Thirty-Day Readmission Rates for Medicare Beneficiaries by Race and Site of Care FREE

Racial disparities in health care are well documented,¹ and eliminating them remains a national priority.² Reducing readmissions has become a policy focus because it represents an opportunity to simultaneously improve quality and reduce costs, yet little is known about racial disparities in this area. While at least one study has found that in aggregate, across all conditions, black patients have slightly increased odds of readmission,³ others have found no such association.⁴ We are unaware of prior work on racial disparities in readmission rates at the national level for common medical conditions.

Beyond simply describing whether disparities exist, there is also an increasing urgency to understand why these disparities exist. One possibility is that site of care plays a role. Prior studies have found that care for minorities is highly concentrated: a small number of hospitals provide a disproportionate share of the care for minority patients, and these hospitals appear to have worse performance on processes of care,^{5 - 8} although data on outcomes are mixed.^{4 ,9 - 10} Thus, if black patients have higher readmission rates than white patients, it may be because these patients receive care at low-quality hospitals rather than because of race itself.

Understanding whether, and why, black patients have higher readmission rates for common, publicly reported conditions can help improve the design of interventions that target the most vulnerable patients and hospitals. Therefore, we sought to answer 3 questions: first, are there disparities in readmission rates between elderly black and white patients admitted for acute myocardial infarction (MI), congestive heart failure (CHF), or pneumonia? Second, if these disparities exist, are they related primarily to race itself or primarily to the site where care is provided? And finally, if disparities based on the site of care do exist, are they associated with particular structural features of the hospitals that disproportionately care for minorities (such as size or teaching status), or markers of financial stress, such as public ownership or disproportionately caring for the poor?

We used the Medicare Provider Analysis Review (MedPAR) 100% files to examine all hospitalizations with the primary discharge diagnoses of acute MI, CHF, or pneumonia occurring between January 1, 2006, and November 30, 2008 (International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9 ] codes for acute MI, 410.xx, excluding 410.x2; for CHF, 398.91, 404.x1, 404.x3, and 428.0-428.9; and for pneumonia, 480-486), for Medicare fee-for-service beneficiaries aged 65 years or older. Discharges occurring in December 2008 were excluded because we lacked a full 30 days of follow-up. Only patients surviving to discharge were included.

We excluded patients discharged from federal hospitals and those located outside the 50 states and the District of Columbia. Our final sample consisted of 3 163 011 discharges: for acute MI, 579 492 discharges from 4322 hospitals; for CHF, 1 346 768 discharges from 4560 hospitals; and for pneumonia, 1 236 751 discharges from 4588 hospitals. Patient race was categorized based on self-report, and, as has been the convention in other studies using these data, nonblack patients were categorized as white.^{11 - 12}

We used the 2007 American Hospital Association survey to identify hospitals' size, nurse-to-census ratio, ownership, proportion of hospitalized patients with Medicaid or Medicare, membership in a hospital system, teaching status, location, and census region. Nurse-to-census ratios were calculated by dividing the number of full-time equivalent nurses by 1000 patient-days.¹³ We obtained hospitals' Disproportionate Share Index (a marker of caring for the poor) from the Medicare Impact File. We examined, using the Hospital Quality Alliance (HQA) data, each hospital's performance on processes of care during 2007 and assigned a summary score to each hospital for each condition using standard methods (eTable 1).¹⁴

Our primary outcome was risk-adjusted odds of all-cause 30-day readmission; the unit of analysis was the patient. We also examined risk-adjusted 30-day readmissions with the same diagnosis as the index admission. Each patient's likelihood of readmission was adjusted using the Elixhauser risk-adjustment scheme, a validated tool developed by the Agency for Healthcare Research and Quality (AHRQ) that was designed to be used with administrative data.^{15 - 17} The Elixhauser approach has been widely used in the field,^{18 - 23} and details are provided in the eAppendix and eTable 2. In a sensitivity analysis, we used the Charlson comorbidity index for the risk adjustments; the results were very similar, so we present only the Elixhauser-adjusted model.

