Lancaster EPrints

Modelling spatio-temporal variation in exposure to particulate matter : a two-stage approach.

Fanshawe, Thomas R. and Diggle, Peter J. and Rushton, S. and Sanderson, R. and Lurz, P. W. W. and Glinianaia, S. V. and Pearce, M. S. and Parker, L. and Charlton, M. and Pless-Mulloli, T. (2008) Modelling spatio-temporal variation in exposure to particulate matter : a two-stage approach. Environmetrics, 19 (6). pp. 549-566.

Full text not available from this repository.

Abstract

Studies investigating associations between air pollution exposure and health outcomes benefit from the estimation of exposures at the individual level, but explicit consideration of the spatio-temporal variation in exposure is relatively new in air pollution epidemiology.We address the problem of estimating spatially and temporally varying particulate matter concentrations (black smoke = BS = PM4) using data routinely collected from 20 monitoring stations in Newcastle-upon-Tyne between 1961 and 1992.We propose a two-stage strategy for modelling BS levels. In the first stage, we use a dynamic linear model to describe the long-term trend and seasonal variation in area-wide average BS levels. In the second stage, we account for the spatio-temporal variation between monitors around the area-wide average in a linear model that incorporates a range of spatio-temporal covariates available throughout the study area, and test for evidence of residual spatio-temporal correlation.We then use the model to assign time-aggregated predictions of BS exposure, with associated prediction variances, to each singleton pregnancy that occurred in the study area during this period, guided by dates of conception and birth and mothers’ residential locations. In work to be reported separately, these exposure estimates will be used to investigate relationships between maternal exposure to BS during pregnancy and a range of birth outcomes. Our analysis demonstrates how suitable covariates can be used to explain residual spatio-temporal variation in individual-level exposure, thereby reducing the need to model the residual spatio-temporal correlation explicitly.

Item Type: Article
Journal or Publication Title: Environmetrics
Uncontrolled Keywords: dynamic linear model • environmental epidemiology • exposure estimation • particulate matter • spatio-temporal process
Subjects: R Medicine > R Medicine (General)
Departments: Faculty of Health and Medicine > Medicine
VC's Office
Faculty of Health and Medicine > Health Research
ID Code: 28225
Deposited By: Dr TR Fanshawe
Deposited On: 18 Nov 2009 10:10
Refereed?: Yes
Published?: Published
Last Modified: 26 Jul 2012 16:46
Identification Number:
URI: http://eprints.lancs.ac.uk/id/eprint/28225

Actions (login required)

View Item