Spatial variable selection methods for investigating acute health effects of fine particulate matter components

Abstract

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.

Figure 1

Posterior median coefficients (in % RR increase per IQR increase) for each model. Each row includes coefficients for the labeled pollutant; each column indicates one county. The counties are grouped according to the eight EPA subregions, defined as Northeast (NE), Mid Atlantic (Mid Atl), South Atlantic (S Atl), East South Central (ESC), West South Central (WSC), East North Central (ENC), West North Central (WNC), Mountain (Mtn), and Pacific (Pac). This figure appears in color in the electronic version of this article.

Figure 2

Posterior median county-specific % risk increase for an IQR increase in elemental carbon from the SpVS model. This figure appears in color in the electronic version of this article.

Figure 3

City-specific means (open dot), median (filled dot), and 95% posterior intervals (vertical lines) for the elemental carbon effect, arranged by EPA subregion, defined as Northeast (NE), Mid Atlantic (Mid Atl), South Atlantic (S Atl), East South Central (ESC), West South Central (WSC), East North Central (ENC), West North Central (WNC), Mountain (Mtn), and Pacific (Pac). The national average estimates are given for α_k (triangle), α_kπ_k (square), and average of the 115 β_k(s) (diamond). This figure appears in color in the electronic version of this article.