An application of density estimation to geographical epidemiology

Abstract

A relative risk function over a geographical region is defined and it is shown that it can be estimated effectively using kernel density estimation separately for the spatial distribution of disease cases and for a sample of controls. This procedure is demonstrated using data on childhood leukaemia in the vicinity of the Sellafield nuclear reprocessing plant in Cumbria, U.K. Various modifications to the method are proposed, including the use of an adaptive kernel. The final plot demonstrates a sharp peak at Sellafield and a reasonably smooth surface over the rest of the region, despite the small number of cases in the series.