Spatial-Temporal Modeling of the Critical Windows of Air Pollution Exposure for Preterm Birth

• Activity Number: 137
• Type: Contributed
• Date/Time: Monday, July 30, 2012 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics and the Environment
• Author(s): Joshua Warren*+ and Montserrat Fuentes and Amy Herring and Peter Langlois
• Companies: The University of North Carolina at Chapel Hill and North Carolina State University and The University of North Carolina at Chapel Hill and Texas Department of State Health Services
• Keywords: spatial statistics ; particulate matter ; ozone ; probit spatial model ; multivariate statistics

Abstract

Exposure to high levels of air pollution during the pregnancy is associated with increased probability of preterm birth (PTB), a major cause of infant morbidity and mortality. New statistical methodology is required to specifically determine when a particular pollutant impacts the PTB outcome, to determine the role of different pollutants, and to characterize the spatial variability in these results. We introduce a new Bayesian spatial model for PTB which identifies susceptible windows throughout the pregnancy jointly for multiple pollutants while allowing these windows to vary continuously across space and time. A directional Bayesian approach is implemented to correctly characterize the uncertainty of the climatic and pollution variables throughout the modeling process. We apply our methods to geo-coded birth outcome data from the state of Texas (2002-2004). Our results indicate the susceptible window for higher preterm probabilities is mid-first trimester for the fine PM and beginning of the first trimester for the ozone.