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

Author

Listed:
  • Kimberly A. Kaufeld

    (Los Alamos National Laboratory, P.O. Box 1663, Los Alamos, NM 87545, USA)

  • Montse Fuentes

    (Department of Biostatistics and Statistics and Operations Research, Virginia Commonwealth University, Richmond, VA 23284, USA)

  • Brian J. Reich

    (Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA)

  • Amy H. Herring

    (Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA)

  • Gary M. Shaw

    (Division of Neonatology, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA)

  • Maria A. Terres

    (The Climate Corporation, San Francisco, CA 94103, USA)

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

Suggested Citation

  • Kimberly A. Kaufeld & Montse Fuentes & Brian J. Reich & Amy H. Herring & Gary M. Shaw & Maria A. Terres, 2017. "A Multivariate Dynamic Spatial Factor Model for Speciated Pollutants and Adverse Birth Outcomes," IJERPH, MDPI, vol. 14(9), pages 1-16, September.
    • Handle: RePEc:gam:jijerp:v:14:y:2017:i:9:p:1046-:d:111548
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    References listed on IDEAS

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    1. E.S. Neeley & W.F. Christensen & S.R. Sain, 2014. "A Bayesian spatial factor analysis approach for combining climate model ensembles," Environmetrics, John Wiley & Sons, Ltd., vol. 25(7), pages 483-497, November.
    2. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    3. Joshua Warren & Montserrat Fuentes & Amy Herring & Peter Langlois, 2012. "Spatial-Temporal Modeling of the Association between Air Pollution Exposure and Preterm Birth: Identifying Critical Windows of Exposure," Biometrics, The International Biometric Society, vol. 68(4), pages 1157-1167, December.
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