Controlling for localised spatio-temporal autocorrelation in long-term air pollution and health studies

Lee, D. and Mitchell, R. (2014) Controlling for localised spatio-temporal autocorrelation in long-term air pollution and health studies. Statistical Methods in Medical Research, 23(6), pp. 488-506. (doi:10.1177/0962280214527384)

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Publisher's URL: http://dx.doi.org/10.1177/0962280214527384

Abstract

Estimating the long-term health impact of air pollution using an ecological spatio-temporal study design is a challenging task, due to the presence of residual spatio-temporal autocorrelation in the health counts after adjusting for the covariate effects. This autocorrelation is commonly modelled by a set of random effects represented by a Gaussian Markov random field (GMRF) prior distribution, as part of a hierarchical Bayesian model. However, GMRF models typically assume the random effects are globally smooth in space and time, and thus are likely to be collinear to any spatially and temporally smooth covariates such as air pollution. Such collinearity leads to poor estimation performance of the estimated fixed effects, and motivated by this epidemiological problem, this paper proposes new GMRF methodology to allow for localised spatio-temporal smoothing. This means random effects that are either geographically or temporally adjacent are allowed to be autocorrelated or conditionally independent, which allows more flexible autocorrelation structures to be represented. This increased flexibility results in improved fixed effects estimation compared with global smoothing models, which is evidenced by our simulation study. The methodology is then applied to the motivating study investigating the long-term effects of air pollution on respiratory ill health in Greater Glasgow, Scotland between 2007 and 2011.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Mitchell, Professor Richard and Lee, Professor Duncan
Authors: Lee, D., and Mitchell, R.
College/School:College of Medical Veterinary and Life Sciences > Institute of Health and Wellbeing > Public Health
College of Science and Engineering > School of Mathematics and Statistics
Journal Name:Statistical Methods in Medical Research
Publisher:SAGE
ISSN:0962-2802
ISSN (Online):1477-0334
Copyright Holders:Copyright © 2014 The Authors
First Published:First published in Statistical Methods in Medical Research
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