A Bayesian localized conditional autoregressive model for estimating the health effects of air pollution

Lee, D., Rushworth, A. and Sahu, S. K. (2014) A Bayesian localized conditional autoregressive model for estimating the health effects of air pollution. Biometrics, 70(2), pp. 419-429. (doi:10.1111/biom.12156) (PMID:24571082) (PMCID:PMC4282098)

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Publisher's URL: http://dx.doi.org/10.1111/biom.12156

Abstract

Estimation of the long-term health effects of air pollution is a challenging task, especially when modeling spatial small-area disease incidence data in an ecological study design. The challenge comes from the unobserved underlying spatial autocorrelation structure in these data, which is accounted for using random effects modeled by a globally smooth conditional autoregressive model. These smooth random effects confound the effects of air pollution, which are also globally smooth. To avoid this collinearity a Bayesian localized conditional autoregressive model is developed for the random effects. This localized model is flexible spatially, in the sense that it is not only able to model areas of spatial smoothness, but also it is able to capture step changes in the random effects surface. This methodological development allows us to improve the estimation performance of the covariate effects, compared to using traditional conditional auto-regressive models. These results are established using a simulation study, and are then illustrated with our motivating study on air pollution and respiratory ill health in Greater Glasgow, Scotland in 2011. The model shows substantial health effects of particulate matter air pollution and nitrogen dioxide, whose effects have been consistently attenuated by the currently available globally smooth models.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Rushworth, Mr Alastair and Lee, Professor Duncan
Authors: Lee, D., Rushworth, A., and Sahu, S. K.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Biometrics
Publisher:Wiley-Blackwell
ISSN:0006-341X
ISSN (Online):1541-0420
Copyright Holders:Copyright © 2014 The Authors
First Published:First published in Biometrics 70(2):419-429
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
588351A rigorous statistical framework for estimating the long-term health effects of air pollution.Duncan LeeEngineering & Physical Sciences Research Council (EPSRC)EP/J017442/1M&S - STATISTICS