RACIAL DISPARITY IN MORTALITY RATES IN A SAMPLE OF THE U.S. MEDICARE POPULATION

Yijie Zhou, Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health
Francesca Dominici, Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health
Thomas A. Louis, Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health

Support for this work was provided by the National Institute for Environmental Health Sciences (ES012054-03) and the Environmental Protection Agency (RD83054801).

Abstract

Racial differences in mortality adjusted for socioeconomic status (SES) are not well understood. We study the joint relations between race, SES, and mortality risk. We construct a data set that links, at individual and zip code levels, three government databases: Medicare, U.S. Census and Medicare Current Beneficiary Survey (MCBS). 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 the Black-White disparity in risk of death, as well as whether this disparity is accounted by differentiation in both individual-level and zip code-level SES. We define population level attributable risk (AR), relative attributable risk (RAR) and odds ratio (OR) of death comparing Blacks versus Whites from model predicted probabilities, and estimate these parameters of interest using a Bayes approach via Markov Chain Monte Carlo (MCMC). The results show that for the Medicare population being studied, there is a statistically 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 SES. In addition, when we adjust for SES we find statistically significant reduction in AR but not in RAR and OR. This suggests that SES does not explain the association between race and risk of death for the Medicare population who are 65 years and older. However, reducing the SES differences between the black and white populations will reduce their differences in mortality risks.