Disease mapping.

The mapping of disease incidence and prevalence has long been a part of public health, epidemiology, and the study of disease in human populations (Koch, 2005). In this chapter, we focus on the challenge of obtaining reliable statistical estimates of local disease risk based on counts of observed cases within small administrative districts or regions coupled with potentially relevant background information (e.g., the number of individuals at risk and, possibly, covariate information, such as the regional age distribution, measures of socioeconomic status, or ambient levels of pollution). Our goals are twofold: we want statistically precise (i.e., low variance) local estimates of disease risk for each region, and we also want the regions to be “small” in order to maintain geographic resolution (i.e., we want the map to show local detail as well as broad trends). The fundamental problem in meeting both goals is that they are directly at odds with one another: the areas are not only “small” in geographic area (relative to the area of the full spatial domain of interest) resulting in a detailed map, but also “small” in terms of local sample size, resulting in deteriorated local statistical precision.