Differential Impact of Hospital and Community Factors on Medicare Readmission Penalties

• Published in: Health services research 2018-12, Vol.53 (6), p.4416-4436
• Main Authors: Aswani, Monica S., Kilgore, Meredith L., Becker, David J., Redden, David T., Sen, Bisakha, Blackburn, Justin
• Format: Article
• Language: English
• Subjects: Acute services; Admission and discharge; Analysis; Bias; Data collection; Data entry; Data processing; Data sources; Design standards; Dual MEDICAID MEDICARE Eligibility; Extraction; Fines & penalties; Government programs; Health disparities; Heterogeneity; Home health care; Hospital admission and discharge; Hospital Readmissions Reduction Program; Hospitals; Hospitals - statistics & numerical data; Humans; Identification methods; Inequality; Information processing; Medicaid; Medical care; Medical care quality; Medicare; Medicare - statistics & numerical data; Models, Statistical; Nursing homes; Patient Readmission - statistics & numerical data; Penalties; Quality management; Quality of care; Random effects; Readmission; Risk Adjustment; United States
• ISSN: 0017-9124; 1475-6773
• DOI: 10.1111/1475-6773.13030

Objective To identify hospital/county characteristics and sources of regional heterogeneity associated with readmission penalties. Data Sources/Study Setting Acute care hospitals under the Hospital Readmissions Reduction Program from fiscal years 2013 to 2018 were linked to data from the Annual Hospital Association, Centers for Medicare and Medicaid Services, Medicare claims, Hospital Compare, Nursing Home Compare, Area Resource File, Health Inequity Project, and Long‐term Care Focus. The final sample contained 3,156 hospitals in 1,504 counties. Data Collection/Extraction Methods Data sources were combined using Medicare hospital identifiers or Federal Information Processing Standard codes. Study Design A two‐level hierarchical model with correlated random effects, also known as the Mundlak correction, was employed with hospitals nested within counties. Principal Findings Over a third of the variation in readmission penalties was attributed to the county level. Patient sociodemographics and the surrounding access to and quality of care were significantly associated with penalties. Hospital measures of Medicare volume, percentage dual‐eligible and Black patients, and patient experience were correlated with unobserved area‐level factors that also impact penalties. Conclusions As the readmission risk adjustment does not include any community‐level characteristics or geographic controls, the resulting endogeneity bias has the potential to disparately penalize certain hospitals.