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DOI10.1002/sim.6891
Bayesian multinomial probit modeling of daily windows of susceptibility for maternal PM2.5 exposure and congenital heart defects
Warren, Joshua L.1; Stingone, Jeanette A.2; Herring, Amy H.3; Luben, Thomas J.4; Fuentes, Montserrat5; Aylsworth, Arthur S.6; Langlois, Peter H.7; Botto, Lorenzo D.8; Correa, Adolfo9; Olshan, Andrew F.10
发表日期2016-07-20
ISSN0277-6715
卷号35期号:16页码:2786-2801
英文摘要

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 mu m (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 (c) 2016 John Wiley & Sons, Ltd.


英文关键词air pollution;Bayesian modeling;birth defects;critical windows;Gaussian process
语种英语
WOS记录号WOS:000378549200008
来源期刊STATISTICS IN MEDICINE
来源机构美国环保署
文献类型期刊论文
条目标识符http://gcip.llas.ac.cn/handle/2XKMVOVA/61734
作者单位1.Yale Univ, Sch Publ Hlth, Dept Biostat, POB 208034, New Haven, CT 06520 USA;
2.Icahn Sch Med Mt Sinai, Dept Prevent Med, New York, NY 10029 USA;
3.UNC Gillings Sch Global Publ Hlth, Dept Biostat, Chapel Hill, NC USA;
4.USA Environm Protect Agcy, Natl Ctr Environm Assessment, Off Res & Dev, Res Triangle Pk, NC USA;
5.N Carolina State Univ, Dept Stat, Raleigh, NC 27695 USA;
6.Univ N Carolina, Dept Pediat & Genet, Chapel Hill, NC USA;
7.Texas Dept State Hlth Serv, Texas Ctr Birth Defects Res & Prevent, Austin, TX USA;
8.Univ Utah, Dept Pediat, Div Med Genet, Salt Lake City, UT USA;
9.Univ Mississippi, Med Ctr, Dept Pediat, Jackson, MS 39216 USA;
10.UNC Gillings Sch Global Publ Hlth, Dept Epidemiol, Chapel Hill, NC USA
推荐引用方式
GB/T 7714
Warren, Joshua L.,Stingone, Jeanette A.,Herring, Amy H.,et al. Bayesian multinomial probit modeling of daily windows of susceptibility for maternal PM2.5 exposure and congenital heart defects[J]. 美国环保署,2016,35(16):2786-2801.
APA Warren, Joshua L..,Stingone, Jeanette A..,Herring, Amy H..,Luben, Thomas J..,Fuentes, Montserrat.,...&Olshan, Andrew F..(2016).Bayesian multinomial probit modeling of daily windows of susceptibility for maternal PM2.5 exposure and congenital heart defects.STATISTICS IN MEDICINE,35(16),2786-2801.
MLA Warren, Joshua L.,et al."Bayesian multinomial probit modeling of daily windows of susceptibility for maternal PM2.5 exposure and congenital heart defects".STATISTICS IN MEDICINE 35.16(2016):2786-2801.
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