The geography of Medicare's hospital value-based purchasing in relation to market demographics

Abstract

Objective: To illustrate the association between the sociodemographic characteristics of hospital markets and the geographic patterns of Medicare hospital value-based purchasing (HVBP) scores. Data Sources and Study Setting: This is a secondary analysis of United States hospitals with a HVBP Total Performance Score (TPS) for 2019 in the Centers for Medicare and Medicaid Services (CMS) Hospital Compare database (4/2021 release) and American Community Survey (ACS) data for 2015–2019. Study Design: This is a cross-sectional study using spatial multivariable autoregressive models with HVBP TPS and component domain scores as dependent variables and hospital market demographics as the independent variables. Data Collection/Extraction Methods: We calculated hospital market demographics using ZIP code level data from the ACS, weighted the 2019 CMS inpatient Hospital Service Area file. Principal Findings: Spatial autoregressive models using eight nearest neighbors with diversity index, race and ethnicity distribution, families in poverty, unemployment, and lack of health insurance among residents ages 19–64 years provided the best model fit. Diversity index had the highest statistically significant contribution to lower TPS (ß = −12.79, p < 0.0001), followed by the percent of the population coded to “non-Hispanic, some other race” (ß = −2.59, p < 0.0023), and the percent of families in poverty (ß = −0.26, p < 0.0001). Percent of the population was non-Hispanic American Indian/Alaskan Native (ß = 0.35, p < 0.0001) and percent non-Hispanic Asian (ß = 0.12, p < 0.02071) were associated with higher TPS. Lower predicted TPS was observed in large urban cities throughout the US as well as in states throughout the Southeastern US. Similar geographic patterns were observed for the predicted Patient Safety, Person and Community Engagement, and Efficiency and Cost Reduction domain scores but are not for predicted Clinical Outcomes scores. Conclusions: The lower predicted scores seen in cities and in the Southeastern region potentially reflect an inherent—that is, structural—association between market sociodemographics and HVBP scores.