Bayesian cluster detection via adjacency modelling

Anderson, C; Lee, D; Dean, N

Bayesian cluster detection via adjacency modelling

Anderson, C

Lee, D Dean, N

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Publication Type:: Journal Article
Citation:: Spatial and Spatio-temporal Epidemiology, 2016, 16 pp. 11 - 20
Issue Date:: 2016-02-01

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Field	Value	Language
dc.contributor.author	Anderson, C https://orcid.org/0000-0003-1554-5727	en_US
dc.contributor.author	Lee, D	en_US
dc.contributor.author	Dean, N	en_US
dc.date.available	2015-11-12	en_US
dc.date.issued	2016-02-01	en_US
dc.identifier.citation	Spatial and Spatio-temporal Epidemiology, 2016, 16 pp. 11 - 20	en_US
dc.identifier.issn	1877-5845	en_US
dc.identifier.uri	http://hdl.handle.net/10453/122647
dc.description.abstract	© 2015 Elsevier Ltd. 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.	en_US
dc.relation.ispartof	Spatial and Spatio-temporal Epidemiology	en_US
dc.relation.isbasedon	10.1016/j.sste.2015.11.005	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Pulmonary Disease, Chronic Obstructive	en_US
dc.subject.mesh	Cluster Analysis	en_US
dc.subject.mesh	Models, Statistical	en_US
dc.subject.mesh	Bayes Theorem	en_US
dc.subject.mesh	Risk	en_US
dc.subject.mesh	Disease Outbreaks	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	England	en_US
dc.subject.mesh	Female	en_US
dc.subject.mesh	Male	en_US
dc.subject.mesh	Spatial Analysis	en_US
dc.title	Bayesian cluster detection via adjacency modelling	en_US
dc.type	Journal Article
utslib.citation.volume	16	en_US
utslib.for	1117 Public Health and Health Services	en_US
utslib.for	0707 Veterinary Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	16	en_US

Abstract:

© 2015 Elsevier Ltd. 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.

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http://hdl.handle.net/10453/122647