findings first be considered when risk stratifying patients before hospital discharge in terms of readmission risk. Patients with depression and schizophrenia would seem to merit postdischarge interventions to decrease their potentially avoidable readmissions. Compulsory community treatment (a feature of treatment in Canada and Australia that is ordered by clinicians) has been shown to decrease mortality due to medical illness in patients who have been hospitalized and are psychiatrically ill, and addition of these services to postdischarge care may be useful.[23] Inpatient physicians could work to ensure follow‐up not just with medical providers but with robust outpatient mental health programs to decrease potentially avoidable readmission risk, and administrators could work to ensure close linkages with these community resources. Studies evaluating the impact of these types of interventions would need to be conducted. Patients with polypharmacy, including psychiatric medications, may benefit from interventions to improve medication safety, such as enhanced medication reconciliation and pharmacist counseling.[26]
Our study suggests that patients with depression, those with schizophrenia, and those who have increased numbers of prescribed psychiatric medications should be considered at high risk for readmission for medical illnesses. Targeting interventions to these patients may be fruitful in preventing avoidable readmissions.
Acknowledgements
The authors thank Dr. Yves Eggli for screening the database for potentially avoidable readmissions using the SQLape algorithm.
Disclosures
Dr. Donz was supported by the Swiss National Science Foundation and the Swiss Foundation for MedicalBiological Scholarships. The authors otherwise have no conflicts of interest to disclose. The content is solely the responsibility of the authors and does not necessarily represent the official views of the US Department of Veterans Affairs.
Readmissions to the hospital are common and costly.[1] However, identifying patients prospectively who are likely to be readmitted and who may benefit from interventions to reduce readmission risk has proven challenging, with published risk scores having only moderate ability to discriminate between patients likely and unlikely to be readmitted.[2] One reason for this may be that published studies have not typically focused on patients who are cognitively impaired, psychiatrically ill, have low health or English literacy, or have poor social supports, all of whom may represent a substantial fraction of readmitted patients.[2, 3, 4, 5]
Psychiatric disease, in particular, may contribute to increased readmission risk for nonpsychiatric (medical) illness, and is associated with increased utilization of healthcare resources.[6, 7, 8, 9, 10, 11] For example, patients with mental illness who were discharged from New York hospitals were more likely to be rehospitalized and had more costly readmissions than patients without these comorbidities, including a length of stay nearly 1 day longer on average.[7] An unmet need for treatment of substance abuse was projected to cost Tennessee $772 million of excess healthcare costs in 2000, mostly incurred through repeat hospitalizations and emergency department (ED) visits.[10]
Despite this, few investigators have considered the role of psychiatric disease and/or substance abuse in medical readmission risk. The purpose of the current study was to evaluate the role of psychiatric illness and substance abuse in unselected medical patients to determine their relative contributions to 30‐day all‐cause readmissions (ACR) and potentially avoidable readmissions (PAR).
METHODS
Patients and Setting
We conducted a retrospective cohort study of consecutive adult patients discharged from medicine services at Brigham and Women's Hospital (BWH), a 747‐bed tertiary referral center and teaching hospital, between July 1, 2009 and June 30, 2010. Most patients are cared for by resident housestaff teams at BWH (approximately 25% are cared for by physician assistants working directly with attending physicians), and approximately half receive primary care in the Partners system, which has a shared electronic medical record (EMR). Outpatient mental health services are provided by Partners‐associated mental health professionals including those at McLean Hospital and MassHealth (Medicaid)‐associated sites through the Massachusetts Behavioral Health Partnership. Exclusion criteria were death in the hospital or discharge to another acute care facility. We also excluded patients who left against medical advice (AMA). The study protocol was approved by the Partners Institutional Review Board.
Outcome
The primary outcomes were ACR and PAR within 30 days of discharge. First, we identified all 30‐day readmissions to BWH or to 2 other hospitals in the Partners Healthcare Network (previous studies have shown that 80% of all readmitted patients are readmitted to 1 of these 3 hospitals).[12] For patients with multiple readmissions, only the first readmission was included in the dataset.
To find potentially avoidable readmissions, administrative and billing data for these patients were processed using the SQLape (SQLape s.a.r.l., Corseaux, Switzerland) algorithm, which identifies PAR by excluding patients who undergo planned follow‐up treatment (such as a cycle of planned chemotherapy) or are readmitted for conditions unrelated in any way to the index hospitalization.[13, 14] Common complications of treatment are categorized as potentially avoidable, such as development of a deep venous thrombosis, a decubitus ulcer after prolonged bed rest, or bleeding complications after starting anticoagulation. Although the algorithm identifies theoretically preventable readmissions, the algorithm does not quantify how preventable they are, and these are thus referred to as potentially avoidable. This is similar to other admission metrics, such as the Agency for Healthcare Research and Quality's prevention quality indicators, which are created from a list of ambulatory care‐sensitive conditions.[15] SQLape has the advantage of being a specific tool for readmissions. Patients with 30‐day readmissions identified by SQLape as planned or unlikely to be avoidable were excluded in the PAR analysis, although still included in ACR analysis. In each case, the comparison group is patients without any readmission.
Predictors
Our predictors of interest included the overall prevalence of a psychiatric diagnosis or diagnosis of substance abuse, the presence of specific psychiatric diagnoses, and prescription of psychiatric medications to help assess the independent contribution of these comorbidities to readmission risk.
We used a combination of easily obtainable inpatient and outpatient clinical and administrative data to identify relevant patients. Patients were considered likely to be psychiatrically ill if they: (1) had a psychiatric diagnosis on their Partners outpatient EMR problem list and were prescribed a medication to treat that condition as an outpatient, or (2) had an International Classification of Diseases, 9th Revision diagnosis of a psychiatric illness at hospital discharge. Patients were considered to have moderate probability of disease if they: (1) had a psychiatric diagnosis on their outpatient problem list, or (2) were prescribed a medication intended to treat a psychiatric condition as an outpatient. Patients were considered unlikely to have psychiatric disease if none of these criteria were met. Patients were considered likely to have a substance abuse disorder if they had this diagnosis on their outpatient EMR, or were prescribed a medication to treat this condition (eg, buprenorphine/naloxone), or received inpatient consultation from a substance abuse treatment team during their inpatient hospitalization, and were considered unlikely if none of these were true. We also evaluated individual categories of psychiatric illness (schizophrenia, depression, anxiety, bipolar disorder) and of psychotropic medications (antidepressants, antipsychotics, anxiolytics).
Potential Confounders
Data on potential confounders, based on prior literature,[16, 17] collected at the index admission were derived from electronic administrative, clinical, and billing sources, including the Brigham Integrated Computer System and the Partners Clinical Data Repository. They included patient age, gender, ethnicity, primary language, marital status, insurance status, living situation prior to admission, discharge location, length of stay, Elixhauser comorbidity index,[18] total number of medications prescribed, and number of prior admissions and ED visits in the prior year.
Statistical Analysis
Bivariate comparisons of each of the predictors of ACR and PAR risk (ie, patients with a 30‐day ACR or PAR vs those not readmitted within 30 days) were conducted using 2 trend tests for ordinal predictors (eg, likelihood of psychiatric disease), and 2 or Fisher exact test for dichotomous predictors (eg, receipt of inpatient substance abuse counseling).
We then used multivariate logistic regression analysis to adjust for all of the potential confounders noted above, entering each variable related to psychiatric illness into the model separately (eg, likely psychiatric illness, number of psychiatric medications). In a secondary analysis, we removed potentially collinear variables from the final model; as this did not alter the results, the full model is presented. We also conducted a secondary analysis where we included patients who left against medical advice (AMA), which also did not alter the results. Two‐sided P values <0.05 were considered significant, and all analyses were performed using the SAS version 9.2 (SAS Institute, Inc., Cary, NC).
RESULTS
There were 7984 unique patients discharged during the study period. Patients were generally white and English speaking; just over half of admissions came from the ED (Table 1). Of note, nearly all patients were insured, as are almost all patients in Massachusetts. They had high degrees of comorbid illness and large numbers of prescribed medications. Nearly 30% had at least 1 hospital admission within the prior year.
| Characteristic | All Patients, N (%) | Not Readmitted, N (%) | ACR, N (%) | PAR N (%)a |
|---|---|---|---|---|
| ||||
| Study cohort | 6987 (100) | 5727 (72) | 1260 (18) | 388 (5.6) |
| Age, y | ||||
| <50 | 1663 (23.8) | 1343 (23.5) | 320 (25.4) | 85 (21.9) |
| 5165 | 2273 (32.5) | 1859 (32.5) | 414 (32.9) | 136 (35.1) |
| 6679 | 1444 (20.7) | 1176 (20.5) | 268 (18.6) | 80 (20.6) |
| >80 | 1607 (23.0) | 1349 (23.6) | 258 (16.1) | 87 (22.4) |
| Female | 3604 (51.6) | 2967 (51.8) | 637 (50.6) | 206 (53.1) |
| Race | ||||
| White | 5126 (73.4) | 4153 (72.5) | 973 (77.2) | 300 (77.3) |
| Black | 1075 (15.4) | 899 (15.7) | 176 (14.0) | 53 (13.7) |
| Hispanic | 562 (8.0) | 477 (8.3) | 85 (6.8) | 28 (7.2) |
| Other | 224 (3.2) | 198 (3.5) | 26 (2.1) | 7 (1.8) |
| Primary language | ||||
| English | 6345 (90.8) | 5180 (90.5) | 1165 (92.5) | 356 (91.8) |
| Marital status | ||||
| Married | 3642 (52.1) | 2942 (51.4) | 702 (55.7) | 214 (55.2) |
| Single, never married | 1662 (23.8) | 1393 (24.3) | 269 (21.4) | 73 (18.8) |
| Previously married | 1683 (24.1) | 1386 (24.2) | 289 (22.9) | 101 (26.0) |
| Insurance | ||||
| Medicare | 3550 (50.8) | 2949 (51.5) | 601 (47.7) | 188 (48.5) |
| Medicaid | 539 (7.7) | 430 (7.5) | 109 (8.7) | 33 (8.5) |
| Private | 2892 (41.4) | 2344 (40.9) | 548 (43.5) | 167 (43.0) |
| Uninsured | 6 (0.1) | 4 (0.1) | 2 (0.1) | 0 (0) |
| Source of index admission | ||||
| Clinic or home | 2136 (30.6) | 1711 (29.9) | 425 (33.7) | 117 (30.2) |
| Emergency department | 3592 (51.4) | 2999 (52.4) | 593 (47.1) | 181 (46.7) |
| Nursing facility | 1204 (17.2) | 977 (17.1) | 227 (18.0) | 84 (21.7) |
| Other | 55 (0.1) | 40 (0.7) | 15 (1.1) | 6 (1.6) |
| Length of stay, d | ||||
| 02 | 1757 (25.2) | 1556 (27.2) | 201 (16.0) | 55 (14.2) |
| 34 | 2200 (31.5) | 1842 (32.2) | 358 (28.4) | 105 (27.1) |
| 57 | 1521 (21.8) | 1214 (21.2) | 307 (24.4) | 101 (26.0) |
| >7 | 1509 (21.6) | 1115 (19.5) | 394 (31.3) | 127 (32.7) |
| Elixhauser comorbidity index score | ||||
| 01 | 1987 (28.4) | 1729 (30.2) | 258 (20.5) | 66 (17.0) |
| 27 | 1773 (25.4) | 1541 (26.9) | 232 (18.4) | 67 (17.3) |
| 813 | 1535 (22.0) | 1212 (21.2) | 323 (25.6) | 86 (22.2) |
| >13 | 1692 (24.2) | 1245 (21.7) | 447 (35.5) | 169 (43.6) |
| Medications prescribed as outpatient | ||||
| 06 | 1684 (24.1) | 1410 (24.6) | 274 (21.8) | 72 (18.6) |
| 79 | 1601 (22.9) | 1349 (23.6) | 252 (20.0) | 77 (19.9) |
| 1013 | 1836 (26.3) | 1508 (26.3) | 328 (26.0) | 107 (27.6) |
| >13 | 1866 (26.7) | 1460 (25.5) | 406 (32.2) | 132 (34.0) |
| Number of admissions in past year | ||||
| 0 | 4816 (68.9) | 4032 (70.4) | 784 (62.2) | 279 (71.9) |
| 15 | 2075 (29.7) | 1640 (28.6) | 435 (34.5) | 107 (27.6) |
| >5 | 96 (1.4) | 55 (1.0) | 41 (3.3) | 2 (0.5) |
| Number of ED visits in past year | ||||
| 0 | 4661 (66.7) | 3862 (67.4) | 799 (63.4) | 261 (67.3) |
| 15 | 2326 (33.3) | 1865 (32.6) | 461 (36.6) | 127 (32.7) |
All‐Cause Readmissions
After exclusion of 997 patients who died, were discharged to skilled nursing or rehabilitation facilities, or left AMA, 6987 patients were included (Figure 1). Of these, 1260 had a readmission (18%). Approximately half were considered unlikely to be psychiatrically ill, 22% were considered moderately likely, and 29% likely (Table 2).
| All‐Cause Readmission Analysis | Potentially Avoidable Readmission Analysis | |||||
|---|---|---|---|---|---|---|
| No. in Cohort (%) | % of Patients With ACR | P Valuea | No. in Cohort (%) | % of Patients With PAR | P Valuea | |
| ||||||
| Entire cohort | 6987 | 18.0 | 6115 | 6.3 | ||
| Likelihood of psychiatric illness | ||||||
| Unlikely | 3424 (49) | 16.5 | 3026 (49) | 5.6 | ||
| Moderate | 1564 (22) | 23.5 | 1302 (21) | 7.1 | ||
| Likely | 1999 (29) | 16.4 | 1787 (29) | 6.4 | ||
| Likely versus unlikely | 0.87 | 0.20 | ||||
| Moderate+likely versus unlikely | 0.001 | 0.02 | ||||
| Likelihood of substance abuse | 0.01 | 0.20 | ||||
| Unlikely | 5804 (83) | 18.7 | 5104 (83) | 6.5 | ||
| Likely | 1183 (17) | 14.8 | 1011 (17) | 5.4 | 0.14 | |
| Number of prescribed outpatient psychotropic medications | <0.001 | 0.04 | ||||
| 0 | 4420 (63) | 16.3 | 3931 (64) | 5.9 | ||
| 1 | 1725 (25) | 20.4 | 1481 (24) | 7.2 | ||
| 2 | 781 (11) | 22.3 | 653 (11) | 7.0 | ||
| >2 | 61 (1) | 23.0 | 50 (1) | 6.0 | ||
| Prescribed antidepressant | 1474 (21) | 20.6 | 0.005 | 1248 (20) | 6.2 | 0.77 |
| Prescribed antipsychotic | 375 (5) | 22.4 | 0.02 | 315 (5) | 7.6 | 0.34 |
| Prescribed mood stabilizer | 81 (1) | 18.5 | 0.91 | 69 (1) | 4.4 | 0.49 |
| Prescribed anxiolytic | 1814 (26) | 21.8 | <0.001 | 1537 (25) | 7.7 | 0.01 |
| Prescribed stimulant | 101 (2) | 26.7 | 0.02 | 83 (1) | 10.8 | 0.09 |
| Prescribed pharmacologic treatment for substance abuse | 79 (1) | 25.3 | 0.09 | 60 (1) | 1.7 | 0.14 |
| Number of psychiatric diagnoses on outpatient problem list | 0.31 | 0.74 | ||||
| 0 | 6405 (92) | 18.2 | 5509 (90) | 6.3 | ||
| 1 or more | 582 (8) | 16.5 | 474 (8) | 7.0 | ||
| Outpatient diagnosis of substance abuse | 159 (2) | 13.2 | 0.11 | 144 (2) | 4.2 | 0.28 |
| Outpatient diagnosis of any psychiatric illness | 582 (8) | 16.5 | 0.31 | 517 (8) | 8.0 | 0.73 |
| Discharge diagnosis of depression | 774 (11) | 17.7 | 0.80 | 690 (11) | 7.7 | 0.13 |
| Discharge diagnosis of schizophrenia | 56 (1) | 23.2 | 0.31 | 50 (1) | 14 | 0.03 |
| Discharge diagnosis of bipolar disorder | 101 (1) | 10.9 | 0.06 | 92 (2) | 2.2 | 0.10 |
| Discharge diagnosis of anxiety | 1192 (17) | 15.0 | 0.003 | 1080 (18) | 6.2 | 0.83 |
| Discharge diagnosis of substance abuse | 885 (13) | 14.8 | 0.008 | 803 (13) | 6.1 | 0.76 |
| Discharge diagnosis of any psychiatric illness | 1839 (26) | 16.0 | 0.008 | 1654 (27) | 6.6 | 0.63 |
| Substance abuse consultation as inpatient | 284 (4) | 14.4 | 0.11 | 252 (4) | 3.6 | 0.07 |
In bivariate analysis (Table 2), likelihood of psychiatric illness (P<0.01) and increasing numbers of prescribed outpatient psychiatric medications (P<0.01) were significantly associated with ACR. In multivariate analysis, each additional prescribed outpatient psychiatric medication increased ACR risk (odds ratio [OR]: 1.10, 95% confidence interval [CI]: 1.01‐1.20) or any prescription of an anxiolytic in particular (OR: 1.16, 95% CI: 1.001.35) was associated with increased risk of ACR, whereas discharge diagnoses of anxiety (OR: 0.82, 95% CI: 0.68‐0.99) and substance abuse (OR: 0.80, 95% CI: 0.65‐0.99) were associated with lower risk of ACR (Table 3).
| ACR, OR (95% CI) | PAR, OR (95% CI)a | |
|---|---|---|
| ||
| Likely psychiatric disease | 0.97 (0.82‐1.14) | 1.20 (0.92‐1.56) |
| Likely and possible psychiatric disease | 1.07 (0.94‐1.22) | 1.18 (0.94‐1.47) |
| Likely substance abuse | 0.83 (0.69‐0.99) | 0.85 (0.63‐1.16) |
| Psychiatric diagnosis on outpatient problem list | 0.97 (0.76‐1.23) | 1.04 (0.70‐1.55) |
| Substance abuse diagnosis on outpatient problem list | 0.63 (0.39‐1.02) | 0.65 (0.28‐1.52) |
| Increasing number of prescribed psychiatric medications | 1.10 (1.01‐1.20) | 1.00 (0.86‐1.16) |
| Outpatient prescription for antidepressant | 1.10 (0.94‐1.29) | 0.86 (0.66‐1.13) |
| Outpatient prescription for antipsychotic | 1.03 (0.79‐1.34) | 0.93 (0.59‐1.45) |
| Outpatient prescription for anxiolytic | 1.16 (1.001.35) | 1.13 (0.88‐1.44) |
| Outpatient prescription for methadone or buprenorphine | 1.15 (0.67‐1.98) | 0.18 (0.03‐1.36) |
| Discharge diagnosis of depression | 1.06 (0.86‐1.30) | 1.49 (1.09‐2.04) |
| Discharge diagnosis of schizophrenia | 1.43 (0.75‐2.74) | 2.63 (1.13‐6.13) |
| Discharge diagnosis of bipolar disorder | 0.53 (0.28‐1.02) | 0.35 (0.09‐1.45) |
| Discharge diagnosis of anxiety | 0.82 (0.68‐0.99) | 1.11 (0.83‐1.49) |
| Discharge diagnosis of substance abuse | 0.80 (0.65‐0.99) | 1.05 (0.75‐1.46) |
| Discharge diagnosis of any psychiatric illness | 0.88 (0.75‐1.02) | 1.22 (0.96‐1.56) |
| Addiction team consult while inpatient | 0.82 (0.58‐1.17) | 0.58 (0.29‐1.17) |
Potentially Avoidable Readmissions
After further exclusion of 872 patients who had unavoidable readmissions according to the SQLape algorithm, 6115 patients remained. Of these, 388 had a PAR within 30 days (6.3%, Table 1).
In bivariate analysis (Table 2), the likelihood of psychiatric illness (P=0.02), number of outpatient psychiatric medications (P=0.04), and prescription of anxiolytics (P=0.01) were significantly associated with PAR, as they were with ACR. A discharge diagnosis of schizophrenia was also associated with PAR (P=0.03).
In multivariate analysis, only discharge diagnoses of depression (OR: 1.49, 95% CI: 1.09‐2.04) and schizophrenia (OR: 2.63, 95% CI: 1.13‐6.13) were associated with PAR.
DISCUSSION
Comorbid psychiatric illness was common among patients admitted to the medicine wards. Patients with documented discharge diagnoses of depression or schizophrenia were at highest risk for a potentially avoidable 30‐day readmission, whereas those prescribed more psychiatric medications were at increased risk for ACR. These findings were independent of a comprehensive set of risk factors among medicine inpatients in this retrospective cohort study.
This study extends prior work indicating patients with psychiatric disease have increased healthcare utilization,[6, 7, 8, 9, 10, 11] by identifying at least 2 subpopulations of the psychiatrically ill (those with depression and schizophrenia) at particularly high risk for 30‐day PAR. To our knowledge, this is the first study to identify schizophrenia as a predictor of hospital readmission for medical illnesses. One prior study prospectively identified depression as increasing the 90‐day risk of readmission 3‐fold, although medication usage was not assessed,[6] and our report strengthens this association.
