A Bayesian localised conditional auto-regressive model for estimating the health effects of air pollution
A Bayesian localised conditional auto-regressive model for estimating the health effects of air pollution
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.
419-429
Lee, Duncan
e1d07a84-b5ae-4e19-ac35-599cf4fc8bd1
Rushworth, Alastair
d06244d9-e152-499a-8dae-aedb8c99bda8
Sahu, Sujit K.
33f1386d-6d73-4b60-a796-d626721f72bf
June 2014
Lee, Duncan
e1d07a84-b5ae-4e19-ac35-599cf4fc8bd1
Rushworth, Alastair
d06244d9-e152-499a-8dae-aedb8c99bda8
Sahu, Sujit K.
33f1386d-6d73-4b60-a796-d626721f72bf
Lee, Duncan, Rushworth, Alastair and Sahu, Sujit K.
(2014)
A Bayesian localised conditional auto-regressive model for estimating the health effects of air pollution.
Biometrics, 70 (2), .
(doi: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.
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biom12156
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Accepted/In Press date: 2013
e-pub ahead of print date: 24 February 2014
Published date: June 2014
Organisations:
Statistical Sciences Research Institute
Identifiers
Local EPrints ID: 353919
URI: http://eprints.soton.ac.uk/id/eprint/353919
PURE UUID: 01d145e9-22e2-438a-9049-f7ab359024a4
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Date deposited: 25 Jun 2013 15:01
Last modified: 16 Mar 2024 03:15
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Author:
Duncan Lee
Author:
Alastair Rushworth
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