Title: Spatial clustering of average risks and risk trends in Bayesian disease mapping
Authors: Craig Anderson - University of Glasgow (United Kingdom) [presenting]
Nema Dean - University of Glasgow (United Kingdom)
Abstract: Spatio-temporal disease mapping focuses on estimating the spatial pattern in disease risk across a set of non-overlapping areal units over a fixed period of time. The key aim is to identify areas which have a high average level of disease risk or where disease risk is increasing over time, thus allowing public health interventions to be focused on these areas. Such aims are well suited to the statistical approach of clustering, and while much research has been done in this area in a purely spatial setting, only a handful of approaches have focused on spatio-temporal clustering of disease risk. We outline a new modelling approach for clustering spatio-temporal disease risk data, by clustering areas based on both their mean risk levels and the behaviour of their temporal trends. The efficacy of the methodology is established by a simulation study, and is illustrated by a study of respiratory disease risk in Glasgow, Scotland.