Data Sources and Study Population
We used the 2007–2014 state inpatient data from the Healthcare Cost and Utilization Project (HCUP) to identify hospitalizations for Medicare beneficiaries initially admitted for the conditions targeted by HRRP: acute myocardial infarction, congestive heart failure, and pneumonia. The HCUP state inpatient data include administrative data on hospital procedures, diagnoses, and charges, as well as patient characteristics, including age, gender, and race for all hospital admissions in the state.12 We included all inpatient discharges with any of the three targeted diagnoses, for patients aged 65 and older with Medicare as their primary payer from five states (Arkansas, Florida, Nebraska, New York, and Washington), discharged alive, who did not leave against medical advice (n = 1,745,686). We used these five states because the discharge records contain revisit indicators, which are needed to calculate the days between the index hospitalization and subsequent hospitalizations for the same patient, and because these states had data available for 2007 through 2014. We also performed sensitivity analysis using data from Massachusetts, which has revisit indicators back to 2011, and Iowa, which has revisit indicators back to 2009. We supplemented the HCUP data with data from the 2011 American Hospital Association Annual Survey and the CMS impact file to obtain hospital characteristics.
Thirty-Day Readmissions
We calculated 30-day readmissions following the published guidelines set by the Centers for Medicare and Medicaid Services.13 A hospitalization was identified as a readmission for any condition within 30 days of a previous hospital discharge for one of the targeted conditions. We excluded transfers to other hospitals and potentially planned readmissions, which were defined using the CMS-published algorithms. Generally, planned readmissions include non-acute admissions for typically scheduled procedures and other types of care (e.g., rehabilitation, transplant surgery).14
HRRP Passage
HRRP was announced along with the passage of the Affordable Care Act in March 2010. The program was not fully implemented until 2012, and the first rounds of penalties were not imposed until 2013. The 2013 penalties included a lookback period back to 2008. Although there are many possible cut points to look at the impact of the program, previous research has suggested most of the impact of HRRP was actually felt immediately after the March 2010 announcement, with readmissions declining at the fastest rate between 2010 and 2012, and then plateauing around the time of the full implementation of the program in 2012.2, 3, 5 Thus, we used 2010 as the relevant cut point to descriptively examine what happened to disparities in readmissions following the announcement of HRRP.
Hospital Characteristics
We examined how a hospital’s status as a safety-net provider may play a role in the change in readmission disparities over time. In order to make simple comparisons, we split hospitals into two groups. Following earlier work, we used the Disproportionate Share Hospital (DSH) index from the CMS impact file to identify safety-net hospitals as those hospitals in the top quartile of the DSH index.15, 16 The DSH index is the sum of the percentage of beneficiaries treated at the hospital who receive both Medicare Part A and Supplemental Security Income benefits and the percentage who receive Medicaid, but not Medicare benefits. Although for simplicity, we focus on safety-net hospitals, we note that safety-net status is also strongly correlated with other concepts, such as highly penalized hospitals and minority-serving hospitals. For instance, although safety-net hospitals made up approximately 25% of all hospitals, they made up 47% of highly penalized hospitals (defined as having penalties above the 50th percentile) in our sample. Additionally, 77% of safety-net hospitals were minority-serving hospitals (defined as having greater than 40% of discharges among Black or Hispanic patients) compared with just 11% of non-safety-net hospitals.
Individual Characteristics
We focused on comparisons between non-Hispanic Whites and non-Hispanic Blacks, and excluded Hispanics, Asians, and other races from our analysis since the reliability of race data is highest among these groups in state hospital discharge data,17 and this is consistent with the previous literature.10 We abstracted age, sex, and race from the discharge data. In order to adjust for patient complexity, we calculated the Elixhauser Comorbidity Index, summing the total number of the 30 Elixhauser diagnoses reported during the index admission using ICD-9 codes.18
Statistical Analysis
We used logistic regressions to model the individual likelihood of readmission as a function of race by year interactions, controlling for age, sex, and Elixhauser Comorbidity Index. Using the regression model, we calculated the adjusted readmission rate by calculating the average likelihood of readmission for each year for Whites and Blacks at the mean values of the covariates using the margins command in Stata 13.1. We then repeated this analysis, stratifying by hospital safety-net status and HRRP penalty status by using separate regression models on the restricted sample. In sensitivity analyses, we examined the state-level Black-White differences in state-level trends in readmissions using separate linear regressions for each state. Because state-level data had smaller sample sizes and a higher degree of variability, we examined the overall annual trend using a continuous year variable, and an interaction of that variable with race. All analyses were done in SAS 9.3 and Stata 13.1.