Geographical Analysis of Disease in Small Areas Using Hierarchical Bayesian Models

The purpose of this chapter is to review and compare two techniques to map the mortality risk of a disease in small geographical areas. The first one is a classical approach consisting of mapping standardized mortality ratios, which are maximum likelihood estimates of the relative risk under a Poisson model of death counts. In a second step, the authors consider a Bayesian approach that assumes a hierarchical model where the death counts follow a Poisson distribution conditioned by the prior information. These methods have been applied to the study of geographical variation in men’s lung cancer mortality from 1978 to 1998 in Galicia, Spain. Mapping mortality using the first method has important drawbacks, and there are difficulties to distinguish the mortality pattern. The Bayesian methodology produces smoother maps with a clear mortality pattern and has many advantages over the classical approach.