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A Bayesian hierarchical approach to evaluate multiple pollutants and spina bifida
Swartz, M. D., Cai, Y., Chan, W., Symanski, E., Mitchell, L. E., Danysh, H. E., Langlois, P. H., & Lupo, P. J. (2015). Air toxics and birth defects: A Bayesian hierarchical approach to evaluate multiple pollutants and spina bifida. Environmental Health, 14, Article 16. https://doi.org/10.1186/1476-069X-14-16
Background: While 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).
Methods: The 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 (
Results: Twenty-five HAPs were selected in the final analysis to represent "bins" of highly correlated HAPs (rho > 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).
Conclusions: Overall 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.