There are several possible explanations why these two subpopulations in particular would be more predisposed to readmissions that are potentially avoidable. It is known that patients with schizophrenia, for example, live on average 20 years less than the general population, and most of this excess mortality is due to medical illnesses.[20, 21] Reasons for this may include poor healthcare access, adverse effects of medication, and socioeconomic factors among others.[21, 22] All of these reasons may contribute to the increased PAR risk in this population, mediated, for example, by decreased ability to adhere to postdischarge care plans. Successful community‐based interventions to decrease these inequities have been described and could serve as a model for addressing the increased readmission risk in this population.[23]
Our finding that patients with a greater number of prescribed psychiatric medications are at increased risk for ACR may be expected, given other studies that have highlighted the crucial importance of medications in postdischarge adverse events, including readmissions.[24] Indeed, medication‐related errors and toxicities are the most common postdischarge adverse events experienced by patients.[25] Whether psychiatric medications are particularly prone to causing postdischarge adverse events or whether these medications represent greater psychiatric comorbidity cannot be answered by this study.
It was surprising but reassuring that substance abuse was not a predictor of short‐term readmissions as identified using our measures; in fact, a discharge diagnosis of substance abuse was associated with lower risk of ACR than comparator patients. It seems unlikely that we would have inadequate power to find such a result, as we found a statistically significant negative association in the ACR population, and 17% of our population overall was considered likely to have a substance abuse comorbidity. However, it is likely the burden of disease was underestimated given that we did not try to determine the contribution of long‐term substance abuse to medical diseases that may increase readmission risk (eg, liver cirrhosis from alcohol use). Unlike other conditions in our study, patients with substance abuse diagnoses at BWH can be seen by a dedicated multidisciplinary team while an inpatient to start treatment and plan for postdischarge follow‐up; this may have played a role in our findings.
A discharge diagnosis of anxiety was also somewhat protective against readmission, whereas a prescription of an anxiolytic (predominantly benzodiazepines) increased risk; many patients prescribed a benzodiazepine do not have a Diagnostic and Statistical Manual of Mental Disorders4th Edition (DSM‐IV) diagnosis of anxiety disorder, and thus these findings may reflect different patient populations. Discharging physicians may have used anxiety as a discharge diagnosis in patients in whom they suspected somatic complaints without organic basis; these patients may be at lower risk of readmission.
Discharge diagnoses of psychiatric illnesses were associated with ACR and PAR in our study, but outpatient diagnoses were not. This likely reflects greater severity of illness (documentation as a treated diagnosis on discharge indicates the illness was relevant during the hospitalization), but may also reflect inaccuracies of diagnosis and lack of assessment of severity in outpatient coding, which would bias toward null findings. Although many of the patients in our study were seen by primary care doctors within the Partners system, some patients had outside primary care physicians and we did not have access to these records. This may also have decreased our ability to find associations.
The findings of our study should be interpreted in the context of the study design. Our study was retrospective, which limited our ability to conclusively diagnose psychiatric disease presence or severity (as is true of most institutions, validated psychiatric screening was not routinely used at our institutions on hospital admission or discharge). However, we used a conservative scale to classify the likelihood of patients having psychiatric or substance abuse disorders, and we used other metrics to establish the presence of illness, such as the number of prescribed medications, inpatient consultation with a substance abuse service, and hospital discharge diagnoses. This approach also allowed us to quickly identify a large cohort unaffected by selection bias. Our study was single center, potentially limiting generalizability. Although we capture at least 80% of readmissions, we were not able to capture all readmissions, and we cannot rule out that patients readmitted elsewhere are different than those readmitted within the Partners system. Last, the SQLape algorithm is not perfectly sensitive or specific in identifying avoidable readmissions,[13] but it does eliminate many readmissions that are clearly unavoidable, creating an enriched cohort of patients whose readmissions are more likely to be avoidable and therefore potentially actionable.
We suggest that our study findings first be considered when risk stratifying patients before hospital discharge in terms of readmission risk. Patients with depression and schizophrenia would seem to merit postdischarge interventions to decrease their potentially avoidable readmissions. Compulsory community treatment (a feature of treatment in Canada and Australia that is ordered by clinicians) has been shown to decrease mortality due to medical illness in patients who have been hospitalized and are psychiatrically ill, and addition of these services to postdischarge care may be useful.[23] Inpatient physicians could work to ensure follow‐up not just with medical providers but with robust outpatient mental health programs to decrease potentially avoidable readmission risk, and administrators could work to ensure close linkages with these community resources. Studies evaluating the impact of these types of interventions would need to be conducted. Patients with polypharmacy, including psychiatric medications, may benefit from interventions to improve medication safety, such as enhanced medication reconciliation and pharmacist counseling.[26]
Our study suggests that patients with depression, those with schizophrenia, and those who have increased numbers of prescribed psychiatric medications should be considered at high risk for readmission for medical illnesses. Targeting interventions to these patients may be fruitful in preventing avoidable readmissions.
Acknowledgements
The authors thank Dr. Yves Eggli for screening the database for potentially avoidable readmissions using the SQLape algorithm.
Disclosures
Dr. Donz was supported by the Swiss National Science Foundation and the Swiss Foundation for MedicalBiological Scholarships. The authors otherwise have no conflicts of interest to disclose. The content is solely the responsibility of the authors and does not necessarily represent the official views of the US Department of Veterans Affairs.
- , , . Rehospitalizations among patients in the Medicare fee‐for‐service program. N Engl J Med. 2009;360(14):1418–1428.
- , , , et al. Risk prediction models for hospital readmission: a systematic review. JAMA. 2011;306(15):1688–1698.
- , . Hospital readmission as an accountability measure. JAMA. 2011;305(5):504–505.
- , , , , , . Postdischarge environmental and socioeconomic factors and the likelihood of early hospital readmission among community‐dwelling Medicare beneficiaries. Gerontologist. 2008;48(4):495–504.
- , , , , . Low health literacy and health outcomes: an updated systematic review. Ann Intern Med. 2011;155(2):97–107.
- , , , et al. Depression is a risk factor for rehospitalization in medical inpatients. Prim Care Companion J Clin Psychiatry. 2007;9(4):256–262.
- , , , . Mental illness and hospitalization for ambulatory care sensitive medical conditions. Med Care. 2008;46(12):1249–1256.
- , , , , , . Substance use treatment barriers for patients with frequent hospital admissions. J Subst Abuse Treat. 2010;38(1):22–30.
- , , , , . Managed care and the quality of substance abuse treatment. J Ment Health Policy Econ. 2002;5(4):163–174.
- , , , , . Unmet substance abuse treatment need, health services utilization, and cost: a population‐based emergency department study. Ann Emerg Med. 2005;45(2):118–127.
- , , , . Elderly Medicare inpatients with substance use disorders: characteristics and predictors of hospital readmissions over a four‐year interval. J Stud Alcohol. 2000;61(6):891–895.
- , , , et al. Rationale and design of the Pharmacist Intervention for Low Literacy in Cardiovascular Disease (PILL‐CVD) study. Circ Cardiovasc Qual Outcomes. 2010;3(2):212–219.
- , , , , , . Validation of the potentially avoidable hospital readmission rate as a routine indicator of the quality of hospital care. Med Care. 2006;44(11);972–981.
- , , , , , . Measuring potentially avoidable hospital readmissions. J Clin Epidemiol. 2002;55:573–587.
- Agency for Healthcare Research and Quality Quality Indicators. (April 7, 2006). Prevention Quality Indicators (PQI) Composite Measure Workgroup Final Report. Available at: http://www.qualityindicators.ahrq.gov/modules/pqi_resources.aspx. Accessed June 1, 2012.
- , , , et al. Hospital readmission in general medicine patients: a prediction model. J Gen Intern Med. 2010;25(3):211–219.
- , , , et al. Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community. CMAJ. 2010;182(6):551–557.
- , , , . Comorbidity measures for use with administrative data. Med Care. Jan 1998;36(1):8–27.
- , , , , eds. Morbidity and mortality in people with serious mental illness. October 2006. National Association of State Mental Health Directors, Medical Directors Council. Available at: http://www.nasmhpd.org/docs/publications/MDCdocs/Mortality%20and%20 Mo rbidity%20Final%20Report%208.18.08.pdf. Accessed January 13, 2013.
- , , , . Mortality in individuals who have had psychiatric treatment: population‐based study in Nova Scotia. Br J Psychiat. 2005;187:552–558.
- , , , , , . Inequitable access for mentally ill patients to some medically necessary procedures. CMAJ. 2007;176(6):779–784.
- , , . Quality of medical care for people with and without comorbid mental illness and substance misuse: systematic review of comparative studies. Br J Psychiat. 2009;194:491–499.
- , , , , , . Reducing all‐cause mortality among patients with psychiatric disorders: a population‐based study. CMAJ. 2013;185(1):E50–E56.
- , , , et al. Incidence of potentially avoidable urgent readmissions and their relation to all‐cause urgent readmissions. CMAJ. 2011;183(14):E1067–E1072.
- , , , , . The incidence and severity of adverse events affecting patients after discharge from the hospital. Ann Intern Med. 2003;138(3):161–167.
- , , , . Hospital‐based medication reconciliation practices: a systematic review. Arch Intern Med. 2012;172(14):1057–1069.
- , , . Rehospitalizations among patients in the Medicare fee‐for‐service program. N Engl J Med. 2009;360(14):1418–1428.
- , , , et al. Risk prediction models for hospital readmission: a systematic review. JAMA. 2011;306(15):1688–1698.
- , . Hospital readmission as an accountability measure. JAMA. 2011;305(5):504–505.
- , , , , , . Postdischarge environmental and socioeconomic factors and the likelihood of early hospital readmission among community‐dwelling Medicare beneficiaries. Gerontologist. 2008;48(4):495–504.
- , , , , . Low health literacy and health outcomes: an updated systematic review. Ann Intern Med. 2011;155(2):97–107.
- , , , et al. Depression is a risk factor for rehospitalization in medical inpatients. Prim Care Companion J Clin Psychiatry. 2007;9(4):256–262.
- , , , . Mental illness and hospitalization for ambulatory care sensitive medical conditions. Med Care. 2008;46(12):1249–1256.
- , , , , , . Substance use treatment barriers for patients with frequent hospital admissions. J Subst Abuse Treat. 2010;38(1):22–30.
- , , , , . Managed care and the quality of substance abuse treatment. J Ment Health Policy Econ. 2002;5(4):163–174.
- , , , , . Unmet substance abuse treatment need, health services utilization, and cost: a population‐based emergency department study. Ann Emerg Med. 2005;45(2):118–127.
- , , , . Elderly Medicare inpatients with substance use disorders: characteristics and predictors of hospital readmissions over a four‐year interval. J Stud Alcohol. 2000;61(6):891–895.
- , , , et al. Rationale and design of the Pharmacist Intervention for Low Literacy in Cardiovascular Disease (PILL‐CVD) study. Circ Cardiovasc Qual Outcomes. 2010;3(2):212–219.
- , , , , , . Validation of the potentially avoidable hospital readmission rate as a routine indicator of the quality of hospital care. Med Care. 2006;44(11);972–981.
- , , , , , . Measuring potentially avoidable hospital readmissions. J Clin Epidemiol. 2002;55:573–587.
- Agency for Healthcare Research and Quality Quality Indicators. (April 7, 2006). Prevention Quality Indicators (PQI) Composite Measure Workgroup Final Report. Available at: http://www.qualityindicators.ahrq.gov/modules/pqi_resources.aspx. Accessed June 1, 2012.
- , , , et al. Hospital readmission in general medicine patients: a prediction model. J Gen Intern Med. 2010;25(3):211–219.
- , , , et al. Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community. CMAJ. 2010;182(6):551–557.
- , , , . Comorbidity measures for use with administrative data. Med Care. Jan 1998;36(1):8–27.
- , , , , eds. Morbidity and mortality in people with serious mental illness. October 2006. National Association of State Mental Health Directors, Medical Directors Council. Available at: http://www.nasmhpd.org/docs/publications/MDCdocs/Mortality%20and%20 Mo rbidity%20Final%20Report%208.18.08.pdf. Accessed January 13, 2013.
- , , , . Mortality in individuals who have had psychiatric treatment: population‐based study in Nova Scotia. Br J Psychiat. 2005;187:552–558.
- , , , , , . Inequitable access for mentally ill patients to some medically necessary procedures. CMAJ. 2007;176(6):779–784.
- , , . Quality of medical care for people with and without comorbid mental illness and substance misuse: systematic review of comparative studies. Br J Psychiat. 2009;194:491–499.
- , , , , , . Reducing all‐cause mortality among patients with psychiatric disorders: a population‐based study. CMAJ. 2013;185(1):E50–E56.
- , , , et al. Incidence of potentially avoidable urgent readmissions and their relation to all‐cause urgent readmissions. CMAJ. 2011;183(14):E1067–E1072.
- , , , , . The incidence and severity of adverse events affecting patients after discharge from the hospital. Ann Intern Med. 2003;138(3):161–167.
- , , , . Hospital‐based medication reconciliation practices: a systematic review. Arch Intern Med. 2012;172(14):1057–1069.
Copyright © 2013 Society of Hospital Medicine
Should I retire early?
Much has been written of the widespread concern among America’s physicians over upcoming changes in our health care system. Dire predictions of impending doom have prompted many to consider early retirement.
I do not share such concerns, for what that is worth; but if you do, and you are serious about retiring sooner than planned, now would be a great time to take a close look at your financial situation.
Many doctors have a false sense of security about their money; most of us save too little. We either miscalculate or underestimate how much we’ll need to last through retirement.
We tend to live longer than we think we will, and as such we run the risk of outliving our savings. And we don’t face facts about long-term care. Not nearly enough of us have long-term care insurance, or the means to self-fund an extended long-term care situation.
Many people lack a clear idea of where their retirement income will come from, and even when they do, they don’t know how to manage their savings correctly. Doctors in particular are notorious for not understanding investments. Many attempt to manage their practice’s retirement plans with inadequate knowledge of how the investments within their plans work.
So how will you know if you can safely retire before Obamacare gets up to speed? Of course, as with everything else, it depends. But to arrive at any sort of reliable ballpark figure, you’ll need to know three things: (1) how much you realistically expect to spend annually after retirement; (2) how much principal you will need to generate that annual income; and (3) how far your present savings are from that target figure.
An oft-quoted rule of thumb is that in retirement you should plan to spend about 70% of what you are spending now. In my opinion, that’s nonsense. While a few significant expenses, such as disability and malpractice insurance premiums, will be eliminated, other expenses, such as travel, recreation, and medical care (including long-term care insurance, which no one should be without), will increase. My wife and I are assuming we will spend about the same in retirement as we spend now, and I suggest you do too.
Once you know how much money you will spend per year, you can calculate how much money – in interest- and dividend-producing assets – will be needed to generate that amount.
Ideally, you will want to spend only the interest and dividends; by leaving the principal untouched you will never run short, even if you retire at an unusually young age, or longevity runs in your family (or both). Most financial advisers use the 5% rule: You can safely assume a minimum average of 5% annual return on your nest egg. So if you want to spend $100,000 per year, you will need $2 million in assets; for $200,000, you’ll need $4 million.
This is where you may discover – if your present savings are a long way from your target figure – that early retirement is not a realistic option. Better, though, to make that unpleasant discovery now, rather than face the frightening prospect of running out of money at an advanced age. Don’t be tempted to close a wide gap in a hurry with high-return/high-risk investments, which often backfire, leaving you further than ever from retirement.
Of course, it goes without saying that debt can destroy the best-laid retirement plans. If you carry significant debt, pay it off as soon as possible, and certainly before you retire.
Even if you have no plans to retire in the immediate future, it is never too soon to think about retirement. Young physicians often defer contributing to their retirement plans because they want to save for a new house, or college for their children. But there are tangible tax benefits that you get now, because your contributions usually reduce your taxable income, and your investment grows tax-free until you take it out.
For long-term planning, the most foolproof strategy – seldom employed, because it’s boring – is to sock away a fixed amount per month (after your retirement plan has been funded) in a mutual fund. For example, $1,000 per month for 25 years with the market earning 10% overall comes to almost $2 million, with the power of compounded interest working for you.
Dr. Eastern practices dermatology and dermatologic surgery in Belleville, N.J.
Much has been written of the widespread concern among America’s physicians over upcoming changes in our health care system. Dire predictions of impending doom have prompted many to consider early retirement.
I do not share such concerns, for what that is worth; but if you do, and you are serious about retiring sooner than planned, now would be a great time to take a close look at your financial situation.
Many doctors have a false sense of security about their money; most of us save too little. We either miscalculate or underestimate how much we’ll need to last through retirement.
We tend to live longer than we think we will, and as such we run the risk of outliving our savings. And we don’t face facts about long-term care. Not nearly enough of us have long-term care insurance, or the means to self-fund an extended long-term care situation.
Many people lack a clear idea of where their retirement income will come from, and even when they do, they don’t know how to manage their savings correctly. Doctors in particular are notorious for not understanding investments. Many attempt to manage their practice’s retirement plans with inadequate knowledge of how the investments within their plans work.
So how will you know if you can safely retire before Obamacare gets up to speed? Of course, as with everything else, it depends. But to arrive at any sort of reliable ballpark figure, you’ll need to know three things: (1) how much you realistically expect to spend annually after retirement; (2) how much principal you will need to generate that annual income; and (3) how far your present savings are from that target figure.
An oft-quoted rule of thumb is that in retirement you should plan to spend about 70% of what you are spending now. In my opinion, that’s nonsense. While a few significant expenses, such as disability and malpractice insurance premiums, will be eliminated, other expenses, such as travel, recreation, and medical care (including long-term care insurance, which no one should be without), will increase. My wife and I are assuming we will spend about the same in retirement as we spend now, and I suggest you do too.
Once you know how much money you will spend per year, you can calculate how much money – in interest- and dividend-producing assets – will be needed to generate that amount.
Ideally, you will want to spend only the interest and dividends; by leaving the principal untouched you will never run short, even if you retire at an unusually young age, or longevity runs in your family (or both). Most financial advisers use the 5% rule: You can safely assume a minimum average of 5% annual return on your nest egg. So if you want to spend $100,000 per year, you will need $2 million in assets; for $200,000, you’ll need $4 million.
This is where you may discover – if your present savings are a long way from your target figure – that early retirement is not a realistic option. Better, though, to make that unpleasant discovery now, rather than face the frightening prospect of running out of money at an advanced age. Don’t be tempted to close a wide gap in a hurry with high-return/high-risk investments, which often backfire, leaving you further than ever from retirement.
Of course, it goes without saying that debt can destroy the best-laid retirement plans. If you carry significant debt, pay it off as soon as possible, and certainly before you retire.
Even if you have no plans to retire in the immediate future, it is never too soon to think about retirement. Young physicians often defer contributing to their retirement plans because they want to save for a new house, or college for their children. But there are tangible tax benefits that you get now, because your contributions usually reduce your taxable income, and your investment grows tax-free until you take it out.
For long-term planning, the most foolproof strategy – seldom employed, because it’s boring – is to sock away a fixed amount per month (after your retirement plan has been funded) in a mutual fund. For example, $1,000 per month for 25 years with the market earning 10% overall comes to almost $2 million, with the power of compounded interest working for you.
Dr. Eastern practices dermatology and dermatologic surgery in Belleville, N.J.
Much has been written of the widespread concern among America’s physicians over upcoming changes in our health care system. Dire predictions of impending doom have prompted many to consider early retirement.
I do not share such concerns, for what that is worth; but if you do, and you are serious about retiring sooner than planned, now would be a great time to take a close look at your financial situation.
Many doctors have a false sense of security about their money; most of us save too little. We either miscalculate or underestimate how much we’ll need to last through retirement.
We tend to live longer than we think we will, and as such we run the risk of outliving our savings. And we don’t face facts about long-term care. Not nearly enough of us have long-term care insurance, or the means to self-fund an extended long-term care situation.
Many people lack a clear idea of where their retirement income will come from, and even when they do, they don’t know how to manage their savings correctly. Doctors in particular are notorious for not understanding investments. Many attempt to manage their practice’s retirement plans with inadequate knowledge of how the investments within their plans work.
So how will you know if you can safely retire before Obamacare gets up to speed? Of course, as with everything else, it depends. But to arrive at any sort of reliable ballpark figure, you’ll need to know three things: (1) how much you realistically expect to spend annually after retirement; (2) how much principal you will need to generate that annual income; and (3) how far your present savings are from that target figure.
An oft-quoted rule of thumb is that in retirement you should plan to spend about 70% of what you are spending now. In my opinion, that’s nonsense. While a few significant expenses, such as disability and malpractice insurance premiums, will be eliminated, other expenses, such as travel, recreation, and medical care (including long-term care insurance, which no one should be without), will increase. My wife and I are assuming we will spend about the same in retirement as we spend now, and I suggest you do too.
Once you know how much money you will spend per year, you can calculate how much money – in interest- and dividend-producing assets – will be needed to generate that amount.
Ideally, you will want to spend only the interest and dividends; by leaving the principal untouched you will never run short, even if you retire at an unusually young age, or longevity runs in your family (or both). Most financial advisers use the 5% rule: You can safely assume a minimum average of 5% annual return on your nest egg. So if you want to spend $100,000 per year, you will need $2 million in assets; for $200,000, you’ll need $4 million.
This is where you may discover – if your present savings are a long way from your target figure – that early retirement is not a realistic option. Better, though, to make that unpleasant discovery now, rather than face the frightening prospect of running out of money at an advanced age. Don’t be tempted to close a wide gap in a hurry with high-return/high-risk investments, which often backfire, leaving you further than ever from retirement.
