Advancing Dose–Response Assessment Methods for Environmental Regulatory Impact Analysis: A Bayesian Belief Network Approach Applied to Inorganic Arsenic

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• 作者：Joseph W. Zabinski ; Gonzalo Garcia-Vargas ; Marisela Rubio-Andrade ; Rebecca C. Fry ; Jacqueline MacDonald Gibson
• 刊名：Environmental Science & Technology Letters
• 出版年：2016
• 出版时间：May 10, 2016
• 年：2016
• 卷：3
• 期：5
• 页码：200-204
• 全文大小：310K
• 年卷期：0

文摘

Dose–response functions used in regulatory risk assessment are based on studies of whole organisms and fail to incorporate genetic and metabolic data. Bayesian belief networks (BBNs) could provide a powerful framework for incorporating such data, but no prior research has examined this possibility. To address this gap, we develop a BBN-based model predicting birthweight at gestational age from arsenic exposure via drinking water and maternal metabolic indicators using a cohort of 200 pregnant women from an arsenic-endemic region of Mexico. We compare BBN predictions to those of prevailing slope-factor and reference-dose approaches. The BBN outperforms prevailing approaches in balancing false-positive and false-negative rates. Whereas the slope-factor approach had 2% sensitivity and 99% specificity and the reference-dose approach had 100% sensitivity and 0% specificity, the BBN’s sensitivity and specificity were 71 and 30%, respectively. BBNs offer a promising opportunity to advance health risk assessment by incorporating modern genetic and metabolic data.