Small area estimation of racial disparities in diabetes using multilevel reweighted regression

Introduction: The agenda to reduce racial health disparities has been set primarily at the national and state levels. These levels may be too far removed from the individual level where health outcomes are realized. This disconnect may be slowing the progress made in reducing these disparities. We use a small area analysis technique to fill the void for county level disparities data. Methods: Behavioral Risk Factor Surveillance System data is used to estimate the prevalence of diabetes by county among Non-Hispanic Whites and Non-Hispanic Blacks. A modified weighting system was developed based on demographics at the county-level. A multilevel reweighted regression model is fit to obtain county level prevalence estimates by race. To examine whether racial disparities exist at the county-level, these rates are compared using risk difference and rate ratio. Results: The District of Columbia was ranked as having the largest average disparity in both absolute and relative terms (risk difference and risk ratio). Based on the average risk difference of counties within a state, the next five states with the largest average disparity are: Massachusetts, Kansas, Ohio, North Carolina, and Kentucky. The next five states with the largest average relative disparity, calculated with rate ratio, were: Massachusetts, Colorado, Kansas, Illinois, and Ohio. Discussion: Addressing disparities based on factors such as race/ethnicity, geographic location, and socioeconomic status is a current public health priority. This study takes a first step in developing the statistical infrastructure needed to target disparities interventions and resources to the local areas with greatest need.