Of course, it goes without saying that debt can destroy the best-laid retirement plans. If you carry significant debt, pay it off as soon as possible, and certainly before you retire.
Even if you have no plans to retire in the immediate future, it is never too soon to think about retirement. Young physicians often defer contributing to their retirement plans because they want to save for a new house, or college for their children. But there are tangible tax benefits that you get now, because your contributions usually reduce your taxable income, and your investment grows tax-free until you take it out.
For long-term planning, the most foolproof strategy – seldom employed, because it’s boring – is to sock away a fixed amount per month (after your retirement plan has been funded) in a mutual fund. For example, $1,000 per month for 25 years with the market earning 10% overall comes to almost $2 million, with the power of compounded interest working for you.
Dr. Eastern practices dermatology and dermatologic surgery in Belleville, N.J.
Managing symptoms of depression
Diana looked at her pill bottles and wondered why she was on all these medications when she did not feel any better. She looked at the five bottles: bupropion, paroxetine, diazepam, alprazolam, and zolpidem. She thought about the side effects she was experiencing.
She had been taking this cocktail, in various dosages, for the best part of a year now. Her depression remained unchanged. She made a decision that she would tell her psychiatrist that she wanted off the medications at her next visit. She would then ask for other treatments. She had found many therapies offered on the Internet for treatment of depression, and she hoped her psychiatrist would be able to help her decide which therapies might be best suited for her. Perhaps she would agree to stay on one medication as a compromise as she knew her psychiatrist thought treatment of depression with medication to be important.
Up to 30% of patients with depression do not respond to multiple treatment trials and are considered to have treatment-resistant depression. Most treatment trials for these patients focus on symptom reduction as a goal. This emphasis on symptom reduction often leads to tunnel vision, where other evidence-based treatments become marginalized by psychiatrists. Thus, patients like Diana end up on multiple medications, without an integrated approach to assessment or discussion of combined treatments (medications and psychotherapy).
Dr. Gabor Keitner, who practices in Providence, R.I., and is a member of the Association of Family Psychiatrists, offers a new program aimed at helping patients manage their depression. His philosophical stance is that depression is a chronic illness and that expecting symptoms to be cured with medications is, for most patients, a false hope perpetuated by a consumer society, where the pharmaceutical industry has dominated the education of patients, their families, and the psychiatric profession. He conceptualizes depression, like other chronic medical illnesses, such as diabetes or hypertension, with a similar range of severity. Therefore, the assessment and treatment of depression requires a more nuanced approach.
He is scheduled to present his Management of Depression (MOD) program at this year’s American Psychiatric Association meeting in San Francisco. His MOD program focuses on how a patient such as Diana can build a satisfying life with meaningful goals and relationships – even if her depressive symptoms persist.
In his pilot study, 30 patients with treatment-resistant depression were randomized to treatment as usual (TAU, n = 13) or the MOD program (n = 17) for 12 weeks. The patients in the MOD group had significant improvement in perception of social support (P < .034) and purpose in life (P < .038) scores, in contrast to the TAU group. The MOD group participated in nine adjunctive sessions of disease management focused therapy. The Scales of Psychological Well-Being measured purpose in life, life goals, and meaning. Social support was measured with the Multidimensional Scale of Perceived Social Support. Depression severity was measured by the Montgomery-Åsberg Depression Rating Scale. Patients were assessed at baseline and week 12. Both groups of patients had significant improvements in their depressive symptoms (TAU 35.46 to 25.9 P < .010; MOD 31.88 to 22.41 P < .001) but continued to experience moderate levels of depression. Adjunctive treatment focusing on functioning, life meaning, and relationships, as opposed to symptom reduction, will help Diana to have a more satisfying life, despite her symptoms of depression.
Measuring relational functioning briefly
In another session, Dr. Keitner is slated to present "The Brief Multidimensional Assessment Scale (BMAS): A Mental Health Check Up," coauthored with Abigail K. Mansfield Maraccio, Ph.D., and Joan Kelley. This scale evaluates global mental health outcomes, including quality of life, symptoms, functioning, and relationships. This measure can be used to assess the clinical status of patients at every health encounter and over the course of an illness. Most available scales are either too long for routine clinical use, focus on a narrow range of symptoms, or focus on specific diagnostic groups. Best of all, this new scale takes less than a minute to complete.
The BMAS was tested against The Outcome Questionnaire–45 (OQ45) with 248 psychiatric outpatients as part of their standard ongoing care. Internal consistency was evaluated with Cronbach’s alpha, which was .75 for the four items. Test-retest reliability was assessed using Pearson’s r and ranged from .45 (symptom severity, which can fluctuate daily) to .79 (quality of life) for each of the BMAS items. Concurrent and convergent validity was analyzed with Pearson product moment correlations between BMAS and OQ45 scales. All correlations were significant for the relevant dimensions.
The BMAS demonstrated acceptable reliability, especially for such a brief measure. It also demonstrated concurrent and convergent validity with a much longer commonly used clinical outcome scale. The BMAS is a useful assessment tool for patients with any clinical condition for which it is desirable to track how the patient is experiencing his or her life situation at a given point in time and when there is a desire to monitor change over time. Notably, BMAS includes health relationships as a measure of good clinical outcome.
A daughter’s documentary about her father
One media workshop slated for the APA meeting will be offered by three members of the Association of Family Psychiatrists: Dr. Michael S. Ascher, Dr. Ira Glick, and Dr. Igor Galynker. They will present a film, "Unlisted: A Story of Schizophrenia." This is a soul-searching examination of responsibility – of parents and children, physicians and patients, and of society and citizens – toward those afflicted with severe mental illness. The film was made by Dr. Delaney Ruston, a Seattle general physician who documents the rebuilding of her relationship with her father. "Unlisted" examines the challenging family dynamics that are present when schizophrenia occurs. Dr. Ruston works hard to overcome the obstacles in accessing appropriate treatment for her father, and her documentary exposes the many failings of the American mental health system as experienced by the families. Dr. Ruston traces the progression of her father’s illness. She studies his medical files and narrates from his autobiographical surrealist novel. In beautifully portrayed scenes, "Unlisted" enters the inner life of Richard Ruston with a clarity and affection missing from many films about people with mental illness.
In summary, family-oriented patient care can be delivered in many ways, from focusing on relational improvement in individual work, to being aware of how to assess and measure relational functioning briefly at each visit, to being able to listen to the accounts of family members and invite them into the treatment room.
Dr. Heru is with the department of psychiatry at the University of Colorado at Denver, Aurora. She is editor of the recently published book, "Working With Families in Medical Settings: A Multidisciplinary Guide for Psychiatrists and Other Health Professions" (New York: Routledge, March 2013), and has been a member of the Association of Family Psychiatrists since 2002.
Diana looked at her pill bottles and wondered why she was on all these medications when she did not feel any better. She looked at the five bottles: bupropion, paroxetine, diazepam, alprazolam, and zolpidem. She thought about the side effects she was experiencing.
She had been taking this cocktail, in various dosages, for the best part of a year now. Her depression remained unchanged. She made a decision that she would tell her psychiatrist that she wanted off the medications at her next visit. She would then ask for other treatments. She had found many therapies offered on the Internet for treatment of depression, and she hoped her psychiatrist would be able to help her decide which therapies might be best suited for her. Perhaps she would agree to stay on one medication as a compromise as she knew her psychiatrist thought treatment of depression with medication to be important.
Up to 30% of patients with depression do not respond to multiple treatment trials and are considered to have treatment-resistant depression. Most treatment trials for these patients focus on symptom reduction as a goal. This emphasis on symptom reduction often leads to tunnel vision, where other evidence-based treatments become marginalized by psychiatrists. Thus, patients like Diana end up on multiple medications, without an integrated approach to assessment or discussion of combined treatments (medications and psychotherapy).
Dr. Gabor Keitner, who practices in Providence, R.I., and is a member of the Association of Family Psychiatrists, offers a new program aimed at helping patients manage their depression. His philosophical stance is that depression is a chronic illness and that expecting symptoms to be cured with medications is, for most patients, a false hope perpetuated by a consumer society, where the pharmaceutical industry has dominated the education of patients, their families, and the psychiatric profession. He conceptualizes depression, like other chronic medical illnesses, such as diabetes or hypertension, with a similar range of severity. Therefore, the assessment and treatment of depression requires a more nuanced approach.
He is scheduled to present his Management of Depression (MOD) program at this year’s American Psychiatric Association meeting in San Francisco. His MOD program focuses on how a patient such as Diana can build a satisfying life with meaningful goals and relationships – even if her depressive symptoms persist.
In his pilot study, 30 patients with treatment-resistant depression were randomized to treatment as usual (TAU, n = 13) or the MOD program (n = 17) for 12 weeks. The patients in the MOD group had significant improvement in perception of social support (P < .034) and purpose in life (P < .038) scores, in contrast to the TAU group. The MOD group participated in nine adjunctive sessions of disease management focused therapy. The Scales of Psychological Well-Being measured purpose in life, life goals, and meaning. Social support was measured with the Multidimensional Scale of Perceived Social Support. Depression severity was measured by the Montgomery-Åsberg Depression Rating Scale. Patients were assessed at baseline and week 12. Both groups of patients had significant improvements in their depressive symptoms (TAU 35.46 to 25.9 P < .010; MOD 31.88 to 22.41 P < .001) but continued to experience moderate levels of depression. Adjunctive treatment focusing on functioning, life meaning, and relationships, as opposed to symptom reduction, will help Diana to have a more satisfying life, despite her symptoms of depression.
Measuring relational functioning briefly
In another session, Dr. Keitner is slated to present "The Brief Multidimensional Assessment Scale (BMAS): A Mental Health Check Up," coauthored with Abigail K. Mansfield Maraccio, Ph.D., and Joan Kelley. This scale evaluates global mental health outcomes, including quality of life, symptoms, functioning, and relationships. This measure can be used to assess the clinical status of patients at every health encounter and over the course of an illness. Most available scales are either too long for routine clinical use, focus on a narrow range of symptoms, or focus on specific diagnostic groups. Best of all, this new scale takes less than a minute to complete.
The BMAS was tested against The Outcome Questionnaire–45 (OQ45) with 248 psychiatric outpatients as part of their standard ongoing care. Internal consistency was evaluated with Cronbach’s alpha, which was .75 for the four items. Test-retest reliability was assessed using Pearson’s r and ranged from .45 (symptom severity, which can fluctuate daily) to .79 (quality of life) for each of the BMAS items. Concurrent and convergent validity was analyzed with Pearson product moment correlations between BMAS and OQ45 scales. All correlations were significant for the relevant dimensions.
The BMAS demonstrated acceptable reliability, especially for such a brief measure. It also demonstrated concurrent and convergent validity with a much longer commonly used clinical outcome scale. The BMAS is a useful assessment tool for patients with any clinical condition for which it is desirable to track how the patient is experiencing his or her life situation at a given point in time and when there is a desire to monitor change over time. Notably, BMAS includes health relationships as a measure of good clinical outcome.
A daughter’s documentary about her father
One media workshop slated for the APA meeting will be offered by three members of the Association of Family Psychiatrists: Dr. Michael S. Ascher, Dr. Ira Glick, and Dr. Igor Galynker. They will present a film, "Unlisted: A Story of Schizophrenia." This is a soul-searching examination of responsibility – of parents and children, physicians and patients, and of society and citizens – toward those afflicted with severe mental illness. The film was made by Dr. Delaney Ruston, a Seattle general physician who documents the rebuilding of her relationship with her father. "Unlisted" examines the challenging family dynamics that are present when schizophrenia occurs. Dr. Ruston works hard to overcome the obstacles in accessing appropriate treatment for her father, and her documentary exposes the many failings of the American mental health system as experienced by the families. Dr. Ruston traces the progression of her father’s illness. She studies his medical files and narrates from his autobiographical surrealist novel. In beautifully portrayed scenes, "Unlisted" enters the inner life of Richard Ruston with a clarity and affection missing from many films about people with mental illness.
In summary, family-oriented patient care can be delivered in many ways, from focusing on relational improvement in individual work, to being aware of how to assess and measure relational functioning briefly at each visit, to being able to listen to the accounts of family members and invite them into the treatment room.
Dr. Heru is with the department of psychiatry at the University of Colorado at Denver, Aurora. She is editor of the recently published book, "Working With Families in Medical Settings: A Multidisciplinary Guide for Psychiatrists and Other Health Professions" (New York: Routledge, March 2013), and has been a member of the Association of Family Psychiatrists since 2002.
Diana looked at her pill bottles and wondered why she was on all these medications when she did not feel any better. She looked at the five bottles: bupropion, paroxetine, diazepam, alprazolam, and zolpidem. She thought about the side effects she was experiencing.
She had been taking this cocktail, in various dosages, for the best part of a year now. Her depression remained unchanged. She made a decision that she would tell her psychiatrist that she wanted off the medications at her next visit. She would then ask for other treatments. She had found many therapies offered on the Internet for treatment of depression, and she hoped her psychiatrist would be able to help her decide which therapies might be best suited for her. Perhaps she would agree to stay on one medication as a compromise as she knew her psychiatrist thought treatment of depression with medication to be important.
Up to 30% of patients with depression do not respond to multiple treatment trials and are considered to have treatment-resistant depression. Most treatment trials for these patients focus on symptom reduction as a goal. This emphasis on symptom reduction often leads to tunnel vision, where other evidence-based treatments become marginalized by psychiatrists. Thus, patients like Diana end up on multiple medications, without an integrated approach to assessment or discussion of combined treatments (medications and psychotherapy).
Dr. Gabor Keitner, who practices in Providence, R.I., and is a member of the Association of Family Psychiatrists, offers a new program aimed at helping patients manage their depression. His philosophical stance is that depression is a chronic illness and that expecting symptoms to be cured with medications is, for most patients, a false hope perpetuated by a consumer society, where the pharmaceutical industry has dominated the education of patients, their families, and the psychiatric profession. He conceptualizes depression, like other chronic medical illnesses, such as diabetes or hypertension, with a similar range of severity. Therefore, the assessment and treatment of depression requires a more nuanced approach.
He is scheduled to present his Management of Depression (MOD) program at this year’s American Psychiatric Association meeting in San Francisco. His MOD program focuses on how a patient such as Diana can build a satisfying life with meaningful goals and relationships – even if her depressive symptoms persist.
In his pilot study, 30 patients with treatment-resistant depression were randomized to treatment as usual (TAU, n = 13) or the MOD program (n = 17) for 12 weeks. The patients in the MOD group had significant improvement in perception of social support (P < .034) and purpose in life (P < .038) scores, in contrast to the TAU group. The MOD group participated in nine adjunctive sessions of disease management focused therapy. The Scales of Psychological Well-Being measured purpose in life, life goals, and meaning. Social support was measured with the Multidimensional Scale of Perceived Social Support. Depression severity was measured by the Montgomery-Åsberg Depression Rating Scale. Patients were assessed at baseline and week 12. Both groups of patients had significant improvements in their depressive symptoms (TAU 35.46 to 25.9 P < .010; MOD 31.88 to 22.41 P < .001) but continued to experience moderate levels of depression. Adjunctive treatment focusing on functioning, life meaning, and relationships, as opposed to symptom reduction, will help Diana to have a more satisfying life, despite her symptoms of depression.
Measuring relational functioning briefly
In another session, Dr. Keitner is slated to present "The Brief Multidimensional Assessment Scale (BMAS): A Mental Health Check Up," coauthored with Abigail K. Mansfield Maraccio, Ph.D., and Joan Kelley. This scale evaluates global mental health outcomes, including quality of life, symptoms, functioning, and relationships. This measure can be used to assess the clinical status of patients at every health encounter and over the course of an illness. Most available scales are either too long for routine clinical use, focus on a narrow range of symptoms, or focus on specific diagnostic groups. Best of all, this new scale takes less than a minute to complete.
The BMAS was tested against The Outcome Questionnaire–45 (OQ45) with 248 psychiatric outpatients as part of their standard ongoing care. Internal consistency was evaluated with Cronbach’s alpha, which was .75 for the four items. Test-retest reliability was assessed using Pearson’s r and ranged from .45 (symptom severity, which can fluctuate daily) to .79 (quality of life) for each of the BMAS items. Concurrent and convergent validity was analyzed with Pearson product moment correlations between BMAS and OQ45 scales. All correlations were significant for the relevant dimensions.
The BMAS demonstrated acceptable reliability, especially for such a brief measure. It also demonstrated concurrent and convergent validity with a much longer commonly used clinical outcome scale. The BMAS is a useful assessment tool for patients with any clinical condition for which it is desirable to track how the patient is experiencing his or her life situation at a given point in time and when there is a desire to monitor change over time. Notably, BMAS includes health relationships as a measure of good clinical outcome.
A daughter’s documentary about her father
One media workshop slated for the APA meeting will be offered by three members of the Association of Family Psychiatrists: Dr. Michael S. Ascher, Dr. Ira Glick, and Dr. Igor Galynker. They will present a film, "Unlisted: A Story of Schizophrenia." This is a soul-searching examination of responsibility – of parents and children, physicians and patients, and of society and citizens – toward those afflicted with severe mental illness. The film was made by Dr. Delaney Ruston, a Seattle general physician who documents the rebuilding of her relationship with her father. "Unlisted" examines the challenging family dynamics that are present when schizophrenia occurs. Dr. Ruston works hard to overcome the obstacles in accessing appropriate treatment for her father, and her documentary exposes the many failings of the American mental health system as experienced by the families. Dr. Ruston traces the progression of her father’s illness. She studies his medical files and narrates from his autobiographical surrealist novel. In beautifully portrayed scenes, "Unlisted" enters the inner life of Richard Ruston with a clarity and affection missing from many films about people with mental illness.
In summary, family-oriented patient care can be delivered in many ways, from focusing on relational improvement in individual work, to being aware of how to assess and measure relational functioning briefly at each visit, to being able to listen to the accounts of family members and invite them into the treatment room.
Dr. Heru is with the department of psychiatry at the University of Colorado at Denver, Aurora. She is editor of the recently published book, "Working With Families in Medical Settings: A Multidisciplinary Guide for Psychiatrists and Other Health Professions" (New York: Routledge, March 2013), and has been a member of the Association of Family Psychiatrists since 2002.
Only doctors can save America
Dr. Ezekiel J. Emanuel, one of the brains behind Obamacare, has a blunt message for his fellow physicians:
Only you can save America.
He's not just talking about medicine. As might befit someone who holds a faculty title at the business-oriented Wharton School at the University of Pennsylvania, Dr. Emanuel spent much of his keynote address here at the American College of Physicians' annual meeting in San Francisco talking about the U.S. economy. The enormous impact of runaway spending on U.S. health care threatens "everything we care about," including access to health care, state funds available for education, corporate wages for the middle class, and the fiscal health of the nation, he said.
"More than any other group in America, doctors have the power to solve our long-term economic challenges to ensure a prosperous future," Dr. Emanuel said.
If the U.S. health care system were a country, its nearly $3 trillion economy in 2012 would be the fifth largest in the world, behind only the U.S. as a whole, China, Japan, and Germany. "We spend more on health care in this country than the 66 million French spend on everything in their society," he said. "It is an astounding number how much we spend on health care."
Take just the federal portions of Medicare and Medicaid, excluding state spending, and you've still got the 16th largest economy in the world, bigger than the economies of Switzerland, Turkey, or the Netherlands, for example. The impact of any other fiscal variable on the U.S. economy, including Social Security, is swamped by the impact of health care costs, said Dr. Emanuel, who is also chair of medical ethics and health policy at the University of Pennsylvania, Philadelphia.
Per person, the United States far outspends other countries when it comes to health care, and the proportion of the gross domestic product consumed by health care keeps getting larger and larger.
Dr. Emanuel served as a special adviser for health policy to the director of the federal Office of Management and Budget in 2009-2011 - during the design, passage, and first steps to implementation of the Patient Protection and Affordable Care Act (commonly known as Obamacare) - and he seemed to address some critics in absentia who have claimed that health care reform will lead to unwanted rationing of care. There's no need to ration, Dr. Emanuel said. Switzerland doesn't ration care, and it spends far less per capita for what is considered quality health care. "We can do a better job in this country of controlling costs without the need to ration care," he said.
The only way to really control costs is to transform the way U.S. health care is delivered, he said. Ten percent of U.S. patients account for 63% of dollars spent on health care. "You know who they are - people with congestive heart failure, COPD, diabetes, adult asthma, coronary artery disease, cancer. People with chronic multiple chronic illnesses. That's where the money's going. That's where the uneven quality is," and that's where health care delivery needs to improve, he said.
Dr. Emanuel proposed six essential components to transforming the health care system. Among them: The focus needs to be on cost according to value, and getting rid of services with no value. The system must focus on patients' needs, not on physicians' schedules or other concerns. And the system must evolve toward clinicians working as teams including allied health professionals, not as individuals. "We are not going to be, going forward, one-sies and two-sies in practice" anymore, he said.
Greater emphasis on delivering health care via organizations and systems, standardization of processes, and transparency around price and quality will be essential, he added.
Transparency in pricing and quality isn't just something consumers will want. Physicians will want it in order to refer patients to quality care and set prices appropriately, Dr. Emanuel argued. "I think this is inevitable, and I think it's going to happen faster than you think," he said.
