title: Bayesian multinomial probit modeling of daily windows of susceptibility for maternal PM2.5 exposure and congenital heart defects.
abstract: Epidemiologic studies suggest that maternal ambient air pollution exposure during critical periods of pregnancy is associated with adverse effects on fetal development. In this work, we introduce new methodology for identifying critical periods of development during post-conception gestational weeks 2-8 where elevated exposure to particulate matter less than 2.5 µm (PM2.5 ) adversely impacts development of the heart. Past studies have focused on highly aggregated temporal levels of exposure during the pregnancy and have failed to account for anatomical similarities between the considered congenital heart defects. We introduce a multinomial probit model in the Bayesian setting that allows for joint identification of susceptible daily periods during pregnancy for 12 types of congenital heart defects with respect to maternal PM2.5 exposure. We apply the model to a dataset of mothers from the National Birth Defect Prevention Study where daily PM2.5 exposures from post-conception gestational weeks 2-8 are assigned using predictions from the downscaler pollution model. This approach is compared with two aggregated exposure models that define exposure as the average value over post-conception gestational weeks 2-8 and the average over individual weeks, respectively. Results suggest an association between increased PM2.5 exposure on post-conception gestational day 53 with the development of pulmonary valve stenosis and exposures during days 50 and 51 with tetralogy of Fallot. Significant associations are masked when using the aggregated exposure models. Simulation study results suggest that the findings are robust to multiple sources of error. The general form of the model allows for different exposures and health outcomes to be considered in future applications. Copyright © 2016 John Wiley & Sons, Ltd.
authors: Department of Biostatistics, Yale School of Public Health, New Haven, CT, U.S.A.
authors: Joshua L
authors: JL
authors: Warren
authors: Department of Preventive Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, U.S.A.
authors: Jeanette A
authors: JA
authors: Stingone
authors: Department of Biostatistics, UNC Gillings School of Global Public Health, Chapel Hill, NC, U.S.A.
authors: Amy H
authors: AH
authors: Herring
authors: National Center for Environmental Assessment, Office of Research and Development, USA Environmental Protection Agency, Research Triangle Park, NC, U.S.A.
authors: Thomas J
authors: TJ
authors: Luben
authors: Department of Statistics, North Carolina State University, Raleigh, NC, U.S.A.
authors: Montserrat
authors: M
authors: Fuentes
authors: Department of Pediatrics and Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, U.S.A.
authors: Arthur S
authors: AS
authors: Aylsworth
authors: Texas Center for Birth Defects Research and Prevention, Texas Department of State Health Services, Austin, TX, U.S.A.
authors: Peter H
authors: PH
authors: Langlois
authors: Division of Medical Genetics, Department of Pediatrics, University of Utah, Salt Lake City, UT, U.S.A.
authors: Lorenzo D
authors: LD
authors: Botto
authors: Department of Pediatrics, University of Mississippi Medical Center, Jackson, MS, U.S.A.
authors: Adolfo
authors: A
authors: Correa
authors: Department of Epidemiology, UNC Gillings School of Global Public Health, Chapel Hill, NC, U.S.A.
authors: Andrew F
authors: AF
authors: Olshan
mesh_terms: D000393:Air Pollutants / Q000633:toxicity*; D001499:Bayes Theorem; D005260:Female; D006330:Heart Defects, Congenital / Q000453:epidemiology*; D006801:Humans; D007231:Infant, Newborn; D018811:Maternal Exposure; D015233:Models, Statistical; D052638:Particulate Matter; D011247:Pregnancy
chemical_list: D000393:Air Pollutants; D052638:Particulate Matter
keywords: Bayesian modeling; Gaussian process; air pollution; birth defects; critical windows