Issues in the statistical analysis of small area health data

The availability of geographically indexed health and population data, with advances in computing, geographical information systems and statistical methodology, have opened the way for serious exploration of small area health statistics based on routine data. Such analyses may be used to address specific questions concerning health in relation to sources of pollution, to investigate clustering of disease or for hypothesis generation. We distinguish four types of analysis: disease mapping; geographic correlation studies; the assessment of risk in relation to a prespecified point or line source, and cluster detection and disease clustering. A general framework for the statistical analysis of small area studies will be considered. This framework assumes that populations at risk arise from inhomogeneous Poisson processes. Disease cases are then realizations of a thinned Poisson process where the risk of disease depends on the characteristics of the person, time and spatial location. Difficulties of analysis and interpretation due to data inaccuracies and aggregation will be addressed with particular reference to ecological bias and confounding. The use of errors-in-variables modelling in small area analyses will be discussed.