Most U.S. physicians are stuck in fee-for-service payment systems, which don't provide the incentives needed for change, he said. Doctors "as a group" should push for changes to the payment system, which will increase physician autonomy but also will assign more financial risk to physicians. "I see no way of getting out of that," Dr. Emanuel said.
In his eyes, if doctors don't push for changes in how health care is delivered, we basically can kiss the U.S. economy and future prosperity good-bye. "Doctors are the only people who can re-engineer the delivery system," he said. "If you don't do it, it ain't gonna happen. It's that simple," he said. All previous reform efforts that did not have physician leadership have failed.
"You have to lead this," he explained.
No one should expect that reforming the fifth-largest economy in the world could be accomplished in just a few years, however. "It's going to take this decade," Dr. Emanuel predicted.
Dr. Emanuel reported having no financial disclosures.
Twitter: @sherryboschert
Dr. Ezekiel J. Emanuel, one of the brains behind Obamacare, has a blunt message for his fellow physicians:
Only you can save America.
He's not just talking about medicine. As might befit someone who holds a faculty title at the business-oriented Wharton School at the University of Pennsylvania, Dr. Emanuel spent much of his keynote address here at the American College of Physicians' annual meeting in San Francisco talking about the U.S. economy. The enormous impact of runaway spending on U.S. health care threatens "everything we care about," including access to health care, state funds available for education, corporate wages for the middle class, and the fiscal health of the nation, he said.
"More than any other group in America, doctors have the power to solve our long-term economic challenges to ensure a prosperous future," Dr. Emanuel said.
If the U.S. health care system were a country, its nearly $3 trillion economy in 2012 would be the fifth largest in the world, behind only the U.S. as a whole, China, Japan, and Germany. "We spend more on health care in this country than the 66 million French spend on everything in their society," he said. "It is an astounding number how much we spend on health care."
Take just the federal portions of Medicare and Medicaid, excluding state spending, and you've still got the 16th largest economy in the world, bigger than the economies of Switzerland, Turkey, or the Netherlands, for example. The impact of any other fiscal variable on the U.S. economy, including Social Security, is swamped by the impact of health care costs, said Dr. Emanuel, who is also chair of medical ethics and health policy at the University of Pennsylvania, Philadelphia.
Per person, the United States far outspends other countries when it comes to health care, and the proportion of the gross domestic product consumed by health care keeps getting larger and larger.
Dr. Emanuel served as a special adviser for health policy to the director of the federal Office of Management and Budget in 2009-2011 - during the design, passage, and first steps to implementation of the Patient Protection and Affordable Care Act (commonly known as Obamacare) - and he seemed to address some critics in absentia who have claimed that health care reform will lead to unwanted rationing of care. There's no need to ration, Dr. Emanuel said. Switzerland doesn't ration care, and it spends far less per capita for what is considered quality health care. "We can do a better job in this country of controlling costs without the need to ration care," he said.
The only way to really control costs is to transform the way U.S. health care is delivered, he said. Ten percent of U.S. patients account for 63% of dollars spent on health care. "You know who they are - people with congestive heart failure, COPD, diabetes, adult asthma, coronary artery disease, cancer. People with chronic multiple chronic illnesses. That's where the money's going. That's where the uneven quality is," and that's where health care delivery needs to improve, he said.
Dr. Emanuel proposed six essential components to transforming the health care system. Among them: The focus needs to be on cost according to value, and getting rid of services with no value. The system must focus on patients' needs, not on physicians' schedules or other concerns. And the system must evolve toward clinicians working as teams including allied health professionals, not as individuals. "We are not going to be, going forward, one-sies and two-sies in practice" anymore, he said.
Greater emphasis on delivering health care via organizations and systems, standardization of processes, and transparency around price and quality will be essential, he added.
Transparency in pricing and quality isn't just something consumers will want. Physicians will want it in order to refer patients to quality care and set prices appropriately, Dr. Emanuel argued. "I think this is inevitable, and I think it's going to happen faster than you think," he said.
Most U.S. physicians are stuck in fee-for-service payment systems, which don't provide the incentives needed for change, he said. Doctors "as a group" should push for changes to the payment system, which will increase physician autonomy but also will assign more financial risk to physicians. "I see no way of getting out of that," Dr. Emanuel said.
In his eyes, if doctors don't push for changes in how health care is delivered, we basically can kiss the U.S. economy and future prosperity good-bye. "Doctors are the only people who can re-engineer the delivery system," he said. "If you don't do it, it ain't gonna happen. It's that simple," he said. All previous reform efforts that did not have physician leadership have failed.
"You have to lead this," he explained.
No one should expect that reforming the fifth-largest economy in the world could be accomplished in just a few years, however. "It's going to take this decade," Dr. Emanuel predicted.
Dr. Emanuel reported having no financial disclosures.
Twitter: @sherryboschert
Dr. Ezekiel J. Emanuel, one of the brains behind Obamacare, has a blunt message for his fellow physicians:
Only you can save America.
He's not just talking about medicine. As might befit someone who holds a faculty title at the business-oriented Wharton School at the University of Pennsylvania, Dr. Emanuel spent much of his keynote address here at the American College of Physicians' annual meeting in San Francisco talking about the U.S. economy. The enormous impact of runaway spending on U.S. health care threatens "everything we care about," including access to health care, state funds available for education, corporate wages for the middle class, and the fiscal health of the nation, he said.
"More than any other group in America, doctors have the power to solve our long-term economic challenges to ensure a prosperous future," Dr. Emanuel said.
If the U.S. health care system were a country, its nearly $3 trillion economy in 2012 would be the fifth largest in the world, behind only the U.S. as a whole, China, Japan, and Germany. "We spend more on health care in this country than the 66 million French spend on everything in their society," he said. "It is an astounding number how much we spend on health care."
Take just the federal portions of Medicare and Medicaid, excluding state spending, and you've still got the 16th largest economy in the world, bigger than the economies of Switzerland, Turkey, or the Netherlands, for example. The impact of any other fiscal variable on the U.S. economy, including Social Security, is swamped by the impact of health care costs, said Dr. Emanuel, who is also chair of medical ethics and health policy at the University of Pennsylvania, Philadelphia.
Per person, the United States far outspends other countries when it comes to health care, and the proportion of the gross domestic product consumed by health care keeps getting larger and larger.
Dr. Emanuel served as a special adviser for health policy to the director of the federal Office of Management and Budget in 2009-2011 - during the design, passage, and first steps to implementation of the Patient Protection and Affordable Care Act (commonly known as Obamacare) - and he seemed to address some critics in absentia who have claimed that health care reform will lead to unwanted rationing of care. There's no need to ration, Dr. Emanuel said. Switzerland doesn't ration care, and it spends far less per capita for what is considered quality health care. "We can do a better job in this country of controlling costs without the need to ration care," he said.
The only way to really control costs is to transform the way U.S. health care is delivered, he said. Ten percent of U.S. patients account for 63% of dollars spent on health care. "You know who they are - people with congestive heart failure, COPD, diabetes, adult asthma, coronary artery disease, cancer. People with chronic multiple chronic illnesses. That's where the money's going. That's where the uneven quality is," and that's where health care delivery needs to improve, he said.
Dr. Emanuel proposed six essential components to transforming the health care system. Among them: The focus needs to be on cost according to value, and getting rid of services with no value. The system must focus on patients' needs, not on physicians' schedules or other concerns. And the system must evolve toward clinicians working as teams including allied health professionals, not as individuals. "We are not going to be, going forward, one-sies and two-sies in practice" anymore, he said.
Greater emphasis on delivering health care via organizations and systems, standardization of processes, and transparency around price and quality will be essential, he added.
Transparency in pricing and quality isn't just something consumers will want. Physicians will want it in order to refer patients to quality care and set prices appropriately, Dr. Emanuel argued. "I think this is inevitable, and I think it's going to happen faster than you think," he said.
Most U.S. physicians are stuck in fee-for-service payment systems, which don't provide the incentives needed for change, he said. Doctors "as a group" should push for changes to the payment system, which will increase physician autonomy but also will assign more financial risk to physicians. "I see no way of getting out of that," Dr. Emanuel said.
In his eyes, if doctors don't push for changes in how health care is delivered, we basically can kiss the U.S. economy and future prosperity good-bye. "Doctors are the only people who can re-engineer the delivery system," he said. "If you don't do it, it ain't gonna happen. It's that simple," he said. All previous reform efforts that did not have physician leadership have failed.
"You have to lead this," he explained.
No one should expect that reforming the fifth-largest economy in the world could be accomplished in just a few years, however. "It's going to take this decade," Dr. Emanuel predicted.
Dr. Emanuel reported having no financial disclosures.
Twitter: @sherryboschert
Patient Prediction Model Trims Avoidable Hospital Readmissions
A new prediction model that uses a familiar phrase can help identify potentially avoidable hospital patient readmissions, according to a report in JAMA Internal Medicine.
The retrospective cohort study, "Potentially Avoidable 30-Day Hospital Readmissions in Medical Patients," used a model dubbed HOSPITAL to create a score that targets patients most likely to benefit from pre-discharge interventions. The model is based on seven factors: hemoglobin at discharge, discharge from an oncology service, sodium levels at discharge, procedure during the index admission, index type of admission, number of admissions in the prior 12 months, and length of stay. The HOSPITAL score had fair discriminatory power (C statistic 0.71) and good calibration, the authors noted.
"By definition, these [interventions] are expensive and you really want to reserve them for the patients that are most likely to benefit," says study co-author Jeffrey Schnipper, MD, MPH, FHM, director of clinical research and an associate physician in the general medicine division at Brigham and Women's Hospital in Boston.
The study identified 879 potentially avoidable discharges out of 10,731 eligible discharges, or 8.5%. The estimated potentially avoidable readmission risk was 18%. Dr. Schnipper says that in absolute reduction, the model could cut 2% to 3% of readmissions.
"This is an evolution of sophistication in how we think about this work," Dr. Schnipper adds. "Not all patients have a preventable readmission. Maybe some of those patients are more likely to benefit. The next step is to prove it. That's the gold standard and that’s our next study." TH
Visit our website for more information on 30-day readmissions.
A new prediction model that uses a familiar phrase can help identify potentially avoidable hospital patient readmissions, according to a report in JAMA Internal Medicine.
The retrospective cohort study, "Potentially Avoidable 30-Day Hospital Readmissions in Medical Patients," used a model dubbed HOSPITAL to create a score that targets patients most likely to benefit from pre-discharge interventions. The model is based on seven factors: hemoglobin at discharge, discharge from an oncology service, sodium levels at discharge, procedure during the index admission, index type of admission, number of admissions in the prior 12 months, and length of stay. The HOSPITAL score had fair discriminatory power (C statistic 0.71) and good calibration, the authors noted.
"By definition, these [interventions] are expensive and you really want to reserve them for the patients that are most likely to benefit," says study co-author Jeffrey Schnipper, MD, MPH, FHM, director of clinical research and an associate physician in the general medicine division at Brigham and Women's Hospital in Boston.
The study identified 879 potentially avoidable discharges out of 10,731 eligible discharges, or 8.5%. The estimated potentially avoidable readmission risk was 18%. Dr. Schnipper says that in absolute reduction, the model could cut 2% to 3% of readmissions.
"This is an evolution of sophistication in how we think about this work," Dr. Schnipper adds. "Not all patients have a preventable readmission. Maybe some of those patients are more likely to benefit. The next step is to prove it. That's the gold standard and that’s our next study." TH
Visit our website for more information on 30-day readmissions.
A new prediction model that uses a familiar phrase can help identify potentially avoidable hospital patient readmissions, according to a report in JAMA Internal Medicine.
The retrospective cohort study, "Potentially Avoidable 30-Day Hospital Readmissions in Medical Patients," used a model dubbed HOSPITAL to create a score that targets patients most likely to benefit from pre-discharge interventions. The model is based on seven factors: hemoglobin at discharge, discharge from an oncology service, sodium levels at discharge, procedure during the index admission, index type of admission, number of admissions in the prior 12 months, and length of stay. The HOSPITAL score had fair discriminatory power (C statistic 0.71) and good calibration, the authors noted.
"By definition, these [interventions] are expensive and you really want to reserve them for the patients that are most likely to benefit," says study co-author Jeffrey Schnipper, MD, MPH, FHM, director of clinical research and an associate physician in the general medicine division at Brigham and Women's Hospital in Boston.
The study identified 879 potentially avoidable discharges out of 10,731 eligible discharges, or 8.5%. The estimated potentially avoidable readmission risk was 18%. Dr. Schnipper says that in absolute reduction, the model could cut 2% to 3% of readmissions.
"This is an evolution of sophistication in how we think about this work," Dr. Schnipper adds. "Not all patients have a preventable readmission. Maybe some of those patients are more likely to benefit. The next step is to prove it. That's the gold standard and that’s our next study." TH
Visit our website for more information on 30-day readmissions.
Hospitals Seek Ways to Defuse Angry Doctors
Everyone is prone to an angry outburst from time to time, and doctors are no exception. With well-documented, negative effects on morale, nurse retention, and patient safety, it's safe to say anger issues crop up from time to time among the nearly 40,000 practicing hospitalists throughout the U.S.
A recent article in Kaiser Health News describes efforts by hospitals to deal with physicians' tirades, such as a three-day counseling program developed at Vanderbilt University in Nashville, Tenn.
"All physicians need to be aware that there should be a 'zero tolerance' attitude for disruptive behavior, hospitalists included, and that disruptive behavior undermines a culture of safety, and therefore can put patients in danger," says Danielle Scheurer, MD, MSCR, SFHM, hospitalist and chief quality officer at Medical University of South Carolina in Charleston and physician editor of The Hospitalist.
In 2009, The Joint Commission issued a sentinel alert about intimidating and disruptive behaviors by physicians and the ways in which hospitals can address the issue.
The problem is not unique to any physician specialty, including hospitalists, says Alan Rosenstein, MD, an internist and disruptive behavior researcher based in San Francisco. A physician's training or personality might contribute to angry outbursts, but excessive workloads will cause pressure, stress, and burnout, which can lead to poor behavior.
"Hospitals can no longer afford to look the other way," Dr. Rosenstein says. "I look at physicians as a precious resource. The organizations they're affiliated with need to be more proactive and empathetic, intervening before the problem reaches the stage of requiring discipline through techniques such as coaching and stress management." TH
Visit our website for more information about the impact of workloads on hospitalists.
Everyone is prone to an angry outburst from time to time, and doctors are no exception. With well-documented, negative effects on morale, nurse retention, and patient safety, it's safe to say anger issues crop up from time to time among the nearly 40,000 practicing hospitalists throughout the U.S.
A recent article in Kaiser Health News describes efforts by hospitals to deal with physicians' tirades, such as a three-day counseling program developed at Vanderbilt University in Nashville, Tenn.
"All physicians need to be aware that there should be a 'zero tolerance' attitude for disruptive behavior, hospitalists included, and that disruptive behavior undermines a culture of safety, and therefore can put patients in danger," says Danielle Scheurer, MD, MSCR, SFHM, hospitalist and chief quality officer at Medical University of South Carolina in Charleston and physician editor of The Hospitalist.
In 2009, The Joint Commission issued a sentinel alert about intimidating and disruptive behaviors by physicians and the ways in which hospitals can address the issue.
The problem is not unique to any physician specialty, including hospitalists, says Alan Rosenstein, MD, an internist and disruptive behavior researcher based in San Francisco. A physician's training or personality might contribute to angry outbursts, but excessive workloads will cause pressure, stress, and burnout, which can lead to poor behavior.
"Hospitals can no longer afford to look the other way," Dr. Rosenstein says. "I look at physicians as a precious resource. The organizations they're affiliated with need to be more proactive and empathetic, intervening before the problem reaches the stage of requiring discipline through techniques such as coaching and stress management." TH
Visit our website for more information about the impact of workloads on hospitalists.
Everyone is prone to an angry outburst from time to time, and doctors are no exception. With well-documented, negative effects on morale, nurse retention, and patient safety, it's safe to say anger issues crop up from time to time among the nearly 40,000 practicing hospitalists throughout the U.S.
A recent article in Kaiser Health News describes efforts by hospitals to deal with physicians' tirades, such as a three-day counseling program developed at Vanderbilt University in Nashville, Tenn.
"All physicians need to be aware that there should be a 'zero tolerance' attitude for disruptive behavior, hospitalists included, and that disruptive behavior undermines a culture of safety, and therefore can put patients in danger," says Danielle Scheurer, MD, MSCR, SFHM, hospitalist and chief quality officer at Medical University of South Carolina in Charleston and physician editor of The Hospitalist.
In 2009, The Joint Commission issued a sentinel alert about intimidating and disruptive behaviors by physicians and the ways in which hospitals can address the issue.
The problem is not unique to any physician specialty, including hospitalists, says Alan Rosenstein, MD, an internist and disruptive behavior researcher based in San Francisco. A physician's training or personality might contribute to angry outbursts, but excessive workloads will cause pressure, stress, and burnout, which can lead to poor behavior.
"Hospitals can no longer afford to look the other way," Dr. Rosenstein says. "I look at physicians as a precious resource. The organizations they're affiliated with need to be more proactive and empathetic, intervening before the problem reaches the stage of requiring discipline through techniques such as coaching and stress management." TH
Visit our website for more information about the impact of workloads on hospitalists.
VIDEO: Five Reasons You Should Attend HM13 in Washington, D.C.
Alstonia scholaris
Alstonia scholaris, a tree that grows 50-80 feet high and belongs to the Apocynaceae family, has a long history of use in traditional and homeopathic medicine, including Ayurvedic medicine in India, where it is known as sapthaparna (Integr. Cancer Ther. 2009;8:273-9), in traditional Chinese medicine (J. Ethnopharmacol. 2010;129:293-8; J. Ethnopharmacol. 2010;129:174-81), and in traditional medicine in Africa and Australia (Integr. Cancer Ther. 2010;9:261-9). The bark contains the alkaloids ditamine, echitamine (or ditaine), and echitanines; and decoctions or other preparations of the bark have been used to treat gastrointestinal conditions (Grieve M. A Modern Herbal (Vol. 1). New York, Dover Publications, 1971, p. 29). Often called the devil’s tree, the bark of A. scholaris also has been used to treat malaria, cutaneous diseases, tumors, ulcers, chronic respiratory conditions (such as asthma and bronchitis), helminthiasis, and agalactia (Chin. J. Integr. Med. 2012 Mar 28 [Epub ahead of print]).
In the study of A. scholaris most directly pertinent to potential dermatologic treatment, Lee et al. found that ethanolic bark extracts of A. scholaris significantly suppressed retinoid-induced skin irritation in vitro and in vivo, in human HaCat keratinocytes. The investigators identified echitamine and loganin as the primary components likely responsible for the anti-inflammatory effects.
Data showed that A. scholaris dose-dependently inhibited the all-trans retinoic acid–induced releases of the pro-inflammatory cytokines monocyte chemoattractant protein-1 (MCP-1) and interleukin-8 (IL-8) in vitro. Also in vitro, A. scholaris extract potently suppressed radiation-induced increases in matrix metalloproteinase-1 (MMP-1). Importantly, in a cumulative irritation patch test, the botanical extract diminished retinol-induced skin irritation while enhancing retinoid activity in blocking MMP-1 expression, which is linked closely to cutaneous aging. The authors concluded that A. scholaris appears to have the dual benefits of decreasing irritation associated with retinoids while augmenting their antiaging impact (Evid. Based Complement. Alternat. Med. 2012;2012:190370).
The leaf extract of A. scholaris has been used to treat cold symptoms and tracheitis, and it has been prescribed in hospitals and approved for commercial over-the-counter sale by the State Food and Drugs Administration of China (SFDA) (J. Ethnopharmacol. 2010;129:293-8; J. Ethnopharmacol. 2010;129:174-81). The broad range of biological properties associated with A. scholaris has been ascribed to particular constituent categories, including alkaloids, flavonoids, and terpenoids (specifically, phenolic acids) (Chin. J. Integr. Med. 2012 Mar 28 [Epub ahead of print]). These properties include, but are reportedly not limited to, antioxidant, anticancer, anti-inflammatory, antistress, analgesic, antimutagenic, hepatoprotective, immunomodulatory, and chemopreventive activity (Integr. Cancer Ther. 2010;9:261-9; Chin. J. Integr. Med. 2012 Mar 28 [Epub ahead of print]). Antineoplastic effects have been linked directly to phytochemical constituents including echitamine, alstonine, pleiocarpamine, O-methylmacralstonine, macralstonine, and lupeol (Integr. Cancer Ther. 2010;9:261-9).
In 2006, Jagetia and Baliga investigated the anticancer activity of A. scholaris alkaloid fractions in vitro in cultured human neoplastic cell lines. They also conducted in vivo studies in tumor-bearing mice. The in vitro data in HeLa cells revealed a time-dependent rise in antineoplastic activity after 24 hours of exposure (25 mcg/mL). Further, once-daily administration of A. scholaris (240 mg/kg) to tumor-bearing mice yielded dose-dependent remissions, although there were toxic presentations at this dosage. The next-lower dose of 210 mg/kg was found to be most effective, with 20% of the mice surviving for as long as 120 days after tumor cell inoculation, compared with none of the control animals treated with saline (Phytother. Res. 2006;20:103-9).
