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Int. J. Environ. Res. Public Health 2017, 14(9), 1046; doi:10.3390/ijerph14091046

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

1
Los Alamos National Laboratory, P.O. Box 1663, Los Alamos, NM 87545, USA
2
Department of Biostatistics and Statistics and Operations Research, Virginia Commonwealth University, Richmond, VA 23284, USA
3
Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA
4
Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA
5
Division of Neonatology, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA
6
The Climate Corporation, San Francisco, CA 94103, USA
*
Author to whom correspondence should be addressed.
Received: 31 May 2017 / Revised: 21 July 2017 / Accepted: 1 September 2017 / Published: 11 September 2017
(This article belongs to the Special Issue Spatial Modelling for Public Health Research)
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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 View Full-Text
Keywords: multivariate; spatiotemporal; birth defects; pollutants; factor analysis multivariate; spatiotemporal; birth defects; pollutants; factor analysis
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Kaufeld, K.A.; Fuentes, M.; Reich, B.J.; Herring, A.H.; Shaw, G.M.; Terres, M.A. A Multivariate Dynamic Spatial Factor Model for Speciated Pollutants and Adverse Birth Outcomes. Int. J. Environ. Res. Public Health 2017, 14, 1046.

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