Gestational exposure to organochlorine compounds and metals and infant birth weight: Effect modification by maternal hardships

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Hu, Tye E. Arbuckle, Patricia A. Janssen, Bruce P. Lanphear, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4244204/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Jun, 2024 Read the published version in Environmental Health → Version 1 posted 10 You are reading this latest preprint version Abstract Background Gestational exposure to toxic environmental chemicals and maternal social hardships are individually associated with impaired fetal growth, but it is unclear whether the effects of environmental chemical exposure on infant birth weight are modified by maternal hardships. Methods We used data from the Maternal-Infant Research on Environmental Chemicals (MIREC) Study, a pan-Canadian cohort of 1982 pregnant females enrolled between 2008 and 2011. We quantified eleven environmental chemical concentrations from two chemical classes – six organochlorine compounds (OCs) and five metals – that were detected in ≥ 70% of blood samples collected during the first trimester. Using birth weight adjusted for gestational age (GA) as our outcome, we assessed nine maternal hardships by questionnaire. Each maternal hardship variable was dichotomized to indicate whether the females experienced the hardship. In our analysis, we used elastic net to select the environmental chemicals, maternal hardships, and 2-way interactions between maternal hardships and environmental chemicals that were most predictive of birth weight. Next, we obtained effect estimates using multiple linear regression, and plotted the relationships by hardship status for visual interpretation. Results Elastic net selected trans -nonachlor, lead, low educational status, minority status, and low supplemental folic acid intake. All were inversely associated with birth weight. Elastic net also selected interaction terms. Among those with increasing environmental chemical exposures and reported hardships, we observed stronger negative associations and a few positive associations. For example, every two-fold increase in lead concentrations was more strongly associated with reduced infant birth weight among participants with low educational status ( β = -100 grams (g); 95% confidence interval (CI): -215, 16), than those with higher educational status ( β = -34 g; 95% CI: -63, -3). In contrast, every two-fold increase in mercury concentrations was associated with slightly higher birth weight among participants with low educational status ( β = 23 g; 95% CI: -25, 71) compared to those with higher educational status ( β = -9 g; 95% CI: -24, 6). Conclusions Our findings suggest that maternal hardships can modify the associations of gestational exposure to some OCs and metals with infant birth weight. Fetal growth Birth weight Maternal hardships Metals Organochlorine compounds Two-hits Chemical mixtures Elastic Net Effect modifications Interactions Figures Figure 1 Figure 2 1. Background Fetal growth is a complex process impacted by various factors including psychosocial stressors, toxic chemicals, nutrition, placental function, and the actions of many intrauterine hormones and growth factors [ 1 ]. Fetal growth restriction, commonly assessed by infant birth weight for a given gestational age [ 3 ], occurs when the fetus does not reach its intrauterine potential for growth and development [ 2 ]. It is an important determinant for the risk of infant mortality and the development of chronic diseases [ 3 ]. Recent research has found that impaired fetal growth is associated with gestational exposure to toxic environmental chemicals, which are ubiquitous in our environment, food sources and personal care products [ 4 , 5 ]. In our previous work, we reported associations of two classes of environmental chemicals – metals and organochlorine compounds (OCs) – with reduced infant birth weight [ 6 ]. We found that first trimester blood concentrations of trans -nonachlor, an organochlorine compound (OC), and lead (Pb), a toxic metal, were inversely associated with birth weight. Every 2-fold increase in trans -nonachlor and Pb concentrations was associated with reduced birth weight by an average of 38 grams (g) (95% confidence interval (CI): -67, -10) and 39 g (95% CI: -69, -9), respectively. The study, however, did not paint a complete picture of the adverse effects on fetal growth because the etiology of impaired fetal growth also includes maternal hardships such as social stress, race, and nutrition [ 7 – 9 ]. Recent research by Goin et al. (2021) reported that hardships such as stressful life events, food insecurity, living alone and facing unplanned pregnancy can interact in pairwise fashion to adversely affect fetal growth [ 11 ]. These 2-way interactions observed are supported by Knudson’s “two-hit” hypothesis, where it was found that two gene mutations are needed for retinoblastoma, a childhood cancer of the retina, to develop [ 13 ]. The major implication of this “two-hit” hypothesis is that two distinct gestational stressors are needed to affect an outcome. Thus, gestational exposure to environmental chemicals and maternal hardships may interact and jointly influence fetal growth. While there is robust evidence of an association between either chemical exposures or maternal hardships and fetal growth, there is a paucity of research on the joint associations of chemical exposures and maternal hardship on fetal growth. Aker et al. (2020) found evidence, among the 752 Puerto Rican females from the Puerto Rico Testsite for Exploring Contamination Threats (PROTECT) cohort, that life event score or stress interacted with bisphenol-S and triclocarban exposures to modify their associations with gestational length [ 10 ]. Although Aker et al. (2020) did not examine metals and OCs specifically, their observation supports further exploration of maternal hardships as an effect modifier in the associations between environmental chemicals and fetal growth. We assessed the independent associations of nine maternal hardships including self-reported minority status, immigrant status, financial strain, low supplemental folic acid intake, low educational status, living status, lone parenthood, experiencing chronic diseases, and being a student, on birth weight and the potential modifying effects of these maternal hardships on the associations between OCs and metals and birth weight among a cohort of 1982 Canadian females and their infants who participated in the Maternal-Infant Research on Environmental Chemicals (MIREC) Study. 2. Methods 2.1. Study participants We analyzed data from the MIREC Study, a cohort study of 1982 pregnant females recruited between 2008 and 2011 from ten Canadian cities. The goal of the MIREC Study was to obtain Canadian biomonitoring data on pregnant females and examine associations between prenatal exposure to environmental chemicals with pregnancy and child health outcomes [ 11 ]. Detailed information on demographic and lifestyle factors were collected from questionnaires administered at recruitment in the first trimester. Eligibility criteria and exclusions are described in Arbuckle et al. (2013) [ 11 ]. For the present study, we included females who had complete socio-demographic information, provided biological samples during the first trimester of the pregnancy, and delivered singleton live births. Participants with missing information were excluded from the analysis. 2.2. Fetal growth measurement To examine fetal growth, we considered infant birth weight adjusted for gestational age (GA) [ 12 , 13 ]. We used cubic splines to allow infant birth weight curves to vary across gestation in a smooth manner [ 13 ]. We could alternatively use GA-specific birth weight z-scores [ 14 ]. However, the interpretation of the unit in standard deviation is not straightforward, which may be difficult for the public to understand and challenging to use in clinical settings [ 15 ]. Both infant birth weight, measured in grams (g), and GA, measured in weeks, were abstracted from the medical records and examined as continuous variables. 2.3. Biomarkers of prenatal environmental chemical exposure We measured environmental chemical exposures using biomarkers in blood samples collected from MIREC participants during the first trimester of pregnancy. Biomarker analysis was conducted at the Toxicology Laboratory of the Institut national de santé publique du Québec (INSPQ) using previously described methodology [ 16 – 18 ]. In our previous analysis, we reported associations between two classes of environmental chemicals – metals and OCs – and reduced infant birth weight [ 6 ]. Hence, in this study, we limited our analysis to OCs and metals only. We retained biomarkers that were detected in at least 70% of the samples [ 19 ]. These included six plasma OCs (polychlorinated biphenyl (PCB) 118, PCB 180, Aroclor 1260 (consists of PCB 138 and 153), dichlorodiphenyldichloroethylene (DDE), oxychlordane, and trans -nonachlor) and five whole blood metals (arsenic (As), cadmium (Cd), mercury (Hg), manganese (Mn), and lead (Pb)). We imputed measurements below the limit of detection (LOD) using the single imputation “fill-in” approach where the log 2 chemical concentrations lower than the LOD were randomly sampled from a truncated lognormal distribution with mean and standard deviation (SD) estimated from the observed data [ 19 ]. This “fill-in” approach for missing biomarkers yields unbiased regression coefficient estimates when 30% or less of the data are below the detection limits [ 19 ]. To account for individual-level variability in lipid levels, we standardized OC concentrations by total plasma lipid concentrations and expressed them in units of ng/g lipids [ 20 ]. Standardization was only applied when presenting the descriptive statistics on the environmental chemical concentrations. Otherwise, total lipids was included as a covariate in regression models [ 20 , 21 ]. Due to the highly correlated nature of PCB 153 and PCB 138, INSPQ summed and multiplied them by a factor of 5.2 to create Aroclor 1260 [ 22 ]. To reduce the potential influence of outliers due to the right skewed distributions of chemical concentrations, the concentrations were log 2 -transformed before inclusion in the models. The transformed unit indicates a two-fold increase in concentrations. Furthermore, before creating the interaction terms with maternal hardships to examine their joint associations, the log 2 -transformed chemical concentrations were median-centered to improve interpretation and decrease the multicollinearity effects, which would affect model convergence and inflate the standard errors [ 23 ]. 2.4. Maternal social hardships We selected maternal social hardship variables based on factors related to social determinants of health [ 24 ]. We used a questionnaire administered at baseline by study personnel [ 11 ] to capture responses that signal indicators of hardships directly affecting the study participants. The eight maternal hardships included self-reported minority status [ 7 , 25 ], immigrant status [ 25 , 26 ], financial strain [ 8 , 25 ], low supplemental folic acid intake [ 27 , 28 ], low educational status [ 7 , 8 ], living status [ 29 , 30 ], lone parenthood [ 31 , 32 ], and experiencing chronic diseases [ 33 , 34 ]. Furthermore, we also examined whether being a student is a potential maternal hardship. Each hardship variable was dichotomized to indicate whether a study participant experienced the hardship or not. Race has definitions ranging from biological to social and its operationalization as a variable in research has been debated [ 35 ]. Race or having minority status, as reported here, is considered a social construct that reflects lived experiences of systematic discrimination and differences among populations. This definition is based on Du Bois’ seminal work (1899) in sociological research on black race and health which provided insights that the racial differences in health are primarily related to social factors [ 36 ]. Therefore, we classified participants who reported their racial or cultural status as non-white as having minority status. Participants who reported their birth country as countries other than Canada were considered immigrants and faced hardship associated with being immigrants. Participants were classified as having financial strain if their reported annual household income was below $ 20,000, which is approximately the 2008 Canada poverty line for a two-person household [ 37 ]. Folate is a key pregnancy nutrient and pregnant individuals with hardship of food insecurity are more likely to have inadequate folic acid supplementation [ 27 , 28 ]. We classified participants with daily folic acid supplementation levels below 400 µg per day as low supplemental folic acid intake given that they did not meet the recommended intakes for females at low-risk for a neural tube defect-affected pregnancy [ 38 ]. Supplemental folic acid intake was assessed via a structured survey conducted at 16-weeks’ gestation where participants were asked to list all the supplements they took in the past 30 days. The product information and intake frequency were used to calculate daily total folic acid consumption from supplements [ 39 ]. However, when supplement information was missing from this 30-day recall form, we then estimated the supplement information using the 24-hour recall form (also completed at 16-weeks’ gestation) or if necessary, the baseline questionnaire, completed between 6- and 13-weeks’ gestation, where participants were asked to list all the supplements they had taken within the past 3 months. Participants who reported their highest educational level achieved as high school diploma or less were classified as having low educational status. For living status, participants who reported not living with a roommate, spouse, partner, or parents were classified as living alone. Participants with marital status as single (i.e., not married or with the same partner for 1 year or more) were classified as having a hardship of lone parenthood. Participants who responded affirmatively to “Do you have any chronic medical condition(s)?” were classified as having chronic disease(s). Here, chronic medical condition was defined as any condition that treatment can manage but not cure. Examples of such conditions include high blood pressure, diabetes, and other pre-existing conditions prior to pregnancy such as asthma, depression, arthritis, heart conditions, and psoriasis. Lastly, we also examined whether being a student is a potential maternal hardship. Participants who answered yes to the question “are you currently attending school?” were classified as being current students. 2.5. Covariates We created a directed acyclic graph (Supplemental Figure S1 ) to identify predictors of infant birth weight and factors associated with both environmental chemical exposure and infant birth weight. Additionally, we identified predictors of maternal hardships and infant birth weight. The following, derived from the baseline questionnaire, were examined: maternal age (under 25, ≥ 25 to < 30, ≥30 to < 35, ≥35 to < 40, and ≥ 40), race (white, non-white), education (high school diploma or less, college or trade school diploma, undergraduate university degree, and graduate university degree), cigarette smoking status (never, current, former, and quit during pregnancy), parity (0, 1, 2, ≥ 3), infant sex (male and female) and pre-pregnancy body mass index (BMI) (underweight, normal, overweight, and obese). We adjusted for the non-linear effect of gestational age (GA) as a covariate on birth weight using the cubic spline approach [ 13 ]. For OC models, we additionally adjusted for plasma lipid concentrations. For models including maternal hardship minority status, we did not adjust for maternal race. For models including maternal hardship low educational status or low income, we did not adjust for maternal education. 2.6. Analytical approach We first tabulated participant characteristics, examined the relations between maternal hardship variables using Cramer's V correlation coefficients, and calculated geometric means and percentiles of the environmental chemicals. We then proceeded to examine the moderating influence of maternal hardships on the relationship between OC and metal exposures and birth weight by using elastic net regression to choose which variables to include in our models. Elastic net is a machine learning method that is built on conventional regression with an added penalty term [ 40 , 41 ]. The penalty term biased the estimates to reduce overfitting and improve prediction on data not used in the model fitting procedure. There are two penalty parameters (i.e., ɑ and λ) that can be tuned to produce the best performing model. The quantity ɑ adjusts the balance between lasso and ridge penalty and has a range of 0 to 1. When ɑ is 1, the model is a lasso model and when ɑ is 0, the model is a ridge regression model. Furthermore, when ɑ is closer to 1, the elastic net model will focus more on selection and when the ɑ is closer to 0, the model can better handle multicollinearity. The second tuning parameter λ controls the magnitude of the penalty where smaller λ leads to estimates that are closer to those of conventional regression method. To determine the optimal degrees of penalization, we used cross-validation and tested the models over a grid of ɑ and λ sequences. As elastic net can account for multicollinearity, we included all the interested variables in our model. Specifically, we created separate models for the OC compounds and the metals. In each model, the dependent variable was birth weight adjusted for gestational age, and the predictor variables included all environmental chemicals in the chemical class, all the maternal hardships, and the interaction terms between the environmental chemicals and the hardships (e.g., Pb x minority status). We used elastic net for variable selection purposes in order to reduce multicollinearity and to ensure that only important variables and interaction terms are included in the model, thus potentially reducing the probability of type I errors. The covariates were adjusted in the elastic net regression model as unpenalized variables. For parameter estimation and calculating 95% CIs, it has been recommended that penalized regression, such as elastic net regression, be used to select the model first and then the selected model be used in an unpenalized regression (i.e., ordinary least squares (OLS) regression models) to quantify the associations of gestational exposures to individual environmental chemicals with infant birth weight, while adjusting for covariates [ 41 , 42 ]. This utilizes the predictive power of the overall modeling procedure while maintaining the interpretability of the individual estimates. We therefore fitted the subset of variables and the interaction terms selected and inferred to be important by elastic net in OLS regression models. The selected environmental chemicals and maternal hardships were entered into the OLS regression models individually and adjusted for covariates. The selected interaction terms were also entered into unpenalized regression models that included both the main effects and the interaction terms and adjusted for covariates. We screened for suggestive associations with p-values of 0.1 and 0.05 and visualized the relationships graphically using interaction plots with 95% confidence interval bands. Furthermore, using Cook’s distance [ 43 ], we identified and removed one influential outlier that negatively affected the models. All analyses were conducted using Microsoft R Open version 3.5.1 and elastic net models were fitted using the glmnet package [ 44 ]. 