Air toxics and birth defects: a Bayesian hierarchical approach to evaluate multiple pollutants and spina bifida

Environmental Health

Research

Peter H Langlois¹  Philip J Lupo²  Heather E Danysh²  Yi Cai³  Michael D Swartz³  Wenyaw Chan³  Laura E Mitchell⁴  Elaine Symanski⁴ 

[1] Birth Defects Epidemiology and Surveillance Branch, Texas Department of State Health Services, Austin, TX, USA;Department of Pediatrics, Section of Hematology-Oncology and Texas Children’s Cancer Center, Baylor College of Medicine, Houston, TX, USA;Division of Biostatistics, University of Texas School of Public Health, Houston, TX, USA;Division of Epidemiology, Human Genetics and Environmental Sciences, University of Texas School of Public Health, Houston, TX, USA;

关键词: Bayesian hierarchical models;    Birth defects;    Hazardous air pollutants;    Maternal exposure;    Multi-pollutant;    Spina bifida;   

DOI  :  10.1186/1476-069X-14-16

 received in 2014-09-02, accepted in 2015-01-29,  发布年份 2015

来源: Springer

【 摘 要 】

BackgroundWhile there is evidence that maternal exposure to benzene is associated with spina bifida in offspring, to our knowledge there have been no assessments to evaluate the role of multiple hazardous air pollutants (HAPs) simultaneously on the risk of this relatively common birth defect. In the current study, we evaluated the association between maternal exposure to HAPs identified by the United States Environmental Protection Agency (U.S. EPA) and spina bifida in offspring using hierarchical Bayesian modeling that includes Stochastic Search Variable Selection (SSVS).MethodsThe Texas Birth Defects Registry provided data on spina bifida cases delivered between 1999 and 2004. The control group was a random sample of unaffected live births, frequency matched to cases on year of birth. Census tract-level estimates of annual HAP levels were obtained from the U.S. EPA’s 1999 Assessment System for Population Exposure Nationwide. Using the distribution among controls, exposure was categorized as high exposure (>95th percentile), medium exposure (5th-95th percentile), and low exposure (<5th percentile, reference). We used hierarchical Bayesian logistic regression models with SSVS to evaluate the association between HAPs and spina bifida by computing an odds ratio (OR) for each HAP using the posterior mean, and a 95% credible interval (CI) using the 2.5th and 97.5th quantiles of the posterior samples. Based on previous assessments, any pollutant with a Bayes factor greater than 1 was selected for inclusion in a final model.ResultsTwenty-five HAPs were selected in the final analysis to represent “bins” of highly correlated HAPs (ρ > 0.80). We identified two out of 25 HAPs with a Bayes factor greater than 1: quinoline (ORhigh = 2.06, 95% CI: 1.11-3.87, Bayes factor = 1.01) and trichloroethylene (ORmedium = 2.00, 95% CI: 1.14-3.61, Bayes factor = 3.79).ConclusionsOverall there is evidence that quinoline and trichloroethylene may be significant contributors to the risk of spina bifida. Additionally, the use of Bayesian hierarchical models with SSVS is an alternative approach in the evaluation of multiple environmental pollutants on disease risk. This approach can be easily extended to environmental exposures, where novel approaches are needed in the context of multi-pollutant modeling.

【 授权许可】

Unknown   
© Swartz et al.; licensee BioMed Central. 2015. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.