Spatial clustering of average risks and risk trends in Bayesian disease mapping

Anderson, C. , Lee, D. and Dean, N. (2017) Spatial clustering of average risks and risk trends in Bayesian disease mapping. Biometrical Journal, 59(1), pp. 41-56. (doi: 10.1002/bimj.201600018) (PMID:27492753)

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.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• 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: Biometrical Journal
• Publisher: Wiley-VCH Verlag
• ISSN: 0323-3847
• ISSN (Online): 1521-4036
• Published Online: 05 August 2016
• Copyright Holders: Copyright © 2016 Wiley-VCH Verlag GmbH and Co.
• First Published: First published in Biometrical Journal 59(1): 41-56
• Publisher Policy: Reproduced in accordance with the publisher copyright policy