Multivariate space-time modelling of multiple air pollutants and their health effects accounting for exposure uncertainty.

ABSTRACT: 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: (1) how to estimate spatially representative pollution concentrations for each areal unit; (2) how to allow for the uncertainty in these estimated concentrations when estimating their health effects; and (3) how to simultaneously estimate the joint effects of multiple correlated pollutants. This article proposes a novel 2-stage Bayesian hierarchical model for addressing these 3 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 mode

SUBMITTER: Huang G 

PROVIDER: S-EPMC5888175 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature