How to Account for the Full Impact of Social Determinants of Health in Medicare Advantage Plans
SummaryAvalere experts recently presented “Risk Adjusting Medicare Advantage Plan Performance Measures for Social Determinants of Health: Are Dual Eligibility and Disability Status Enough?” at the Academy Health 2019 Annual Research Meeting.
Starting in 2017, the Centers for Medicare and Medicaid Services (CMS) began adjusting the Five Star Rating System for Medicare Advantage (MA) health plans for dual eligibility (DE, i.e., eligibility for both Medicare and Medicaid) and disability status (DIS) in an attempt to account for beneficiaries’ social determinants of health (SDH).
This Avalere research examined whether adjusting for DE and DIS is sufficient to account for the full impact of SDH on outcomes compared to adjusting for additional SDH measures. Their work also sought to contrast individual-level SDH drawn from two different neighborhood sizes—census block groups (CBG, which encompass approximately 1500 individuals) vs. 9-digit ZIP code groups (which average 10–11 individuals)—in modeling healthcare outcomes. Research has shown that health behaviors are highly predictable based on near-neighborhood characteristics. Using data averaged over disparate areas can mask the impact of SDH on health outcomes.
The Avalere team studied 1.4 million MA beneficiaries who were continuously enrolled in their health plan in 2015. The data were extracted from a national claims database. Based on their address, beneficiaries were matched to household SDH variables from a commercial market research database drawn from multiple, comprehensive individual and household databases and aggregated at the 9-digit ZIP code level, and to identical SDH variables taken from the U.S. Census Bureau’s American Community Survey, where data are aggregated at the CBG level.
Their research found that adjusting for DE and DIS significantly improved prediction outcomes compared to a model with plan effect only. Adding additional SDH factors at CBG level (e.g., income, education, marital status, home ownership, ethnicity) significantly improved the fit even after controlling for DE and DIS, demonstrating that those 2 factors alone do not fully capture the impact of SDH on outcomes. Using SDH data captured at a more granular 9-digit ZIP code near-neighborhood level further improved the model fit, demonstrating the importance of using accurate data on SDH in predicting health plan performance and healthcare outcomes.
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