Identifying areas with elevated disease incidence rates using empirical Bayes estimators

Abstract

Using maps of observed disease incidence rates to identify regions with potentially elevated risk may be misleading due to the instability of the observed rates in regions with small populations. The authors use a simulation study to examine the use of maps based on observed incidence rates in identifying such high-risk areas as compared to maps based on empirical Bayes and constrained empirical Bayes rate estimates. Because the existence of clusters of areas with elevated risk violates the usual distributional assumptions underlying the empirical Bayes approach, it also examines the robustness of the estimates and the impact of incorrect assumptions on identification of high-risk regions. The simulation results indicate that the observed incidence rates were quite sensitive in terms of identifying areas with truly elevated rates. It illustrates an application of these results with an analysis of the geographic distribution of brain cancer mortality among counties in Ohio.