Bayesian spatial modeling of the association between speciated fine particles and mortality

 

Particulate matter (PM) has been linked to a range of serious cardiovascular and respiratory health problems, including premature mortality. The main objective of our research is to quantify uncertainties about the impacts of fine PM exposure on mortality. We develop a multivariate spatial regression model for the estimation of the risk of mortality associated with fine PM and its components across all counties in the coterminous U.S. We characterize different sources of uncertainty in the data and model the spatial structure of the mortality data and the speciated fine PM. We consider a flexible Bayesian hierarchical model for a space-time series of counts (mortality) by constructing a likelihood-based version of a generalized Poisson regression model, that combines methods for point-level misaligned data and change of support regression. Our results seem to suggest an increase by a factor of two in the risk of mortality due to fine particles with respect to coarse particles. Our study also shows that in the Western U.S., the nitrate and crustal components of the speciated fine PM seem to have more impact on mortality than the other components. On the other hand, in the Eastern U.S., sulfate and ammonium explain most of the fine PM effect.

Collaborators: H.R. Song (Biostat, U. of South Carolina), S. Ghosh (Stat., NCSU), D. Holland (EPA) and J. Davis (Marine Earth Atmospheric Science Dept, NCSU).