Pseudo-continuous spatial and spatio-temporal modelling of disease risk

Sanittham, Kamol (2021) Pseudo-continuous spatial and spatio-temporal modelling of disease risk. PhD thesis, University of Glasgow.

Abstract

Disease mapping approach is the statistical methodology used to estimate disease risk over time, and is generally based on areal unit data. Conditional autoregressive (CAR) models are the most common modelling approach for disease data at the areal unit level. Such approaches assume constant disease risk within each areal unit, which may not be realistic. Therefore this study aims to address this problem by creating a pseudo continuous disease risk surface over the Greater Glasgow and Clyde Health Board. A set of regular grid squares is overlaid across the study region and the main focus of this study is to estimate disease risk in each grid square after removing grid squares with zero population. Areal unit data are transformed to the grid level via two novel approaches which are multiple imputation and data augmentation and then use these grid data to fit the standard Leroux CAR model to estimate the spatial patterns in disease risk at the grid level. The multiple imputation approach generates multiple sets of disease counts at the grid level via multinomial sampling, and each dataset is used to fit the CAR model then combine the results to estimate the grid level disease risk. While the data augmentation allows uncertainty in the disease counts by updating them in the MCMC steps. Each method is applied to respiratory hospital admission data from the Greater Glasgow and Clyde Health Board area. The final piece of work of this thesis extends the spatial model to measure health inequality in Glasgow over time. Overall, it was found that disease risk is increasing over time and the areas with higher risk correspond to the deprived areas, while areas with lower risk tend to be the wealthier areas in Glasgow.

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Keywords: Disease mapping, CAR models, grid level.
• Subjects: H Social Sciences > HA Statistics
• Colleges/Schools: College of Science and Engineering > School of Mathematics and Statistics > Statistics
• Supervisor's Name: Lee, Professor Duncan and Anderson, Dr. Craig
• Date of Award: 2021
• Depositing User: Mr Kamol Sanittham
• Unique ID: glathesis:2021-82093
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 31 Mar 2021 13:37
• Last Modified: 31 Mar 2021 13:43
• Thesis DOI: 10.5525/gla.thesis.82093
• URI: https://theses.gla.ac.uk/id/eprint/82093