title: Spatial clustering of average risks and risk trends in Bayesian disease mapping.
abstract: Spatiotemporal disease mapping focuses on estimating the spatial pattern in disease risk across a set of nonoverlapping areal units over a fixed period of time. The key aim of such research is to identify areas that 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 spatiotemporal clustering of disease risk. Therefore, this paper outlines a new modeling approach for clustering spatiotemporal disease risk data, by clustering areas based on both their mean risk levels and the behavior 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.
authors: School of Mathematical and Physical Sciences, University of Technology Sydney, 15 Broadway, Ultimo, NSW, 2007, Australia.|ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS), Richard Berry Building, The University of Melbourne, Parkville, VIC 3010, Australia.
authors: Craig
authors: C
authors: Anderson
authors: School of Mathematics and Statistics, University of Glasgow, University Gardens, Glasgow, G12 8QW, Scotland.
authors: Duncan
authors: D
authors: Lee
authors: School of Mathematics and Statistics, University of Glasgow, University Gardens, Glasgow, G12 8QW, Scotland.
authors: Nema
authors: N
authors: Dean
mesh_terms: D001499:Bayes Theorem; D016000:Cluster Analysis; D006801:Humans; D011634:Public Health / Q000379:methods*; D012306:Risk; D018570:Risk Assessment / Q000379:methods*
keywords: CAR; Clustering; Disease mapping; Spatiotemporal