Materials and methods

2.1. Maternal birth cohort

To compare a BBN-based dose-response assessment approach to the prevailing RfD and slope factor approaches, we used maternal health, demographic, environmental exposure, and birth outcome data from the Biomarkers of Exposure to Arsenic (BEAR) prospective pregnancy cohort.111 This cohort was recruited in 2011-2012 from Gomez Palacio, Mexico, where 400,000 people are exposed to high arsenic levels.112 Participant recruitment methods are described elsewhere.111

For each participant, social workers collected information on age, education, smoking and alcohol consumption behaviors during pregnancy, seafood consumption, and sources of drinking and cooking water. Attending physicians reported infant birthweights and gestational ages at delivery. Maternal urine samples collected at delivery were analyzed for total,

inorganic, and methylated arsenic as described in Laine et al. (2015).113 2.2. Birth outcome measure

Infants with birthweights below the 10th percentile for gestational age are typically classified as small for gestational age.114 We calculated the small-for-gestational age cutoff values using a World Health Organization tool.115 Using this definition, only 14 infants in the cohort were small for gestational age. Due to the small sample size, we developed dose- response models to predict the probability that the birthweight-to-gestational-age (BWGA)

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ratio was below the 25th percentile, an outcome that we designate as “lower BWGA.” Of the 200 infants, 57 were designated as lower BWGA.

2.3. Reference dose approach

Current US environmental policies define the RfD for assessing non-carget risksas116

( 8 )

where NOAEL is the no observable adverse effects level (the largest dose at which no

statistically significant effects are observed) and the UFs are uncertainty factors accounting for interspecies extrapolation, intra-species differences, and uncertainty sources. The current arsenic RfD, 0.3 µg arsenic/(kg body weight-day), was derived from 1968 data on

hyperpigmentation and keratosis incidence in a Taiwanese population exposed to arsenic in drinking water.117 Because this RfD does not consider birth outcomes, we computed an RfD for the BEAR cohort using Equation 1. Consistent with the current RfD, we assumed

UFinter=UFintra=1 and UFother=3.14 In addition, we compared the BBN-based approach with the current regulatory RfD. For both analyses, we assumed that pregnant women drink 0.872 liters/day and weigh 75 kg.118 All women exposed at levels above the RfD were assumed to be at risk of delivering an infant with lower BWGA. Sensitivity and specificity were estimated by comparing the resulting assignment of risk status to the true birth outcome for each

participant.

2.4. Slope factor approach

The current arsenic slope factor, 1,500 kg-day/µg, was developed from data collected in 1968 and 1977 on skin cancer prevalence as a function of arsenic exposure in the previously

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mentioned arsenic-endemic region of Taiwan. Because this slope factor is not applicable for estimating adverse birth outcomes, we estimated a slope factor for lower BWGA risk using the BEAR cohort. Consistent with the general approach for estimating cancer risk slope factors, we computed a maximum likelihood estimator (using Stata) by regressing BWGA against inorganic arsenic exposure concentration in drinking water along with other covariates (total maternal urinary arsenicals, maternal urinary monomethylated arsenic, age, education, alcohol and seafood consumption during pregnancy, smoking during pregnancy, and infant gender). Covariates were chosen based on a prior BEAR cohort analysis.111 BWGA and all covariates measured on continuous scales were treated as continuous. We tested sensitivity and

specificity using via leave-one-out cross-validation: each cohort member was removed from the data set, a regression model was fitted to the remaining 199 members, the model was used to predict BWGA for the corresponding test subject, and this estimate was converted to an indicator of lower BWGA status and compared against the case’s true status.

2.5. BBN approach

A BBN that predicts lower BWGA from the same covariates used in the regression model for the slope factor analysis was constructed using BayesiaLab (Laval, France) software. To the explanatory variables in the regression model, we added urinary inorganic and dimethylated arsenic, which were excluded from the regression model due to multicollinearity. In brief, a BBN is a probabilistic model represented as a directed acyclic graph in which nodes are

variables and edges represent causal dependencies.22,119 A fully parameterized BBN represents the joint probability distributions among the variables. Our team’s biomedical experts

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ingested inorganic arsenic is converted to potentially hazardous arsenic metabolites. The BEAR cohort data were then used to parameterize the model. All BBN variables were discretized (Table 3). We calibrated the posterior probability threshold above which lower BWGA status is assigned to maximize sensitivity first and then specificity. Sensitivity and specificity were tested using a leave-one-out cross-validation approach, in which the BBN was fitted to 199 cases and its prediction of lower BWGA status in the remaining case was compared to the case’s actual status.

Table 3. Nodes in the Bayesian belief network.

Title Name Units States and Prior Distributions

Maternal

Education edu [categorical] [college, highschool, none]; [0.205, 0.540, 0.255]

Mother’s Age age years [[0,22], (22-29], >29]; [0.480, 0.355, 0.165]

Drinking Status drink [categorical] [no, yes]; [0.790, 0.210]

Smoking Status smoke [categorical] [no, yes]; [0.925, 0.075]

Seafood

Consumption fish [categorical] [no, yes]; [0.775, 0.225]

Infant Gender sex [categorical] [female, male]; [0.480, 0.520]

Arsenic in Tap Water dwias μg/L [[0, 19.282], (19.282, 105.591], >105.591]; [0.585, 0.260, 0.055] Total Arsenic in Urine utas μg/L [[0, 31.115], (31.115, 79.291], >79.291]; [0.625, 0.295, 0.080] Urinary Arsenic as IAs ias μg/L [[0, 1.536], (1.536, 4.143], >4.143]]; [0.595, 0.310, 0.095] Urinary Arsenic as MMAs mmas μg/L [[0, 1.869], (1.869, 5.05], >5.05]; [0.650, 0.250, 0.100] Urinary Arsenic as DMAs dmas μg/L [[0, 27.938], (27.938, 70.531], >70.531]; [0.635, 0.290, 0.075] Birthweight for

Gestational Age bwga g/weeks [lower, normal, higher]; [0.285, 0.430, 0.285]