Discrete versus continuous domain models for disease mapping

08/14/2018

∙

by   Garyfallos Konstantinoudis, et al.

∙

0

∙

share

Disease mapping aims to assess variation of disease risk over space and identify high-risk areas. A common approach is to use the Besag-York-Mollié model on data aggregated to administrative areal units. When precise geocodes are available, it is more natural to use Log-Gaussian Cox processes. In a simulation study mimicking childhood leukaemia incidence using actual residential locations of all children in the canton of Zürich, Switzerland, we compare the ability of these models to recover risk surfaces and identify high-risk areas. We then apply both approaches to actual data on childhood leukaemia incidence in the canton of Zürich during 1985-2015.