Boundary detection in disease mapping studies

Lee, D. and Mitchell, R. (2012) Boundary detection in disease mapping studies. Biostatistics, 13(3), pp. 415-426. (doi:10.1093/biostatistics/kxr036)

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Publisher's URL: http://dx.doi.org/10.1093/biostatistics/kxr036

Abstract

In disease mapping, the aim is to estimate the spatial pattern in disease risk over an extended geographical region, so that areas with elevated risks can be identified. A Bayesian hierarchical approach is typically used to produce such maps, which represents the risk surface with a set of random effects that exhibit a single global level of spatial smoothness. However, in complex urban settings, the risk surface is likely to exhibit localized rather than global spatial structure, including areas where the risk varies smoothly over space, as well as boundaries separating populations that are geographically adjacent but have very different risk profiles. Therefore, this paper proposes an approach for capturing localized spatial structure, including the identification of such risk boundaries. The effectiveness of the approach is tested by simulation, before being applied to lung cancer incidence data in Greater Glasgow, UK, between 2001 and 2005

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Mitchell, Professor Richard and Lee, Professor Duncan
Authors: Lee, D., and Mitchell, R.
College/School:College of Medical Veterinary and Life Sciences > Institute of Health and Wellbeing > Public Health
College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Biostatistics
ISSN:1465-4644
ISSN (Online):1468-4357

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
538431Allowing for cliffs and slopes in the risk surface when modelling small-area spatial dataDuncan LeeEconomic & Social Research Council (ESRC)ES/I015604/1M&S - STATISTICS