Modelling categorical covariates in Bayesian disease mapping by partition structures.

Abstract

We consider the problem of mapping the risk from a disease using a series of regional counts of observed and expected cases, and information on potential risk factors. To analyse this problem from a Bayesian viewpoint, we propose a methodology which extends a spatial partition model by including categorical covariate information. Such an extension allows detection of clusters in the residual variation, reflecting further, possibly unobserved, covariates. The methodology is implemented by means of reversible jump Markov chain Monte Carlo sampling. An application is presented in order to illustrate and compare our proposed extensions with a purely spatial partition model. Here we analyse a well-known data set on lip cancer incidence in Scotland.