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.
Highlights
Short-term air pollution studies aim at evaluating the association between the day-to-day variation in ambient air pollution and the day-to-day variation in a health outcome, such as mortality or hospital admissions
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 such concentration is linked to the health outcomes in a time-series perspective
Particle number concentration was measured by condensation particle counter (TSI 3022). We focus on these five pollutants as they are already regulated in ambient air; as a result, they are well monitored, have documented associations to health outcomes [15] and have been showed to need National Ambient Air Quality Standards [16]
Summary
Short-term air pollution studies aim at evaluating the association between the day-to-day variation in ambient air pollution and the day-to-day variation in a health outcome, such as mortality or hospital admissions This involves a time-series approach using data from a particular geographical area that contains daily counts of mortality or morbidity, pollution and meteorological measurements. Our paper novelty lays on its fully Bayesian framework, as the two components are jointly estimated so that uncertainty from the concentration estimates can feed forward into the health effect estimates; at the same time information from the outcome can feedback to the air pollution estimates. The remainder of the paper is structured as follows: section 2 presents the data and the model, section 3 introduces the simulation study, while in section 4 we present the results of our analysis and section 5 covers areas of discussion and concluding remarks
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