Covariate Adjustment and Ranking Methods to Identify Regions with High and Low Mortality Rates
Article first published online: 9 JUN 2009
© 2009, The International Biometric Society No claim to original US government works
Volume 66, Issue 2, pages 613–620, June 2010
How to Cite
Li, H., Graubard, B. I. and Gail, M. H. (2010), Covariate Adjustment and Ranking Methods to Identify Regions with High and Low Mortality Rates. Biometrics, 66: 613–620. doi: 10.1111/j.1541-0420.2009.01284.x
- Issue published online: 1 JUN 2010
- Article first published online: 9 JUN 2009
- Received September 2008. Revised March 2009. Accepted March 2009.
- Incidence rate maps;
- Mortality rate maps;
- Poisson–Gamma model;
- Ranking procedures;
- Standardized incidence ratio;
- Standardized mortality ratio
Summary 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 100γ% and lower 100γ% of relative risks for γ= 0.05, 0.1, and 0.2. We also give analytic approximations to the probabilities of correctly classifying regions in the upper 100γ% 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.