09-1 Methods to Investigate the Effects of Multiple Air Pollution Constituents

This report contains two studies, by Drs. Brent A Coull and Eun Sug Park and their colleagues, and a Commentary discussing each study individually, as well as an Integrative Discussion of the two. 
Part 1. Statistical Learning Methods for the Effects of Multiple Air Pollution Constituents. Brent A. Coull et al.
Part 2. Development of Enhanced Statistical Methods for Assessing Health Effects Associated with an Unknown Number of Major Sources of Multiple Air Pollutants. Eun Sug Park et al. 

This report is Part 3 of HEI Research Report 183, Development of Statistical Methods for Multipollutant Research. It describes a study to develop and apply statistical methods to analyze the effects of multipollutant exposures on health, expanding beyond the two-pollutant approaches used in many studies to date. HEI funded three innovative studies in recent years to improve the tools for analyzing complex multipollutant exposures. In this last report from these studies, John Molitor and colleagues describe a Bayesian framework to identify spatial clusters of air pollution exposures — and other covariates such as socioeconomic status — and estimated pregnancy outcomes associated with those clusters, using a data set for Los Angeles county.