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

TL;DR

This paper introduces a Bayesian hierarchical model to evaluate the individual effects of multiple air pollutants on health outcomes in time-series studies, overcoming limitations of previous methods that aggregate pollutants.

Contribution

It proposes a novel two-component Bayesian hierarchical framework that estimates true pollutant concentrations and assesses their separate health impacts.

Findings

Model accurately estimates pollutant effects in simulations.

Applied to London data, identified specific pollutants linked to cardiovascular mortality.

Framework allows for detailed pollutant-specific health impact analysis.

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 concentration in a two-component model: a pollutant model is specified to estimate the `true' concentration values for if your 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.