title: Spatial analysis of disease--applications.
abstract: The application of spatial statistical analysis to health data has reached adolescence. The theory and the software are both still maturing. We are drawing upon the experiences of the geostatisticians in modeling surfaces and the econometricians in modeling time series. "New and improved" computer algorithms are constantly being provided to implement the evolving theory or to improve the processing in terms of stability, reliability, and efficiency. We will come of age when we have the theory, the software, and the process to reliably produce "generalized spatio-temporal" models suitable for health data. In the meantime, biostatisticians need to acknowledge when their data is not independently distributed and to consider the spatial correlation in their analysis. This chapter provided examples using four available methods. The methods were spatial filtering, identifying clusters using the spatial scan statistic, hierarchical modeling, and conditional autoregression modeling.
authors.forename: B Sue
authors.initials: BS
authors.lastname: Bell
mesh_terms: D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D003710:Demography; D005260:Female; D005317:Fetal Growth Retardation / Q000453:epidemiology; D006801:Humans; D015233:Models, Statistical; D011247:Pregnancy; D012984:Software; D019371:Topography, Medical