Modelling categorical covariates in Bayesian disease mapping by partition structures

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 to detect 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 dataset on lip cancer incidence in Scotland.