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