For each hospital, we calculated the proportion of its Medicare patients who are black and categorized institutions in the highest decile of proportion of black patients as minority serving; the other 90% of hospitals were categorized as nonminority serving. In sensitivity analyses, we examined alternative cut points including the highest quartile and highest 5%; the results were similar, so we present only the results using the highest decile as the cut point.

We compared the characteristics of black vs white patients for each condition and the characteristics of minority vs non–minority-serving hospitals using Wilcoxon tests for continuous data and χ² tests for categorical data. For our primary outcome, risk-adjusted odds of readmission, we created multivariate patient-level logistic regression models; all models included within-hospital clustering. For each condition, we first examined patient race as the primary predictor of readmission and then site of care (minority-serving vs non–minority-serving hospital) as the primary predictor; we then added both patient race and site of care to the model to evaluate their relative contribution to the model of readmission rates. We tested for an interaction between race and site of care for each condition.

We then categorized all patients into 4 categories that we had defined a priori : black patients at minority-serving hospitals, white patients at minority-serving hospitals, black patients at non–minority-serving hospitals, and white patients at non–minority-serving hospitals. We ran logistic regression models using indicator variables to examine the relationship between these groups and odds of readmission, first using only age for risk-adjustment (model 1), and next using our formal risk-adjustment scheme^{15 - 16} (model 2). We added discharge destination (home, nursing or rehabilitation facility, hospice, or other) to our model for each condition, as well as length of stay, to address possible confounding by these factors (model 3),^{24 - 25} and then added hospital characteristics including size, system membership, teaching status, ownership, location, and region (model 4). We then added the proportion of Medicaid patients and each hospital's Disproportionate Share Index^{26 - 27} as proxies for the proportion of poor patients a hospital serves (model 5).²⁸ Finally, we further adjusted for condition-specific HQA scores.

We performed a number of sensitivity analyses. We excluded Hispanics, Asian Americans, and other racial/ethnic groups (4.4% of the patient sample). Furthermore, to address the concern that black patients were less likely to die in the 30 days following an admission and thus might be more likely to be readmitted based on this fact alone, we performed 2 related analyses. First, we censored patients who died between discharge and 30 days of follow-up. Next, we used a composite end point of all cause death or readmission in 30 days as our primary outcome. We also added each patient's number of admissions for the prior year and in-hospital procedures into the model.

To account for multiple comparisons, we considered a 2-sided P value of less than .008 to be significant. All statistical analyses were performed using SAS software version 9.2 (SAS Institute Inc, Cary, North Carolina). This study was granted exemption by the Harvard School of Public Health Institutional Review Board.

Of the 3 163 011 discharges in our sample, 276 681 (8.7%) were for black patients and 2 886 330 (91.3%) were for white patients. For each condition, black patients were younger; more often women; and more likely to have diabetes, hypertension, chronic kidney disease, and obesity and were less likely to have chronic pulmonary disease, valvular heart disease, and depression (Table 1). Roughly 40% of black patients and 6% of white patients were cared for at hospitals designated as minority-serving. A significantly higher proportion of black patients were Medicaid eligible. Black patients were more likely to be discharged home for CHF, but that was less likely after acute MI and pneumonia. Black patients were less likely to die between hospital discharge and 30 days of follow-up for CHF, but there was no difference in this outcome for acute MI or pneumonia.

At minority-serving hospitals, on average, 37% of patients were black compared with 1.4% of patients at non–minority-serving hospitals (Table 2). Minority-serving hospitals were more often large public or for-profit hospitals. Seventy percent of the minority-serving hospitals were located in the South compared with 35% of the non–minority-serving hospitals. Minority-serving hospitals were more often teaching hospitals, served a higher proportion of Medicaid patients, and had a higher Disproportionate Share Index. Minority-serving hospitals had fewer nurses per 1000 patient-days and had somewhat lower performance on HQA measures (Table 2). Length of stay was greater at minority-serving hospitals for each condition.

