title: Advancing Dose-Response Assessment Methods for Environmental Regulatory Impact Analysis: A Bayesian Belief Network Approach Applied to Inorganic Arsenic.
abstract: Dose-response functions used in regulatory risk assessment are based on studies of whole organisms and fail to incorporate genetic and metabolomic 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 metabolomic data.
authors.forename: Joseph W
authors.initials: JW
authors.lastname: Zabinski
authors.forename: Gonzalo
authors.initials: G
authors.lastname: Garcia-Vargas
authors.forename: Marisela
authors.initials: M
authors.lastname: Rubio-Andrade
authors.forename: Rebecca C
authors.initials: RC
authors.lastname: Fry
authors.forename: Jacqueline MacDonald
authors.initials: JM
authors.lastname: Gibson