2.6.1. Supplemental Analyses We conducted additional analyses using first trimester plasma total folate concentrations instead of supplemental folic acid intake. Plasma total folate, a biomarker of folate status, specifically of recent folate intake, was measured by LC-MS/MS, as previously described [ 39 ]. Currently, no cutoff values for either low or high concentrations or a reference range for plasma total folate have been identified for fetal growth [ 45 ] but 25.5 nmol/L was estimated as a potential cut-off for neural tube defects (NTD) risk reduction [ 46 ]. Among the MIREC participants, less than 0.5% (n = 8) had a plasma total folate concentration < 25.5 nmol/L, while approximately 5% (n = 104) reported a low total daily supplemental folic acid intake of < 400µg/day. Therefore, to allow for easier comparison of the low plasma total folate and the low supplemental folic acid intake effects and to mitigate issues with low sample size, we selected the 5th percentile of plasma total folate as the cutoff value to indicate low folate status, which corresponded to 51.5 nmol/L. GA is a potential mediator in the relationship between environmental chemical exposures and birth weight. To rule out any bias or over-adjustment in our models, we conducted additional analyses that excluded GA as a covariate in the models. Lastly, we summed the number of hardships to examine the association between cumulative hardship and birth weight. 3. Results 3.1. Descriptive statistics We included a total of 1982 MIREC participants in the analysis. Fewer than 10% of observations for sociodemographic variables were missing except for income status and pre-pregnancy BMI with missingness of 11% (n = 212) and 13% (n = 264), respectively. 135 (7%) participants had missing birth weight data and 8% had missing chemical concentrations (n OC = 167 and n metals = 159). Due to missing information on a combination of sociodemographic characteristics, chemical concentrations and birth weight, the final sample size was 1565 for the OC model and 1567 for the metal model. Most of the MIREC participants were white (82%), over 30 years of age (70%) with at least an undergraduate degree or higher (63%), never smoked (61%) and had normal BMI (61%). Forty-four percent were nulliparous and 53% had male infants. Maternal characteristics associated with lower birth weight included being non-white, older age, lower educational status, smoking, underweight BMI, and nulliparity. Female infants weighed on average 100 g lower at birth compared to males (Table 1 ). Table 1 Participant sociodemographic characteristics and mean birth weight (grams) among MIREC study participants in Canada, 2008–2011. n (%) Birth weight (g) Mean (SD) Total 1982 (100) 3452 (532) Race Others 339 (17) 3334 (529) White 1517 (77) 3479 (529) Missing 126 (6) - Age ≤24 117 (6) 3404 (617) 25–29 443 (22) 3483 (505) 30–34 655 (33) 3452 (495) 35–39 502 (25) 3444 (570) 40+ 127 (6) 3428 (561) Missing 138 (7) - Education levels High school diploma or less 160 (8) 3381 (571) Some college, trade school, or college diploma 538 (27) 3444 (546) Undergraduate degree 681 (34) 3486 (512) Graduate degree 475 (24) 3435 (528) Missing 128 (6) - Household income ≤ $ 20,000 72 (4) 3348 (515) $ 20,001 - $ 40,000 152 (8) 3445 (572) $ 40,001 - $ 60,000 187 (9) 3458 (543) $ 60,001 - $ 80,000 286 (14) 3457 (530) $ 80,001 - $ 100,000 358 (18) 3440 (531) > $ 100,000 715 (36) 3475 (516) Missing 212 (11) - Smoking status Never 1137 (57) 3442 (520) Current 105 (5) 3366 (657) Former 498 (25) 3496 (517) Quit during Pregnancy 115 (6) 3432 (569) Missing 127 (6) - Parity 0 812 (41) 3407 (546) 1 751 (38) 3496 (508) 2 221 (11) 3469 (559) 3+ 72 (4) 3454 (502) Missing 126 (6) - Pre-pregnancy BMI Underweight 49 (3) 3302 (503) Normal 1040 (53) 3429 (496) Overweight 371 (19) 3515 (521) Obese 258 (13) 3466 (647) Missing 264 (13) - Infant Sex Male 974 (49) 3500 (534) Female 877 (44) 3399 (524) Missing 131 (7) - Types of Hardship Minority status Yes 339 (17) 3333 (529) No 1517 (77) 3479 (529) Missing 126 (6) - Low income Yes 72 (4) 3348 (515) No 1698 (86) 3460 (529) Missing 212 (11) - Low education Yes 160 (8) 3380 (571) No 1649 (83) 3458 (528) Missing 128 (7) - Lone parenthood Yes 87 (4) 3398 (626) No 1769 (89) 3455 (527) Missing 126 (6) - Living alone Yes 36 (2) 3384 (645) No 1820 (92) 3454 (529) Missing 126 (6) - Current student Yes 201 (10) 3461 (484) No 1763 (89) 3451 (537) Missing 18 (1) - Immigrant Yes 370 (19) 3379 (549) No 1612 (81) 3469 (526) Missing 0 (0) - Low supplemental folic acid intake (< 400 \(u\) g/day) Yes 104 (5) 3361 (523) No 1878 (95) 3457 (532) Missing 0 (0) - Having chronic illnesses Yes 491 (25) 3404 (579) No 1476 (75) 3467 (514) Missing 15 (1) - * missing was excluded from the analysis From the Cramer’s V correlation coefficient plot (Figure S2), we observed low to moderate correlations (0 to 0.5) between the maternal hardship variables. The most commonly experienced maternal hardships were having a chronic disease (25%), being an immigrant (19%) and having minority status (18%). The least common maternal hardship was living alone (2%) (Table 1 ). More than half of the participants (57%) reported having at least one maternal hardship(s) (Table S1 ). The most common hardships occurring together were immigrants with minority status at 10%, followed by those with minority status and low educational status, and immigrants who were students, both at 3% (Table S2). All hardships, except for being a student, were associated with lower birth weight (Table 1 ). Furthermore, we detected most chemicals in over 90% of study participants, with the exception of PCB 118 and trans -nonachlor with 74% and 84% detection rates, respectively (Table 2 ). Table 2 Distributions of environmental chemicals measured during the 1st trimester for MIREC study participants from 2008 to 2011 across 10 major cities in Canada. Metabolites %>LOD GM 1 Min 25th 50th 75th Max Organochlorine compounds (ng/g lipids) PCB 118 73.6 2.5 <LOD 1.7 2.3 3.4 37.9 PCB 180 92.7 4.9 <LOD 2.9 4.8 7.8 183.3 Aroclor 1260 97.3 60.7 <LOD 37.0 57.9 90.9 1183.3 DDE 99.0 56.4 <LOD 33.8 49.1 77.9 530.6 Oxychlordane 92.3 2.0 <LOD 1.4 2.1 2.9 17.5 trans - Nonachlor 84.3 3.0 <LOD 2.0 2.9 4.2 34.3 Metals (ug/L) Arsenic 92.5 0.8 <LOD 0.2 0.7 1.1 34.5 Cadmium 97.4 0.2 <LOD 0.5 0.2 0.3 5.1 Mercury 90.5 0.6 <LOD 0.1 0.7 1.4 10.0 Manganese 100.0 8.8 2.0 0.3 8.8 11.0 29.1 Lead 100.0 6.2 1.6 7.1 6.0 8.5 41.4 1 GM = Geometric Mean 3.2. Variables and interaction terms that were selected in the elastic net models of infant birth weight In the OC model, trans -nonachlor and three maternal hardship variables (i.e., low educational status, minority status, and low supplemental folic acid intake) were selected and inferred as important contributors in the elastic net regression of infant birth weight model. Elastic net also identified the following sixteen interaction terms as important: trans -nonachlor x low income, trans -nonachlor x lone parenthood, trans -nonachlor x immigrant status, PCB 118 x minority status, PCB 118 x low supplemental folic acid intake, PCB 118 x low income, PCB 118 x lone parenthood, PCB 180 x immigrant status, PCB 180 x lone parenthood, Aroclor 1260 x minority status, Aroclor 1260 x student status, oxychlordane x minority status, oxychlordane x low education, oxychlordane x low income, oxychlordane x student status, and DDE x low supplemental folic acid intake. In the metal model, Pb and the same three maternal hardship variables (i.e., low educational status, minority status, and low supplemental folic acid intake) were selected. The eleven interaction terms identified as important are: Pb x low education, Pb x minority status, As x low education, As x low income, As x lone parenthood, Hg x low education, Mn x low income, Mn x living alone, Mn x lone parenthood, Mn x student, Cd x minority status. 3.3. Independent associations between environmental chemical exposure and infant birth weight Our elastic net variable selection results agreed with those of Bayesian Kernel Machine Regression method from our previous study [ 6 ]. Both mixture methods indicated the importance of trans -nonachlor and Pb exposures, which were negatively associated with infant birth weight (Table 3 ). Every doubling of trans -nonachlor and Pb concentrations corresponded to a 37 g (95% CI: -65, -8) and a 38 g (95% CI: -68, -8) reduction in birth weight, respectively. Table 3 Differences in mean birth weight (grams) and 95% CI associated with exposure to log2-chemicals and maternal hardships, as selected by elastic net, among the MIREC study participants in Canada, 2008–2011. Unadjusted Adjusted for covariates* Organochlorine Compounds (OCs) trans -Nonachlor -66 (-96, -35) -37 (-65, -8) Metals Lead -56 (-92, -21) -38 (-68, -8) Maternal Hardships Minority status -145 (-208, -83) -78 (-130, -26) Low education -78 (-164, 9) -103 (-185, -21) Low supplemental folic acid intake (< 400 \(u\) g/day) -97 (-205, 12) -61 (-152, 31) *All models were adjusted for gestational age (GA), race, education, age, BMI, smoking, parity, and baby sex. The OC models also adjusted for total lipids. Model assessing hardship of low education and minority status did not adjust for education and race/ethnicity, respectively, as a covariate. 3.4. Independent associations between maternal hardships and infant birth weight We found that all three hardships selected by elastic net (i.e., low educational status, minority status and low supplemental folic acid intake) had negative associations with birth weight (Table 3 ). Infants born to females with a high school diploma or less had lower birth weight ( \(\beta\) = -103 g; 95% CI: -185, -21) compared to those born to more educated females. Infants born to females with minority status had lower birth weight ( \(\beta\) = -78 g; 95% CI: -130, -26) compared to those born to white females. Lastly, infants born to females with low supplemental folic acid intake had lower birth weight ( \(\beta\) = -61 g; 95% CI: -152, 31) compared to those born to females who met or exceeded recommended folic acid intakes. 3.5. Regression analysis of the modifying effect of maternal social hardships on the relationship between environmental chemical exposure and infant birth weight We found that maternal hardships can modify the strength of the relationships (i.e., steeper negative slope and lower birth weight) or the direction of the relationships (i.e., positive slope and higher birth weight) between biomarker of exposure to OCs and metals and birth weight. Although the 95% CIs were imprecise, some associations showed statistical significance at the 0.05 level (Figs. 1 to 2 and Table 4 ). Table 4 The associations between exposure to environmental chemicals (2-fold increase) and birth weight (grams) by maternal hardships, mutually adjusted for one another and covariates, among the MIREC study participants in Canada, 2008–2011. Low education Low income Immigrant Low supplemental folic acid intake (< 400 \(u\) g/day) Lone parenthood Yes n = 160 (9%) No n = 1649 (91%) Yes n = 72 (4%) No n = 1698 (96%) Yes n = 370 (19%) No n = 1612 (81%) Yes n = 104 (5%) No n = 1878 (95%) Yes n = 87 (5%) No n = 1769 (95%) trans - Nonachlor - - -128 (-238, -19)** -39 (-69, -10)** -44 (-90, 2)* -33 (-67, 1)* - - -43 (-151, 64) -36 (-65, -7)** PCB 118 - - 76 (-70, 221) -16 (-43, 12) - - -87 (-194, 19) -7 (-35, 20) -1 (-111, 108) -12 (-39, 15) PCB 180 - - - - -32 (-65, 1)* -8 (-36, 20) - - 29 (-44, 102) -21 (-42, 0)* Aroclor 1260 - - - - - - - - - - Oxychlordane 21 (-69, 111) -30 (-60, -0)** -124 (-236, -12)** -28 (-58, 2)* - - - - - - DDE - - - - - - 26 (-41, 92) -15 (-36, 5) - - Pb -100 (-215, 16)* -34 (-64, -3)** - - - - - - - - Hg 23 (-25, 71) -9 (-24, 6) - - - - - - - - As -58 (-130, 13) -5 (-25, 16) -87 (-189, 15)* -3 (-23, 18) - - - - -100 (-201, 1)* -5 (-25, 15)* Mn - - 192 (-27, 410)* -1 (-46, 44) - - - - -242 (-454, -29)** 20 (-25, 64) Cd - - - - - - - - - - n indicates sample size; - indicates not selected by elastic net models. Each cell contains the difference in birth weight measured in grams and associated 95% confidence intervals. All models were adjusted for maternal education, maternal race, maternal age, maternal pre-pregnancy BMI, maternal smoking status, parity, infant sex and gestational age; Models assessing maternal hardship low education or low income were not adjusted for maternal education; OC models additionally adjusted for total lipids. ** p-value for association < 0.05; * p-value for association < 0.1 Table 4 (con’t). The associations between exposure to environmental chemicals (2-fold increase) and birth weight (grams) by maternal hardships, mutually adjusted for one another and covariates, among the MIREC study participants in Canada, 2008–2011. Minority status Living alone Student Yes n = 160 (9%) No n = 1649 (91%) Yes n = 72 (4%) No n = 1698 (96%) Yes n = 82 (5%) No n = 1439 (95%) trans - Nonachlor - - - - - - PCB118 -28 (-87, 31) -8 (-37, 21) - - - - PCB180 - - - - - - Aroclor 1260 -34 (-80, 13) -17 (-42, 7) - - -48 (-102, 6)* -13 (-37, 11) Oxychlordane 4 (-50, 57) -35 (-68, -3)** - - -93 (-177, -8)** -19 (-49, 11) DDE - - - - - - Pb 18 (-54, 89) -50 (-82, -17)** - - - - Hg - - - - - - As - - - - - - Mn - - 231 (-151, 612) 7 (-37, 50) 81 (-52, 213) -0 (-47, 46) Cd -35 (-79, 9) -8 (-31, 15) - - - - n indicates sample size; - indicates not selected by elastic net models. Each cell contains the difference in birth weight measured in grams and associated 95% confidence intervals. All models were adjusted for maternal education, maternal race, maternal age, maternal pre-pregnancy BMI, maternal smoking status, parity, infant sex and gestational age; Models assessing maternal hardship low education or low income were not adjusted for maternal education; OC models additionally adjusted for total lipids. ** p-value for association < 0.05; * p-value for association < 0.1 Among the OCs, we observed negative (e.g., trans -nonachlor) to no associations (e.g., PCB 118) with birth weight for females who did not report any of the hardships examined. For trans -nonachlor and Aroclor 1260, we observed larger than expected negative associations on mean birth weight among females with hardships (Figs. 1 A and 1 D). For example, each 2-fold increase in urinary trans -nonachlor concentration was associated with a lower mean birth weight of -128 g (95% CI: -238, -19) among those with low income and − 44 g (95% CI: -90, 2) among immigrants (Table 4 ). Meanwhile, infants born to higher income females or Canadian-born females had smaller birth weight reductions of 39 g (95% CI: -69, -10) and 33 g (95% CI: -67, 1), respectively. Similarly, we observed steeper negative slopes for those with increasing PCB 118 concentrations and belonging to a minority group, increasing PCB 118 concentrations and inadequate supplemental folic acid intake, increasing PCB 180 concentrations and immigrant status, and increasing oxychlordane concentrations and low income. Furthermore, students with increasing exposure to environmental chemicals also showed steeper curves and greater birth weight reduction, compared to non-students. However, the dose response curves crossed over indicating that students had initial gains in birth weight at low exposure compared to non-students but as exposure increased, greater reduction in birth weight was observed (Figs. 1 D-ii and E-iv). We observed a change in direction for the following associations: PCB 118 x low income, PCB 180 x lone parenthood, oxychlordane x low education, and DDE x low supplemental folic acid intake. Among those who reported hardships, we saw a gain, on average, in birth weight of at least 21 g (95% CI: -69, 111) for infants born to females with increasing oxychlordane concentrations and low education to a maximum of 76 g (95% CI: -70, 221) for infants born to females with increasing PCB 118 concentrations and low income (Table 4 ). Conversely, among those who did not report a hardship, we saw reductions in mean birth weight of between 15 g (95: CI: -36, 5) for infants born to females with increasing DDE concentrations but had adequate supplemental folic acid intake and 30 g (95: CI: -60, -0) for infants born to females with increasing PCB 118 concentrations but had higher income (Table 4 ). Among the metals, we also observed negative (e.g., Pb) to no associations (e.g., As) with birth weight for those who did not report hardships. For Pb exposure, we observed both a strengthening of association and change in direction when combined with hardships. Among infants born to females with increasing Pb concentrations and low educational status, we observed a larger reduction in mean birth weight ( \(\beta\) = -100 g; 95% CI: -215, 16) compared to those with higher educational status ( \(\beta\) = -34 g; 95% CI: -64, -3) (Fig. 2 A and Table 4 ); while for infants born to females with increasing Pb concentrations and belonging to a minority group, we observed a positive association ( \(\beta\) = 18 g; 95% CI: -54, 89) and a gain of 68 g, on average compared to infants born to white females. For As and Cd exposures, among those with hardships, we observed stronger associations (i.e., steeper slope) with birth weight compared to those who did not report hardships (Fig. 2 ). Similarly to that of Pb, the associations between As and birth weight were stronger (steeper slope) among infants born to females with low educational status ( \(\beta\) = -58 g; 95% CI: -130, 13), low income \((\beta\) = -87 g; 95% CI: -189, 15), and lone parenthood ( \(\beta\) = -100 g; 95% CI: -201, 1) compared to their counterparts (Fig. 2 B and Table 4 ). Furthermore, for Mn and Hg exposures, we observed positive associations among those who experienced hardships. For instance, infants born to females with low educational status, compared to those with higher educational status, had lower birth weight at low Mn or Hg concentration. As Mn or Hg level increased, the dose response curve trended upward leading to higher birth weights at high Mn or Hg concentrations (Figs. 2 C and 2 D). One exception to this pattern is lone parents with increasing Mn concentrations. A 242 g (95% CI: -454, -29) reduction in mean birth weight was observed among infants born to single females compared to females who were not single ( \(\beta\) = 20 g; 95% CI: -25, 64). 