Using an acute-restraint stress model in mice in 2009, Kulkarni and Juvekar evaluated the effects of stress and the impact of a methanolic extract of A. scholaris bark. Pretreatments with the extract of 100, 250, and 500 mg/kg for 7 days were found to exert significant antistress effects. In addition, nootropic activities were observed, with memory functions clearly enhanced in learning tasks. A. scholaris also was associated with significant antioxidant properties. The extract at 200 mcg/mL exhibited maximum scavenging of the stable radical 1,1-diphenyl-2-picrylhydrazyl at 90.11% and the nitric oxide radical at 62.77% (Indian J. Exp. Biol. 2009;47:47-52).
Later in 2009, Jahan et al. reported on their investigation of potential antioxidant and chemopreventive activity displayed by A. scholaris in a two-stage murine model. Skin carcinogenesis development was initiated in Swiss albino mice through one application of 7, 12-dimethyabenz(a)anthrecene (DMBA) and then promoted two weeks later by repeated application of croton oil three times per week through 16 weeks. The investigators found a lower incidence of tumors, tumor yield, tumor burden, and number of papillomas in mice treated with A. scholaris extract as compared to untreated controls (Integr. Cancer Ther. 2009;8:273-9).
In 2010, Shang et al. conducted multiple studies using A. scholaris. In the first published report, they assessed the anti-inflammatory and analgesic properties of the ethanolic leaf extract to validate its use in traditional Chinese medicine and modern clinical medicine. The investigators first determined that analgesic activity was conferred as the ethyl acetate and alkaloid fractions significantly diminished acetic acid-induced reactions in mice and, along with the ethanolic extract, reduced xylene-induced ear edema.
The researchers also performed in vivo and in vitro assessments of anti-inflammatory activity again on xylene-induced ear edema and carrageenan-induced air pouch formation in mice, as well as cyclooxygenase (COX)-1, -2 and 5-LOX inhibition.
In the air pouch model, A. scholaris alkaloids were found to have significantly spurred superoxide dismutase activity while lowering nitric oxide, prostaglandin E2, and malondialdehyde levels. In vitro tests, supporting evidence from animal models, showed that the three primary alkaloids isolated from A. scholaris leaves (picrinine, vallesamine, and scholaricine) inhibited the inflammatory mediators COX-1, COX-2, and 5-LOX. The researchers also noted that the in vitro anti-inflammatory assay results reinforced the notion of these alkaloids as the bioactive fraction of the plant (J. Ethnopharmacol. 2010;129:174-81).
In their second published report that year, Shang et al. investigated the antitussive and anti-asthmatic activities of the ethanolic extract, fractions, and chief alkaloids of A. scholaris leaf.
The researchers tested for antitussive effects using ammonia-induced or sulfur dioxide-induced coughing in mice and citric acid-induced coughing in guinea pigs. They evaluated anti-asthmatic activity via histamine-induced bronchoconstriction in guinea pigs. They also measured phenol red volume in murine tracheas to assess expectorant activity.
The data indicated antitussive activity, with significant alkaloid suppression of ammonia-induced coughing frequency in mice. Latency periods of sulfur dioxide-induced cough in mice and citric acid-induced cough in guinea pigs increased, and cough frequency in guinea pigs decreased.
Anti-asthmatic effects, such as suppression of convulsion, were observed in guinea pigs. In the expectorant assessment, tracheal phenol red production was increased. The researchers identified picrinine as the primary alkaloid responsible for these activities (J. Ethnopharmacol. 2010;129:293-8).
In addition, Jahan and Goyal showed that pretreatment with A. scholaris bark extract protected the bone marrow of mice against radiation-induced chromosomal damage and micronuclei induction (J Environ. Pathol. Toxicol. Oncol. 2010;29:101-11).
Conclusion
Despite the dearth of research on A. scholaris, the existing data are intriguing, particularly the findings that A. scholaris may have the capacity to amplify the anti-aging activity of retinoids while blunting their irritating effects. Although more research is needed to determine the dermatologic value of A. scholaris, the pursuit may potentially prove fruitful.
Dr. Baumann is in private practice in Miami Beach. She did not disclose any conflicts of interest. To respond to this column, or to suggest topics for future columns, write to her at [email protected].
Alstonia scholaris, a tree that grows 50-80 feet high and belongs to the Apocynaceae family, has a long history of use in traditional and homeopathic medicine, including Ayurvedic medicine in India, where it is known as sapthaparna (Integr. Cancer Ther. 2009;8:273-9), in traditional Chinese medicine (J. Ethnopharmacol. 2010;129:293-8; J. Ethnopharmacol. 2010;129:174-81), and in traditional medicine in Africa and Australia (Integr. Cancer Ther. 2010;9:261-9). The bark contains the alkaloids ditamine, echitamine (or ditaine), and echitanines; and decoctions or other preparations of the bark have been used to treat gastrointestinal conditions (Grieve M. A Modern Herbal (Vol. 1). New York, Dover Publications, 1971, p. 29). Often called the devil’s tree, the bark of A. scholaris also has been used to treat malaria, cutaneous diseases, tumors, ulcers, chronic respiratory conditions (such as asthma and bronchitis), helminthiasis, and agalactia (Chin. J. Integr. Med. 2012 Mar 28 [Epub ahead of print]).
In the study of A. scholaris most directly pertinent to potential dermatologic treatment, Lee et al. found that ethanolic bark extracts of A. scholaris significantly suppressed retinoid-induced skin irritation in vitro and in vivo, in human HaCat keratinocytes. The investigators identified echitamine and loganin as the primary components likely responsible for the anti-inflammatory effects.
Data showed that A. scholaris dose-dependently inhibited the all-trans retinoic acid–induced releases of the pro-inflammatory cytokines monocyte chemoattractant protein-1 (MCP-1) and interleukin-8 (IL-8) in vitro. Also in vitro, A. scholaris extract potently suppressed radiation-induced increases in matrix metalloproteinase-1 (MMP-1). Importantly, in a cumulative irritation patch test, the botanical extract diminished retinol-induced skin irritation while enhancing retinoid activity in blocking MMP-1 expression, which is linked closely to cutaneous aging. The authors concluded that A. scholaris appears to have the dual benefits of decreasing irritation associated with retinoids while augmenting their antiaging impact (Evid. Based Complement. Alternat. Med. 2012;2012:190370).
The leaf extract of A. scholaris has been used to treat cold symptoms and tracheitis, and it has been prescribed in hospitals and approved for commercial over-the-counter sale by the State Food and Drugs Administration of China (SFDA) (J. Ethnopharmacol. 2010;129:293-8; J. Ethnopharmacol. 2010;129:174-81). The broad range of biological properties associated with A. scholaris has been ascribed to particular constituent categories, including alkaloids, flavonoids, and terpenoids (specifically, phenolic acids) (Chin. J. Integr. Med. 2012 Mar 28 [Epub ahead of print]). These properties include, but are reportedly not limited to, antioxidant, anticancer, anti-inflammatory, antistress, analgesic, antimutagenic, hepatoprotective, immunomodulatory, and chemopreventive activity (Integr. Cancer Ther. 2010;9:261-9; Chin. J. Integr. Med. 2012 Mar 28 [Epub ahead of print]). Antineoplastic effects have been linked directly to phytochemical constituents including echitamine, alstonine, pleiocarpamine, O-methylmacralstonine, macralstonine, and lupeol (Integr. Cancer Ther. 2010;9:261-9).
In 2006, Jagetia and Baliga investigated the anticancer activity of A. scholaris alkaloid fractions in vitro in cultured human neoplastic cell lines. They also conducted in vivo studies in tumor-bearing mice. The in vitro data in HeLa cells revealed a time-dependent rise in antineoplastic activity after 24 hours of exposure (25 mcg/mL). Further, once-daily administration of A. scholaris (240 mg/kg) to tumor-bearing mice yielded dose-dependent remissions, although there were toxic presentations at this dosage. The next-lower dose of 210 mg/kg was found to be most effective, with 20% of the mice surviving for as long as 120 days after tumor cell inoculation, compared with none of the control animals treated with saline (Phytother. Res. 2006;20:103-9).
Using an acute-restraint stress model in mice in 2009, Kulkarni and Juvekar evaluated the effects of stress and the impact of a methanolic extract of A. scholaris bark. Pretreatments with the extract of 100, 250, and 500 mg/kg for 7 days were found to exert significant antistress effects. In addition, nootropic activities were observed, with memory functions clearly enhanced in learning tasks. A. scholaris also was associated with significant antioxidant properties. The extract at 200 mcg/mL exhibited maximum scavenging of the stable radical 1,1-diphenyl-2-picrylhydrazyl at 90.11% and the nitric oxide radical at 62.77% (Indian J. Exp. Biol. 2009;47:47-52).
Later in 2009, Jahan et al. reported on their investigation of potential antioxidant and chemopreventive activity displayed by A. scholaris in a two-stage murine model. Skin carcinogenesis development was initiated in Swiss albino mice through one application of 7, 12-dimethyabenz(a)anthrecene (DMBA) and then promoted two weeks later by repeated application of croton oil three times per week through 16 weeks. The investigators found a lower incidence of tumors, tumor yield, tumor burden, and number of papillomas in mice treated with A. scholaris extract as compared to untreated controls (Integr. Cancer Ther. 2009;8:273-9).
In 2010, Shang et al. conducted multiple studies using A. scholaris. In the first published report, they assessed the anti-inflammatory and analgesic properties of the ethanolic leaf extract to validate its use in traditional Chinese medicine and modern clinical medicine. The investigators first determined that analgesic activity was conferred as the ethyl acetate and alkaloid fractions significantly diminished acetic acid-induced reactions in mice and, along with the ethanolic extract, reduced xylene-induced ear edema.
The researchers also performed in vivo and in vitro assessments of anti-inflammatory activity again on xylene-induced ear edema and carrageenan-induced air pouch formation in mice, as well as cyclooxygenase (COX)-1, -2 and 5-LOX inhibition.
In the air pouch model, A. scholaris alkaloids were found to have significantly spurred superoxide dismutase activity while lowering nitric oxide, prostaglandin E2, and malondialdehyde levels. In vitro tests, supporting evidence from animal models, showed that the three primary alkaloids isolated from A. scholaris leaves (picrinine, vallesamine, and scholaricine) inhibited the inflammatory mediators COX-1, COX-2, and 5-LOX. The researchers also noted that the in vitro anti-inflammatory assay results reinforced the notion of these alkaloids as the bioactive fraction of the plant (J. Ethnopharmacol. 2010;129:174-81).
In their second published report that year, Shang et al. investigated the antitussive and anti-asthmatic activities of the ethanolic extract, fractions, and chief alkaloids of A. scholaris leaf.
The researchers tested for antitussive effects using ammonia-induced or sulfur dioxide-induced coughing in mice and citric acid-induced coughing in guinea pigs. They evaluated anti-asthmatic activity via histamine-induced bronchoconstriction in guinea pigs. They also measured phenol red volume in murine tracheas to assess expectorant activity.
The data indicated antitussive activity, with significant alkaloid suppression of ammonia-induced coughing frequency in mice. Latency periods of sulfur dioxide-induced cough in mice and citric acid-induced cough in guinea pigs increased, and cough frequency in guinea pigs decreased.
Anti-asthmatic effects, such as suppression of convulsion, were observed in guinea pigs. In the expectorant assessment, tracheal phenol red production was increased. The researchers identified picrinine as the primary alkaloid responsible for these activities (J. Ethnopharmacol. 2010;129:293-8).
In addition, Jahan and Goyal showed that pretreatment with A. scholaris bark extract protected the bone marrow of mice against radiation-induced chromosomal damage and micronuclei induction (J Environ. Pathol. Toxicol. Oncol. 2010;29:101-11).
Conclusion
Despite the dearth of research on A. scholaris, the existing data are intriguing, particularly the findings that A. scholaris may have the capacity to amplify the anti-aging activity of retinoids while blunting their irritating effects. Although more research is needed to determine the dermatologic value of A. scholaris, the pursuit may potentially prove fruitful.
Dr. Baumann is in private practice in Miami Beach. She did not disclose any conflicts of interest. To respond to this column, or to suggest topics for future columns, write to her at [email protected].
Alstonia scholaris, a tree that grows 50-80 feet high and belongs to the Apocynaceae family, has a long history of use in traditional and homeopathic medicine, including Ayurvedic medicine in India, where it is known as sapthaparna (Integr. Cancer Ther. 2009;8:273-9), in traditional Chinese medicine (J. Ethnopharmacol. 2010;129:293-8; J. Ethnopharmacol. 2010;129:174-81), and in traditional medicine in Africa and Australia (Integr. Cancer Ther. 2010;9:261-9). The bark contains the alkaloids ditamine, echitamine (or ditaine), and echitanines; and decoctions or other preparations of the bark have been used to treat gastrointestinal conditions (Grieve M. A Modern Herbal (Vol. 1). New York, Dover Publications, 1971, p. 29). Often called the devil’s tree, the bark of A. scholaris also has been used to treat malaria, cutaneous diseases, tumors, ulcers, chronic respiratory conditions (such as asthma and bronchitis), helminthiasis, and agalactia (Chin. J. Integr. Med. 2012 Mar 28 [Epub ahead of print]).
In the study of A. scholaris most directly pertinent to potential dermatologic treatment, Lee et al. found that ethanolic bark extracts of A. scholaris significantly suppressed retinoid-induced skin irritation in vitro and in vivo, in human HaCat keratinocytes. The investigators identified echitamine and loganin as the primary components likely responsible for the anti-inflammatory effects.
Data showed that A. scholaris dose-dependently inhibited the all-trans retinoic acid–induced releases of the pro-inflammatory cytokines monocyte chemoattractant protein-1 (MCP-1) and interleukin-8 (IL-8) in vitro. Also in vitro, A. scholaris extract potently suppressed radiation-induced increases in matrix metalloproteinase-1 (MMP-1). Importantly, in a cumulative irritation patch test, the botanical extract diminished retinol-induced skin irritation while enhancing retinoid activity in blocking MMP-1 expression, which is linked closely to cutaneous aging. The authors concluded that A. scholaris appears to have the dual benefits of decreasing irritation associated with retinoids while augmenting their antiaging impact (Evid. Based Complement. Alternat. Med. 2012;2012:190370).
The leaf extract of A. scholaris has been used to treat cold symptoms and tracheitis, and it has been prescribed in hospitals and approved for commercial over-the-counter sale by the State Food and Drugs Administration of China (SFDA) (J. Ethnopharmacol. 2010;129:293-8; J. Ethnopharmacol. 2010;129:174-81). The broad range of biological properties associated with A. scholaris has been ascribed to particular constituent categories, including alkaloids, flavonoids, and terpenoids (specifically, phenolic acids) (Chin. J. Integr. Med. 2012 Mar 28 [Epub ahead of print]). These properties include, but are reportedly not limited to, antioxidant, anticancer, anti-inflammatory, antistress, analgesic, antimutagenic, hepatoprotective, immunomodulatory, and chemopreventive activity (Integr. Cancer Ther. 2010;9:261-9; Chin. J. Integr. Med. 2012 Mar 28 [Epub ahead of print]). Antineoplastic effects have been linked directly to phytochemical constituents including echitamine, alstonine, pleiocarpamine, O-methylmacralstonine, macralstonine, and lupeol (Integr. Cancer Ther. 2010;9:261-9).
In 2006, Jagetia and Baliga investigated the anticancer activity of A. scholaris alkaloid fractions in vitro in cultured human neoplastic cell lines. They also conducted in vivo studies in tumor-bearing mice. The in vitro data in HeLa cells revealed a time-dependent rise in antineoplastic activity after 24 hours of exposure (25 mcg/mL). Further, once-daily administration of A. scholaris (240 mg/kg) to tumor-bearing mice yielded dose-dependent remissions, although there were toxic presentations at this dosage. The next-lower dose of 210 mg/kg was found to be most effective, with 20% of the mice surviving for as long as 120 days after tumor cell inoculation, compared with none of the control animals treated with saline (Phytother. Res. 2006;20:103-9).
Using an acute-restraint stress model in mice in 2009, Kulkarni and Juvekar evaluated the effects of stress and the impact of a methanolic extract of A. scholaris bark. Pretreatments with the extract of 100, 250, and 500 mg/kg for 7 days were found to exert significant antistress effects. In addition, nootropic activities were observed, with memory functions clearly enhanced in learning tasks. A. scholaris also was associated with significant antioxidant properties. The extract at 200 mcg/mL exhibited maximum scavenging of the stable radical 1,1-diphenyl-2-picrylhydrazyl at 90.11% and the nitric oxide radical at 62.77% (Indian J. Exp. Biol. 2009;47:47-52).
Later in 2009, Jahan et al. reported on their investigation of potential antioxidant and chemopreventive activity displayed by A. scholaris in a two-stage murine model. Skin carcinogenesis development was initiated in Swiss albino mice through one application of 7, 12-dimethyabenz(a)anthrecene (DMBA) and then promoted two weeks later by repeated application of croton oil three times per week through 16 weeks. The investigators found a lower incidence of tumors, tumor yield, tumor burden, and number of papillomas in mice treated with A. scholaris extract as compared to untreated controls (Integr. Cancer Ther. 2009;8:273-9).
In 2010, Shang et al. conducted multiple studies using A. scholaris. In the first published report, they assessed the anti-inflammatory and analgesic properties of the ethanolic leaf extract to validate its use in traditional Chinese medicine and modern clinical medicine. The investigators first determined that analgesic activity was conferred as the ethyl acetate and alkaloid fractions significantly diminished acetic acid-induced reactions in mice and, along with the ethanolic extract, reduced xylene-induced ear edema.
The researchers also performed in vivo and in vitro assessments of anti-inflammatory activity again on xylene-induced ear edema and carrageenan-induced air pouch formation in mice, as well as cyclooxygenase (COX)-1, -2 and 5-LOX inhibition.
In the air pouch model, A. scholaris alkaloids were found to have significantly spurred superoxide dismutase activity while lowering nitric oxide, prostaglandin E2, and malondialdehyde levels. In vitro tests, supporting evidence from animal models, showed that the three primary alkaloids isolated from A. scholaris leaves (picrinine, vallesamine, and scholaricine) inhibited the inflammatory mediators COX-1, COX-2, and 5-LOX. The researchers also noted that the in vitro anti-inflammatory assay results reinforced the notion of these alkaloids as the bioactive fraction of the plant (J. Ethnopharmacol. 2010;129:174-81).
In their second published report that year, Shang et al. investigated the antitussive and anti-asthmatic activities of the ethanolic extract, fractions, and chief alkaloids of A. scholaris leaf.
The researchers tested for antitussive effects using ammonia-induced or sulfur dioxide-induced coughing in mice and citric acid-induced coughing in guinea pigs. They evaluated anti-asthmatic activity via histamine-induced bronchoconstriction in guinea pigs. They also measured phenol red volume in murine tracheas to assess expectorant activity.
The data indicated antitussive activity, with significant alkaloid suppression of ammonia-induced coughing frequency in mice. Latency periods of sulfur dioxide-induced cough in mice and citric acid-induced cough in guinea pigs increased, and cough frequency in guinea pigs decreased.
Anti-asthmatic effects, such as suppression of convulsion, were observed in guinea pigs. In the expectorant assessment, tracheal phenol red production was increased. The researchers identified picrinine as the primary alkaloid responsible for these activities (J. Ethnopharmacol. 2010;129:293-8).
In addition, Jahan and Goyal showed that pretreatment with A. scholaris bark extract protected the bone marrow of mice against radiation-induced chromosomal damage and micronuclei induction (J Environ. Pathol. Toxicol. Oncol. 2010;29:101-11).
Conclusion
Despite the dearth of research on A. scholaris, the existing data are intriguing, particularly the findings that A. scholaris may have the capacity to amplify the anti-aging activity of retinoids while blunting their irritating effects. Although more research is needed to determine the dermatologic value of A. scholaris, the pursuit may potentially prove fruitful.
Dr. Baumann is in private practice in Miami Beach. She did not disclose any conflicts of interest. To respond to this column, or to suggest topics for future columns, write to her at [email protected].
Early Death or Hospital Readmission
Socioeconomic status (SES) classifies people according to occupation, prior education, or income.[1] Socioeconomic status has been associated with several population‐health outcomes, albeit with geographically inconsistent results.[2] If lower SES is associated with higher readmission rates, then further studies could be done to determine which specific socioeconomic factors are potentially modifiable and whether the provision of additional resources could allay the increased risk associated with those factors.
Nine studies have examined the association between SES and readmissions.[3, 4, 5, 6, 7, 8, 9, 10, 11] These studies varied extensively in methodologies, SES measures, and results. However, results from 1 of these studies[11] were particularly notable given the study's significant association between lower household income and increased risk of acute readmission in a publicly funded, open‐access healthcare system. Given the implications of these results, an accurate and explicit assessment of the association between SES measures and the risk of adverse postdischarge outcomes is important.
We recently developed a model that accurately predicts the risk of 30‐day death or urgent readmission using administrative data.[12] This model did not directly control for any SES factors. In this study, we determined if a commonly used SES measurehousehold‐income quintilewas associated with the risk of early death or urgent readmission after controlling for factors known to influence this outcome.
