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Title: Identifying clusters in Bayesian disease mapping
Author: Anderson, Craig
ISNI:       0000 0004 5361 5615
Awarding Body: University of Glasgow
Current Institution: University of Glasgow
Date of Award: 2015
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This thesis develops statistical methodology for disease mapping, an increasingly important field of spatial epidemiology. Disease mapping has applications in public health by allowing for identification of areas which are at high risk of particular health problems. Such approaches are generally based on areal data, which involves partitioning the study region into a set of non-overlapping areal units and recording counts of disease cases within each areal unit. The majority of approaches assume a spatially smooth risk surface, but this may not be realistic, and there has been recent interest in developing methodology which allows for discontinuities in this structure. This can be done by identifying clusters of areal units with similar disease risks, and allowing for discontinuities between these clusters. The work presented in this thesis develops models to identify such clusters and also estimate disease risk. Three Bayesian hierarchical models are proposed; the first two are based on spatial data at a single time point, while the third extends into the spatio-temporal domain by modelling across multiple time points. Each model is applied to respiratory hospital admission data from the Greater Glasgow and Clyde Health Board area in order to identify clusters which have high disease risk.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: HA Statistics ; RA Public aspects of medicine