3.6. Supplemental analyses results Low first trimester plasma total folate concentrations (i.e., <5th percentile or < 51.5 nmol/L) was associated with a 124 g decrease in birth weight (95% CI: -223, -26) (Table S3) in the adjusted model (Table S4). From the Cramer’s V correlation coefficient plot (Figure S3), we observed low correlations (0 to 0.2) between low plasma total folate and other maternal hardship variables. When we included low plasma total folate (instead of low supplemental folic acid intake) in our elastic net model, the selections of variables by elastic net for both the OC and the metal models were similar except that in the metal models, Hg and immigrant status were additionally selected. In the adjusted models, we observed that every doubling of Hg concentration corresponded to a 6 g (95% CI: -21, 8) decrease in birth weight and infants born to immigrants faced a 21 g (95% CI: -78, 35) reduction in birth weight compared to those born to non-immigrants. Elastic net also identified three interactions associated with Hg as important: Hg x low income, Hg x low plasma total folate and Hg x chronic diseases. The interaction plot of Hg x chronic diseases showed a stronger association (steeper slope) indicating greater detriments in birth weight at higher Hg concentration for those who reported having one or more chronic diseases compared to those who did not report any (Figure S4). Meanwhile, the interaction plots of Hg x low income and Hg x low plasma total folate showed positive associations where the dose response curves for those who reported hardship had positive slopes (Figure S4). Additionally, we found that adjustment for GA as a covariate attenuated the relationships between exposure to chemical concentrations and birth weight (Table S4). Our cumulative hardship analysis found that the proportions of participants who reported no hardship, one hardship, and two or more hardships were 43%, 33%, and 24% respectively (Table S1 ). We observed a negative relationship where a higher number of hardships corresponded to lower mean birth weight (Table S1 ). In the adjusted model, the presence of one hardship or two or more hardships corresponded to lower birth weight of 58 g (95% CI: -106, -11) and 93 g (95% CI: -148, -39), respectively, compared to no hardship (Table S6). 4. Discussion Our findings suggest that maternal hardships that co-occur with gestational exposure to some OCs and metals, may interact and produce greater detrimental effects on fetal growth than either exposure alone. For instance, trans -nonachlor and oxychlordane were more strongly associated with decreased infant birth weight among females with lower income compared to those with higher income. This finding is not unexpected as it has long been recognized that females with lower income have disproportionately higher exposure for environmental stressors as well as higher risk for impaired fetal growth [ 47 ]. For instance, Borders et al. (2007) found, among their population of 1,363 pregnant American females with low income, that maternal social hardships and low birth weight were strongly related. They also reported that pregnant females with lower income are more likely to face food insecurity and consume inadequate supplemental folic acid compared to those with higher income [ 48 ]. This relationship among poverty, food insecurity, and folic acid supplementation is well supported [ 27 , 28 , 49 ]. In our study, 104 (5%) participants had low supplemental folic acid intake; only 10 (1%) experienced both low income and low supplemental folic acid intake. Among those infants born to females with increasing PCB 118 concentrations and low supplemental folic acid intake, we observed that every 2-fold increase in PCB 118 concentration was associated with an 87 g reduction in birth weight among females with low supplemental folic acid intake, compared to a 7 g reduction among females with the recommended supplemental folic acid intake level. Beside maternal income, maternal education also had a strong and positive association with birth weight [ 50 ]. In our study, Pb x low education was one of the selected interaction terms by elastic net. Pb exposure is unequally distributed across populations where higher exposure to Pb is typically found in communities of lower socioeconomic status and among individuals with less access to resources including financial, educational, social, and health [ 51 ]. Furthermore, both Pb exposure and low maternal education can independently contribute to lower birth weight [ 6 , 50 , 52 – 54 ]. In combination, we found that Pb was associated with a greater reduction in birth weight among females with low educational status ( \(\beta\) = -100 g; 95% CI: -215, 16) compared to those who had higher education ( \(\beta\) = -34 g; 95% CI: -64, -3) (Table 4 ). Another hardship that has been known to affect birth weight is immigration status. When we considered the females’ immigration status along with their exposure to OCs (i.e., trans -nonachlor and PCB 180), immigrants showed a lower mean birth weight compared with Canadian-born participants (Fig. 2 A, 2 C and Table 4 ). This birth weight reduction is supported by an increasing number of studies that found that immigration status affects fetal growth [ 55 ] and that the birth weights of babies born to immigrants are generally lower than those of babies born to Canadian-born females [ 56 , 57 ]. For example, South Asian born females tend to give birth to smaller babies than non-migrant females [ 58 ]. However, in a systematic review where Gagnon et al. (2009) explored whether immigrants have poorer infant outcomes compared to Canadian-born females, the authors found that being an immigrant was not a consistent marker for poorer infant outcomes [ 56 ]. Nevertheless, among our MIREC population, immigrants had lower mean birth weights and when considering the effects of environmental chemical exposures, they experienced a slight increase in vulnerability compared with Canadian-born females. Interestingly, we also observed a greater reduction in birth weight among infants born to students with increasing concentrations of OCs (i.e., Aroclor 1260 and PCB 180), compared to those of non-students. There is a dearth of studies on environmental chemical exposures in the context of pregnant students. Available studies typically examined behaviours in reducing exposure [ 59 ], knowledge and awareness of exposures to environmental chemicals [ 60 ], or exposure assessment on campus or in laboratories [ 61 ]. Future work, therefore, should include pregnant students as the intersection of their experiences and exposure status may generate differential impacts on their pregnancy outcomes. The stronger relationships we observed for females with both higher environmental chemical concentrations and a maternal hardship is consistent with Knudson’s “two-hit” hypothesis [ 62 ] where two distinct gestational stressors (e.g., gestational exposures to environmental chemical and maternal hardship) showed different associations with birth weight when combined compared to either “hit” alone. To further explore the impact of cumulative effects, we summed the number of hardships each participant faced and found that as the number of hardships increased, the mean birth weight decreased (Table S1 ). Specifically, the presence of one hardship was associated with a 59 g (95% CI: -105, -12) lower birth weight while the presence of two or more hardships was associated with an 87 g (95% CI: -139, -34) lower birth weight, compared to no hardship (Table S2). While the findings of the present study support the hypothesis that exposure to two stressors can have different effects compared to exposure to a single stressor, these findings may depend on the nature of the stressor(s). For instance, infants born to MIREC participants with increasing Mn or Hg concentrations and reported maternal hardship had higher birth weights compared to those born to participants who did not report hardship. Studies had shown that Mn exhibits a nonlinear inverse U-shaped relationship with birth weight where lower birth weight was observed at both low and high concentrations of blood Mn level [ 63 , 64 ]. Despite that, most studies, including the present one, assumed linear functions. In our previous study, using a flexible Bayesian Kernel Machine Regression method that can accommodate non-linearity, we found that Mn showed a positive and linear relationship with infant birth weight among MIREC participants [ 6 ]. It is possible that we have captured the ascending segment of the inverted U-shaped Mn-birth weight relationship, which showed an association with higher birth weight in our adjusted regression analysis. As a result, when examined by maternal hardship status, we found evidence that infants born to MIREC participants with both higher Mn concentration and low income had a higher birth weight, while those with no reported hardship showed no association with birth weight. However, the pattern doesn't hold true for lone parenthood as infants born to single females experienced approximately 200 g greater birth weight reduction compared to those born to married parents. The limited research on lone parenthood (or single mother families) and environmental chemical exposures has focused on exposure to air pollutants. Two US-based environmental inequality studies reported that single-mother families are more likely to live in neighborhoods with higher pollution compared to other family types (e.g., married or single father families) [ 65 , 66 ]. Another US air pollution study found that living in a poor neighbourhood may increase the risk of exposure to OCs and metals in the air [ 67 ]. We are not aware of any study that has examined the combined effects of lone parenthood and Mn exposure on pregnancy outcomes. Research that assesses the relationship between wider ranges in Mn concentrations and fetal growth is highly recommended to consider the shape of the Mn-birth weight function as well as the impact of lone parenthood. Like Mn, total Hg also showed a positive relationship with birth weight among those with maternal hardships. Although Hg is a toxic chemical that can freely cross the placenta and potentially disrupt a range of important pregnancy processes [ 68 ], in a recent systematic review, Hg was reported to have a minimal to null association with birth weight [ 69 ]. However, one particular study reported that the lowest tertile hair Hg concentration was associated with a higher risk of having infants with low birth weight (adjusted odds ratio (aOR) = 7.2; 95% CI: 1.5, 35.6) compared to those with higher Hg concentration (aOR = 0.52; 95% CI: 0.17, 1.55) [ 70 ]. As Hg exposure generally results from maternal fish consumption, the authors hypothesized that the consumption of contaminated fish with high levels of Hg may have coincided with an increased intake of selenium (Se), an essential element found in fish. Se, in this case, may have moderated the toxic effects of Hg [ 71 , 72 ] and thus resulted in a decreased OR for low-birth-weight infants among those with higher Hg exposure. Other studies have also reported that Hg levels have a positive association with socioeconomic status among females of childbearing age [ 73 , 74 ]. Specifically, pregnant participants with low income and low education consumed more fish per week compared to pregnant participants with higher income and higher educational status, but they were found to have lower blood Hg levels [ 74 ]. This is because the type of fish consumed by different demographic groups can also affect Hg exposure levels [ 74 ]. Therefore, future studies should include Se, the types of fish consumed, and if possible, the whole diet when assessing the potential toxic effects of Hg. Among the MIREC study participants, we observed that maternal hardships can modify the strength of the relationships (i.e., steeper negative slope and lower birth weight) as well as the direction of the relationships (i.e., positive slope and higher birth weight) between biomarkers of exposures to OCs and metals during pregnancy and infant birth weight. To our knowledge, this study is the largest single cohort study conducted on the modifying effect of maternal hardship on the relationship between gestational environmental chemical concentrations and birth weight. With the benefits of a large sample size and the use of elastic net regularization technique, we had higher statistical power to detect associations and identify key variables. However, our findings should be interpreted with caution due to the following limitations. First, we used multivariable linear regression to assess the independent effects of each maternal hardship and environmental chemical on birth weight and did not account for non-linear relationships. Second, we performed post-selection inference by first using elastic net for variable selection, then using OLS to derive statistical inference from the best-fitting model. A limitation of variable selection methods such as elastic net is their instability where any change in observations may change the model selected [ 75 ]. As OLS assumes that we have obtained a perfect model, which may not be the case, the resulting 95% CIs may be inaccurate and too narrow. Accounting for the effects of variable selection is a challenging task and work is underway to develop tools for selective inference so that we can properly assess the strength of the relationships [ 75 ]. Third, we used Lubin’s single imputation approach for measurements below the LOD and the standard errors may be biased. However, as the majority of our environmental chemicals were detected in over 90% of the participants, the impact of bias, if any exists, should be minimal [ 19 ]. Fourth, maternal hardships were based on a positive-negative dichotomy (i.e., yes-no hardship), which produced easy to interpret findings but essentially weighted all hardships equally. This assumption may have overlooked important and differential aspects of the potential effect of each hardship on the outcome. Furthermore, low maternal hardship proportions of 2 to 25% and missing maternal hardship values may bias the effect estimates and reduce our study power to detect a statistically significant association. Regardless, our use of dichotomized hardships served as an important starting point for the conceptualization of maternal hardships and highlighted the importance for future research to include parental hardships when examining the effects of environmental chemical exposure. Additionally, our use of objective maternal hardships may not fully represent a woman’s experience and well-being [ 76 ]. Obtaining qualitative data and additional quantitative data such as food security, stressful life events, and resilience will enhance our understanding of maternal hardships and enrich our current findings. Fifth, we examined only OCs and metals and did not consider co-exposures among the environmental chemicals or hardships. Therefore, we may have missed other environmental chemicals-birth weight associations modified by maternal hardships or any potential 2-way interactions between environmental chemicals or between hardships. Lastly, MIREC is not a representative sample of all Canadian females because it is not population-based [ 11 ]. As a result, the generalizability of our results may be limited. Conclusions While it is widely agreed that fetal growth is determined by multiple factors encompassing genetic, environmental, social and maternal factors, little is known about how these factors work together to maintain the persistent disparities in abnormal fetal growth. We present supporting evidence that maternal hardships may modify the relationship between gestational environmental chemical exposures and infant birth weight and that two “hits” of stressors have different effects compared to a single “hit”. Future research should examine the mixture or cumulative effects of environmental chemicals and non-chemical stressors such as maternal hardships to further advance our knowledge of the mechanisms of impaired fetal growth. Greater knowledge can address the adverse fetal growth outcomes where vulnerable populations are at increased risk for impaired fetal growth. These hardships or social factors that co-occur with gestational environmental chemical exposure can become a source of inequality and have downstream effects on infant health [ 77 ]. Abbreviations aOR: adjusted odds ratio As: arsenic Cd: cadmium CI: confidence interval DDE: dichlorodiphenyldichloroethylene GA: gestational age g: grams Hg: mercury INSPQ: Institut national de santé publique du Québec LOD: limit of detection MIREC: Maternal-Infant Research on Environmental Chemicals Mn: manganese NTD: neural tube defects OC: organochlorines OLS: ordinary least squares OR: odds ratio Pb: lead PCB: polychlorinated biphenyl PROTECT: Puerto Rico Testsite for Exploring Contamination Threats SD: standard deviation Se: selenium Declarations Ethics approval and consent to participate: This research was approved by the ethics review boards from Health Canada, all study sites, and Simon Fraser University. All study participants provided written informed consent after study protocols had been explained. Consent for publication: Not applicable Availability of data and materials: Due to data privacy issues, we are not able to make these data publicly available. Individuals may apply to access the data through the MIREC Biobank (www.mirec-canada.ca/en/research). Competing interests: Dr. McCandless reports financial support was provided by Canadian Institutes of Health Research. Brown University was compensated for Dr. Braun’s services as an expert witness for plaintiffs in litigation related to PFAS-contaminated drinking water; these funds were not paid to Dr. Braun directly. Dr. Braun also reports a relationship with Quest Diagnostics that includes consulting or advisory. Dr. Lanphear serves as an expert witness in cases of lead poisoning and other toxic chemicals, but he receives no financial payment. Funding: This work was supported by a Catalyst Grant from the Canadian Institutes of Health Research (L-CIP-150736). The original MIREC study was supported by the Chemicals Management Plan of the Government of Canada, the Ontario Ministry of the Environment., and a research grant from the Canadian Institutes for Health Research (MOP–81285). Authors' contributions: JH: Conceptualization, Methodology, Formal analysis, Writing - original draft. TA: Writing – review & editing. PJ: Writing – review & editing. BL: Writing – review & editing. JA: Writing – review & editing. JB: Writing – review & editing. AM: Writing – review & editing. AC: Writing – review & editing. LM: Supervision, Funding acquisition, Writing – review & editing. All authors read and approved the final manuscript. Acknowledgements: We would like to thank the participants of the MIREC study, study coordinators, nurses, and research assistants who made this work possible. References Kamai EM, McElrath TF, Ferguson KK. 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Muller C, Sampson RJ, Winter AS. Environmental Inequality: The Social Causes and Consequences of Lead Exposure. Annu Rev Sociol [Internet]. 2018 [cited 2023 Feb 13]; Available from: https://www.annualreviews.org/doi/abs/10.1146/annurev-soc-073117-041222 Genowska A, Motkowski R, Strukcinskaite V, Abramowicz P, Konstantynowicz J. Inequalities in Birth Weight in Relation to Maternal Factors: A Population-Based Study of 3,813,757 Live Births. Int J Environ Res Public Health [Internet]. 