METHODS
Study Setting and Data Sources
This population‐based study took place in Ontario, Canada, between April 1, 2003 and March 31, 2009. All hospital and physician care in Ontario is publicly funded. The study used 2 databases, the Discharge Abstract Database and the Registered Persons Database. The Discharge Abstract Database records information about all nonpsychiatric hospitalizations, including dates of hospital admission and discharge, vital status at end of hospitalization, discharge destination (ie, community, nursing home, or chronic hospital), admission urgency, primary and other diagnoses, and postal code of patient's household. The Registered Persons Database captures basic demographic data about all Ontarians, including date of birth and date of death (if applicable), postal code of residence, and average household‐income quintile of postal code, determined by linking the postal code to Statistics Canada geographical units through the Postal Code Conversion File Plus.[13] The Registered Persons Database captures all deaths regardless of the death location (ie, community vs hospital).
Study Population
This study used patients from a previous analysis that internally validated an index to predict the risk of 30‐day death or urgent readmission.[12] This analysis included a simple random sample of 250,000 adult Ontarians (age >18 years) who were discharged from the hospital to the community between April 1, 2003 and March 31, 2009. These medical and surgical hospitalizations were sampled from the Discharge Abstract Database described above. Psychiatric admissions were excluded because their hospitalizations are captured in a distinct database; obstetrical admissions were also excluded because they have a very low risk of 30‐day death or readmission. We randomly chose 1 index admission per person to ensure that the patient was the unit of analysis.
For the present study, we selected all patients from the previous analysis who were discharged from the hospital in 2006. This year was chosen because the SES indicator we used in the study (average household‐income quintile) was measured during the 2006 Canadian Census and would be most accurate for patients discharged in that year. The present study also limited patients to those with a valid postal code, because this was required to link patients to their neighborhood and their household‐income quintile.
Study Outcome
The study outcome was all‐cause death or urgent readmission within 30 days of discharge from hospital. We combined death with urgent readmission to avoid potential biases that could occur when measuring associations between risk factors and urgent readmission; in analyses having readmission as the sole outcome, the categorization of early deaths that occur prior to readmission as nonevents could minimize the importance of factors (such as severe comorbidities or patient age) that are associated with both early death and readmission.
We linked to the Registered Patients Database to determine each person's 30‐day death status. We linked to the Discharge Abstract Database to determine if patients had been urgently readmitted to any hospital within 30 days of discharge. All deaths were considered regardless of cause. All urgent (ie, nonscheduled) readmissions were included regardless of the reason for admission. Urgent status was determined by the urgency field in the Discharge Abstract Database, for which data abstractors are instructed to classify all nonscheduled admissions as urgent; these admissions frequently include those admitted after presenting to the emergency department.
Study Covariates: Readmission Risk and Neighborhood Household‐Income Quintile
In our primary analysis, we quantified the risk of 30‐day death or urgent readmission using an internally validated index, the LACE+ index: length of stay (L), acuity of the admission (A), comorbidity of the patient (measured with the Charlson Comorbidity Index score (C), and emergency‐department use (E).[12] The LACE+ index predicts the risk of 30‐day all‐cause death or urgent readmission for nonpsychiatric and nonobstetrical admissions. This index includes patient age, sex, comorbidities, and previous hospital and emergency‐department utilization; admission urgency; hospital type; total length of stay (LOS) and days in hospital awaiting placement; and hospitalization diagnostic risk.[14] The index quantified outcome risk as a score that ranged from 17 to 114. It was very discriminatory (C statistic, 77.1%) and was well calibrated (the observed and expected outcome risk was statistically distinct in only 2 of 14 risk groups that contained <2% of the population). The LACE+ quintiles were defined using score distribution from the entire 20032009 cohort.[12]
We used neighborhood income quintile as 1 measure of patient SES. Neighborhood income quintile was calculated by Statistics Canada using the Income Per Person Equivalent (IPPE) determined from the 2006 Canadian census.[13] The IPPE was calculated as total household income divided by the Single Persons Equivalent, which reflects decreased costs per person (and therefore increased available income per household occupant) in households having greater numbers of people. Within each dissemination area (each contains 400700 people), the average IPPE was calculated. Then, within each region (delineated by the Census Metropolitan Area, the Census Agglomeration, or provincial residual areas), dissemination areas were ranked by their average IPPE and then categorized into quintiles. These household‐income quintiles, therefore, are community‐specific and ensure that neighborhood household incomes are categorized based on comparisons within the same community. As such, the income thresholds for quintile categorization will vary between regions. We linked each patient's postal code to their dissemination area using the Postal Code Conversion File Plus[13] to determine their neighborhood income quintile.
Analysis
We described the patient cohort by readmission status. We categorized the expected risk of 30‐day death or urgent readmission to hospital (as determined by the LACE+ score) into quintiles. We used the 2 test and the test for trend to determine the association of these risk quintiles and SES quintiles with observed rates of 30‐day death or urgent readmission. The Cochran‐Mantel‐Haenszel test was used to determine the association of household‐income quintile and outcome risk after adjusting for LACE+ quintile.
To determine how the association between income quintile and outcome changes with increase adjustment, we constructed a series of logistic‐regression models that contained household‐income quintile and the sequential addition of components of the LACE+ score. For each model, we measured the influence of these added covariates on the association between household‐income quintile and early death or urgent readmission. We used orthogonal parameterization (which facilitates the comparison of parameter estimates in a regression model) to measure linear trends in the association of the income quintiles with outcomes.
RESULTS
The original cohort contained 250,000 people, of which 40,827 people (16.3%) were included in the present study (208,995 were excluded because patients were discharged in years other than 2006; 178 were excluded because of invalid postal codes).
Patients are described in Table 1. Patients were middle‐aged and had few documented chronic comorbidities. Of the patients, 37% had been to the emergency department and 12% had been admitted urgently. Most admissions were to large, nonteaching hospitals with a median LOS of 3 days.
| Variable | Value | No Death/Readmission, n=38,189 | Death/Readmission, n=2,638 | Overall, N=40,827 | |||
|---|---|---|---|---|---|---|---|
| |||||||
| Mean age (SD), y | 57.39 (18.3) | 67.17 (17.2) | 58.02 (18.4) | ||||
| Female sex | 20,044 | 52.5% | 1,291 | 48.9% | 21,335 | 52.3% | |
| Charlson index | 0 | 28,908 | 75.7% | 1,238 | 46.9% | 30,146 | 73.8% |
| 1 | 450 | 11.7% | 362 | 13.7% | 4,812 | 11.8% | |
| 2 | 2,668 | 7.0% | 427 | 16.2% | 3,095 | 7.6% | |
| 3+ | 2,163 | 5.7% | 611 | 23.2% | 2,774 | 6.8% | |
| ED visits in previous 6 moths | 0 | 24,599 | 64.4% | 1,210 | 45.9% | 25,809 | 63.2% |
| 12 | 11,262 | 29.5% | 1,008 | 38.2% | 12,270 | 30.1% | |
| 3+ | 2,328 | 6.1% | 420 | 15.9% | 2,748 | 6.7% | |
| Urgent hospitalizations, previous year | 0 | 33,729 | 88.3% | 1,796 | 68.1% | 35,525 | 87.0% |
| 1 | 3,425 | 9.0% | 525 | 19.9% | 3,950 | 9.7% | |
| 1+ | 1,035 | 2.7% | 317 | 12.0% | 1,352 | 3.3% | |
| Elective hospitalizations, previous year | 0 | 35,988 | 94.2% | 2,389 | 90.6% | 38,377 | 94.0% |
| 1 | 1,998 | 5.2% | 213 | 8.1% | 2,211 | 5.4% | |
| 2+ | 203 | 0.5% | 36 | 1.4% | 239 | 0.6% | |
| Hospital type | Nonteaching, large | 20,554 | 53.8% | 1,334 | 50.6% | 21,888 | 53.6% |
| Nonteaching, small | 5,239 | 13.7% | 487 | 18.5% | 5726 | 14.0% | |
| Teaching | 12,396 | 32.5% | 817 | 31.0% | 13,213 | 32.4% | |
| Urgent admit | 23,769 | 62.2% | 2,223 | 84.3% | 25,992 | 63.7% | |
| LOS rounded to nearest day, median (IQR) | 3 (26) | 5 (311) | 3 (26) | ||||
| Any hospital days on ALC | 0 | 646 | 1.7% | 127 | 4.8% | 773 | 1.9% |
| CMG score of index admission | 0 | 27,257 | 71.4% | 1,594 | 60.4% | 28,851 | 70.7% |
| 1+ | 5,218 | 13.7% | 948 | 35.9% | 6,166 | 15.1% | |
| <0 | 5,714 | 15.0% | 96 | 3.6% | 5,810 | 14.2% | |
| LACE+ score of index admission, median (IQR) | 31 (1848) | 61 (4175) | 32 (1951) | ||||
| Household‐income quintile | 1 (poorest) | 7,798 | 20.4% | 621 | 23.5% | 8,419 | 20.6% |
| 2 | 7,812 | 20.5% | 586 | 22.2% | 8,398 | 20.6% | |
| 3 | 7,557 | 19.8% | 484 | 18.3% | 8,041 | 19.7% | |
| 4 | 7,561 | 19.8% | 500 | 19.0% | 8,061 | 19.7% | |
| 5 (richest) | 7,461 | 19.5% | 447 | 16.9% | 7,908 | 19.4% | |
Death or urgent readmission within 30 days occurred in 2638 people (6.5%) (Table 1). Outcome risk increased with age; in males; as comorbidities increased; with greater numbers of emergency‐department visits, urgent admissions, and previous elective admissions; when index admissions were emergent; with longer hospital LOS and increased number of alternate level of care days; and as the diagnostic risk (measured as the Case Mix Group [CMG] score)[14] increased. Outcome risk increased as income quintile became poorer.
Household Income and Risk of 30‐Day Death or Urgent Readmission
People were evenly divided among the income quintiles (Table 2). By itself, household‐income quintile was significantly associated with the risk of early death or urgent hospital readmission (Table 2, column C, 2=27.4, P<0.0001; Mantel‐Haenszel trend 2=24.3, P<0.0001). In the poorest quintile, 7.4% of people had an outcome, compared with 5.6% in the richest quintile (2=19.8, df=1, P<0.0001).
| Risk Quintile of 30‐Day Death or Readmission (LACE+ Points Range) | ||||||
|---|---|---|---|---|---|---|
| 1 (1416) [A] | 2 (1727) | 3 (2839) | 4 (4056) | 5 (57114) [B] | Income Quintile Overall [C] | |
| ||||||
| Income quintile | ||||||
| 1 (poorest) | 18/1,485 (1.2%) | 42/1,667 (2.5%) | 65/1,635 (4.0%) | 117/1,722 (6.8%) | 379/1,910 (19.8%) | 621/8,419 (7.4%) |
| 2 | 21/1,627 (1.3%) | 39/1,665 (2.3%) | 65/1,598 (4.1%) | 130/1,808 (5.2%) | 331/1,700 (19.5%) | 586/8,398 (7.0%) |
| 3 | 18/1,761 (1.0%) | 33/1,665 (2.0%) | 63/1,568 (4.0%) | 96/1,499 (6.4%) | 274/1,548 (17.7%) | 484/8,041 (6.0%) |
| 4 | 27/1,851 (1.5%) | 42/1,698 (2.4%) | 57/1,585 (3.6%) | 110/1,548 (6.1%) | 264/1,379 (19.1%) | 500/8,061 (6.2%) |
| 5 (richest) | 20/1,864 (1.1%) | 32/1,736 (1.8%) | 60/1,468 (4.1%) | 107/1,525 (7.0%) | 228/1,315 (17.3%) | 447/7,908 (5.6%) |
| Risk quintile overall [D] | 104/8,588 (1.2%) | 188/8,431 (2.2%) | 310/7,854 (4.0%) | 560/8,102 (6.9%) | 1476/7,852 (18.8%) | 2,638/40,827 (6.5%) |
However, household income was also strongly associated with LACE+ scores (2=240, P<0.0001; Mantel‐Haenszel trend 2=209, P<0.0001). The number of people in the lowest‐risk quintile increased with income, from 1485 in the poorest quintile to 1864 in the richest quintile (Table 2, column A). In contrast, the number of high‐risk people progressively decreased with income, from 1910 in the poorest quintile to 1315 in the richest quintile (Table 2, column B).
The LACE+ quintile was very strongly associated with outcome risk, as shown in Table 2, row D (2=2703, P<0.0001; Mantel‐Haenszel trend 2=2102, P<0.0001). Within each LACE+ stratum, the risk of death or urgent readmission did not appear to consistently change with income quintile. After adjusting for LACE+ scores, income quintile was no longer associated with 30‐day death or readmission (Cochran‐Mantel‐Haenszel 2=5.9, df=4, P=0.21).
We found no nonlinear associations between household‐income quintile and 30‐day death or readmission after adjusting for the LACE+ score. In addition, the association between LACE+ quintile and outcome did not vary significantly by household‐income quintile (P value for interaction term in logistic regression model=0.5582).
The association between income quintile and 30‐day death or urgent readmission decreased when incrementally controlling for other covariates in the LACE+ model (Figure 1). By itself, all income quintiles except 2 were significantly distinct from the poorest income quintile. The addition of patient age, sex, and hospital type had little effect on the association between income and outcomes. The addition of index admission urgency shifted all point estimates toward unity (Figure 1). Associations between income and death or readmission then remained relatively stable until the addition of number of urgent admissions in the previous year (Figure 1). The subsequent addition of number of emergency visits and comorbidities resulted in none of the income quintiles being statistically distinct from the poorest quintile, as well as a nonsignificant linear trend over the quintiles.
DISCUSSION
Our study shows that the risk of 30‐day death or urgent readmission was higher in people from lower‐income neighborhoods. However, this risk appears to be explained by patient‐level factors that are known to be associated with bad postdischarge outcomes. After accounting for these factors with the LACE+ index, we found no notable changes in the risk of early death or urgent readmission with SES as measured with average neighborhood household income.
Nine previous studies have measured the association between various SES measures and hospital readmission in disparate populations.[3, 4, 5, 6, 7, 8, 9, 10, 11] These studies were done in the United States,[5, 6, 8, 9, 10] the United Kingdom,[3, 7] Australia,[4] and Canada.[11] They used a range of SES indicators (from area‐level measures of household income[5] or deprivation[3] to personal education and income)[8, 9, 10] in diverse patient populations (from a random sample of all hospitalizations[3] to people with disabilities living in New York City)[15] and very different time horizons (capturing hospital readmissions that occurred from within 30 days[5] to 4 years).[10] Of these 9 studies, 5 found no independent association between their SES measure and readmission,[5, 6, 8, 9, 10] and 2 included SES in their final regression model but did not present the modelmaking it impossible to determine if SES significantly influenced outcomes.[3, 15] One study found that the risk of hospital readmission independently increased as a composite measure of area‐level social and economic indicators decreased.[4] A Canadian study[11] measured neighborhood income quintile and showed, after adjusting for patient sex, comorbidities, LOS variance, and previous admissions, that the odds of acute, nonpsychiatric readmission within 30 days of discharge were approximately 10% higher in the lowest versus the highest SES quintile. The ability of this model to adjust for important confounders when associating SES and risk of readmission is uncertain because the model fit was not reported.
Several factors could explain the difference between our study and the previous Canadian analysis showing significantly higher adjusted risk of readmission in patients from the lowest versus the highest SES quintile.[11] First, our analysis had a slightly different outcome, combining early death with urgent readmission (rather than the latter alone). We believe that this combination is important to avoid biased results when associating patient factors with readmission risk.[14] Second, our unit of analysis was the patient, whereas in the previous analysis it was the hospitalization.[11] A recent analysis by our group found that this distinction can change the results on analyses in early postdischarge outcomes.[16] In the present analysis, different results could occur if patients with multiple readmissions were disproportionately prevalent in low‐income neighborhoods. Third, our analysis was limited to Ontario rather than the entire country. Finally, and we believe most importantly, we used a validated model to control for risk of poor outcomes soon after discharge from hospital. Our analysis shows that this risk was strongly associated with neighborhood income (Table 2). This suggests that the association between SES and bad postdischarge outcomes could be explained by factors that independently increase the risk of these outcomes. Adequately controlling for these covariates would then remove variation in readmission risk by SES. We believe that these results highlight the importance of adequately controlling for potential confounders.
We believe that our results are reassuring but not definitive. We found no indication that, in Ontario, people from poorer neighborhoods are systematically more likelyafter considering factors that are known to be associated with early death or urgent readmissionto have a worse outcome early after their discharge from hospital. However, patient income and other SES measures could be associated with early death or readmission for several reasons. First, our study used average neighborhood income quintiles to quantify SES. It is possible that other SES measures (such as education or social deprivation) or patient‐level SES indicators could be significantly associated with early death or readmission.[17, 18] Second, we previously found that approximately only 25% of hospital readmissions are potentially avoidable.[19] Further study is required to determine if patient SES independently influences potentially avoidable hospital readmissions. Third, we cannot be certain how our results might generalize to health populations outside of Ontario. Specifically, SES might play a more important role in regions without universal healthcare in which community‐based healthcare resources that could decrease readmission risk, such as medications or physician follow‐up, are unavailable to those without health insurance coverage. Finally, we found notable confounding between neighborhood income quintile and factors known to be independently associated with early death or urgent readmission (Figure 1). This was especially prominent with index admission urgency, number of previous urgent admissions and emergency visits, and patient comorbidities. These factors have a much stronger association with early death or readmission than neighborhood income quintile. If low neighborhood income actually results in urgent hospital admission, emergency‐department visits, and comorbidities, then the inclusion of these covariates in the model could obscure the influence of neighborhood income on early death or readmission.
In summary, our study found that neighborhood income was not associated with early death or urgent readmission independent of known risk factors. Our analysis indicates that focusing resources on patients in lower‐income neighborhoods is unlikely to change the risk of early postdischarge adverse events. Further study is required to determine if SES is associated with adverse postdischarge outcomes in settings without publicly funded healthcare.
Acknowledgment
Disclosure: Nothing to report.
- Last JM, ed. A Dictionary of Epidemiology. 3rd ed. New York, NY: Oxford University Press; 1995.
- , , , et al. Is income inequality a determinant of population health? Part 1: A systematic review. Milbank Q. 2004;82(1):5–99.
- , , . Identifying patients at high risk of emergency hospital admissions: a logistic regression analysis. J R Soc Med. 2006;99(8):406–414.
- , , , . Using routine inpatient data to identify patients at risk of hospital readmission. BMC Health Serv Res. 2009;9:96.
- , , , , . Risk factors for 30‐day hospital readmission in patients ≥65 years of age. Proc (Bayl Univ Med Cent). 2008;21(4):363–372.
- , , , et al. An automated model to identify heart failure patients at risk for 30‐day readmission or death using electronic medical record data. Med Care. 2010;48(11):981–988.
- , . Improving the management of care for high‐cost Medicaid patients. Health Aff (Millwood). 2007;26(6):1643–1654.
- , . Factors predicting readmission of older general medicine patients. J Gen Intern Med. 1991;6(5):389–393.
- , , , et al. Hospital readmission in general medicine patients: a prediction model. J Gen Intern Med. 2010;25(3):211–219.
- , , , , , . Screening elders for risk of hospital admission. J Am Geriatr Soc. 1993;41(8):811–817.
- Canadian Institute for Health Information. All‐Cause Readmission to Acute Care and Return to the Emergency Department. Ottawa, ON: Canadian Institute for Health Information; 2012:1–64.
- , , . LACE+ index: extension of a validated index to predict early death or unplanned readmission following hospital discharge using administrative data. Open Medicine. 2012;6(2):80–89.
- . PCCF Plus version 5E user's guide. Ottawa ON: Statistics Canada; 2009;82F0086‐XDB.
- , , . Derivation and validation of diagnostic score based on case‐mix groups to predict 30‐day death or urgent readmission. Open Medicine. 2012;6(3):e80–e89.
- , . Executing high‐quality care transitions: a call to do it right. J Hosp Med. 2007;2(5):287–290.
- , , , . Predicting post‐discharge death or readmission: deterioration of model performance in a population having multiple admissions per patient [published online ahead of print November 19, 2012]. J Eval Clin Pract. doi: 10.1111/jep.12012.
- , , , . Patient self‐management of chronic disease in primary care. JAMA. 2002;288(19): 2469–2475.
- , . Multilevel analyses of neighbourhood socioeconomic context and health outcomes: a critical review. J Epidemiol Community Health. 2001;55(2):111–122.
- , , , et al. Incidence of potentially avoidable hospital readmissions and its relationship to all‐cause urgent readmissions. CMAJ. 2011;183(14):E1067–E1072.
- , , , . Validation of a combined comorbidity index. J Clin Epidemiol. 1994;47(11):1245–1251.
Socioeconomic status (SES) classifies people according to occupation, prior education, or income.[1] Socioeconomic status has been associated with several population‐health outcomes, albeit with geographically inconsistent results.[2] If lower SES is associated with higher readmission rates, then further studies could be done to determine which specific socioeconomic factors are potentially modifiable and whether the provision of additional resources could allay the increased risk associated with those factors.
Nine studies have examined the association between SES and readmissions.[3, 4, 5, 6, 7, 8, 9, 10, 11] These studies varied extensively in methodologies, SES measures, and results. However, results from 1 of these studies[11] were particularly notable given the study's significant association between lower household income and increased risk of acute readmission in a publicly funded, open‐access healthcare system. Given the implications of these results, an accurate and explicit assessment of the association between SES measures and the risk of adverse postdischarge outcomes is important.
We recently developed a model that accurately predicts the risk of 30‐day death or urgent readmission using administrative data.[12] This model did not directly control for any SES factors. In this study, we determined if a commonly used SES measurehousehold‐income quintilewas associated with the risk of early death or urgent readmission after controlling for factors known to influence this outcome.
