Covariate Adjustment and Ranking Methods to Identify Regions with High and Low Mortality Rates


Ranking, Sparseness and Clustering in Disease Mapping

Huilin Li (Division of Biostatistics, Department of Population Health, New York University) (Speaker)
Barry I. Graubard (Biostatistics Branch, DCEG, National Cancer Institute)
Mitchell H. Gail (Biostatistics Branch, DCEG, National Cancer Institute)

Identifying regions with the highest and lowest mortality rates and producing the corresponding color-coded maps help epidemiologists identify promising areas for analytic etiological studies. Based on a two-stage Poisson–Gamma model with covariates, we use information on known risk factors, such as smoking prevalence, to adjust mortality rates and reveal residual variation in relative risks that may reflect previously masked etiological associations. In addition to covariate adjustment, we study rankings based on standardized mortality ratios (SMRs), empirical Bayes (EB) estimates, and a posterior percentile ranking (PPR) method and indicate circumstances that warrant the more complex procedures in order to obtain a high probability of correctly classifying the regions with the upper $100gamma%$ and lower $100gamma%$ of relative risks for $gamma = 0.05$, $0.1$, and $0.2$. We also give analytic approximations to the probabilities of correctly classifying regions in the upper $100gamma%$ of relative risks for these three ranking methods. Using data on mortality from heart disease, we found that adjustment for smoking prevalence has an important impact on which regions are classified as high and low risk. With such a common disease, all three ranking methods performed comparably. However, for diseases with smaller event counts, such as cancers, and wide variation in event counts among regions, EB and PPR methods outperform ranking based on SMRs.

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