Lee, D. , Mukhopadhyay, S., Rushworth, A. and Sahu, S. K. (2017) A rigorous statistical framework for spatio-temporal pollution prediction and estimation of its long-term impact on health. Biostatistics, 18(2), pp. 370-385. (doi: 10.1093/biostatistics/kxw048) (PMID:28025181)
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Abstract
In the United Kingdom, air pollution is linked to around 40000 premature deaths each year, but estimating its health effects is challenging in a spatio-temporal study. The challenges include spatial misalignment between the pollution and disease data; uncertainty in the estimated pollution surface; and complex residual spatio-temporal autocorrelation in the disease data. This article develops a two-stage model that addresses these issues. The first stage is a spatio-temporal fusion model linking modeled and measured pollution data, while the second stage links these predictions to the disease data. The methodology is motivated by a new five-year study investigating the effects of multiple pollutants on respiratory hospitalizations in England between 2007 and 2011, using pollution and disease data relating to local and unitary authorities on a monthly time scale.
Item Type: | Articles |
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Additional Information: | The work was funded by the Engineering and Physical Sciences Research Council (EPSRC) grant numbers EP/J017442/1 and EP/J017485/1. |
Status: | Published |
Refereed: | Yes |
Glasgow Author(s) Enlighten ID: | Rushworth, Mr Alastair and Lee, Professor Duncan |
Authors: | Lee, D., Mukhopadhyay, S., Rushworth, A., and Sahu, S. K. |
College/School: | College of Science and Engineering > School of Mathematics and Statistics > Statistics |
Journal Name: | Biostatistics |
Publisher: | Oxford University Press |
ISSN: | 1465-4644 |
ISSN (Online): | 1468-4357 |
Published Online: | 24 December 2016 |
Copyright Holders: | Copyright © 2016 The Authors |
First Published: | First published in Proteome Science 18(2):370-385 |
Publisher Policy: | Reproduced under a Creative Commons License |
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