  In epidemiological disease mapping one aims to estimate the spatio-temporal
pattern in disease risk and identify high-risk clusters, allowing health
interventions to be appropriately targeted. Bayesian spatio-temporal models are
used to estimate smoothed risk surfaces, but this is contrary to the aim of
identifying groups of areal units that exhibit elevated risks compared with
their neighbours. Therefore, in this paper we propose a new Bayesian
hierarchical modelling approach for simultaneously estimating disease risk and
identifying high-risk clusters in space and time. Inference for this model is
based on Markov chain Monte Carlo simulation, using the freely available R
package CARBayesST that has been developed in conjunction with this paper. Our
methodology is motivated by two case studies, the first of which assesses if
there is a relationship between Public health Districts and colon cancer
clusters in Georgia, while the second looks at the impact of the smoking ban in
public places in England on cardiovascular disease clusters.
