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Title: A Multivariate Dynamic Spatial Factor Model for Speciated Pollutants and Adverse Birth Outcomes

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. Here, the proposed methodology is illustrated using pollutant monitoring data from the Environmental Protection Agency and birth records from the National Birth Defect Prevention Study.

Authors:
ORCiD logo [1];  [2];  [3];  [4];  [5];  [6]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  2. Virginia Commonwealth Univ., Richmond, VA (United States)
  3. North Carolina State Univ., Raleigh, NC (United States)
  4. Univ. of North Carolina, Chapel Hill, NC (United States)
  5. Stanford Univ. School of Medicine, Stanford, CA (United States)
  6. The Climate Corp., San Francisco, CA (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1415413
Report Number(s):
LA-UR-17-25966
Journal ID: ISSN 1660-4601; IJERGQ
Grant/Contract Number:
AC52-06NA25396
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
International Journal of Environmental Research and Public Health
Additional Journal Information:
Journal Volume: 14; Journal Issue: 9; Journal ID: ISSN 1660-4601
Publisher:
MDPI
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; 54 ENVIRONMENTAL SCIENCES; Mathematics; multivariate; spatiotemporal; birth defects; pollutants; factor analysis

Citation Formats

Kaufeld, Kimberly Ann, Fuentes, Montse, Reich, Brian J., Herring, Amy H., Shaw, Gary M., and Terres, Maria A.. A Multivariate Dynamic Spatial Factor Model for Speciated Pollutants and Adverse Birth Outcomes. United States: N. p., 2017. Web. doi:10.3390/ijerph14091046.
Kaufeld, Kimberly Ann, Fuentes, Montse, Reich, Brian J., Herring, Amy H., Shaw, Gary M., & Terres, Maria A.. A Multivariate Dynamic Spatial Factor Model for Speciated Pollutants and Adverse Birth Outcomes. United States. doi:10.3390/ijerph14091046.
Kaufeld, Kimberly Ann, Fuentes, Montse, Reich, Brian J., Herring, Amy H., Shaw, Gary M., and Terres, Maria A.. 2017. "A Multivariate Dynamic Spatial Factor Model for Speciated Pollutants and Adverse Birth Outcomes". United States. doi:10.3390/ijerph14091046. https://www.osti.gov/servlets/purl/1415413.
@article{osti_1415413,
title = {A Multivariate Dynamic Spatial Factor Model for Speciated Pollutants and Adverse Birth Outcomes},
author = {Kaufeld, Kimberly Ann and Fuentes, Montse and Reich, Brian J. and Herring, Amy H. and Shaw, Gary M. and Terres, Maria A.},
abstractNote = {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. Here, the proposed methodology is illustrated using pollutant monitoring data from the Environmental Protection Agency and birth records from the National Birth Defect Prevention Study.},
doi = {10.3390/ijerph14091046},
journal = {International Journal of Environmental Research and Public Health},
number = 9,
volume = 14,
place = {United States},
year = 2017,
month = 9
}

Journal Article:
Free Publicly Available Full Text
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