Controlling for localised spatio-temporal autocorrelation in long-term air pollution and health studies.
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...
| Publicado en: | Statistical Methods in Medical Research Vol. 23; no. 6; pp. 488 - 507 |
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| Autores principales: | , |
| Formato: | research Journal Article |
| Publicado: |
Sage Publications Inc.
Dec2014
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=109765202&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109765202 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09622802 31F jtl: Statistical Methods in Medical Research issn: 09622802 maglogo: Y pubinfo: dt: Dec2014 vid: 23 iid: 6 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 109765202 NLM24648100 2012815549 10.1177/0962280214527384 NLM24648100 PMC4272194 109765202 ppf: 488 ppct: 19 formats: tig: atl: Controlling for localised spatio-temporal autocorrelation in long-term air pollution and health studies. aug: au: Lee, Duncan Mitchell, Richard sug: subj: Air Pollution Environmental Exposure Probability Human Linear Regression ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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