title: Exploratory spatial relative risk mapping.
abstract: The many faces of disease mapping include maps of disease case locations, regional counts of cases, and disease risk. Another approach is that of mapping the relative risk. Previous methods to map the relative risk were based on regression models of relative risk, given information about geographical locations and established risk factors. However, spatial epidemiological investigations are often exploratory with limited knowledge about the putative risk factors. Indeed, often the primary motivation for the analysis is to identify unknown geographically varying risk factors. An exploratory approach to mapping the spatial relative risk is to scale the risk map using the background risk in the unexposed (or less-exposed) population. Exposure to unknown spatial risk factors is defined via specific cluster analysis. Identification of spatial disease clusters separates the population into those inside and those outside high risk areas (the exposed and unexposed populations). This exploratory approach to relative risk mapping gives the investigator an impression about the importance and geographical distribution of the unknown spatial risk factors. Two examples illustrate the exploratory relative risk mapping approach using a spatial point data set on pseudorabies in pig-herds and a regional count data set on small fox tapeworm infections in red foxes.
authors.affiliation: Department of Population Medicine, Ontario Veterinary College, University of Guelph, Guelph, Ont., Canada. oberke@uoguelph.ca
authors.forename: Olaf
authors.initials: O
authors.lastname: Berke
mesh_terms: D000818:Animals; D004739:England / Q000453:epidemiology; D005589:Foxes; D015233:Models, Statistical; D011557:Pseudorabies / Q000453:epidemiology* / Q000209:etiology / Q000517:prevention & control*; D012306:Risk; D013029:Space-Time Clustering; D013552:Swine; D013553:Swine Diseases / Q000453:epidemiology / Q000209:etiology / Q000517:prevention & control