Abstract
OBJECTIVE: To examine the extent of segregation between hospitals for Medicare beneficiaries by race, ethnicity, and dual-eligible status over time. DATA SOURCES AND STUDY SETTING: We used Medicare inpatient hospital provider data for fee-for-service (FFS) beneficiaries, and the Dartmouth Atlas of Health Care from 2013 to 2021 nationwide, for hospital referral regions (HRRs), and for and hospital service areas (HSAs). STUDY DESIGN: We conducted time trend analysis with dissimilarity indices (DIs) for Black (DI-Black), Hispanic (DI-Hispanic), non-White (including Black, Hispanic, and other non-White) (DI-non-White), and dual-eligible (DI-Dual) beneficiaries. DIs between hospitals were contextualized and correlated with population compositions and residential DIs. DATA COLLECTION/EXTRACTION METHODS: We included 3177 hospitals with more than 250 Medicare FFS beneficiaries discharged per year. We cross-linked data on hospital-level patient race, ethnicity, and dual-eligible status with geographic data and examined time trends using linear mixed models. PRINCIPAL FINDINGS: Nationwide DIs ranged from 0.23 to 0.53. HRRs and HSAs generally had low segregation (DI medians: 0.08-0.19, highest among Black, then non-White, Hispanic, and dual-eligible beneficiaries). However, some HRRs and HSAs had moderate or high segregation (DI-Black >0.30 in 19.1% of HRRs and 5.8% of HSAs; DI-non-White >0.30 for two HRRs with high American Indian/Alaska Native populations). Time trends indicated small declines in segregation from 2013 to 2021 (0.15%-0.30% per year; all p < 0.001). DI-Dual correlated moderately with non-White populations. CONCLUSIONS: For Medicare FFS, we observe generally low and slightly declining levels of segregation across HRRs and HSAs, with notable exceptions. Improving race reporting and contextualizing select areas of higher segregation with their hospital and residential population compositions can help frame and understand health inequities. Interpretation of HRR-level DI may require additional historical, demographic, and spatial context due to its potential to oversimplify, overstate, or obscure segregation. Future work should identify drivers and mitigators of segregation, including sorting patterns among health systems.