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.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:637684 |
| Date | January 2015 |
| Creators | Anderson, Craig |
| Publisher | University of Glasgow |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://theses.gla.ac.uk/6107/ |
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