Racial disparities in mortality risks adjusted by socioeconomic status (SES) are not well understood. To add to the understanding of racial disparities, we construct and analyze a data set that links, at individual and zip code levels, three government databases: Medicare, Medicare Current Beneficiary Survey and U.S. Census. Our study population includes more than 4 million Medicare enrollees residing in 2095 zip codes in the Northeast region of U.S. We develop hierarchical models to estimate Black-White disparity in risk of death, adjusted by both individual-level and zip codelevel income. We define population-level attributable risk (AR), relative attributable risk (RAR) and odds ratio (OR) of death comparing Blacks versus Whites, and we estimate these parameters using a Bayesian approach via Markov chain Monte Carlo. By applying the multiple imputation method to fill in missing data, our estimates account for the uncertainty from the missing individual-level income data. Results show that for the Medicare population being studied, there is a statistically and substantively significantly higher risk of death for Blacks compared with Whites, in all three measures of AR, RAR, and OR, both adjusted and not adjusted for income. In addition, after adjusting for income we find statistically significant reduction of AR but not of RAR and OR.



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