The long-term health effects of air pollution are often estimated using a spatio-temporal ecological areal unit study, but this design leads to the following statistical challenges: (i) how to estimate spatially representative pollution concentrations for each areal unit; (ii) how to allow for the uncertainty in these estimated concentrations when estimating their health effects; and (iii) how to simultaneously estimate the joint effects of multiple correlated pollutants. This article proposes a novel two-stage Bayesian hierarchical model for addressing these three challenges, with inference based on Markov chain Monte Carlo simulation. The first stage is a multivariate spatio-temporal fusion model for predicting areal level average concentrations of multiple pollutants from both monitored and modelled pollution data. The second stage is a spatio-temporal model for estimating the health impact of multiple correlated pollutants simultaneously, which correctly accounts for the uncertainty in the estimated pollution concentrations. The novel methodology is motivated by a new study of the impact of both particulate matter and nitrogen dioxide concentrations on respiratory ill health in Scotland between 2007 and 2011, and the results suggest that both pollutants exhibit substantial and independent health effects.
Keywords: air pollution and health; multiple pollutant fusion modelling; space-time modelling; uncertainty propagation.