Overall, when we considered our entire group of patients with acute MI, CHF, and pneumonia in a single sample, black patients had 13% higher odds of all-cause 30-day readmission than white patients (odds ratio [OR], 1.13; 95% confidence interval [CI], 1.11-1.14; P < .001); patients discharged from minority-serving hospitals had 23% higher odds of readmission than patients from non–minority-serving hospitals (OR, 1.23; 95% CI, 1.20-1.27; P < .001). When we examined the conditions separately and examined patient race and site of care simultaneously, both factors were significantly associated with readmission rates. Among patients with acute MI, black patients had 13% higher odds of readmission (OR, 1.13; 95% CI, 1.10-1.16; P < .001), irrespective of the site of care, while patients from minority-serving hospitals had 22% higher odds of readmissions (OR, 1.22; 95% CI, 1.17-1.27; P < .001), even accounting for patient race. The results for the other 2 conditions were similar (Table 3). There was no significant interaction between race and site of care (P values for interaction >.10).

Examining readmissions in our prespecified groups, we found that white patients at non–minority-serving hospitals consistently had the lowest odds of readmission and that black patients at minority-serving hospitals, the highest. For example, among patients with acute MI, using white patients at non–minority-serving hospitals as the reference group, black patients at minority-serving hospitals (OR, 1.35, 95% CI, 1.28-1.42), white patients at minority-serving hospitals (OR, 1.23; 95% CI, 1.18-1.29), and black patients at non−minority-serving hospitals (OR, 1.20; 95% CI, 1.16-1.23) had progressively higher odds of readmission (P < .001 for each). The results for CHF and pneumonia were similar (Table 4). When we further adjusted these analyses for discharge destination, length of stay, and key hospital characteristics, we found comparable results. Further adjusting for markers of caring for the poor had only modest effects, with the exception of CHF, in which the disparity between black and white patients at non–minority-serving hospitals was no longer statistically significant (Table 4). Finally, adjusting for a hospital's HQA score did not affect readmission rates (data not shown).

When we examined race, site of care, and same-cause readmissions, we found similar results for both acute MI and CHF. Among patients with acute MI, black patients had 13% higher odds of readmission than white patients (OR, 1.13; 95% CI, 1.07-1.20), controlling for site of care, and patients discharged from minority-serving hospitals had 15% higher odds of readmission than patients discharged from non–minority-serving hospitals (OR, 1.15; 95% CI, 1.06-1.25), controlling for race. The findings were similar for CHF, but not for pneumonia, where the differences were not statistically significant (eTable 3A). Our 4-group analyses were similar as well; among patients with acute MI, using white patients at non–minority-serving hospitals as our reference group, black patients at minority-serving hospitals (OR, 1.30; 95% CI, 1.17-1.45), white patients at minority-serving hospitals (OR, 1.15; 95% CI, 1.05-1.25), and black patients at non−minority-serving hospitals (OR, 1.13; 95% CI, 1.06-1.21) all had significantly higher odds of readmission (P < .001 for each). These results were similar for CHF, but were not significant for pneumonia (eTable 3B).

In sensitivity analyses, we found that excluding Hispanics, Asian-Americans, and other nonwhite, nonblack racial or ethnic groups did not significantly change our results (eTable 4A and B). Excluding patients who died between discharge and 30 days or considering a composite outcome of death or readmission, as well as adding prior hospitalizations and in-hospital procedures to our model, eliminated the disparities in 1 subgroup: for patients with CHF at non–minority-serving hospitals, there were no racial disparities in readmissions. However, the disparities persisted for patients with CHF at minority-serving hospitals and for patients with acute MI or pneumonia at either type of hospital (eTable 5A and B, eTable 6A and B, and eTable 7A and B).

We found that elderly black Medicare patients had higher odds of 30-day readmission than white patients for acute MI, CHF, and pneumonia. These disparities were related to race itself as well as to the site where care was provided: black patients had a 13% higher odds of readmission than white patients, while patients discharged from minority-serving hospitals had a 23% higher odds of readmission than patients discharged from non–minority-serving hospitals.

