Analyzing the Association Between Socioeconomic Factors and Hospital Readmission Rates in California

Background: The Affordable Care Act (ACA) established the Hospital Readmissions Reduction Program in which hospitals receive up to a 1% reimbursement penalty for excessive 30-day readmission rates in acute myocardial infarctions (AMI), heart failure (HF), and pneumonia (PN). Many hospitals have voiced their concerns that the penalty disproportionately affects hospitals serving large, socioeconomically disadvantaged populations.\ud \ud Objective: To determine the bivariate associations between hospital readmission rates for AMI, HF, and PN, and three socioeconomic factors: education, poverty, and income.\ud \ud Design and Setting: This study involved a cross-sectional analysis of FY13 Medicare Provider Analysis and Review (MedPAR) files for California hospitals in the Hospital Readmissions Reduction Program.\ud \ud Population: California acute care hospitals (n=191) with reported AMI, HF, or PN 30-day readmission rates for Medicare beneficiaries between 2008 to 2011.\ud \ud Analysis: Each socioeconomic factor was categorized by bed size (Small=X<218, Large=X>218), and then each category was divided into quartiles. A t-test comparing the top 25% to the bottom 25% was conducted, along with a general linearized model (GLM). Three analyses were conducted: an unadjusted association between readmission rate and socioeconomic factor, an association adjusted for hospital size and geographic classification, and an association adjusted for hospital size, geographic classification, and DSH payment.\ud \ud Results: Poverty and income showed statistically significant results for all three quality measures, while education only showed significant results for large hospitals for AMI and HF, and both large and small hospitals for PN. All quality measures showed greater associations as the association was adjusted for the covariates.\ud \ud Limitations: This analysis only includes California hospitals, and has limited granularity due to the use of hospital-level and county-level data sets.\ud \ud Conclusion: The association between readmissions and poverty and readmissions and income showed significant associations for all three quality measures, especially after adjusting for the covariates.\ud \ud Public Health Significance: As the Centers for Medicare and Medicaid Services (CMS) increases the readmission penalty in future years, it is important to continually revise the penalty methodology to exclude factors outside of a hospital’s control in order to avoid unintended consequences that may widen health disparities by discouraging hospitals from serving socioeconomically disadvantaged populations