Excess Readmission vs Excess Penalties: Maximum Readmission Penalties as a Function of Socioeconomics and Geography
BACKGROUND: The Hospital Readmission Reduction Program (HRRP) penalizes hospitals with “excess” readmissions up to 3% of Medicare reimbursement. Approximately 75% of eligible hospitals received penalties, worth an estimated $428 million, in fiscal year 2015.
OBJECTIVE: To identify demographic and socioeconomic disparities between matched and localized maximum-penalty and no-penalty hospitals.
DESIGN: A case-control study in which cases included were hospitals to receive the maximum 3% penalty under the HRRP during the 2015 fiscal year. Controls were drawn from no-penalty hospitals and matched to cases by hospital characteristics (primary analysis) or geographic proximity (secondary analysis).
SETTING: A selectiion of 3383 US hospitals eligible for HRRP. PARTICIPANTS: Thirty-nine case and 39 control hospitals from the HRRP cohort.
MEASUREMENTS: Socioeconomic status variables were collected by the American Community Survey. Hospital and health system characteristics were drawn from Centers for Medicare and Medicaid Services, American Hospital Association, and Dartmouth Atlas of Health Care. The statistical analysis was conducted using Student t tests.
RESULTS: Thirty-nine hospitals received a maximum penalty. Relative to controls, maximum-penalty hospitals in counties with lower SES profiles are defined by increased poverty rates (19.1% vs 15.5%, P = 0.015) and lower rates of high school graduation (82.2% vs 87.5%, P = 0.001). County level age, sex, and ethnicity distributions were similar between cohorts.
CONCLUSION: Cases were more likely than controls to be in counties with low socioeconomic status; highlighting potential unintended consequences of national benchmarks for phenomena underpinned by environmental factors; specifically, whether maximum penalties under the HRRP are a consequence of underperforming hospitals or a manifestation of underserved communities. Journal of Hospital Medicine 2017;12:610-617. © 2017 Society of Hospital Medicine
Strengths and Weaknesses
Matching is a strength of the study. Primary analysis matched case and controls by hospital characteristics, generating cohorts similar across a spectrum of hospital metrics. Therefore, variation in readmission rates was less likely confounded by hospital characteristics. The secondary analysis was matched by geography in an effort to adjust for unmeasured, regional factors, including obesity and cost of living that may confound an association between SES and health outcomes. Geographic matching adds strength to our assertion that SES drives distinction between maximum-penalty hospitals and nonpenalty hospitals.
One weakness was the regional unit of analysis for socioeconomic and Dartmouth Atlas data, which is not a precise profile of the corresponding hospital. Each hospital was assigned a county-level socioeconomic profile. A more robust methodology would analyze patient-level SES data; this was impractical given a cohort of 78 hospitals. Regional health outcomes data limits analysis of readmission penalties as a function of hospital quality. Instead, regional data facilitated associations between readmission and population health, consistent with the aim of our study.
We analyzed 116 of 3668 hospitals eligible for the HRRP (3.2%), limiting the generalizability of our findings. Eighty-four percent of hospitals in the primary analysis have below the median number of beds, and none of them are teaching hospitals. Our analysis, restricted to maximum-penalty and no-penalty cohorts, does not address potential association between submaximal readmission penalties and socioeconomics.
Both matching techniques potentially controlled for similar SES factors and skewed our results towards null, especially in terms of race and ethnicity. Geographic matching generated 7 pairs (18%) within in the same county; both maximum-penalty and no-penalty hospitals were assigned the same socioeconomic profile, as well as 6 pairs (15%) within the same HSA, and both cases and controls had identical Dartmouth Atlas health outcomes profiles. We retained these pairs in our analysis to avoid artificially inflating SES and population health differences between cohorts.
Thirty-nine hospitals received a maximum penalty in the 3rd year of the HRRP. Relative to geographically matched no-penalty hospitals, maximum-penalty hospitals were more likely to be rural and located in counties with less educational attainment, more poverty and more poorly controlled chronic disease. In contrast to nationwide studies, a matched analysis plan revealed no difference between the cohorts in terms of race and ethnicity and provided evidence that maximum penalty hospitals had higher rates of age-, sex-, and race-adjusted hospital-wide mortality.
Our results highlight potential consequences of nationally derived benchmarks for phenomena underpinned by social, behavioral, and environmental factors and raise the question of whether maximum HRRP penalties are a consequence of underperforming hospitals or a manifestation of underserved communities. We are encouraged by MedPAC’s proposal to stratify HRRP by SES, yet recommend further small-area geographic analyses to better align quality measures, penalties, and incentives with resources and needs of distinct populations.
Acknowledgments
The authors thank William Hisey, who laid the foundation for the analysis and without whom the project would not have been possible.
DISCLOSURE
The authors certify that none of the material in this manuscript has been previously published and that none of this material is currently under consideration for publication elsewhere. This project received no funding. None of the authors on this manuscript have any commercial relationships to disclose in relation to this manuscript. All authors have reviewed and approved this manuscript and have contributed significantly to the design, conduct, and/or analysis of the research. No authors have any financial interests to disclose. No authors have any potential conflicts of interest to disclose. No authors have financial or personal relationships with any of the subject material presented in the manuscript.