METHODS
Study Setting and Data Sources
This population‐based study took place in Ontario, Canada, between April 1, 2003 and March 31, 2009. All hospital and physician care in Ontario is publicly funded. The study used 2 databases, the Discharge Abstract Database and the Registered Persons Database. The Discharge Abstract Database records information about all nonpsychiatric hospitalizations, including dates of hospital admission and discharge, vital status at end of hospitalization, discharge destination (ie, community, nursing home, or chronic hospital), admission urgency, primary and other diagnoses, and postal code of patient's household. The Registered Persons Database captures basic demographic data about all Ontarians, including date of birth and date of death (if applicable), postal code of residence, and average household‐income quintile of postal code, determined by linking the postal code to Statistics Canada geographical units through the Postal Code Conversion File Plus.[13] The Registered Persons Database captures all deaths regardless of the death location (ie, community vs hospital).
Study Population
This study used patients from a previous analysis that internally validated an index to predict the risk of 30‐day death or urgent readmission.[12] This analysis included a simple random sample of 250,000 adult Ontarians (age >18 years) who were discharged from the hospital to the community between April 1, 2003 and March 31, 2009. These medical and surgical hospitalizations were sampled from the Discharge Abstract Database described above. Psychiatric admissions were excluded because their hospitalizations are captured in a distinct database; obstetrical admissions were also excluded because they have a very low risk of 30‐day death or readmission. We randomly chose 1 index admission per person to ensure that the patient was the unit of analysis.
For the present study, we selected all patients from the previous analysis who were discharged from the hospital in 2006. This year was chosen because the SES indicator we used in the study (average household‐income quintile) was measured during the 2006 Canadian Census and would be most accurate for patients discharged in that year. The present study also limited patients to those with a valid postal code, because this was required to link patients to their neighborhood and their household‐income quintile.
Study Outcome
The study outcome was all‐cause death or urgent readmission within 30 days of discharge from hospital. We combined death with urgent readmission to avoid potential biases that could occur when measuring associations between risk factors and urgent readmission; in analyses having readmission as the sole outcome, the categorization of early deaths that occur prior to readmission as nonevents could minimize the importance of factors (such as severe comorbidities or patient age) that are associated with both early death and readmission.
We linked to the Registered Patients Database to determine each person's 30‐day death status. We linked to the Discharge Abstract Database to determine if patients had been urgently readmitted to any hospital within 30 days of discharge. All deaths were considered regardless of cause. All urgent (ie, nonscheduled) readmissions were included regardless of the reason for admission. Urgent status was determined by the urgency field in the Discharge Abstract Database, for which data abstractors are instructed to classify all nonscheduled admissions as urgent; these admissions frequently include those admitted after presenting to the emergency department.
Study Covariates: Readmission Risk and Neighborhood Household‐Income Quintile
In our primary analysis, we quantified the risk of 30‐day death or urgent readmission using an internally validated index, the LACE+ index: length of stay (L), acuity of the admission (A), comorbidity of the patient (measured with the Charlson Comorbidity Index score (C), and emergency‐department use (E).[12] The LACE+ index predicts the risk of 30‐day all‐cause death or urgent readmission for nonpsychiatric and nonobstetrical admissions. This index includes patient age, sex, comorbidities, and previous hospital and emergency‐department utilization; admission urgency; hospital type; total length of stay (LOS) and days in hospital awaiting placement; and hospitalization diagnostic risk.[14] The index quantified outcome risk as a score that ranged from 17 to 114. It was very discriminatory (C statistic, 77.1%) and was well calibrated (the observed and expected outcome risk was statistically distinct in only 2 of 14 risk groups that contained <2% of the population). The LACE+ quintiles were defined using score distribution from the entire 20032009 cohort.[12]
We used neighborhood income quintile as 1 measure of patient SES. Neighborhood income quintile was calculated by Statistics Canada using the Income Per Person Equivalent (IPPE) determined from the 2006 Canadian census.[13] The IPPE was calculated as total household income divided by the Single Persons Equivalent, which reflects decreased costs per person (and therefore increased available income per household occupant) in households having greater numbers of people. Within each dissemination area (each contains 400700 people), the average IPPE was calculated. Then, within each region (delineated by the Census Metropolitan Area, the Census Agglomeration, or provincial residual areas), dissemination areas were ranked by their average IPPE and then categorized into quintiles. These household‐income quintiles, therefore, are community‐specific and ensure that neighborhood household incomes are categorized based on comparisons within the same community. As such, the income thresholds for quintile categorization will vary between regions. We linked each patient's postal code to their dissemination area using the Postal Code Conversion File Plus[13] to determine their neighborhood income quintile.
Analysis
We described the patient cohort by readmission status. We categorized the expected risk of 30‐day death or urgent readmission to hospital (as determined by the LACE+ score) into quintiles. We used the 2 test and the test for trend to determine the association of these risk quintiles and SES quintiles with observed rates of 30‐day death or urgent readmission. The Cochran‐Mantel‐Haenszel test was used to determine the association of household‐income quintile and outcome risk after adjusting for LACE+ quintile.
To determine how the association between income quintile and outcome changes with increase adjustment, we constructed a series of logistic‐regression models that contained household‐income quintile and the sequential addition of components of the LACE+ score. For each model, we measured the influence of these added covariates on the association between household‐income quintile and early death or urgent readmission. We used orthogonal parameterization (which facilitates the comparison of parameter estimates in a regression model) to measure linear trends in the association of the income quintiles with outcomes.
RESULTS
The original cohort contained 250,000 people, of which 40,827 people (16.3%) were included in the present study (208,995 were excluded because patients were discharged in years other than 2006; 178 were excluded because of invalid postal codes).
Patients are described in Table 1. Patients were middle‐aged and had few documented chronic comorbidities. Of the patients, 37% had been to the emergency department and 12% had been admitted urgently. Most admissions were to large, nonteaching hospitals with a median LOS of 3 days.
| Variable | Value | No Death/Readmission, n=38,189 | Death/Readmission, n=2,638 | Overall, N=40,827 | |||
|---|---|---|---|---|---|---|---|
| |||||||
| Mean age (SD), y | 57.39 (18.3) | 67.17 (17.2) | 58.02 (18.4) | ||||
| Female sex | 20,044 | 52.5% | 1,291 | 48.9% | 21,335 | 52.3% | |
| Charlson index | 0 | 28,908 | 75.7% | 1,238 | 46.9% | 30,146 | 73.8% |
| 1 | 450 | 11.7% | 362 | 13.7% | 4,812 | 11.8% | |
| 2 | 2,668 | 7.0% | 427 | 16.2% | 3,095 | 7.6% | |
| 3+ | 2,163 | 5.7% | 611 | 23.2% | 2,774 | 6.8% | |
| ED visits in previous 6 moths | 0 | 24,599 | 64.4% | 1,210 | 45.9% | 25,809 | 63.2% |
| 12 | 11,262 | 29.5% | 1,008 | 38.2% | 12,270 | 30.1% | |
| 3+ | 2,328 | 6.1% | 420 | 15.9% | 2,748 | 6.7% | |
| Urgent hospitalizations, previous year | 0 | 33,729 | 88.3% | 1,796 | 68.1% | 35,525 | 87.0% |
| 1 | 3,425 | 9.0% | 525 | 19.9% | 3,950 | 9.7% | |
| 1+ | 1,035 | 2.7% | 317 | 12.0% | 1,352 | 3.3% | |
| Elective hospitalizations, previous year | 0 | 35,988 | 94.2% | 2,389 | 90.6% | 38,377 | 94.0% |
| 1 | 1,998 | 5.2% | 213 | 8.1% | 2,211 | 5.4% | |
| 2+ | 203 | 0.5% | 36 | 1.4% | 239 | 0.6% | |
| Hospital type | Nonteaching, large | 20,554 | 53.8% | 1,334 | 50.6% | 21,888 | 53.6% |
| Nonteaching, small | 5,239 | 13.7% | 487 | 18.5% | 5726 | 14.0% | |
| Teaching | 12,396 | 32.5% | 817 | 31.0% | 13,213 | 32.4% | |
| Urgent admit | 23,769 | 62.2% | 2,223 | 84.3% | 25,992 | 63.7% | |
| LOS rounded to nearest day, median (IQR) | 3 (26) | 5 (311) | 3 (26) | ||||
| Any hospital days on ALC | 0 | 646 | 1.7% | 127 | 4.8% | 773 | 1.9% |
| CMG score of index admission | 0 | 27,257 | 71.4% | 1,594 | 60.4% | 28,851 | 70.7% |
| 1+ | 5,218 | 13.7% | 948 | 35.9% | 6,166 | 15.1% | |
| <0 | 5,714 | 15.0% | 96 | 3.6% | 5,810 | 14.2% | |
| LACE+ score of index admission, median (IQR) | 31 (1848) | 61 (4175) | 32 (1951) | ||||
| Household‐income quintile | 1 (poorest) | 7,798 | 20.4% | 621 | 23.5% | 8,419 | 20.6% |
| 2 | 7,812 | 20.5% | 586 | 22.2% | 8,398 | 20.6% | |
| 3 | 7,557 | 19.8% | 484 | 18.3% | 8,041 | 19.7% | |
| 4 | 7,561 | 19.8% | 500 | 19.0% | 8,061 | 19.7% | |
| 5 (richest) | 7,461 | 19.5% | 447 | 16.9% | 7,908 | 19.4% | |
Death or urgent readmission within 30 days occurred in 2638 people (6.5%) (Table 1). Outcome risk increased with age; in males; as comorbidities increased; with greater numbers of emergency‐department visits, urgent admissions, and previous elective admissions; when index admissions were emergent; with longer hospital LOS and increased number of alternate level of care days; and as the diagnostic risk (measured as the Case Mix Group [CMG] score)[14] increased. Outcome risk increased as income quintile became poorer.
Household Income and Risk of 30‐Day Death or Urgent Readmission
People were evenly divided among the income quintiles (Table 2). By itself, household‐income quintile was significantly associated with the risk of early death or urgent hospital readmission (Table 2, column C, 2=27.4, P<0.0001; Mantel‐Haenszel trend 2=24.3, P<0.0001). In the poorest quintile, 7.4% of people had an outcome, compared with 5.6% in the richest quintile (2=19.8, df=1, P<0.0001).
| Risk Quintile of 30‐Day Death or Readmission (LACE+ Points Range) | ||||||
|---|---|---|---|---|---|---|
| 1 (1416) [A] | 2 (1727) | 3 (2839) | 4 (4056) | 5 (57114) [B] | Income Quintile Overall [C] | |
| ||||||
| Income quintile | ||||||
| 1 (poorest) | 18/1,485 (1.2%) | 42/1,667 (2.5%) | 65/1,635 (4.0%) | 117/1,722 (6.8%) | 379/1,910 (19.8%) | 621/8,419 (7.4%) |
| 2 | 21/1,627 (1.3%) | 39/1,665 (2.3%) | 65/1,598 (4.1%) | 130/1,808 (5.2%) | 331/1,700 (19.5%) | 586/8,398 (7.0%) |
| 3 | 18/1,761 (1.0%) | 33/1,665 (2.0%) | 63/1,568 (4.0%) | 96/1,499 (6.4%) | 274/1,548 (17.7%) | 484/8,041 (6.0%) |
| 4 | 27/1,851 (1.5%) | 42/1,698 (2.4%) | 57/1,585 (3.6%) | 110/1,548 (6.1%) | 264/1,379 (19.1%) | 500/8,061 (6.2%) |
| 5 (richest) | 20/1,864 (1.1%) | 32/1,736 (1.8%) | 60/1,468 (4.1%) | 107/1,525 (7.0%) | 228/1,315 (17.3%) | 447/7,908 (5.6%) |
| Risk quintile overall [D] | 104/8,588 (1.2%) | 188/8,431 (2.2%) | 310/7,854 (4.0%) | 560/8,102 (6.9%) | 1476/7,852 (18.8%) | 2,638/40,827 (6.5%) |
However, household income was also strongly associated with LACE+ scores (2=240, P<0.0001; Mantel‐Haenszel trend 2=209, P<0.0001). The number of people in the lowest‐risk quintile increased with income, from 1485 in the poorest quintile to 1864 in the richest quintile (Table 2, column A). In contrast, the number of high‐risk people progressively decreased with income, from 1910 in the poorest quintile to 1315 in the richest quintile (Table 2, column B).
The LACE+ quintile was very strongly associated with outcome risk, as shown in Table 2, row D (2=2703, P<0.0001; Mantel‐Haenszel trend 2=2102, P<0.0001). Within each LACE+ stratum, the risk of death or urgent readmission did not appear to consistently change with income quintile. After adjusting for LACE+ scores, income quintile was no longer associated with 30‐day death or readmission (Cochran‐Mantel‐Haenszel 2=5.9, df=4, P=0.21).
We found no nonlinear associations between household‐income quintile and 30‐day death or readmission after adjusting for the LACE+ score. In addition, the association between LACE+ quintile and outcome did not vary significantly by household‐income quintile (P value for interaction term in logistic regression model=0.5582).
The association between income quintile and 30‐day death or urgent readmission decreased when incrementally controlling for other covariates in the LACE+ model (Figure 1). By itself, all income quintiles except 2 were significantly distinct from the poorest income quintile. The addition of patient age, sex, and hospital type had little effect on the association between income and outcomes. The addition of index admission urgency shifted all point estimates toward unity (Figure 1). Associations between income and death or readmission then remained relatively stable until the addition of number of urgent admissions in the previous year (Figure 1). The subsequent addition of number of emergency visits and comorbidities resulted in none of the income quintiles being statistically distinct from the poorest quintile, as well as a nonsignificant linear trend over the quintiles.
DISCUSSION
Our study shows that the risk of 30‐day death or urgent readmission was higher in people from lower‐income neighborhoods. However, this risk appears to be explained by patient‐level factors that are known to be associated with bad postdischarge outcomes. After accounting for these factors with the LACE+ index, we found no notable changes in the risk of early death or urgent readmission with SES as measured with average neighborhood household income.
Nine previous studies have measured the association between various SES measures and hospital readmission in disparate populations.[3, 4, 5, 6, 7, 8, 9, 10, 11] These studies were done in the United States,[5, 6, 8, 9, 10] the United Kingdom,[3, 7] Australia,[4] and Canada.[11] They used a range of SES indicators (from area‐level measures of household income[5] or deprivation[3] to personal education and income)[8, 9, 10] in diverse patient populations (from a random sample of all hospitalizations[3] to people with disabilities living in New York City)[15] and very different time horizons (capturing hospital readmissions that occurred from within 30 days[5] to 4 years).[10] Of these 9 studies, 5 found no independent association between their SES measure and readmission,[5, 6, 8, 9, 10] and 2 included SES in their final regression model but did not present the modelmaking it impossible to determine if SES significantly influenced outcomes.[3, 15] One study found that the risk of hospital readmission independently increased as a composite measure of area‐level social and economic indicators decreased.[4] A Canadian study[11] measured neighborhood income quintile and showed, after adjusting for patient sex, comorbidities, LOS variance, and previous admissions, that the odds of acute, nonpsychiatric readmission within 30 days of discharge were approximately 10% higher in the lowest versus the highest SES quintile. The ability of this model to adjust for important confounders when associating SES and risk of readmission is uncertain because the model fit was not reported.
Several factors could explain the difference between our study and the previous Canadian analysis showing significantly higher adjusted risk of readmission in patients from the lowest versus the highest SES quintile.[11] First, our analysis had a slightly different outcome, combining early death with urgent readmission (rather than the latter alone). We believe that this combination is important to avoid biased results when associating patient factors with readmission risk.[14] Second, our unit of analysis was the patient, whereas in the previous analysis it was the hospitalization.[11] A recent analysis by our group found that this distinction can change the results on analyses in early postdischarge outcomes.[16] In the present analysis, different results could occur if patients with multiple readmissions were disproportionately prevalent in low‐income neighborhoods. Third, our analysis was limited to Ontario rather than the entire country. Finally, and we believe most importantly, we used a validated model to control for risk of poor outcomes soon after discharge from hospital. Our analysis shows that this risk was strongly associated with neighborhood income (Table 2). This suggests that the association between SES and bad postdischarge outcomes could be explained by factors that independently increase the risk of these outcomes. Adequately controlling for these covariates would then remove variation in readmission risk by SES. We believe that these results highlight the importance of adequately controlling for potential confounders.
We believe that our results are reassuring but not definitive. We found no indication that, in Ontario, people from poorer neighborhoods are systematically more likelyafter considering factors that are known to be associated with early death or urgent readmissionto have a worse outcome early after their discharge from hospital. However, patient income and other SES measures could be associated with early death or readmission for several reasons. First, our study used average neighborhood income quintiles to quantify SES. It is possible that other SES measures (such as education or social deprivation) or patient‐level SES indicators could be significantly associated with early death or readmission.[17, 18] Second, we previously found that approximately only 25% of hospital readmissions are potentially avoidable.[19] Further study is required to determine if patient SES independently influences potentially avoidable hospital readmissions. Third, we cannot be certain how our results might generalize to health populations outside of Ontario. Specifically, SES might play a more important role in regions without universal healthcare in which community‐based healthcare resources that could decrease readmission risk, such as medications or physician follow‐up, are unavailable to those without health insurance coverage. Finally, we found notable confounding between neighborhood income quintile and factors known to be independently associated with early death or urgent readmission (Figure 1). This was especially prominent with index admission urgency, number of previous urgent admissions and emergency visits, and patient comorbidities. These factors have a much stronger association with early death or readmission than neighborhood income quintile. If low neighborhood income actually results in urgent hospital admission, emergency‐department visits, and comorbidities, then the inclusion of these covariates in the model could obscure the influence of neighborhood income on early death or readmission.
In summary, our study found that neighborhood income was not associated with early death or urgent readmission independent of known risk factors. Our analysis indicates that focusing resources on patients in lower‐income neighborhoods is unlikely to change the risk of early postdischarge adverse events. Further study is required to determine if SES is associated with adverse postdischarge outcomes in settings without publicly funded healthcare.
Acknowledgment
Disclosure: Nothing to report.
Socioeconomic status (SES) classifies people according to occupation, prior education, or income.[1] Socioeconomic status has been associated with several population‐health outcomes, albeit with geographically inconsistent results.[2] If lower SES is associated with higher readmission rates, then further studies could be done to determine which specific socioeconomic factors are potentially modifiable and whether the provision of additional resources could allay the increased risk associated with those factors.
Nine studies have examined the association between SES and readmissions.[3, 4, 5, 6, 7, 8, 9, 10, 11] These studies varied extensively in methodologies, SES measures, and results. However, results from 1 of these studies[11] were particularly notable given the study's significant association between lower household income and increased risk of acute readmission in a publicly funded, open‐access healthcare system. Given the implications of these results, an accurate and explicit assessment of the association between SES measures and the risk of adverse postdischarge outcomes is important.
We recently developed a model that accurately predicts the risk of 30‐day death or urgent readmission using administrative data.[12] This model did not directly control for any SES factors. In this study, we determined if a commonly used SES measurehousehold‐income quintilewas associated with the risk of early death or urgent readmission after controlling for factors known to influence this outcome.
METHODS
Study Setting and Data Sources
This population‐based study took place in Ontario, Canada, between April 1, 2003 and March 31, 2009. All hospital and physician care in Ontario is publicly funded. The study used 2 databases, the Discharge Abstract Database and the Registered Persons Database. The Discharge Abstract Database records information about all nonpsychiatric hospitalizations, including dates of hospital admission and discharge, vital status at end of hospitalization, discharge destination (ie, community, nursing home, or chronic hospital), admission urgency, primary and other diagnoses, and postal code of patient's household. The Registered Persons Database captures basic demographic data about all Ontarians, including date of birth and date of death (if applicable), postal code of residence, and average household‐income quintile of postal code, determined by linking the postal code to Statistics Canada geographical units through the Postal Code Conversion File Plus.[13] The Registered Persons Database captures all deaths regardless of the death location (ie, community vs hospital).
Study Population
This study used patients from a previous analysis that internally validated an index to predict the risk of 30‐day death or urgent readmission.[12] This analysis included a simple random sample of 250,000 adult Ontarians (age >18 years) who were discharged from the hospital to the community between April 1, 2003 and March 31, 2009. These medical and surgical hospitalizations were sampled from the Discharge Abstract Database described above. Psychiatric admissions were excluded because their hospitalizations are captured in a distinct database; obstetrical admissions were also excluded because they have a very low risk of 30‐day death or readmission. We randomly chose 1 index admission per person to ensure that the patient was the unit of analysis.
For the present study, we selected all patients from the previous analysis who were discharged from the hospital in 2006. This year was chosen because the SES indicator we used in the study (average household‐income quintile) was measured during the 2006 Canadian Census and would be most accurate for patients discharged in that year. The present study also limited patients to those with a valid postal code, because this was required to link patients to their neighborhood and their household‐income quintile.
Study Outcome
The study outcome was all‐cause death or urgent readmission within 30 days of discharge from hospital. We combined death with urgent readmission to avoid potential biases that could occur when measuring associations between risk factors and urgent readmission; in analyses having readmission as the sole outcome, the categorization of early deaths that occur prior to readmission as nonevents could minimize the importance of factors (such as severe comorbidities or patient age) that are associated with both early death and readmission.