2022;19. Available from: http://dx.doi.org/10.3390/ijerph19031384 Wang D, Fu X, Zhang J, Xu C, Hu Q, Lin W. Association between blood lead level during pregnancy and birth weight: A meta-analysis. Am J Ind Med. 2020;63:1085–94. Woods MM, Lanphear BP, Braun JM, McCandless LC. Gestational exposure to endocrine disrupting chemicals in relation to infant birth weight: a Bayesian analysis of the HOME Study. Environ Health. 2017;16:115. Racape J, Schoenborn C, Sow M, Alexander S, De Spiegelaere M. Are all immigrant mothers really at risk of low birth weight and perinatal mortality? The crucial role of socio-economic status. BMC Pregnancy Childbirth. 2016;16:75. Gagnon AJ, Zimbeck M, Zeitlin J, ROAM Collaboration, Alexander S, Blondel B, et al. Migration to western industrialised countries and perinatal health: a systematic review. Soc Sci Med. 2009;69:934–46. Vangen S, Stoltenberg C, Skjaerven R, Magnus P, Harris JR, Stray-Pedersen B. The heavier the better? Birthweight and perinatal mortality in different ethnic groups. Int J Epidemiol. 2002;31:654–60. Seaton SE, Yadav KD, Field DJ, Khunti K, Manktelow BN. Birthweight centile charts for South Asian infants born in the UK. Neonatology. 2011;100:398–403. Yoon C-M, Kim H-J. Influencing Factors of Behavior for Reducing Exposure to Endocrine Disrupting Chemicals and Demand for Related Education. Eur J Investig Health Psychol Educ. 2022;12:295–305. Chan LM, Chalupka SM, Barrett R. Female college student awareness of exposures to environmental toxins in personal care products and their effect on preconception health. Workplace Health Saf. 2015;63:64–70. Guo J, Zhou Y, Wang Y, Chen Y, Zhang B, Zhang J. Methylsiloxanes risk assessment combining external and internal exposure for college students. Sci Total Environ. 2022;845:157379. Knudson AG Jr. Mutation and cancer: statistical study of retinoblastoma. Proc Natl Acad Sci U S A. 1971;68:820–3. Eum J-H, Cheong H-K, Ha E-H, Ha M, Kim Y, Hong Y-C, et al. Maternal blood manganese level and birth weight: a MOCEH birth cohort study. Environ Health. 2014;13:31. Zota AR, Ettinger AS, Bouchard M, Amarasiriwardena CJ, Schwartz J, Hu H, et al. Maternal blood manganese levels and infant birth weight. Epidemiology. 2009;20:367–73. Downey L. SINGLE MOTHER FAMILIES AND INDUSTRIAL POLLUTION IN METROPOLITAN AMERICA. Sociol Spectr. 2005;25:651–75. Downey L, Hawkins B. Single-Mother Families and Air Pollution: A National Study. Soc Sci Q. 2008;89:523–36. Wodtke GT, Ard K, Bullock C, White K, Priem B. Concentrated poverty, ambient air pollution, and child cognitive development. Sci Adv. 2022;8:eadd0285. Gundacker C, Hengstschläger M. The role of the placenta in fetal exposure to heavy metals. Wien Med Wochenschr. 2012;162:201–6. Dack K, Fell M, Taylor CM, Havdahl A, Lewis SJ. Mercury and Prenatal Growth: A Systematic Review. Int J Environ Res Public Health [Internet]. 2021;18. Available from: http://dx.doi.org/10.3390/ijerph18137140 Baldewsingh GK, Wickliffe JK, van Eer ED, Shankar A, Hindori-Mohangoo AD, Harville EW, et al. Prenatal Mercury Exposure in Pregnant Women from Suriname’s Interior and Its Effects on Birth Outcomes. Int J Environ Res Public Health [Internet]. 2020;17. Available from: http://dx.doi.org/10.3390/ijerph17114032 Mahaffey KR, Sunderland EM, Chan HM, Choi AL, Grandjean P, Mariën K, et al. Balancing the benefits of n-3 polyunsaturated fatty acids and the risks of methylmercury exposure from fish consumption. Nutr Rev. 2011;69:493–508. Peterson SA, Ralston NVC, Peck DV, Van Sickle J, Robertson JD, Spate VL, et al. How might selenium moderate the toxic effects of mercury in stream fish of the western U.S.? Environ Sci Technol. 2009;43:3919–25. Knobeloch L, Anderson HA, Imm P, Peters D, Smith A. Fish consumption, advisory awareness, and hair mercury levels among women of childbearing age. Environ Res. 2005;97:220–7. Miranda ML, Edwards S, Maxson PJ. Mercury levels in an urban pregnant population in Durham County, North Carolina. Int J Environ Res Public Health. 2011;8:698–712. Taylor J, Tibshirani RJ. Statistical learning and selective inference. Proc Natl Acad Sci U S A. 2015;112:7629–34. Western M, Tomaszewski W. Subjective Wellbeing, Objective Wellbeing and Inequality in Australia. PLoS One. 2016;11:e0163345. Goin DE, Izano MA, Eick SM, Padula AM, DeMicco E, Woodruff TJ, et al. Maternal Experience of Multiple Hardships and Fetal Growth: Extending Environmental Mixtures Methodology to Social Exposures. Epidemiology. 2021;32:18–26. Haffner MJ, Oakes P, Demerdash A, Yammine KC, Watanabe K, Loukas M, et al. Formaldehyde exposure and its effects during pregnancy: Recommendations for laboratory attendance based on available data. Clinical Anatomy. 2015;28(8):972-979. Available from: https://doi.org/10.1002/ca.22623. Additional Declarations Competing interest reported. Brown University was compensated for Dr. Braun’s services as an expert witness for plaintiffs in litigation related to PFAS-contaminated drinking water; these funds were not paid to Dr. Braun directly. Dr. Braun also reports a relationship with Quest Diagnostics that includes consulting or advisory. Dr. Lanphear serves as an expert witness in cases of lead poisoning and other toxic chemicals, but he receives no financial payment. Supplementary Files SupplementalOCMetalMaternalHardshipsEnvHealth.docx Cite Share Download PDF Status: Published Journal Publication published 30 Jun, 2024 Read the published version in Environmental Health → Version 1 posted Editorial decision: Revision requested 12 May, 2024 Reviews received at journal 10 May, 2024 Reviews received at journal 03 May, 2024 Reviewers agreed at journal 17 Apr, 2024 Reviewers agreed at journal 16 Apr, 2024 Reviewers agreed at journal 15 Apr, 2024 Reviewers invited by journal 10 Apr, 2024 Submission checks completed at journal 09 Apr, 2024 Editor assigned by journal 09 Apr, 2024 First submitted to journal 09 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4244204","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":290255589,"identity":"ad707abb-9684-4267-b8aa-53aa88668d51","order_by":0,"name":"Janice M.Y. Hu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYBAC9gYGhgNAOrGBvYdILYxwLTxnSNDCANYikUOslvazBw/8qGBI3HDz7cEPDH/sGOTdDzB++IFPS09ewsGeMwz1G27nJUswtiUzGJ5JYJbE5y/GhhyDw4xtQFtu55gx/204wGA4g4GNgQeflv43QC3/QA47Y8bA8AeihfEPHi2CM0C2NAC13OABamE7wCAvwcDGjM8WaYk3Bgd7jkkkzjyTYwzyC48BT2KztAweLXz8OcYfftTYJPYdP2MICjE5+fbDBz++waMFCiTgLB6DA7C4IhrIk6phFIyCUTAKhj0AAIaHTJe2pRi7AAAAAElFTkSuQmCC","orcid":"","institution":"Environmental Health Science and Research Bureau, Health Canada","correspondingAuthor":true,"prefix":"","firstName":"Janice","middleName":"M.Y.","lastName":"Hu","suffix":""},{"id":290255590,"identity":"8bf653e7-e896-4aa4-a32f-fe77e69594c1","order_by":1,"name":"Tye E. Arbuckle","email":"","orcid":"","institution":"Environmental Health Science and Research Bureau, Health Canada","correspondingAuthor":false,"prefix":"","firstName":"Tye","middleName":"E.","lastName":"Arbuckle","suffix":""},{"id":290255592,"identity":"2b0234a6-5cdc-41c5-8741-955cfc99955c","order_by":2,"name":"Patricia A. Janssen","email":"","orcid":"","institution":"University of British Columbia","correspondingAuthor":false,"prefix":"","firstName":"Patricia","middleName":"A.","lastName":"Janssen","suffix":""},{"id":290255594,"identity":"b319f853-9894-4acd-97c6-fed5e1c758d8","order_by":3,"name":"Bruce P. Lanphear","email":"","orcid":"","institution":"Simon Fraser University","correspondingAuthor":false,"prefix":"","firstName":"Bruce","middleName":"P.","lastName":"Lanphear","suffix":""},{"id":290255595,"identity":"8c538f19-3ccf-48bc-955c-53865b1b5a15","order_by":4,"name":"Joshua D. Alampi","email":"","orcid":"","institution":"Simon Fraser University","correspondingAuthor":false,"prefix":"","firstName":"Joshua","middleName":"D.","lastName":"Alampi","suffix":""},{"id":290255596,"identity":"11c7427f-817c-4ed5-ab51-b74f484dd854","order_by":5,"name":"Joseph M. Braun","email":"","orcid":"","institution":"Brown University","correspondingAuthor":false,"prefix":"","firstName":"Joseph","middleName":"M.","lastName":"Braun","suffix":""},{"id":290255597,"identity":"77531f0a-bbd3-4a96-8adb-2471eef6bf3e","order_by":6,"name":"Amanda J. MacFarlane","email":"","orcid":"","institution":"Texas A\u0026M Agriculture, Food, and Nutrition Evidence Center","correspondingAuthor":false,"prefix":"","firstName":"Amanda","middleName":"J.","lastName":"MacFarlane","suffix":""},{"id":290255598,"identity":"e600a1b0-782b-4b7e-a5af-34b6c30eabae","order_by":7,"name":"Aimin Chen","email":"","orcid":"","institution":"University of Pennsylvania Perelman School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Aimin","middleName":"","lastName":"Chen","suffix":""},{"id":290255599,"identity":"40b70e65-6010-4300-8dd3-1553dbf61eb4","order_by":8,"name":"Lawrence C. McCandless","email":"","orcid":"","institution":"Simon Fraser University","correspondingAuthor":false,"prefix":"","firstName":"Lawrence","middleName":"C.","lastName":"McCandless","suffix":""}],"badges":[],"createdAt":"2024-04-09 23:29:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4244204/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4244204/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12940-024-01095-x","type":"published","date":"2024-07-01T00:20:18+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":54928035,"identity":"2f0a4d05-9cd9-410c-9176-5679c0441833","added_by":"auto","created_at":"2024-04-18 17:36:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1577343,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction plots as selected by elastic net showing the differences in mean birth weight (grams) and 95% confidence intervals (shaded bands) associated with exposures to organochlorine compounds during the first trimester across different levels of maternal hardships. \u0026nbsp;\u003cstrong\u003eA.\u003c/strong\u003eInteraction plots for \u003cem\u003etrans\u003c/em\u003e-nonachlor. \u0026nbsp;\u003cstrong\u003eB.\u003c/strong\u003e Interaction plots for PCB 118. \u003cstrong\u003eC.\u003c/strong\u003e Interaction plots for PCB 180. \u0026nbsp;\u003cstrong\u003eD.\u003c/strong\u003e Interaction plots for Aroclor 1260.\u003cstrong\u003e E.\u003c/strong\u003e Interaction plots for Oxychlordane. \u0026nbsp;\u003cstrong\u003eF.\u003c/strong\u003e Interaction plot for DDE.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4244204/v1/3d84da267174f4624a56572e.png"},{"id":54928034,"identity":"a4ce6792-7709-46f2-a842-499922c4b42b","added_by":"auto","created_at":"2024-04-18 17:36:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1003985,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction plots as selected by elastic net showing the differences in mean birth weight (grams) and 95% confidence intervals (shaded bands) associated with exposures to metals during the first trimester across different levels of maternal hardships. \u0026nbsp;\u003cstrong\u003eA.\u003c/strong\u003e Interaction plots for Pb. \u003cstrong\u003eB.\u003c/strong\u003e Interaction plots for As. \u003cstrong\u003eC.\u003c/strong\u003e Interaction plot for Hg. \u003cstrong\u003eD.\u003c/strong\u003eInteraction plots for Mn. \u003cstrong\u003eE.\u003c/strong\u003e Interaction plot for Cd.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4244204/v1/cf4230b612facf33e7b608d3.png"},{"id":59446797,"identity":"d1fd827e-a35e-477f-9dd6-6eebcbb21348","added_by":"auto","created_at":"2024-07-02 00:20:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4155666,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4244204/v1/e68b4b8f-c520-4cba-afd9-9781dfcae7ad.pdf"},{"id":54929008,"identity":"d14b027e-d8a6-49c5-a880-aba5e0da50ad","added_by":"auto","created_at":"2024-04-18 17:44:13","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":250207,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalOCMetalMaternalHardshipsEnvHealth.docx","url":"https://assets-eu.researchsquare.com/files/rs-4244204/v1/3d75af3a36886789b0f2f1df.docx"}],"financialInterests":"Competing interest reported. Brown University was compensated for Dr. Braun’s services as an expert witness for plaintiffs in litigation related to PFAS-contaminated drinking water; these funds were not paid to Dr. Braun directly. Dr. Braun also reports a relationship with Quest Diagnostics that includes consulting or advisory. Dr. Lanphear serves as an expert witness in cases of lead poisoning and other toxic chemicals, but he receives no financial payment.","formattedTitle":"Gestational exposure to organochlorine compounds and metals and infant birth weight: Effect modification by maternal hardships","fulltext":[{"header":"1. Background","content":"\u003cp\u003eFetal growth is a complex process impacted by various factors including psychosocial stressors, toxic chemicals, nutrition, placental function, and the actions of many intrauterine hormones and growth factors [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Fetal growth restriction, commonly assessed by infant birth weight for a given gestational age [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], occurs when the fetus does not reach its intrauterine potential for growth and development [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. It is an important determinant for the risk of infant mortality and the development of chronic diseases [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecent research has found that impaired fetal growth is associated with gestational exposure to toxic environmental chemicals, which are ubiquitous in our environment, food sources and personal care products [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In our previous work, we reported associations of two classes of environmental chemicals \u0026ndash; metals and organochlorine compounds (OCs) \u0026ndash; with reduced infant birth weight [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. We found that first trimester blood concentrations of \u003cem\u003etrans\u003c/em\u003e-nonachlor, an organochlorine compound (OC), and lead (Pb), a toxic metal, were inversely associated with birth weight. Every 2-fold increase in \u003cem\u003etrans\u003c/em\u003e-nonachlor and Pb concentrations was associated with reduced birth weight by an average of 38 grams (g) (95% confidence interval (CI): -67, -10) and 39 g (95% CI: -69, -9), respectively. The study, however, did not paint a complete picture of the adverse effects on fetal growth because the etiology of impaired fetal growth also includes maternal hardships such as social stress, race, and nutrition [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecent research by Goin et al. (2021) reported that hardships such as stressful life events, food insecurity, living alone and facing unplanned pregnancy can interact in pairwise fashion to adversely affect fetal growth [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These 2-way interactions observed are supported by Knudson\u0026rsquo;s \u0026ldquo;two-hit\u0026rdquo; hypothesis, where it was found that two gene mutations are needed for retinoblastoma, a childhood cancer of the retina, to develop [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The major implication of this \u0026ldquo;two-hit\u0026rdquo; hypothesis is that two distinct gestational stressors are needed to affect an outcome. Thus, gestational exposure to environmental chemicals and maternal hardships may interact and jointly influence fetal growth.\u003c/p\u003e \u003cp\u003eWhile there is robust evidence of an association between either chemical exposures or maternal hardships and fetal growth, there is a paucity of research on the joint associations of chemical exposures and maternal hardship on fetal growth. Aker et al. (2020) found evidence, among the 752 Puerto Rican females from the Puerto Rico Testsite for Exploring Contamination Threats (PROTECT) cohort, that life event score or stress interacted with bisphenol-S and triclocarban exposures to modify their associations with gestational length [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Although Aker et al. (2020) did not examine metals and OCs specifically, their observation supports further exploration of maternal hardships as an effect modifier in the associations between environmental chemicals and fetal growth.\u003c/p\u003e \u003cp\u003e We assessed the independent associations of nine maternal hardships including self-reported minority status, immigrant status, financial strain, low supplemental folic acid intake, low educational status, living status, lone parenthood, experiencing chronic diseases, and being a student, on birth weight and the potential modifying effects of these maternal hardships on the associations between OCs and metals and birth weight among a cohort of 1982 Canadian females and their infants who participated in the Maternal-Infant Research on Environmental Chemicals (MIREC) Study.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study participants\u003c/h2\u003e \u003cp\u003eWe analyzed data from the MIREC Study, a cohort study of 1982 pregnant females recruited between 2008 and 2011 from ten Canadian cities. The goal of the MIREC Study was to obtain Canadian biomonitoring data on pregnant females and examine associations between prenatal exposure to environmental chemicals with pregnancy and child health outcomes [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Detailed information on demographic and lifestyle factors were collected from questionnaires administered at recruitment in the first trimester. Eligibility criteria and exclusions are described in Arbuckle et al. (2013) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. For the present study, we included females who had complete socio-demographic information, provided biological samples during the first trimester of the pregnancy, and delivered singleton live births. Participants with missing information were excluded from the analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Fetal growth measurement\u003c/h2\u003e \u003cp\u003eTo examine fetal growth, we considered infant birth weight adjusted for gestational age (GA) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. We used cubic splines to allow infant birth weight curves to vary across gestation in a smooth manner [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. We could alternatively use GA-specific birth weight z-scores [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, the interpretation of the unit in standard deviation is not straightforward, which may be difficult for the public to understand and challenging to use in clinical settings [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Both infant birth weight, measured in grams (g), and GA, measured in weeks, were abstracted from the medical records and examined as continuous variables.