Spatial Bayesian Hierarchical Modeling of the Association Between Air Pollution Exposures and Birth Outcomes

• Activity Number: 418
• Type: Topic Contributed
• Date/Time: Tuesday, August 11, 2015 : 2:00 PM to 3:50 PM
• Sponsor: Scientific and Public Affairs Advisory Committee
• Author(s): An-Ting Jhuang* and Montserrat Fuentes and Brian J. Reich and Amy Herring
• Companies: North Carolina State University and North Carolina State University and North Carolina State University and The University of North Carolina at Chapel Hill

Abstract

Recent studies have linked maternal exposure to ambient air pollution during pregnancy with adverse birth outcomes including preterm birth, low birth weight, and birth defects. However, there are remaining uncertainties and methodological challenges in understanding the effects of particulate matter (PM) exposure on neonatal health including uncertainty of exposure measurement errors using environmental monitoring data, the role of different PM constituents, the variation in PM sources, the identification of a critical window of exposure, and the residential mobility. Our access to geocoded addresses for the pregnant women gives us an unprecedented opportunity to examine potential effects of air pollution on fetal development. We develop and validate a Bayesian hierarchical multivariate spatiotemporal framework for prediction and modeling of speciated fine PM simultaneously with co-pollutants. Under this framework, we introduce shrinkage methods and propose a hierarchical Bayesian framework for exposure modeling and assessment of health risks that not only allows potentially spatially-varying effects, but also investigates susceptible windows of exposure.