Spatial-Temporal Association between Daily Mortality and Exposure to Particulate
Eric Kalendra, Department of Statistics, North Carolina State University
Fine particulate matter (PM2.5) is a mixture of pollutants that has been linked to serious health problems, including premature mortality. Since the chemical composition of PM2.5 varies across space and time, the association between PM2.5 and mortality might also be expected to vary with space and season. This study uses a unique spatial data architecture consisting of geocoded North Carolina mortality data for 2001-2002, combined with US Census 2000 data. We study the association between mortality and air pollution exposure using different metrics (monitoring data and air quality numerical models) to characterize the pollution exposure. We develop and implement a novel statistical multi-stage Bayesian framework that provides a very broad, flexible approach to studying the spatiotemporal associations between mortality and population exposure to daily PM2.5 mass, while accounting for different sources of uncertainty. Most of the pollution-mortality risk assessment has been done using aggregated mortality and pollution data (e.g., at the county level), and that can lead to significant ecological bias and error in the estimated risk. In this work, we study and estimate this bias and error. We also provide a mathematical adjustment for analysis that uses aggregated data to reduce the error in the risk assessment due to ecological bias. We present results for the State of North Carolina.