Disease mapping using Bayesian hierarchical models

description and analysis of geographically indexed health data with respect to\ud demographic, environmental, behavioural, socioeconomic, genetic, and infectious\ud risk factors (Elliott andWartenberg 2004). Disease maps can be useful for estimating\ud relative risk; ecological analyses, incorporating area and/or individual-level covariates;\ud or cluster analyses (Lawson 2009). As aggregated data are often more readily\ud available, one common method of mapping disease is to aggregate the counts of\ud disease at some geographical areal level, and present them as choropleth maps\ud (Devesa et al. 1999; Population Health Division 2006). Therefore, this chapter will\ud focus exclusively on methods appropriate for areal data..