Bayesian cluster detection via adjacency modelling

Anderson, C., Lee, D. and Dean, N. (2016) Bayesian cluster detection via adjacency modelling. Spatial and Spatio-Temporal Epidemiology, 16, pp. 11-20. (doi: 10.1016/j.sste.2015.11.005) (PMID:26919751)

128154.pdf - Accepted Version



Disease mapping aims to estimate the spatial pattern in disease risk across an area, identifying units which have elevated disease risk. Existing methods use Bayesian hierarchical models with spatially smooth conditional autoregressive priors to estimate risk, but these methods are unable to identify the geographical extent of spatially contiguous high-risk clusters of areal units. Our proposed solution to this problem is a two-stage approach, which produces a set of potential cluster structures for the data and then chooses the optimal structure via a Bayesian hierarchical model. The first stage uses a spatially adjusted hierarchical agglomerative clustering algorithm. The second stage fits a Poisson log-linear model to the data to estimate the optimal cluster structure and the spatial pattern in disease risk. The methodology was applied to a study of chronic obstructive pulmonary disease (COPD) in local authorities in England, where a number of high risk clusters were identified.

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.
Subjects:Q Science > QA Mathematics
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Spatial and Spatio-Temporal Epidemiology
Published Online:18 December 2015
Copyright Holders:Copyright © 2015 Elsevier Ltd.
First Published:First published in Spatial and Spatio-Temporal Epidemiology 16: 11-20
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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