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December 2018 A locally adaptive process-convolution model for estimating the health impact of air pollution
Duncan Lee
Ann. Appl. Stat. 12(4): 2540-2558 (December 2018). DOI: 10.1214/18-AOAS1167

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.

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Duncan Lee. "A locally adaptive process-convolution model for estimating the health impact of air pollution." Ann. Appl. Stat. 12 (4) 2540 - 2558, December 2018. https://doi.org/10.1214/18-AOAS1167

Information

Received: 1 October 2017; Revised: 1 February 2018; Published: December 2018
First available in Project Euclid: 13 November 2018

zbMATH: 07029465
MathSciNet: MR3875711
Digital Object Identifier: 10.1214/18-AOAS1167

Rights: Copyright © 2018 Institute of Mathematical Statistics

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Vol.12 • No. 4 • December 2018
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