Craig Anderson (University of Glasgow) – Modelling spatially misaligned disease count data with multiple severities

Abstract: There is substantial public health interest in mapping spatial patterns of disease risk, particularly in terms of identifying regions of high risk which may benefit from intervention. The majority of existing studies relate to a single severity of disease outcome (eg hospitalisation), without considering any other severities of the same disease (eg mild cases treated in a primary care setting). In reality, there will be correlation between severity levels, and it can be useful to develop a unified model which can account for different severities simultaneously.

An additional challenge is that data for these different severity levels are often collected at different geographical resolutions. It is therefore necessary to rescale data to a common spatial resolution to provide comparable inference. This talk will outline a novel spatially smoothed data augmented Markov chain Monte Carlo algorithm which addresses these challenges. The model will be demonstrated using a study of respiratory disease risk in Scotland in 2017.

This work is co-authored with Dr Duncan Lee (University of Glasgow) and was originally published in Biometrics in 2023. https://eprints.gla.ac.uk/276601/1/276601.pdf

Lee, D. and Anderson, C. (2023) Delivering spatially comparable inference on the risks of multiple severities of respiratory disease from spatially misaligned disease count data. Biometrics, 79(3), pp. 2691-2704. (doi: 10.1111/biom.13739) (PMID:35972420)