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

Anderson, C; Lee, D; Dean, N

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

Anderson, C

Lee, D Dean, N

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Publication Type:: Journal Article
Citation:: Biometrical Journal, 2017, 59 (1), pp. 41 - 56
Issue Date:: 2017-01-01

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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	2016-06-21	en_US
dc.date.issued	2017-01-01	en_US
dc.identifier.citation	Biometrical Journal, 2017, 59 (1), pp. 41 - 56	en_US
dc.identifier.issn	0323-3847	en_US
dc.identifier.uri	http://hdl.handle.net/10453/125261
dc.description.abstract	© 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim 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.	en_US
dc.relation.ispartof	Biometrical Journal	en_US
dc.relation.isbasedon	10.1002/bimj.201600018	en_US
dc.subject.classification	Statistics & Probability	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Cluster Analysis	en_US
dc.subject.mesh	Bayes Theorem	en_US
dc.subject.mesh	Risk	en_US
dc.subject.mesh	Risk Assessment	en_US
dc.subject.mesh	Public Health	en_US
dc.title	Spatial clustering of average risks and risk trends in Bayesian disease mapping	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	59	en_US
utslib.for	0104 Statistics	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.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	59	en_US

Abstract:

© 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim 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.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/125261