Advancing Dose–Response Assessment Methods for Environmental Regulatory Impact Analysis: A Bayesian Belief Network Approach Applied to Inorganic Arsenic
2016
Zabinski, Joseph W. | Garcia-Vargas, Gonzalo | Rubio-Andrade, Marisela | Fry, Rebecca C. | MacDonald Gibson, Jacqueline
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.
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