A Multivariate Dynamic Spatial Factor Model for Speciated Pollutants and Adverse Birth Outcomes
Year: 2017
Type: article
Abstract: Evidence suggests that exposure to elevated concentrations of air pollution during pregnancy is associated with increased risks of birth defects and other adverse birth outcomes. While current regulations put limits on total PM2.5 concentrations, there are many speciated pollutants within this size class that likely have distinct effects on perinatal health. However, due to correlations between these speciated pollutants, it can be difficult to decipher their effects in a model for birth outcomes. To combat this difficulty, we develop a multivariate spatio-temporal Bayesian model for speciated particulate matter using dynamic spatial factors. These spatial factors can then be interpolated to the pregnant women's homes to be used to model birth defects. The birth defect model allows the impact of pollutants to vary across different weeks of the pregnancy in order to identify susceptible periods. The proposed methodology is illustrated using pollutant monitoring data from the Environmental Protection Agency and birth records from the National Birth Defect Prevention Study.
Language: English
Subfield: Health, Toxicology and Mutagenesis
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,016
Awards: G1437436894, Data Integration Methods for Environmental Exposures with Applications to Air Pollution and Asthma Morbidity, G6111997424, CALIFORNIA CENTER OF BD-STEPS - FINDING CAUSES AND PREVENTIVES OF BIRTH DEFECTS, Carolina Population Center, Space-time Modeling for Linking Climate Change,Pollutant Exposure, Built Environm
OpenAlex ID: W2755198041