Identifying clusters in Bayesian disease mapping

Anderson, C., Lee, D. and Dean, N. (2014) Identifying clusters in Bayesian disease mapping. Biostatistics, 15(3), pp. 457-469. (doi: 10.1093/biostatistics/kxu005) (PMID:24622038)

Full text not currently available from Enlighten.


Disease mapping is the field of spatial epidemiology interested in estimating the spatial pattern in disease risk across nnn areal units. One aim is to identify units exhibiting elevated disease risks, so that public health interventions can be made. Bayesian hierarchical models with a spatially smooth conditional autoregressive prior are used for this purpose, but they cannot identify the spatial extent of high-risk clusters. Therefore, we propose a two-stage solution to this problem, with the first stage being a spatially adjusted hierarchical agglomerative clustering algorithm. This algorithm is applied to data prior to the study period, and produces nnn potential cluster structures for the disease data. The second stage fits a separate Poisson log-linear model to the study data for each cluster structure, which allows for step-changes in risk where two clusters meet. The most appropriate cluster structure is chosen by model comparison techniques, specifically by minimizing the Deviance Information Criterion. The efficacy of the methodology is established by a simulation study, and is illustrated by a study of respiratory disease risk in Glasgow, Scotland.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Dean, Dr Nema and Lee, Professor Duncan and Anderson, Dr Craig
Authors: Anderson, C., Lee, D., and Dean, N.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Biostatistics
Publisher:Oxford University Press
ISSN (Online):1468-4357

University Staff: Request a correction | Enlighten Editors: Update this record