Understanding why health care disparities exist is the key first step in eliminating them. Persistent racial disparities in health care utilization and outcomes are well-documented,¹ and Healthy People 2010, the federal government's set of published health objectives, includes the elimination of health disparities as an overarching goal.² Furthermore, reducing readmissions has become a top priority for policy makers, and to that end, the recently passed Patient Protection and Affordable Care Act (PPACA)²⁹ authorizes financial penalties for hospitals performing poorly on this measure. However, until now, we have had little information on whether there are disparities in readmission rates and why they might exist.

Despite ongoing interest in understanding disparities, much of the previous work has focused on differential outcomes between racial groups, without taking into account the systems within which care is delivered. Given that care for black patients is concentrated among a small number of hospitals,⁵ understanding how outcomes vary as a function of where patients receive care can help policy makers target interventions. We found that the association of readmission rates with the site of care was consistently greater than the association with race, suggesting that racial disparities in readmissions are, at least in part, a systems problem—the hospital at which a patient receives care appears to be at least as important as his/her race.

It is unclear why patients discharged from hospitals that serve a high proportion of black patients had higher odds of readmission. Adjusting for differences in structural characteristics such as teaching status, size, and ownership had little effect on our primary findings. Similarly, adjusting for the proportion of Medicaid patients and hospitals' Disproportionate Share Index did not explain the differences between hospitals, suggesting that either our measures of financial stress are inadequate or that the higher readmission rates among these hospitals are due to other factors, such as a failure to prioritize quality or inadequate focus on transitions of care and coordination of care. Several studies have found that interventions beginning in the hospital and focusing on transitional care can reduce readmissions,^{30 - 32} but whether minority-serving hospitals engage in such programs as often or as effectively as non–minority-serving hospitals is unclear.

Factors beyond hospitals' control might explain our findings. Chronic medical illness requires close outpatient management. Early outpatient follow-up after hospitalization³³ as well as disease management and patient education^{34 - 36} can reduce readmissions among both white and minority populations. It may be that availability of high-quality outpatient care is limited for patients discharged from minority-serving hospitals; these issues should be better understood before hospitals are held solely accountable for high readmission rates.

Others have examined the role of site of care in determining patient outcomes. For example, black patients may have worse outcomes than white patients following major surgeries,^{37 - 38} but taking features of the surgeon and hospital into account explains some of those gaps.^{38 - 40} For Medicare patients with acute MI, hospitals serving a high proportion of black patients have higher 90-day mortality rates,⁴¹ and for pneumonia, these hospitals are less likely to provide timely antibiotics.⁴² Others have found that racial disparities in the quality of medical care, as measured by HQA metrics, may be due, in part, to where minorities and whites receive care.^{43 - 44}

We are unaware of prior work that has focused on readmissions and site of care. Given that reducing readmissions has the potential to both improve quality and decrease costs, this measure has gained support as an important component of tracking hospital performance. It is critical to understand how recently enacted policies, especially those that penalize hospitals with high readmission rates, might impact disparities in care. Our findings suggest that minority-serving hospitals might be disproportionately affected by such penalties.

Our study has limitations. Because we used administrative data, our risk adjustment may have been limited in its ability to account for variations in severity of illness across racial groups and across hospitals. We lacked data on the specific medications and nonprocedural treatments that patients received during their hospitalization and were unable to assess if these were different between black and white patients. Because we lacked data on transitions of care and outpatient care, we could not assess whether our findings were due to inadequacies in these areas. Our sample was limited to Medicare patients; although these patients make up the majority of admissions for CHF, acute MI, and pneumonia,^{45 - 46} whether our findings apply to readmissions for younger patients is unclear. Finally, we could not assess whether the relationships we found were causal or rather simply markers of other unmeasured factors that may influence readmission rates.

We found that older black Medicare patients in the United States had higher 30-day readmission rates than white patients for 3 common medical conditions and that these differences were related, in part, to higher readmission rates among hospitals that disproportionately care for black patients. These associations persisted even after accounting for a series of potential confounders including markers of caring for poor patients, suggesting that measured features of hospitals and lower reimbursements alone are unlikely to explain these gaps. Our findings that racial disparities in readmissions are related to both patient race and the site where care is provided should spur clinical leaders and policy makers to find new ways to reduce disparities in this important health outcome.