We linked to the Registered Patients Database to determine each person's 30‐day death status. We linked to the Discharge Abstract Database to determine if patients had been urgently readmitted to any hospital within 30 days of discharge. All deaths were considered regardless of cause. All urgent (ie, nonscheduled) readmissions were included regardless of the reason for admission. Urgent status was determined by the urgency field in the Discharge Abstract Database, for which data abstractors are instructed to classify all nonscheduled admissions as urgent; these admissions frequently include those admitted after presenting to the emergency department.
Study Covariates: Readmission Risk and Neighborhood Household‐Income Quintile
In our primary analysis, we quantified the risk of 30‐day death or urgent readmission using an internally validated index, the LACE+ index: length of stay (L), acuity of the admission (A), comorbidity of the patient (measured with the Charlson Comorbidity Index score (C), and emergency‐department use (E).[12] The LACE+ index predicts the risk of 30‐day all‐cause death or urgent readmission for nonpsychiatric and nonobstetrical admissions. This index includes patient age, sex, comorbidities, and previous hospital and emergency‐department utilization; admission urgency; hospital type; total length of stay (LOS) and days in hospital awaiting placement; and hospitalization diagnostic risk.[14] The index quantified outcome risk as a score that ranged from 17 to 114. It was very discriminatory (C statistic, 77.1%) and was well calibrated (the observed and expected outcome risk was statistically distinct in only 2 of 14 risk groups that contained <2% of the population). The LACE+ quintiles were defined using score distribution from the entire 20032009 cohort.[12]
We used neighborhood income quintile as 1 measure of patient SES. Neighborhood income quintile was calculated by Statistics Canada using the Income Per Person Equivalent (IPPE) determined from the 2006 Canadian census.[13] The IPPE was calculated as total household income divided by the Single Persons Equivalent, which reflects decreased costs per person (and therefore increased available income per household occupant) in households having greater numbers of people. Within each dissemination area (each contains 400700 people), the average IPPE was calculated. Then, within each region (delineated by the Census Metropolitan Area, the Census Agglomeration, or provincial residual areas), dissemination areas were ranked by their average IPPE and then categorized into quintiles. These household‐income quintiles, therefore, are community‐specific and ensure that neighborhood household incomes are categorized based on comparisons within the same community. As such, the income thresholds for quintile categorization will vary between regions. We linked each patient's postal code to their dissemination area using the Postal Code Conversion File Plus[13] to determine their neighborhood income quintile.
Analysis
We described the patient cohort by readmission status. We categorized the expected risk of 30‐day death or urgent readmission to hospital (as determined by the LACE+ score) into quintiles. We used the 2 test and the test for trend to determine the association of these risk quintiles and SES quintiles with observed rates of 30‐day death or urgent readmission. The Cochran‐Mantel‐Haenszel test was used to determine the association of household‐income quintile and outcome risk after adjusting for LACE+ quintile.
To determine how the association between income quintile and outcome changes with increase adjustment, we constructed a series of logistic‐regression models that contained household‐income quintile and the sequential addition of components of the LACE+ score. For each model, we measured the influence of these added covariates on the association between household‐income quintile and early death or urgent readmission. We used orthogonal parameterization (which facilitates the comparison of parameter estimates in a regression model) to measure linear trends in the association of the income quintiles with outcomes.
RESULTS
The original cohort contained 250,000 people, of which 40,827 people (16.3%) were included in the present study (208,995 were excluded because patients were discharged in years other than 2006; 178 were excluded because of invalid postal codes).
Patients are described in Table 1. Patients were middle‐aged and had few documented chronic comorbidities. Of the patients, 37% had been to the emergency department and 12% had been admitted urgently. Most admissions were to large, nonteaching hospitals with a median LOS of 3 days.
| Variable | Value | No Death/Readmission, n=38,189 | Death/Readmission, n=2,638 | Overall, N=40,827 | |||
|---|---|---|---|---|---|---|---|
| |||||||
| Mean age (SD), y | 57.39 (18.3) | 67.17 (17.2) | 58.02 (18.4) | ||||
| Female sex | 20,044 | 52.5% | 1,291 | 48.9% | 21,335 | 52.3% | |
| Charlson index | 0 | 28,908 | 75.7% | 1,238 | 46.9% | 30,146 | 73.8% |
| 1 | 450 | 11.7% | 362 | 13.7% | 4,812 | 11.8% | |
| 2 | 2,668 | 7.0% | 427 | 16.2% | 3,095 | 7.6% | |
| 3+ | 2,163 | 5.7% | 611 | 23.2% | 2,774 | 6.8% | |
| ED visits in previous 6 moths | 0 | 24,599 | 64.4% | 1,210 | 45.9% | 25,809 | 63.2% |
| 12 | 11,262 | 29.5% | 1,008 | 38.2% | 12,270 | 30.1% | |
| 3+ | 2,328 | 6.1% | 420 | 15.9% | 2,748 | 6.7% | |
| Urgent hospitalizations, previous year | 0 | 33,729 | 88.3% | 1,796 | 68.1% | 35,525 | 87.0% |
| 1 | 3,425 | 9.0% | 525 | 19.9% | 3,950 | 9.7% | |
| 1+ | 1,035 | 2.7% | 317 | 12.0% | 1,352 | 3.3% | |
| Elective hospitalizations, previous year | 0 | 35,988 | 94.2% | 2,389 | 90.6% | 38,377 | 94.0% |
| 1 | 1,998 | 5.2% | 213 | 8.1% | 2,211 | 5.4% | |
| 2+ | 203 | 0.5% | 36 | 1.4% | 239 | 0.6% | |
| Hospital type | Nonteaching, large | 20,554 | 53.8% | 1,334 | 50.6% | 21,888 | 53.6% |
| Nonteaching, small | 5,239 | 13.7% | 487 | 18.5% | 5726 | 14.0% | |
| Teaching | 12,396 | 32.5% | 817 | 31.0% | 13,213 | 32.4% | |
| Urgent admit | 23,769 | 62.2% | 2,223 | 84.3% | 25,992 | 63.7% | |
| LOS rounded to nearest day, median (IQR) | 3 (26) | 5 (311) | 3 (26) | ||||
| Any hospital days on ALC | 0 | 646 | 1.7% | 127 | 4.8% | 773 | 1.9% |
| CMG score of index admission | 0 | 27,257 | 71.4% | 1,594 | 60.4% | 28,851 | 70.7% |
| 1+ | 5,218 | 13.7% | 948 | 35.9% | 6,166 | 15.1% | |
| <0 | 5,714 | 15.0% | 96 | 3.6% | 5,810 | 14.2% | |
| LACE+ score of index admission, median (IQR) | 31 (1848) | 61 (4175) | 32 (1951) | ||||
| Household‐income quintile | 1 (poorest) | 7,798 | 20.4% | 621 | 23.5% | 8,419 | 20.6% |
| 2 | 7,812 | 20.5% | 586 | 22.2% | 8,398 | 20.6% | |
| 3 | 7,557 | 19.8% | 484 | 18.3% | 8,041 | 19.7% | |
| 4 | 7,561 | 19.8% | 500 | 19.0% | 8,061 | 19.7% | |
| 5 (richest) | 7,461 | 19.5% | 447 | 16.9% | 7,908 | 19.4% | |
Death or urgent readmission within 30 days occurred in 2638 people (6.5%) (Table 1). Outcome risk increased with age; in males; as comorbidities increased; with greater numbers of emergency‐department visits, urgent admissions, and previous elective admissions; when index admissions were emergent; with longer hospital LOS and increased number of alternate level of care days; and as the diagnostic risk (measured as the Case Mix Group [CMG] score)[14] increased. Outcome risk increased as income quintile became poorer.
Household Income and Risk of 30‐Day Death or Urgent Readmission
People were evenly divided among the income quintiles (Table 2). By itself, household‐income quintile was significantly associated with the risk of early death or urgent hospital readmission (Table 2, column C, 2=27.4, P<0.0001; Mantel‐Haenszel trend 2=24.3, P<0.0001). In the poorest quintile, 7.4% of people had an outcome, compared with 5.6% in the richest quintile (2=19.8, df=1, P<0.0001).
| Risk Quintile of 30‐Day Death or Readmission (LACE+ Points Range) | ||||||
|---|---|---|---|---|---|---|
| 1 (1416) [A] | 2 (1727) | 3 (2839) | 4 (4056) | 5 (57114) [B] | Income Quintile Overall [C] | |
| ||||||
| Income quintile | ||||||
| 1 (poorest) | 18/1,485 (1.2%) | 42/1,667 (2.5%) | 65/1,635 (4.0%) | 117/1,722 (6.8%) | 379/1,910 (19.8%) | 621/8,419 (7.4%) |
| 2 | 21/1,627 (1.3%) | 39/1,665 (2.3%) | 65/1,598 (4.1%) | 130/1,808 (5.2%) | 331/1,700 (19.5%) | 586/8,398 (7.0%) |
| 3 | 18/1,761 (1.0%) | 33/1,665 (2.0%) | 63/1,568 (4.0%) | 96/1,499 (6.4%) | 274/1,548 (17.7%) | 484/8,041 (6.0%) |
| 4 | 27/1,851 (1.5%) | 42/1,698 (2.4%) | 57/1,585 (3.6%) | 110/1,548 (6.1%) | 264/1,379 (19.1%) | 500/8,061 (6.2%) |
| 5 (richest) | 20/1,864 (1.1%) | 32/1,736 (1.8%) | 60/1,468 (4.1%) | 107/1,525 (7.0%) | 228/1,315 (17.3%) | 447/7,908 (5.6%) |
| Risk quintile overall [D] | 104/8,588 (1.2%) | 188/8,431 (2.2%) | 310/7,854 (4.0%) | 560/8,102 (6.9%) | 1476/7,852 (18.8%) | 2,638/40,827 (6.5%) |
However, household income was also strongly associated with LACE+ scores (2=240, P<0.0001; Mantel‐Haenszel trend 2=209, P<0.0001). The number of people in the lowest‐risk quintile increased with income, from 1485 in the poorest quintile to 1864 in the richest quintile (Table 2, column A). In contrast, the number of high‐risk people progressively decreased with income, from 1910 in the poorest quintile to 1315 in the richest quintile (Table 2, column B).
The LACE+ quintile was very strongly associated with outcome risk, as shown in Table 2, row D (2=2703, P<0.0001; Mantel‐Haenszel trend 2=2102, P<0.0001). Within each LACE+ stratum, the risk of death or urgent readmission did not appear to consistently change with income quintile. After adjusting for LACE+ scores, income quintile was no longer associated with 30‐day death or readmission (Cochran‐Mantel‐Haenszel 2=5.9, df=4, P=0.21).
We found no nonlinear associations between household‐income quintile and 30‐day death or readmission after adjusting for the LACE+ score. In addition, the association between LACE+ quintile and outcome did not vary significantly by household‐income quintile (P value for interaction term in logistic regression model=0.5582).
The association between income quintile and 30‐day death or urgent readmission decreased when incrementally controlling for other covariates in the LACE+ model (Figure 1). By itself, all income quintiles except 2 were significantly distinct from the poorest income quintile. The addition of patient age, sex, and hospital type had little effect on the association between income and outcomes. The addition of index admission urgency shifted all point estimates toward unity (Figure 1). Associations between income and death or readmission then remained relatively stable until the addition of number of urgent admissions in the previous year (Figure 1). The subsequent addition of number of emergency visits and comorbidities resulted in none of the income quintiles being statistically distinct from the poorest quintile, as well as a nonsignificant linear trend over the quintiles.
DISCUSSION
Our study shows that the risk of 30‐day death or urgent readmission was higher in people from lower‐income neighborhoods. However, this risk appears to be explained by patient‐level factors that are known to be associated with bad postdischarge outcomes. After accounting for these factors with the LACE+ index, we found no notable changes in the risk of early death or urgent readmission with SES as measured with average neighborhood household income.
Nine previous studies have measured the association between various SES measures and hospital readmission in disparate populations.[3, 4, 5, 6, 7, 8, 9, 10, 11] These studies were done in the United States,[5, 6, 8, 9, 10] the United Kingdom,[3, 7] Australia,[4] and Canada.[11] They used a range of SES indicators (from area‐level measures of household income[5] or deprivation[3] to personal education and income)[8, 9, 10] in diverse patient populations (from a random sample of all hospitalizations[3] to people with disabilities living in New York City)[15] and very different time horizons (capturing hospital readmissions that occurred from within 30 days[5] to 4 years).[10] Of these 9 studies, 5 found no independent association between their SES measure and readmission,[5, 6, 8, 9, 10] and 2 included SES in their final regression model but did not present the modelmaking it impossible to determine if SES significantly influenced outcomes.[3, 15] One study found that the risk of hospital readmission independently increased as a composite measure of area‐level social and economic indicators decreased.[4] A Canadian study[11] measured neighborhood income quintile and showed, after adjusting for patient sex, comorbidities, LOS variance, and previous admissions, that the odds of acute, nonpsychiatric readmission within 30 days of discharge were approximately 10% higher in the lowest versus the highest SES quintile. The ability of this model to adjust for important confounders when associating SES and risk of readmission is uncertain because the model fit was not reported.
Several factors could explain the difference between our study and the previous Canadian analysis showing significantly higher adjusted risk of readmission in patients from the lowest versus the highest SES quintile.[11] First, our analysis had a slightly different outcome, combining early death with urgent readmission (rather than the latter alone). We believe that this combination is important to avoid biased results when associating patient factors with readmission risk.[14] Second, our unit of analysis was the patient, whereas in the previous analysis it was the hospitalization.[11] A recent analysis by our group found that this distinction can change the results on analyses in early postdischarge outcomes.[16] In the present analysis, different results could occur if patients with multiple readmissions were disproportionately prevalent in low‐income neighborhoods. Third, our analysis was limited to Ontario rather than the entire country. Finally, and we believe most importantly, we used a validated model to control for risk of poor outcomes soon after discharge from hospital. Our analysis shows that this risk was strongly associated with neighborhood income (Table 2). This suggests that the association between SES and bad postdischarge outcomes could be explained by factors that independently increase the risk of these outcomes. Adequately controlling for these covariates would then remove variation in readmission risk by SES. We believe that these results highlight the importance of adequately controlling for potential confounders.
We believe that our results are reassuring but not definitive. We found no indication that, in Ontario, people from poorer neighborhoods are systematically more likelyafter considering factors that are known to be associated with early death or urgent readmissionto have a worse outcome early after their discharge from hospital. However, patient income and other SES measures could be associated with early death or readmission for several reasons. First, our study used average neighborhood income quintiles to quantify SES. It is possible that other SES measures (such as education or social deprivation) or patient‐level SES indicators could be significantly associated with early death or readmission.[17, 18] Second, we previously found that approximately only 25% of hospital readmissions are potentially avoidable.[19] Further study is required to determine if patient SES independently influences potentially avoidable hospital readmissions. Third, we cannot be certain how our results might generalize to health populations outside of Ontario. Specifically, SES might play a more important role in regions without universal healthcare in which community‐based healthcare resources that could decrease readmission risk, such as medications or physician follow‐up, are unavailable to those without health insurance coverage. Finally, we found notable confounding between neighborhood income quintile and factors known to be independently associated with early death or urgent readmission (Figure 1). This was especially prominent with index admission urgency, number of previous urgent admissions and emergency visits, and patient comorbidities. These factors have a much stronger association with early death or readmission than neighborhood income quintile. If low neighborhood income actually results in urgent hospital admission, emergency‐department visits, and comorbidities, then the inclusion of these covariates in the model could obscure the influence of neighborhood income on early death or readmission.
In summary, our study found that neighborhood income was not associated with early death or urgent readmission independent of known risk factors. Our analysis indicates that focusing resources on patients in lower‐income neighborhoods is unlikely to change the risk of early postdischarge adverse events. Further study is required to determine if SES is associated with adverse postdischarge outcomes in settings without publicly funded healthcare.
Acknowledgment
Disclosure: Nothing to report.
- Last JM, ed. A Dictionary of Epidemiology. 3rd ed. New York, NY: Oxford University Press; 1995.
- , , , et al. Is income inequality a determinant of population health? Part 1: A systematic review. Milbank Q. 2004;82(1):5–99.
- , , . Identifying patients at high risk of emergency hospital admissions: a logistic regression analysis. J R Soc Med. 2006;99(8):406–414.
- , , , . Using routine inpatient data to identify patients at risk of hospital readmission. BMC Health Serv Res. 2009;9:96.
- , , , , . Risk factors for 30‐day hospital readmission in patients ≥65 years of age. Proc (Bayl Univ Med Cent). 2008;21(4):363–372.
- , , , et al. An automated model to identify heart failure patients at risk for 30‐day readmission or death using electronic medical record data. Med Care. 2010;48(11):981–988.
- , . Improving the management of care for high‐cost Medicaid patients. Health Aff (Millwood). 2007;26(6):1643–1654.
- , . Factors predicting readmission of older general medicine patients. J Gen Intern Med. 1991;6(5):389–393.
- , , , et al. Hospital readmission in general medicine patients: a prediction model. J Gen Intern Med. 2010;25(3):211–219.
- , , , , , . Screening elders for risk of hospital admission. J Am Geriatr Soc. 1993;41(8):811–817.
- Canadian Institute for Health Information. All‐Cause Readmission to Acute Care and Return to the Emergency Department. Ottawa, ON: Canadian Institute for Health Information; 2012:1–64.
- , , . LACE+ index: extension of a validated index to predict early death or unplanned readmission following hospital discharge using administrative data. Open Medicine. 2012;6(2):80–89.
- . PCCF Plus version 5E user's guide. Ottawa ON: Statistics Canada; 2009;82F0086‐XDB.
- , , . Derivation and validation of diagnostic score based on case‐mix groups to predict 30‐day death or urgent readmission. Open Medicine. 2012;6(3):e80–e89.
- , . Executing high‐quality care transitions: a call to do it right. J Hosp Med. 2007;2(5):287–290.
- , , , . Predicting post‐discharge death or readmission: deterioration of model performance in a population having multiple admissions per patient [published online ahead of print November 19, 2012]. J Eval Clin Pract. doi: 10.1111/jep.12012.
- , , , . Patient self‐management of chronic disease in primary care. JAMA. 2002;288(19): 2469–2475.
- , . Multilevel analyses of neighbourhood socioeconomic context and health outcomes: a critical review. J Epidemiol Community Health. 2001;55(2):111–122.
- , , , et al. Incidence of potentially avoidable hospital readmissions and its relationship to all‐cause urgent readmissions. CMAJ. 2011;183(14):E1067–E1072.
- , , , . Validation of a combined comorbidity index. J Clin Epidemiol. 1994;47(11):1245–1251.
- Last JM, ed. A Dictionary of Epidemiology. 3rd ed. New York, NY: Oxford University Press; 1995.
- , , , et al. Is income inequality a determinant of population health? Part 1: A systematic review. Milbank Q. 2004;82(1):5–99.
- , , . Identifying patients at high risk of emergency hospital admissions: a logistic regression analysis. J R Soc Med. 2006;99(8):406–414.
- , , , . Using routine inpatient data to identify patients at risk of hospital readmission. BMC Health Serv Res. 2009;9:96.
- , , , , . Risk factors for 30‐day hospital readmission in patients ≥65 years of age. Proc (Bayl Univ Med Cent). 2008;21(4):363–372.
- , , , et al. An automated model to identify heart failure patients at risk for 30‐day readmission or death using electronic medical record data. Med Care. 2010;48(11):981–988.
- , . Improving the management of care for high‐cost Medicaid patients. Health Aff (Millwood). 2007;26(6):1643–1654.
- , . Factors predicting readmission of older general medicine patients. J Gen Intern Med. 1991;6(5):389–393.
- , , , et al. Hospital readmission in general medicine patients: a prediction model. J Gen Intern Med. 2010;25(3):211–219.
- , , , , , . Screening elders for risk of hospital admission. J Am Geriatr Soc. 1993;41(8):811–817.
- Canadian Institute for Health Information. All‐Cause Readmission to Acute Care and Return to the Emergency Department. Ottawa, ON: Canadian Institute for Health Information; 2012:1–64.
- , , . LACE+ index: extension of a validated index to predict early death or unplanned readmission following hospital discharge using administrative data. Open Medicine. 2012;6(2):80–89.
- . PCCF Plus version 5E user's guide. Ottawa ON: Statistics Canada; 2009;82F0086‐XDB.
- , , . Derivation and validation of diagnostic score based on case‐mix groups to predict 30‐day death or urgent readmission. Open Medicine. 2012;6(3):e80–e89.
- , . Executing high‐quality care transitions: a call to do it right. J Hosp Med. 2007;2(5):287–290.
- , , , . Predicting post‐discharge death or readmission: deterioration of model performance in a population having multiple admissions per patient [published online ahead of print November 19, 2012]. J Eval Clin Pract. doi: 10.1111/jep.12012.
- , , , . Patient self‐management of chronic disease in primary care. JAMA. 2002;288(19): 2469–2475.
- , . Multilevel analyses of neighbourhood socioeconomic context and health outcomes: a critical review. J Epidemiol Community Health. 2001;55(2):111–122.
- , , , et al. Incidence of potentially avoidable hospital readmissions and its relationship to all‐cause urgent readmissions. CMAJ. 2011;183(14):E1067–E1072.
- , , , . Validation of a combined comorbidity index. J Clin Epidemiol. 1994;47(11):1245–1251.
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