013 - Examining Sources of Post-Acute Care Inequities with Layered Target Matching

Objective: To examine factors associated with racial inequities in discharge location, skilled nursing facility (SNF) utilization, and readmissions.
Data Sources: A 20% sample of longitudinal Medicare claims from 2016 to 2018.
Study Design: We present layered target matching, a method for studying sources of inequities. Layered target matching examines a fixed target population profile representing any race, ethnicity, or vulnerable population, sequentially adjusting for sets of characteristics that may contribute to inequities these groups endure. We demonstrate the method in a study of racial inequities in post-acute care use and readmissions, sequentially matching first to a demographics target, then a richer target adding reasons for admission and clinical characteristics on admission, and finally a third target further adding hospital characteristics and complications experienced during the hospitalization. This process helps clarify potential sources of differences in post-acute care use and readmissions, enabling policy makers to address them more efficiently. Using recent methods for describing the populations implicitly targeted by linear regression, we also investigate the implications of choice of adjustment method for disparities research in populations with limited covariate overlap; specifically, the highly segregated cities of Chicago and Detroit.
Data Collection/Extraction Methods: We studied Black and Non-Hispanic White fee-for-service Medicare beneficiaries aged 66+ admitted to short-term acute-care hospitals for qualifying diagnoses or procedures between 1/1/2016 and 11/30/2018.
Principal Findings: Admitted Black patients tended to be younger, had significantly higher rates of risk factors such as diabetes, stroke, or renal disease, and were much more frequently admitted to large or academic hospitals. Relative to demographically similar White patients, Black patients were significantly more likely to be discharged to SNFs (21.8% vs. 19.3%, difference=2.5%, P<0.0001) and to receive any SNF care within 30 days of discharge (25.3% vs. 22.4%, difference=2.9%, P<0.0001). Black patients were also significantly more likely to experience 30-day readmission (18.7% vs. 14.5%, difference=4.2%, P<0.0001). Differences in reasons for hospitalization and risk factors explained most of the differences in discharge location, post-acute care use, and readmission rates, while additional adjustment for differences in hospital characteristics and complications made little difference for any of the measures studied. Finally, the target populations implied by conventional regression models fit on the highly segregated cities of Chicago and Detroit appeared to be much healthier than the sample of Black patients in each city, with the implied means of some important risk factors such as emergent admission and renal disease being approximately 10% lower than those in the true samples.
Conclusions: We found significant Black-White differences in discharge to SNFs, SNF utilization, and readmission rates. Using layered target matching, we found that differences in risk factors and reasons for hospitalization explained most of these differences, while differences in hospitals did not materially impact the differences. These findings suggest that policies targeting preventive care and reducing differences in occurrences of admissions requiring more intensive post-acute care may possibly be more successful than policies targeting hospitals.

Matching Methods

Risk Adjustment for Clinical Outcomes

Health Equity

Racial/Ethnic Differences in Health and Health Care

Quality of Care