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Biomarkers of prenatal environmental chemical exposure\u003c/h2\u003e \u003cp\u003eWe measured environmental chemical exposures using biomarkers in blood samples collected from MIREC participants during the first trimester of pregnancy. Biomarker analysis was conducted at the Toxicology Laboratory of the Institut national de sant\u0026eacute; publique du Qu\u0026eacute;bec (INSPQ) using previously described methodology [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn our previous analysis, we reported associations between two classes of environmental chemicals \u0026ndash; metals and OCs \u0026ndash; and reduced infant birth weight [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Hence, in this study, we limited our analysis to OCs and metals only. We retained biomarkers that were detected in at least 70% of the samples [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. These included six plasma OCs (polychlorinated biphenyl (PCB) 118, PCB 180, Aroclor 1260 (consists of PCB 138 and 153), dichlorodiphenyldichloroethylene (DDE), oxychlordane, and \u003cem\u003etrans\u003c/em\u003e-nonachlor) and five whole blood metals (arsenic (As), cadmium (Cd), mercury (Hg), manganese (Mn), and lead (Pb)).\u003c/p\u003e \u003cp\u003eWe imputed measurements below the limit of detection (LOD) using the single imputation \u0026ldquo;fill-in\u0026rdquo; approach where the log\u003csub\u003e2\u003c/sub\u003e chemical concentrations lower than the LOD were randomly sampled from a truncated lognormal distribution with mean and standard deviation (SD) estimated from the observed data [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. This \u0026ldquo;fill-in\u0026rdquo; approach for missing biomarkers yields unbiased regression coefficient estimates when 30% or less of the data are below the detection limits [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo account for individual-level variability in lipid levels, we standardized OC concentrations by total plasma lipid concentrations and expressed them in units of ng/g lipids [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Standardization was only applied when presenting the descriptive statistics on the environmental chemical concentrations. Otherwise, total lipids was included as a covariate in regression models [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDue to the highly correlated nature of PCB 153 and PCB 138, INSPQ summed and multiplied them by a factor of 5.2 to create Aroclor 1260 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. To reduce the potential influence of outliers due to the right skewed distributions of chemical concentrations, the concentrations were log\u003csub\u003e2\u003c/sub\u003e-transformed before inclusion in the models. The transformed unit indicates a two-fold increase in concentrations. Furthermore, before creating the interaction terms with maternal hardships to examine their joint associations, the log\u003csub\u003e2\u003c/sub\u003e-transformed chemical concentrations were median-centered to improve interpretation and decrease the multicollinearity effects, which would affect model convergence and inflate the standard errors [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Maternal social hardships\u003c/h2\u003e \u003cp\u003eWe selected maternal social hardship variables based on factors related to social determinants of health [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. We used a questionnaire administered at baseline by study personnel [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] to capture responses that signal indicators of hardships directly affecting the study participants. The eight maternal hardships included self-reported minority status [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], immigrant status [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], financial strain [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], low supplemental folic acid intake [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], low educational status [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], living status [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], lone parenthood [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], and experiencing chronic diseases [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Furthermore, we also examined whether being a student is a potential maternal hardship. Each hardship variable was dichotomized to indicate whether a study participant experienced the hardship or not.\u003c/p\u003e \u003cp\u003eRace has definitions ranging from biological to social and its operationalization as a variable in research has been debated [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Race or having minority status, as reported here, is considered a social construct that reflects lived experiences of systematic discrimination and differences among populations. This definition is based on Du Bois\u0026rsquo; seminal work (1899) in sociological research on black race and health which provided insights that the racial differences in health are primarily related to social factors [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Therefore, we classified participants who reported their racial or cultural status as non-white as having minority status.\u003c/p\u003e \u003cp\u003eParticipants who reported their birth country as countries other than Canada were considered immigrants and faced hardship associated with being immigrants. Participants were classified as having financial strain if their reported annual household income was below \u003cspan\u003e$\u003c/span\u003e20,000, which is approximately the 2008 Canada poverty line for a two-person household [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Folate is a key pregnancy nutrient and pregnant individuals with hardship of food insecurity are more likely to have inadequate folic acid supplementation [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. We classified participants with daily folic acid supplementation levels below 400 \u0026micro;g per day as low supplemental folic acid intake given that they did not meet the recommended intakes for females at low-risk for a neural tube defect-affected pregnancy [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Supplemental folic acid intake was assessed via a structured survey conducted at 16-weeks\u0026rsquo; gestation where participants were asked to list all the supplements they took in the past 30 days. The product information and intake frequency were used to calculate daily total folic acid consumption from supplements [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. However, when supplement information was missing from this 30-day recall form, we then estimated the supplement information using the 24-hour recall form (also completed at 16-weeks\u0026rsquo; gestation) or if necessary, the baseline questionnaire, completed between 6- and 13-weeks\u0026rsquo; gestation, where participants were asked to list all the supplements they had taken within the past 3 months.\u003c/p\u003e \u003cp\u003eParticipants who reported their highest educational level achieved as high school diploma or less were classified as having low educational status. For living status, participants who reported not living with a roommate, spouse, partner, or parents were classified as living alone. Participants with marital status as single (i.e., not married or with the same partner for 1 year or more) were classified as having a hardship of lone parenthood. Participants who responded affirmatively to \u0026ldquo;Do you have any chronic medical condition(s)?\u0026rdquo; were classified as having chronic disease(s). Here, chronic medical condition was defined as any condition that treatment can manage but not cure. Examples of such conditions include high blood pressure, diabetes, and other pre-existing conditions prior to pregnancy such as asthma, depression, arthritis, heart conditions, and psoriasis. Lastly, we also examined whether being a student is a potential maternal hardship. Participants who answered yes to the question \u0026ldquo;are you currently attending school?\u0026rdquo; were classified as being current students.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Covariates\u003c/h2\u003e \u003cp\u003eWe created a directed acyclic graph (Supplemental Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) to identify predictors of infant birth weight and factors associated with both environmental chemical exposure and infant birth weight. Additionally, we identified predictors of maternal hardships and infant birth weight. The following, derived from the baseline questionnaire, were examined: maternal age (under 25, \u0026ge;\u0026thinsp;25 to \u0026lt;\u0026thinsp;30, \u0026ge;30 to \u0026lt;\u0026thinsp;35, \u0026ge;35 to \u0026lt;\u0026thinsp;40, and \u0026ge;\u0026thinsp;40), race (white, non-white), education (high school diploma or less, college or trade school diploma, undergraduate university degree, and graduate university degree), cigarette smoking status (never, current, former, and quit during pregnancy), parity (0, 1, 2, \u0026ge;\u0026thinsp;3), infant sex (male and female) and pre-pregnancy body mass index (BMI) (underweight, normal, overweight, and obese). We adjusted for the non-linear effect of gestational age (GA) as a covariate on birth weight using the cubic spline approach [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. For OC models, we additionally adjusted for plasma lipid concentrations. For models including maternal hardship minority status, we did not adjust for maternal race. For models including maternal hardship low educational status or low income, we did not adjust for maternal education.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Analytical approach\u003c/h2\u003e \u003cp\u003e We first tabulated participant characteristics, examined the relations between maternal hardship variables using Cramer's V correlation coefficients, and calculated geometric means and percentiles of the environmental chemicals. We then proceeded to examine the moderating influence of maternal hardships on the relationship between OC and metal exposures and birth weight by using elastic net regression to choose which variables to include in our models.\u003c/p\u003e \u003cp\u003eElastic net is a machine learning method that is built on conventional regression with an added penalty term [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The penalty term biased the estimates to reduce overfitting and improve prediction on data not used in the model fitting procedure. There are two penalty parameters (i.e., ɑ and λ) that can be tuned to produce the best performing model. The quantity ɑ adjusts the balance between lasso and ridge penalty and has a range of 0 to 1. When ɑ is 1, the model is a lasso model and when ɑ is 0, the model is a ridge regression model. Furthermore, when ɑ is closer to 1, the elastic net model will focus more on selection and when the ɑ is closer to 0, the model can better handle multicollinearity. The second tuning parameter λ controls the magnitude of the penalty where smaller λ leads to estimates that are closer to those of conventional regression method. To determine the optimal degrees of penalization, we used cross-validation and tested the models over a grid of ɑ and λ sequences. As elastic net can account for multicollinearity, we included all the interested variables in our model. Specifically, we created separate models for the OC compounds and the metals. In each model, the dependent variable was birth weight adjusted for gestational age, and the predictor variables included all environmental chemicals in the chemical class, all the maternal hardships, and the interaction terms between the environmental chemicals and the hardships (e.g., Pb x minority status). We used elastic net for variable selection purposes in order to reduce multicollinearity and to ensure that only important variables and interaction terms are included in the model, thus potentially reducing the probability of type I errors. The covariates were adjusted in the elastic net regression model as unpenalized variables.\u003c/p\u003e \u003cp\u003eFor parameter estimation and calculating 95% CIs, it has been recommended that penalized regression, such as elastic net regression, be used to select the model first and then the selected model be used in an unpenalized regression (i.e., ordinary least squares (OLS) regression models) to quantify the associations of gestational exposures to individual environmental chemicals with infant birth weight, while adjusting for covariates [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. This utilizes the predictive power of the overall modeling procedure while maintaining the interpretability of the individual estimates. We therefore fitted the subset of variables and the interaction terms selected and inferred to be important by elastic net in OLS regression models. The selected environmental chemicals and maternal hardships were entered into the OLS regression models individually and adjusted for covariates. The selected interaction terms were also entered into unpenalized regression models that included both the main effects and the interaction terms and adjusted for covariates. We screened for suggestive associations with p-values of 0.1 and 0.05 and visualized the relationships graphically using interaction plots with 95% confidence interval bands.\u003c/p\u003e \u003cp\u003eFurthermore, using Cook\u0026rsquo;s distance [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], we identified and removed one influential outlier that negatively affected the models. All analyses were conducted using Microsoft R Open version 3.5.1 and elastic net models were fitted using the \u003cem\u003eglmnet\u003c/em\u003e package [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.6.1. Supplemental Analyses\u003c/h2\u003e \u003cp\u003eWe conducted additional analyses using first trimester plasma total folate concentrations instead of supplemental folic acid intake. Plasma total folate, a biomarker of folate status, specifically of recent folate intake, was measured by LC-MS/MS, as previously described [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Currently, no cutoff values for either low or high concentrations or a reference range for plasma total folate have been identified for fetal growth [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] but 25.5 nmol/L was estimated as a potential cut-off for neural tube defects (NTD) risk reduction [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Among the MIREC participants, less than 0.5% (n\u0026thinsp;=\u0026thinsp;8) had a plasma total folate concentration\u0026thinsp;\u0026lt;\u0026thinsp;25.5 nmol/L, while approximately 5% (n\u0026thinsp;=\u0026thinsp;104) reported a low total daily supplemental folic acid intake of \u0026lt;\u0026thinsp;400\u0026micro;g/day. Therefore, to allow for easier comparison of the low plasma total folate and the low supplemental folic acid intake effects and to mitigate issues with low sample size, we selected the 5th percentile of plasma total folate as the cutoff value to indicate low folate status, which corresponded to 51.5 nmol/L.\u003c/p\u003e \u003cp\u003eGA is a potential mediator in the relationship between environmental chemical exposures and birth weight. To rule out any bias or over-adjustment in our models, we conducted additional analyses that excluded GA as a covariate in the models. Lastly, we summed the number of hardships to examine the association between cumulative hardship and birth weight.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003e3.1. Descriptive statistics\u003c/h2\u003e\n \u003cp\u003eWe included a total of 1982 MIREC participants in the analysis. Fewer than 10% of observations for sociodemographic variables were missing except for income status and pre-pregnancy BMI with missingness of 11% (n\u0026thinsp;=\u0026thinsp;212) and 13% (n\u0026thinsp;=\u0026thinsp;264), respectively. 135 (7%) participants had missing birth weight data and 8% had missing chemical concentrations (n\u003csub\u003eOC\u003c/sub\u003e = 167 and n\u003csub\u003emetals\u003c/sub\u003e = 159). Due to missing information on a combination of sociodemographic characteristics, chemical concentrations and birth weight, the final sample size was 1565 for the OC model and 1567 for the metal model.\u003c/p\u003e\n \u003cp\u003eMost of the MIREC participants were white (82%), over 30 years of age (70%) with at least an undergraduate degree or higher (63%), never smoked (61%) and had normal BMI (61%). Forty-four percent were nulliparous and 53% had male infants. Maternal characteristics associated with lower birth weight included being non-white, older age, lower educational status, smoking, underweight BMI, and nulliparity. Female infants weighed on average 100 g lower at birth compared to males (Table \u003cspan\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eParticipant sociodemographic characteristics and mean birth weight (grams) among MIREC study participants in Canada, 2008\u0026ndash;2011.