Multi-site time series studies have reported evidence of an association between short term exposure to particulate matter (PM) and adverse health effects, but the effect size varies across the United States. Variability in the effect may partially be due to differing community level exposure and health characteristics, but also due to the chemical composition of PM which is known to vary greatly by location and time. The objective of this article is to identify particularly harmful components of this chemical mixture. Because of the large number of highly-correlated components, we must incorporate some regularization into a statistical model. We assume that, at each spatial location, the regression coefficients come from a mixture model with the flavor of stochastic search variable selection, but utilize a copula to share information about variable inclusion and effect magnitude across locations. The model differs from current spatial variable selection techniques by accommodating both local and global variable selection. The model is used to study the association between fine PM (PM <2.5μm) components, measured at 115 counties nationally over the period 2000-2008, and cardiovascular emergency room admissions among Medicare patients.
Keywords: Bayesian modeling; Fine particulate matter; Spatial data; Stochastic search variable selection.
© 2014, The International Biometric Society.