Hierarchical Models for Mapping Ohio Lung Cancer Rates

The mapping of geographical variation in disease occurrence plays an important role in assess ing environmental justice i e the equitable sharing of adverse e ects of pollution across socio demographic subpopulations Bayes and empirical Bayes methods can be used to obtain stable small area estimates while retaining geographic and demographic resolution In this study we fo cus on modeling spatial patterns of disease rates incorporating demographic variables of interest such as gender and race We employ a Bayesian hierarchical modeling approach which uses a Markov chain Monte Carlo computational method to obtain the joint posterior distribution of the model parameters We use this approach to construct smoothed maps of lung cancer mortality in Ohio counties in Our approach also facilitates a cross validatory comparison between the normal and Poisson likelihoods often t uncritically to data of this type Finally we uncover ev idence of changing spatial structure in the rates over the year period suggesting a spatio temporal hierarchical model as a new possibility