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBirth weight (g)\u003c/p\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1982 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3452 (532)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e339 (17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3334 (529)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1517 (77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3479 (529)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e126 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e117 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3404 (617)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u0026ndash;29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e443 (22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3483 (505)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u0026ndash;34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e655 (33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3452 (495)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u0026ndash;39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e502 (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3444 (570)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e127 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3428 (561)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e138 (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation levels\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh school diploma or less\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e160 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3381 (571)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSome college, trade school,\u003c/p\u003e\n \u003cp\u003eor college diploma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e538 (27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3444 (546)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUndergraduate degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e681 (34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3486 (512)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGraduate degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e475 (24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3435 (528)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e128 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHousehold income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le; \u003cspan\u003e$\u003c/span\u003e20,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3348 (515)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e20,001 - \u003cspan\u003e$\u003c/span\u003e40,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e152 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3445 (572)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e40,001 - \u003cspan\u003e$\u003c/span\u003e60,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e187 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3458 (543)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e60,001 - \u003cspan\u003e$\u003c/span\u003e80,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e286 (14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3457 (530)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e80,001 - \u003cspan\u003e$\u003c/span\u003e100,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e358 (18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3440 (531)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u003cspan\u003e$\u003c/span\u003e100,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e715 (36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3475 (516)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e212 (11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSmoking status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1137 (57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3442 (520)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e105 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3366 (657)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFormer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e498 (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3496 (517)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQuit during\u003c/p\u003e\n \u003cp\u003ePregnancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e115 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3432 (569)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e127 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e812 (41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3407 (546)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e751 (38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3496 (508)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e221 (11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3469 (559)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3454 (502)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e126 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePre-pregnancy BMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnderweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3302 (503)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1040 (53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3429 (496)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOverweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e371 (19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3515 (521)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e258 (13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3466 (647)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e264 (13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInfant Sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e974 (49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3500 (534)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e877 (44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3399 (524)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e131 (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTypes of Hardship\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMinority status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e339 (17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3333 (529)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1517 (77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3479 (529)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e126 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3348 (515)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1698 (86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3460 (529)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e212 (11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e160 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3380 (571)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1649 (83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3458 (528)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e128 (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLone parenthood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3398 (626)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1769 (89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3455 (527)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e126 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLiving alone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3384 (645)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1820 (92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3454 (529)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e126 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent student\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e201 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3461 (484)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1763 (89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3451 (537)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eImmigrant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e370 (19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3379 (549)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1612 (81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3469 (526)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eLow supplemental folic acid intake (\u0026lt;\u0026thinsp;400 \u003cspan\u003e\u003cspan\u003e\\(u\\)\u003c/span\u003e\u003c/span\u003eg/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3361 (523)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1878 (95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3457 (532)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHaving chronic illnesses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e491 (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3404 (579)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1476 (75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3467 (514)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e* missing was excluded from the analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eFrom the Cramer\u0026rsquo;s V correlation coefficient plot (Figure S2), we observed low to moderate correlations (0 to 0.5) between the maternal hardship variables. The most commonly experienced maternal hardships were having a chronic disease (25%), being an immigrant (19%) and having minority status (18%). The least common maternal hardship was living alone (2%) (Table \u003cspan\u003e1\u003c/span\u003e). More than half of the participants (57%) reported having at least one maternal hardship(s) (Table \u003cspan\u003eS1\u003c/span\u003e). The most common hardships occurring together were immigrants with minority status at 10%, followed by those with minority status and low educational status, and immigrants who were students, both at 3% (Table S2). All hardships, except for being a student, were associated with lower birth weight (Table \u003cspan\u003e1\u003c/span\u003e). Furthermore, we detected most chemicals in over 90% of study participants, with the exception of PCB 118 and \u003cem\u003etrans\u003c/em\u003e-nonachlor with 74% and 84% detection rates, respectively (Table \u003cspan\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eDistributions of environmental chemicals measured during the 1st trimester for MIREC study participants from 2008 to 2011 across 10 major cities in Canada.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMetabolites\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u0026gt;LOD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGM\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e25th\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e50th\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e75th\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003e\u003cstrong\u003eOrganochlorine compounds (ng/g lipids)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePCB 118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;LOD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePCB 180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;LOD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e183.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAroclor 1260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;LOD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1183.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDDE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;LOD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e530.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOxychlordane\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;LOD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003etrans\u003c/em\u003e-\u003c/p\u003e\n \u003cp\u003eNonachlor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;LOD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003e\u003cstrong\u003eMetals (ug/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArsenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;LOD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCadmium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;LOD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMercury\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;LOD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eManganese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLead\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003e\u003csup\u003e1\u003c/sup\u003eGM = Geometric Mean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003e3.2. Variables and interaction terms that were selected in the elastic net models of infant birth weight\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIn the OC model, \u003cem\u003etrans\u003c/em\u003e-nonachlor and three maternal hardship variables (i.e., low educational status, minority status, and low supplemental folic acid intake) were selected and inferred as important contributors in the elastic net regression of infant birth weight model. Elastic net also identified the following sixteen interaction terms as important: \u003cem\u003etrans\u003c/em\u003e-nonachlor x low income, \u003cem\u003etrans\u003c/em\u003e-nonachlor x lone parenthood, \u003cem\u003etrans\u003c/em\u003e-nonachlor x immigrant status, PCB 118 x minority status, PCB 118 x low supplemental folic acid intake, PCB 118 x low income, PCB 118 x lone parenthood, PCB 180 x immigrant status, PCB 180 x lone parenthood, Aroclor 1260 x minority status, Aroclor 1260 x student status, oxychlordane x minority status, oxychlordane x low education, oxychlordane x low income, oxychlordane x student status, and DDE x low supplemental folic acid intake.\u003c/p\u003e\n \u003cp\u003eIn the metal model, Pb and the same three maternal hardship variables (i.e., low educational status, minority status, and low supplemental folic acid intake) were selected. The eleven interaction terms identified as important are: Pb x low education, Pb x minority status, As x low education, As x low income, As x lone parenthood, Hg x low education, Mn x low income, Mn x living alone, Mn x lone parenthood, Mn x student, Cd x minority status.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003e3.3. Independent associations between environmental chemical exposure and infant birth weight\u003c/h2\u003e\n \u003cp\u003eOur elastic net variable selection results agreed with those of Bayesian Kernel Machine Regression method from our previous study [\u003cspan\u003e6\u003c/span\u003e]. Both mixture methods indicated the importance of \u003cem\u003etrans\u003c/em\u003e-nonachlor and Pb exposures, which were negatively associated with infant birth weight (Table \u003cspan\u003e3\u003c/span\u003e). Every doubling of \u003cem\u003etrans\u003c/em\u003e-nonachlor and Pb concentrations corresponded to a 37 g (95% CI: -65, -8) and a 38 g (95% CI: -68, -8) reduction in birth weight, respectively.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eDifferences in mean birth weight (grams) and 95% CI associated with exposure to log2-chemicals and maternal hardships, as selected by elastic net, among the MIREC study participants in Canada, 2008\u0026ndash;2011.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnadjusted\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAdjusted for\u003c/p\u003e\n \u003cp\u003ecovariates*\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eOrganochlorine Compounds (OCs)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003etrans\u003c/em\u003e-Nonachlor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-66 (-96, -35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-37 (-65, -8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eMetals\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLead\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-56 (-92, -21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-38 (-68, -8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaternal Hardships\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMinority status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-145 (-208, -83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-78 (-130, -26)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-78 (-164, 9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-103 (-185, -21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow supplemental folic acid intake\u003c/p\u003e\n \u003cp\u003e(\u0026lt;\u0026thinsp;400 \u003cspan\u003e\u003cspan\u003e\\(u\\)\u003c/span\u003e\u003c/span\u003eg/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-97 (-205, 12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-61 (-152, 31)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e*All models were adjusted for gestational age (GA), race, education, age, BMI, smoking, parity, and baby sex. \u0026nbsp;The OC models also adjusted for total lipids. Model assessing hardship of low education and minority status did not adjust for education and race/ethnicity, respectively, as a covariate.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003e3.4. Independent associations between maternal hardships and infant birth weight\u003c/h2\u003e\n \u003cp\u003eWe found that all three hardships selected by elastic net (i.e., low educational status, minority status and low supplemental folic acid intake) had negative associations with birth weight (Table \u003cspan\u003e3\u003c/span\u003e). Infants born to females with a high school diploma or less had lower birth weight (\u003cspan\u003e\u003cspan\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e = -103 g; 95% CI: -185, -21) compared to those born to more educated females. Infants born to females with minority status had lower birth weight (\u003cspan\u003e\u003cspan\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e = -78 g; 95% CI: -130, -26) compared to those born to white females. Lastly, infants born to females with low supplemental folic acid intake had lower birth weight (\u003cspan\u003e\u003cspan\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e = -61 g; 95% CI: -152, 31) compared to those born to females who met or exceeded recommended folic acid intakes.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e3.5. Regression analysis of the modifying effect of maternal social hardships on the relationship between environmental chemical exposure and infant birth weight\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eWe found that maternal hardships can modify the strength of the relationships (i.e., steeper negative slope and lower birth weight) or the direction of the relationships (i.e., positive slope and higher birth weight) between biomarker of exposure to OCs and metals and birth weight. Although the 95% CIs were imprecise, some associations showed statistical significance at the 0.05 level (Figs. \u003cspan\u003e1\u003c/span\u003e to \u003cspan\u003e2\u003c/span\u003e and Table \u003cspan\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eThe associations between exposure to environmental chemicals (2-fold increase) and birth weight (grams) by maternal hardships, mutually adjusted for one another and covariates, among the MIREC study participants in Canada, 2008\u0026ndash;2011.