Bayesian Modelling of Spatial Heterogeneity in Disease Maps with Application to German Cancer Mortality Data

This paper starts with a short overview of basic concepts in
disease mapping such as relative risk and age-standardization. Then
two recent methods for advanced statistical analysis of areal
summary measures of health outcomes are reviewed which overcome
difficulties in traditional mapping methods. Both methods account
for spatial correlation in an hierarchical Bayesian framework and
use computer-intensive Markov chain Monte Carlo methods for
statistical inference. The methods are compared through analyses of
cancer mortality data from Germany, 1986-1990.