A locally adaptive process-convolution model for estimating the health impact of air pollution

Lee, D. (2018) A locally adaptive process-convolution model for estimating the health impact of air pollution. Annals of Applied Statistics, 12(4), pp. 2540-2558. (doi:10.1214/18-AOAS1167)

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Abstract

Most epidemiological air pollution studies focus on severe outcomes such as hospitalisations or deaths, but this underestimates the impact of air pollution by ignoring ill health treated in primary care. This paper quantifies the impact of air pollution on the rates of respiratory medication prescribed in primary care in Scotland, which is a proxy measure for the prevalence of less severe respiratory disease. A novel bivariate spatiotemporal process-convolution model is proposed, which: (i) has increased computational efficiency via a tapering function based on nearest neighbourhoods; and (ii) has locally adaptive weights that outperform traditional distance-decay kernels. The results show significant effects of particulate matter on respiratory prescription rates which are consistent with severe endpoint studies.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Lee, Professor Duncan
Authors: Lee, D.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Annals of Applied Statistics
Publisher:Institute of Mathematical Statistics
ISSN:1932-6157
ISSN (Online):1941-7330
Copyright Holders:Copyright © 2018 Institute of Mathematical Statistics
First Published:First published in Annals of Applied Statistics 12(4):2540-2558
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
647701A flexible class of Bayesian spatio-temporal models for cluster detection, trend estimation and forecasting of disease risk.Duncan LeeMedical Research Council (MRC)MR/L022184/1M&S - STATISTICS