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"11\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLow education\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLow income\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eImmigrant\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLow supplemental folic acid intake (\u0026lt;\u0026thinsp;400 \u003cspan\u003e\u003cspan\u003e\\(u\\)\u003c/span\u003e\u003c/span\u003eg/day)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLone parenthood\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;160\u003c/p\u003e\n \u003cp\u003e(9%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;1649\u003c/p\u003e\n \u003cp\u003e(91%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;72\u003c/p\u003e\n \u003cp\u003e(4%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;1698\u003c/p\u003e\n \u003cp\u003e(96%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;370\u003c/p\u003e\n \u003cp\u003e(19%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;1612\u003c/p\u003e\n \u003cp\u003e(81%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;104\u003c/p\u003e\n \u003cp\u003e(5%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;1878\u003c/p\u003e\n \u003cp\u003e(95%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;87\u003c/p\u003e\n \u003cp\u003e(5%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;1769\u003c/p\u003e\n \u003cp\u003e(95%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003etrans\u003c/strong\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNonachlor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-128 (-238, -19)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-39 (-69, -10)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-44 (-90, 2)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-33 (-67, 1)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-43 (-151, 64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-36 (-65, -7)**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePCB 118\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76 (-70, 221)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-16 (-43, 12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-87 (-194, 19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7 (-35, 20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1 (-111, 108)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-12 (-39, 15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePCB 180\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-32 (-65, 1)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8 (-36, 20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29 (-44, 102)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-21 (-42, 0)*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAroclor 1260\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOxychlordane\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21 (-69, 111)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-30 (-60, -0)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-124 (-236, -12)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-28 (-58, 2)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDDE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26 (-41, 92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-15 (-36, 5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePb\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-100 (-215, 16)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-34 (-64, -3)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHg\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23 (-25, 71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9 (-24, 6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-58 (-130, 13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5 (-25, 16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-87 (-189, 15)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3 (-23, 18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-100 (-201, 1)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5 (-25, 15)*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMn\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e192 (-27, 410)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1 (-46, 44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-242 (-454, -29)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 (-25, 64)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCd\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"11\"\u003e\n \u003cp\u003en indicates sample size; - indicates not selected by elastic net models.\u003c/p\u003e\n \u003cp\u003eEach cell contains the difference in birth weight measured in grams and associated 95% confidence intervals.\u003c/p\u003e\n \u003cp\u003eAll models were adjusted for maternal education, maternal race, maternal age, maternal pre-pregnancy BMI, maternal smoking status, parity, infant sex and gestational age; Models assessing maternal hardship low education or low income were not adjusted for maternal education; OC models additionally adjusted for total lipids.\u003c/p\u003e\n \u003cp\u003e** p-value for association\u0026thinsp;\u0026lt;\u0026thinsp;0.05; * p-value for association\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003e(con\u0026rsquo;t). The associations between exposure to environmental chemicals (2-fold increase) and birth weight (grams) by maternal hardships, mutually adjusted for one another and covariates, among the MIREC study participants in Canada, 2008\u0026ndash;2011.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMinority status\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLiving alone\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eStudent\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;160\u003c/p\u003e\n \u003cp\u003e(9%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;1649\u003c/p\u003e\n \u003cp\u003e(91%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;72\u003c/p\u003e\n \u003cp\u003e(4%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;1698\u003c/p\u003e\n \u003cp\u003e(96%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;82\u003c/p\u003e\n \u003cp\u003e(5%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;1439\u003c/p\u003e\n \u003cp\u003e(95%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003etrans\u003c/strong\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNonachlor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePCB118\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-28 (-87, 31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8 (-37, 21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePCB180\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAroclor 1260\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-34 (-80, 13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-17 (-42, 7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-48 (-102, 6)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-13 (-37, 11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOxychlordane\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (-50, 57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-35 (-68, -3)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-93 (-177, -8)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-19 (-49, 11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDDE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePb\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (-54, 89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-50 (-82, -17)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHg\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMn\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e231 (-151, 612)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (-37, 50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81 (-52, 213)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0 (-47, 46)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCd\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-35 (-79, 9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8 (-31, 15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003en indicates sample size; - indicates not selected by elastic net models.\u003c/p\u003e\n \u003cp\u003eEach cell contains the difference in birth weight measured in grams and associated 95% confidence intervals.\u003c/p\u003e\n \u003cp\u003eAll models were adjusted for maternal education, maternal race, maternal age, maternal pre-pregnancy BMI, maternal smoking status, parity, infant sex and gestational age; Models assessing maternal hardship low education or low income were not adjusted for maternal education; OC models additionally adjusted for total lipids.\u003c/p\u003e\n \u003cp\u003e** p-value for association\u0026thinsp;\u0026lt;\u0026thinsp;0.05; * p-value for association\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eAmong the OCs, we observed negative (e.g., \u003cem\u003etrans\u003c/em\u003e-nonachlor) to no associations (e.g., PCB 118) with birth weight for females who did not report any of the hardships examined. For \u003cem\u003etrans\u003c/em\u003e-nonachlor and Aroclor 1260, we observed larger than expected negative associations on mean birth weight among females with hardships (Figs. \u003cspan\u003e1\u003c/span\u003eA and \u003cspan\u003e1\u003c/span\u003eD). For example, each 2-fold increase in urinary \u003cem\u003etrans\u003c/em\u003e-nonachlor concentration was associated with a lower mean birth weight of -128 g (95% CI: -238, -19) among those with low income and \u0026minus;\u0026thinsp;44 g (95% CI: -90, 2) among immigrants (Table \u003cspan\u003e4\u003c/span\u003e). Meanwhile, infants born to higher income females or Canadian-born females had smaller birth weight reductions of 39 g (95% CI: -69, -10) and 33 g (95% CI: -67, 1), respectively. Similarly, we observed steeper negative slopes for those with increasing PCB 118 concentrations and belonging to a minority group, increasing PCB 118 concentrations and inadequate supplemental folic acid intake, increasing PCB 180 concentrations and immigrant status, and increasing oxychlordane concentrations and low income. Furthermore, students with increasing exposure to environmental chemicals also showed steeper curves and greater birth weight reduction, compared to non-students. However, the dose response curves crossed over indicating that students had initial gains in birth weight at low exposure compared to non-students but as exposure increased, greater reduction in birth weight was observed (Figs. \u003cspan\u003e1\u003c/span\u003eD-ii and E-iv).\u003c/p\u003e\n \u003cp\u003eWe observed a change in direction for the following associations: PCB 118 x low income, PCB 180 x lone parenthood, oxychlordane x low education, and DDE x low supplemental folic acid intake. Among those who reported hardships, we saw a gain, on average, in birth weight of at least 21 g (95% CI: -69, 111) for infants born to females with increasing oxychlordane concentrations and low education to a maximum of 76 g (95% CI: -70, 221) for infants born to females with increasing PCB 118 concentrations and low income (Table \u003cspan\u003e4\u003c/span\u003e). Conversely, among those who did not report a hardship, we saw reductions in mean birth weight of between 15 g (95: CI: -36, 5) for infants born to females with increasing DDE concentrations but had adequate supplemental folic acid intake and 30 g (95: CI: -60, -0) for infants born to females with increasing PCB 118 concentrations but had higher income (Table \u003cspan\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eAmong the metals, we also observed negative (e.g., Pb) to no associations (e.g., As) with birth weight for those who did not report hardships. For Pb exposure, we observed both a strengthening of association and change in direction when combined with hardships. Among infants born to females with increasing Pb concentrations and low educational status, we observed a larger reduction in mean birth weight (\u003cspan\u003e\u003cspan\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e = -100 g; 95% CI: -215, 16) compared to those with higher educational status (\u003cspan\u003e\u003cspan\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e = -34 g; 95% CI: -64, -3) (Fig. \u003cspan\u003e2\u003c/span\u003eA and Table \u003cspan\u003e4\u003c/span\u003e); while for infants born to females with increasing Pb concentrations and belonging to a minority group, we observed a positive association (\u003cspan\u003e\u003cspan\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e = 18 g; 95% CI: -54, 89) and a gain of 68 g, on average compared to infants born to white females. For As and Cd exposures, among those with hardships, we observed stronger associations (i.e., steeper slope) with birth weight compared to those who did not report hardships (Fig. \u003cspan\u003e2\u003c/span\u003e). Similarly to that of Pb, the associations between As and birth weight were stronger (steeper slope) among infants born to females with low educational status (\u003cspan\u003e\u003cspan\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e = -58 g; 95% CI: -130, 13), low income \u003cspan\u003e\u003cspan\u003e\\((\\beta\\)\u003c/span\u003e\u003c/span\u003e = -87 g; 95% CI: -189, 15), and lone parenthood (\u003cspan\u003e\u003cspan\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e = -100 g; 95% CI: -201, 1) compared to their counterparts (Fig. \u003cspan\u003e2\u003c/span\u003eB and Table \u003cspan\u003e4\u003c/span\u003e). Furthermore, for Mn and Hg exposures, we observed positive associations among those who experienced hardships. For instance, infants born to females with low educational status, compared to those with higher educational status, had lower birth weight at low Mn or Hg concentration. As Mn or Hg level increased, the dose response curve trended upward leading to higher birth weights at high Mn or Hg concentrations (Figs. \u003cspan\u003e2\u003c/span\u003eC and \u003cspan\u003e2\u003c/span\u003eD). One exception to this pattern is lone parents with increasing Mn concentrations. A 242 g (95% CI: -454, -29) reduction in mean birth weight was observed among infants born to single females compared to females who were not single (\u003cspan\u003e\u003cspan\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e = 20 g; 95% CI: -25, 64).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\"\u003e\n \u003ch2\u003e3.6. Supplemental analyses results\u003c/h2\u003e\n \u003cp\u003eLow first trimester plasma total folate concentrations (i.e., \u0026lt;5th percentile or \u0026lt;\u0026thinsp;51.5 nmol/L) was associated with a 124 g decrease in birth weight (95% CI: -223, -26) (Table S3) in the adjusted model (Table S4). From the Cramer\u0026rsquo;s V correlation coefficient plot (Figure S3), we observed low correlations (0 to 0.2) between low plasma total folate and other maternal hardship variables.\u003c/p\u003e\n \u003cp\u003eWhen we included low plasma total folate (instead of low supplemental folic acid intake) in our elastic net model, the selections of variables by elastic net for both the OC and the metal models were similar except that in the metal models, Hg and immigrant status were additionally selected. In the adjusted models, we observed that every doubling of Hg concentration corresponded to a 6 g (95% CI: -21, 8) decrease in birth weight and infants born to immigrants faced a 21 g (95% CI: -78, 35) reduction in birth weight compared to those born to non-immigrants. Elastic net also identified three interactions associated with Hg as important: Hg x low income, Hg x low plasma total folate and Hg x chronic diseases. The interaction plot of Hg x chronic diseases showed a stronger association (steeper slope) indicating greater detriments in birth weight at higher Hg concentration for those who reported having one or more chronic diseases compared to those who did not report any (Figure S4). Meanwhile, the interaction plots of Hg x low income and Hg x low plasma total folate showed positive associations where the dose response curves for those who reported hardship had positive slopes (Figure S4). Additionally, we found that adjustment for GA as a covariate attenuated the relationships between exposure to chemical concentrations and birth weight (Table S4).\u003c/p\u003e\n \u003cp\u003eOur cumulative hardship analysis found that the proportions of participants who reported no hardship, one hardship, and two or more hardships were 43%, 33%, and 24% respectively (Table \u003cspan\u003eS1\u003c/span\u003e). We observed a negative relationship where a higher number of hardships corresponded to lower mean birth weight (Table \u003cspan\u003eS1\u003c/span\u003e). In the adjusted model, the presence of one hardship or two or more hardships corresponded to lower birth weight of 58 g (95% CI: -106, -11) and 93 g (95% CI: -148, -39), respectively, compared to no hardship (Table S6).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eOur findings suggest that maternal hardships that co-occur with gestational exposure to some OCs and metals, may interact and produce greater detrimental effects on fetal growth than either exposure alone. For instance, \u003cem\u003etrans\u003c/em\u003e-nonachlor and oxychlordane were more strongly associated with decreased infant birth weight among females with lower income compared to those with higher income. This finding is not unexpected as it has long been recognized that females with lower income have disproportionately higher exposure for environmental stressors as well as higher risk for impaired fetal growth [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. For instance, Borders et al. (2007) found, among their population of 1,363 pregnant American females with low income, that maternal social hardships and low birth weight were strongly related. They also reported that pregnant females with lower income are more likely to face food insecurity and consume inadequate supplemental folic acid compared to those with higher income [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. This relationship among poverty, food insecurity, and folic acid supplementation is well supported [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. In our study, 104 (5%) participants had low supplemental folic acid intake; only 10 (1%) experienced both low income and low supplemental folic acid intake. Among those infants born to females with increasing PCB 118 concentrations and low supplemental folic acid intake, we observed that every 2-fold increase in PCB 118 concentration was associated with an 87 g reduction in birth weight among females with low supplemental folic acid intake, compared to a 7 g reduction among females with the recommended supplemental folic acid intake level.\u003c/p\u003e \u003cp\u003eBeside maternal income, maternal education also had a strong and positive association with birth weight [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. In our study, Pb x low education was one of the selected interaction terms by elastic net. Pb exposure is unequally distributed across populations where higher exposure to Pb is typically found in communities of lower socioeconomic status and among individuals with less access to resources including financial, educational, social, and health [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Furthermore, both Pb exposure and low maternal education can independently contribute to lower birth weight [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan additionalcitationids=\"CR53\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. In combination, we found that Pb was associated with a greater reduction in birth weight among females with low educational status (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e = -100 g; 95% CI: -215, 16) compared to those who had higher education (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e = -34 g; 95% CI: -64, -3) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnother hardship that has been known to affect birth weight is immigration status. When we considered the females\u0026rsquo; immigration status along with their exposure to OCs (i.e., \u003cem\u003etrans\u003c/em\u003e-nonachlor and PCB 180), immigrants showed a lower mean birth weight compared with Canadian-born participants (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eC and Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This birth weight reduction is supported by an increasing number of studies that found that immigration status affects fetal growth [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] and that the birth weights of babies born to immigrants are generally lower than those of babies born to Canadian-born females [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. For example, South Asian born females tend to give birth to smaller babies than non-migrant females [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. However, in a systematic review where Gagnon et al. (2009) explored whether immigrants have poorer infant outcomes compared to Canadian-born females, the authors found that being an immigrant was not a consistent marker for poorer infant outcomes [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Nevertheless, among our MIREC population, immigrants had lower mean birth weights and when considering the effects of environmental chemical exposures, they experienced a slight increase in vulnerability compared with Canadian-born females. Interestingly, we also observed a greater reduction in birth weight among infants born to students with increasing concentrations of OCs (i.e., Aroclor 1260 and PCB 180), compared to those of non-students. There is a dearth of studies on environmental chemical exposures in the context of pregnant students. Available studies typically examined behaviours in reducing exposure [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], knowledge and awareness of exposures to environmental chemicals [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e], or exposure assessment on campus or in laboratories [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Future work, therefore, should include pregnant students as the intersection of their experiences and exposure status may generate differential impacts on their pregnancy outcomes.