期刊
SPATIAL AND SPATIO-TEMPORAL EPIDEMIOLOGY
卷 10, 期 -, 页码 29-38出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.sste.2014.05.001
关键词
Air pollution; Gaussian Markov Random Fields; Respiratory disease; Spatio-temporal autocorrelation
资金
- Engineering and Physical Sciences Research Council (EPSRC) [EP/J017442/1]
- EPSRC [EP/J017442/1] Funding Source: UKRI
- Division Of Mathematical Sciences
- Direct For Mathematical & Physical Scien [1107046] Funding Source: National Science Foundation
It has long been known that air pollution is harmful to human health, as many epidemiological studies have been conducted into its effects. Collectively, these studies have investigated both the acute and chronic effects of pollution, with the latter typically based on individual level cohort designs that can be expensive to implement. As a result of the increasing availability of small-area statistics, ecological spatio-temporal study designs are also being used, with which a key statistical problem is allowing for residual spatio-temporal autocorrelation that remains after the covariate effects have been removed. We present a new model for estimating the effects of air pollution on human health, which allows for residual spatio-temporal autocorrelation, and a study into the long-term effects of air pollution on human health in Greater London, England. The individual and joint effects of different pollutants are explored, via the use of single pollutant models and multiple pollutant indices. (C) 2014 The Authors. Published by Elsevier Ltd.
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