A Bayesian space–time model for clustering areal units based on their disease trends

Napier, G. , Lee, D. , Robertson, C. and Lawson, A. (2019) A Bayesian space–time model for clustering areal units based on their disease trends. Biostatistics, 20(4), pp. 681-697. (doi: 10.1093/biostatistics/kxy024) (PMID:29917057) (PMCID:PMC6797054)

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Population-level disease risk across a set of non-overlapping areal units varies in space and time, and a large research literature has developed methodology for identifying clusters of areal units exhibiting elevated risks. However, almost no research has extended the clustering paradigm to identify groups of areal units exhibiting similar temporal disease trends. We present a novel Bayesian hierarchical mixture model for achieving this goal, with inference based on a Metropolis-coupled Markov chain Monte Carlo ((MC) 3 ) algorithm. The effectiveness of the (MC) 3 algorithm compared to a standard Markov chain Monte Carlo implementation is demonstrated in a simulation study, and the methodology is motivated by two important case studies in the United Kingdom. The first concerns the impact on measles susceptibility of the discredited paper linking the measles, mumps, and rubella vaccination to an increased risk of Autism and investigates whether all areas in the Scotland were equally affected. The second concerns respiratory hospitalizations and investigates over a 10 year period which parts of Glasgow have shown increased, decreased, and no change in risk.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Lee, Professor Duncan and Napier, Dr Gary
Authors: Napier, G., Lee, D., Robertson, C., and Lawson, A.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Biostatistics
Publisher:Oxford University Press
ISSN (Online):1468-4357
Published Online:18 June 2018
Copyright Holders:Copyright © 2018 The Authors
First Published:First published in Biostatistics 20(4): 681-697
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
647701A flexible class of Bayesian spatio-temporal models for cluster detection, trend estimation and forecasting of disease risk.Duncan LeeMedical Research Council (MRC)MR/L022184/1M&S - STATISTICS