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PLoS One. 2019 Mar 4;14(3):e0212565. doi: 10.1371/journal.pone.0212565. eCollection 2019.

A hierarchical modelling approach to assess multi pollutant effects in time-series studies.

Author information

1
MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College, St Mary's Campus, London, United Kingdom.
2
Department of Mathematics, Imperial College, South Kensington Campus, London, United Kingdom.
3
Centre for Environmental Health and Sustainability, University of Leicester, Leicester, United Kingdom.
4
MRC-PHE Centre for Environment and Health, Environmental Research Group, King's College, London, United Kingdom.

Abstract

When assessing the short-term effect of air pollution on health outcomes, it is common practice to consider one pollutant at a time, due to their high correlation. Multi pollutant methods have been recently proposed, mainly consisting of collapsing the different pollutants into air quality indexes or clustering the pollutants and then evaluating the effect of each cluster on the health outcome. A major drawback of such approaches is that it is not possible to evaluate the health impact of each pollutant. In this paper we propose the use of the Bayesian hierarchical framework to deal with multi pollutant concentrations in a two-component model: a pollutant model is specified to estimate the 'true' concentration values for each pollutant and then such concentration is linked to the health outcomes in a time-series perspective. Through a simulation study we evaluate the model performance and we apply the modelling framework to investigate the effect of six pollutants on cardiovascular mortality in Greater London in 2011-2012.

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