Risk Adjustment And Promoting Health Equity In Population-Based Payment: Concepts And Evidence

• Published in: Health affairs (Millwood, Va.) Va.), 2023-01, Vol.42 (1), p.105-9
• Main Authors: McWilliams, J Michael, Weinreb, Gabe, Ding, Lin, Ndumele, Chima D, Wallace, Jacob
• Format: Article
• Language: English
• Subjects: Accuracy; Adjustment; Artificial intelligence; Benchmarks; Beneficiaries; Capitation; Disadvantaged; Disadvantaged groups; Educational attainment; Ethnic differences; Ethnic factors; Ethnicity; Expenditures; Fairness; Functional status; Health care access; Health care expenditures; Health care policy; Health disparities; Health education; Health promotion; Health services; Health status; Incentives; Medicare; Minority groups; Payment systems; Payments; Population; Populations; Race; Racial differences; Racial inequality; Residents; Risk; Risk adjustment; Risk analysis; Risk factors; Social exclusion; Social factors; Social goals
• ISSN: 0278-2715; 1544-5208
• DOI: 10.1377/hlthaff.2022.00916

The objective of risk adjustment is not to predict spending accurately but to support the social goals of a payment system, which include equity. Setting population-based payments at accurate predictions risks entrenching spending levels that are insufficient to mitigate the impact of social determinants on health care use and effectiveness. Instead, to advance equity, payments must be set above current levels of spending for historically disadvantaged groups. In analyses intended to guide such reallocations, we found that current risk adjustment for the community-dwelling Medicare population overpredicts annual spending for Black and Hispanic beneficiaries by $376-$1,264. The risk-adjusted spending for these populations is lower than spending for White beneficiaries despite the former populations' worse risk-adjusted health and functional status. Thus, continued movement from fee-for-service to population-based payment models that omit race and ethnicity from risk adjustment (as current models do) should result in sizable resource reallocations and incentives that support efforts to address racial and ethnic disparities in care. We found smaller overpredictions for lesseducated beneficiaries and communities with higher proportions of residents who are Black, Hispanic, or less educated, suggesting that additional payment adjustments that depart from predictive accuracy are needed to support health equity. These findings also suggest that adding social risk factors as predictors to spending models used for risk adjustment may be counterproductive or accomplish little.