\u003c/p\u003e \u003cp\u003eThe stronger relationships we observed for females with both higher environmental chemical concentrations and a maternal hardship is consistent with Knudson\u0026rsquo;s \u0026ldquo;two-hit\u0026rdquo; hypothesis [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e] where two distinct gestational stressors (e.g., gestational exposures to environmental chemical and maternal hardship) showed different associations with birth weight when combined compared to either \u0026ldquo;hit\u0026rdquo; alone. To further explore the impact of cumulative effects, we summed the number of hardships each participant faced and found that as the number of hardships increased, the mean birth weight decreased (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Specifically, the presence of one hardship was associated with a 59 g (95% CI: -105, -12) lower birth weight while the presence of two or more hardships was associated with an 87 g (95% CI: -139, -34) lower birth weight, compared to no hardship (Table S2). While the findings of the present study support the hypothesis that exposure to two stressors can have different effects compared to exposure to a single stressor, these findings may depend on the nature of the stressor(s). For instance, infants born to MIREC participants with increasing Mn or Hg concentrations and reported maternal hardship had higher birth weights compared to those born to participants who did not report hardship.\u003c/p\u003e \u003cp\u003eStudies had shown that Mn exhibits a nonlinear inverse U-shaped relationship with birth weight where lower birth weight was observed at both low and high concentrations of blood Mn level [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Despite that, most studies, including the present one, assumed linear functions. In our previous study, using a flexible Bayesian Kernel Machine Regression method that can accommodate non-linearity, we found that Mn showed a positive and linear relationship with infant birth weight among MIREC participants [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. It is possible that we have captured the ascending segment of the inverted U-shaped Mn-birth weight relationship, which showed an association with higher birth weight in our adjusted regression analysis. As a result, when examined by maternal hardship status, we found evidence that infants born to MIREC participants with both higher Mn concentration and low income had a higher birth weight, while those with no reported hardship showed no association with birth weight. However, the pattern doesn't hold true for lone parenthood as infants born to single females experienced approximately 200 g greater birth weight reduction compared to those born to married parents. The limited research on lone parenthood (or single mother families) and environmental chemical exposures has focused on exposure to air pollutants. Two US-based environmental inequality studies reported that single-mother families are more likely to live in neighborhoods with higher pollution compared to other family types (e.g., married or single father families) [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Another US air pollution study found that living in a poor neighbourhood may increase the risk of exposure to OCs and metals in the air [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. We are not aware of any study that has examined the combined effects of lone parenthood and Mn exposure on pregnancy outcomes. Research that assesses the relationship between wider ranges in Mn concentrations and fetal growth is highly recommended to consider the shape of the Mn-birth weight function as well as the impact of lone parenthood.\u003c/p\u003e \u003cp\u003eLike Mn, total Hg also showed a positive relationship with birth weight among those with maternal hardships. Although Hg is a toxic chemical that can freely cross the placenta and potentially disrupt a range of important pregnancy processes [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e], in a recent systematic review, Hg was reported to have a minimal to null association with birth weight [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. However, one particular study reported that the lowest tertile hair Hg concentration was associated with a higher risk of having infants with low birth weight (adjusted odds ratio (aOR)\u0026thinsp;=\u0026thinsp;7.2; 95% CI: 1.5, 35.6) compared to those with higher Hg concentration (aOR\u0026thinsp;=\u0026thinsp;0.52; 95% CI: 0.17, 1.55) [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. As Hg exposure generally results from maternal fish consumption, the authors hypothesized that the consumption of contaminated fish with high levels of Hg may have coincided with an increased intake of selenium (Se), an essential element found in fish. Se, in this case, may have moderated the toxic effects of Hg [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e] and thus resulted in a decreased OR for low-birth-weight infants among those with higher Hg exposure. Other studies have also reported that Hg levels have a positive association with socioeconomic status among females of childbearing age [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. Specifically, pregnant participants with low income and low education consumed more fish per week compared to pregnant participants with higher income and higher educational status, but they were found to have lower blood Hg levels [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. This is because the type of fish consumed by different demographic groups can also affect Hg exposure levels [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. Therefore, future studies should include Se, the types of fish consumed, and if possible, the whole diet when assessing the potential toxic effects of Hg.\u003c/p\u003e \u003cp\u003eAmong the MIREC study participants, we observed that maternal hardships can modify the strength of the relationships (i.e., steeper negative slope and lower birth weight) as well as the direction of the relationships (i.e., positive slope and higher birth weight) between biomarkers of exposures to OCs and metals during pregnancy and infant birth weight. To our knowledge, this study is the largest single cohort study conducted on the modifying effect of maternal hardship on the relationship between gestational environmental chemical concentrations and birth weight. With the benefits of a large sample size and the use of elastic net regularization technique, we had higher statistical power to detect associations and identify key variables. However, our findings should be interpreted with caution due to the following limitations. First, we used multivariable linear regression to assess the independent effects of each maternal hardship and environmental chemical on birth weight and did not account for non-linear relationships. Second, we performed post-selection inference by first using elastic net for variable selection, then using OLS to derive statistical inference from the best-fitting model. A limitation of variable selection methods such as elastic net is their instability where any change in observations may change the model selected [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. As OLS assumes that we have obtained a perfect model, which may not be the case, the resulting 95% CIs may be inaccurate and too narrow. Accounting for the effects of variable selection is a challenging task and work is underway to develop tools for selective inference so that we can properly assess the strength of the relationships [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. Third, we used Lubin\u0026rsquo;s single imputation approach for measurements below the LOD and the standard errors may be biased. However, as the majority of our environmental chemicals were detected in over 90% of the participants, the impact of bias, if any exists, should be minimal [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Fourth, maternal hardships were based on a positive-negative dichotomy (i.e., yes-no hardship), which produced easy to interpret findings but essentially weighted all hardships equally. This assumption may have overlooked important and differential aspects of the potential effect of each hardship on the outcome. Furthermore, low maternal hardship proportions of 2 to 25% and missing maternal hardship values may bias the effect estimates and reduce our study power to detect a statistically significant association. Regardless, our use of dichotomized hardships served as an important starting point for the conceptualization of maternal hardships and highlighted the importance for future research to include parental hardships when examining the effects of environmental chemical exposure. Additionally, our use of objective maternal hardships may not fully represent a woman\u0026rsquo;s experience and well-being [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. Obtaining qualitative data and additional quantitative data such as food security, stressful life events, and resilience will enhance our understanding of maternal hardships and enrich our current findings. Fifth, we examined only OCs and metals and did not consider co-exposures among the environmental chemicals or hardships. Therefore, we may have missed other environmental chemicals-birth weight associations modified by maternal hardships or any potential 2-way interactions between environmental chemicals or between hardships. Lastly, MIREC is not a representative sample of all Canadian females because it is not population-based [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. As a result, the generalizability of our results may be limited.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eWhile it is widely agreed that fetal growth is determined by multiple factors encompassing genetic, environmental, social and maternal factors, little is known about how these factors work together to maintain the persistent disparities in abnormal fetal growth. We present supporting evidence that maternal hardships may modify the relationship between gestational environmental chemical exposures and infant birth weight and that two \u0026ldquo;hits\u0026rdquo; of stressors have different effects compared to a single \u0026ldquo;hit\u0026rdquo;. Future research should examine the mixture or cumulative effects of environmental chemicals and non-chemical stressors such as maternal hardships to further advance our knowledge of the mechanisms of impaired fetal growth. Greater knowledge can address the adverse fetal growth outcomes where vulnerable populations are at increased risk for impaired fetal growth. These hardships or social factors that co-occur with gestational environmental chemical exposure can become a source of inequality and have downstream effects on infant health [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e].\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eaOR: adjusted odds ratio\u003c/p\u003e\n\u003cp\u003eAs: arsenic\u003c/p\u003e\n\u003cp\u003eCd: cadmium\u003c/p\u003e\n\u003cp\u003eCI: confidence interval\u003c/p\u003e\n\u003cp\u003eDDE: dichlorodiphenyldichloroethylene\u003c/p\u003e\n\u003cp\u003eGA: gestational age\u003c/p\u003e\n\u003cp\u003eg: grams\u003c/p\u003e\n\u003cp\u003eHg: mercury\u003c/p\u003e\n\u003cp\u003eINSPQ: Institut national de sant\u0026eacute; publique du Qu\u0026eacute;bec\u003c/p\u003e\n\u003cp\u003eLOD: limit of detection\u003c/p\u003e\n\u003cp\u003eMIREC: Maternal-Infant Research on Environmental Chemicals\u003c/p\u003e\n\u003cp\u003eMn: manganese\u003c/p\u003e\n\u003cp\u003eNTD: neural tube defects\u003c/p\u003e\n\u003cp\u003eOC: organochlorines\u003c/p\u003e\n\u003cp\u003eOLS: ordinary least squares\u003c/p\u003e\n\u003cp\u003eOR: odds ratio\u003c/p\u003e\n\u003cp\u003ePb: lead\u003c/p\u003e\n\u003cp\u003ePCB: polychlorinated biphenyl\u003c/p\u003e\n\u003cp\u003ePROTECT: Puerto Rico Testsite for Exploring Contamination Threats\u003c/p\u003e\n\u003cp\u003eSD: standard deviation\u003c/p\u003e\n\u003cp\u003eSe: selenium\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eThis research was approved by the ethics review boards from Health Canada, all study sites, and Simon Fraser University. \u0026nbsp;All study participants provided written informed consent after study protocols had been explained.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eDue to data privacy issues, we are not able to make these data publicly available. Individuals may apply to access the data through the MIREC Biobank (www.mirec-canada.ca/en/research).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e Dr. McCandless reports financial support was provided by Canadian Institutes of Health Research. Brown University was compensated for Dr. Braun\u0026rsquo;s services as an expert witness for plaintiffs in litigation related to PFAS-contaminated drinking water; these funds were not paid to Dr. Braun directly. Dr. Braun also reports a relationship with Quest Diagnostics that includes consulting or advisory. Dr. Lanphear serves as an expert witness in cases of lead poisoning and other toxic chemicals, but he receives no financial payment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis work was supported by a Catalyst Grant from the Canadian Institutes of Health Research (L-CIP-150736). \u0026nbsp;The original MIREC study was supported by the Chemicals Management Plan of the Government of Canada, the Ontario Ministry of the Environment., and a research grant from the Canadian Institutes for Health Research (MOP\u0026ndash;81285).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u0026nbsp;\u003c/strong\u003eJH: Conceptualization, Methodology, Formal analysis, Writing - original draft. TA: Writing \u0026ndash; review \u0026amp; editing. PJ: Writing \u0026ndash; review \u0026amp; editing. BL: Writing \u0026ndash; review \u0026amp; editing. JA: Writing \u0026ndash; review \u0026amp; editing. JB: Writing \u0026ndash; review \u0026amp; editing. AM: Writing \u0026ndash; review \u0026amp; editing. AC: Writing \u0026ndash; review \u0026amp; editing. LM: Supervision, Funding acquisition, Writing \u0026ndash; review \u0026amp; editing.\u0026nbsp;All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e We would like to thank the participants of the MIREC study, study coordinators, nurses, and research assistants who made this work possible.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKamai EM, McElrath TF, Ferguson KK. Fetal growth in environmental epidemiology: mechanisms, limitations, and a review of associations with biomarkers of non-persistent chemical exposures during pregnancy. 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Maternal Experience of Multiple Hardships and Fetal Growth: Extending Environmental Mixtures Methodology to Social Exposures. Epidemiology. 2021;32:18\u0026ndash;26.\u003c/li\u003e\n\u003cli\u003eHaffner MJ, Oakes P, Demerdash A, Yammine KC, Watanabe K, Loukas M, et al. Formaldehyde exposure and its effects during pregnancy: Recommendations for laboratory attendance based on available data. Clinical Anatomy. 2015;28(8):972-979. Available from: https://doi.org/10.1002/ca.22623.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"enhe","sideBox":"Learn more about [Environmental Health](http://ehjournal.biomedcentral.com)","snPcode":"12940","submissionUrl":"https://submission.nature.com/new-submission/12940/3","title":"Environmental Health","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Fetal growth, Birth weight, Maternal hardships, Metals, Organochlorine compounds, Two-hits, Chemical mixtures, Elastic Net, Effect modifications, Interactions","lastPublishedDoi":"10.21203/rs.3.rs-4244204/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4244204/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eGestational exposure to toxic environmental chemicals and maternal social hardships are individually associated with impaired fetal growth, but it is unclear whether the effects of environmental chemical exposure on infant birth weight are modified by maternal hardships.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe used data from the Maternal-Infant Research on Environmental Chemicals (MIREC) Study, a pan-Canadian cohort of 1982 pregnant females enrolled between 2008 and 2011. We quantified eleven environmental chemical concentrations from two chemical classes \u0026ndash; six organochlorine compounds (OCs) and five metals \u0026ndash; that were detected in \u0026ge;\u0026thinsp;70% of blood samples collected during the first trimester. Using birth weight adjusted for gestational age (GA) as our outcome, we assessed nine maternal hardships by questionnaire. Each maternal hardship variable was dichotomized to indicate whether the females experienced the hardship. In our analysis, we used elastic net to select the environmental chemicals, maternal hardships, and 2-way interactions between maternal hardships and environmental chemicals that were most predictive of birth weight. Next, we obtained effect estimates using multiple linear regression, and plotted the relationships by hardship status for visual interpretation.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eElastic net selected \u003cem\u003etrans\u003c/em\u003e-nonachlor, lead, low educational status, minority status, and low supplemental folic acid intake. All were inversely associated with birth weight. Elastic net also selected interaction terms. Among those with increasing environmental chemical exposures and reported hardships, we observed stronger negative associations and a few positive associations. For example, every two-fold increase in lead concentrations was more strongly associated with reduced infant birth weight among participants with low educational status (\u003cem\u003eβ\u003c/em\u003e = -100 grams (g); 95% confidence interval (CI): -215, 16), than those with higher educational status (\u003cem\u003eβ\u003c/em\u003e = -34 g; 95% CI: -63, -3). In contrast, every two-fold increase in mercury concentrations was associated with slightly higher birth weight among participants with low educational status (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;23 g; 95% CI: -25, 71) compared to those with higher educational status (\u003cem\u003eβ\u003c/em\u003e = -9 g; 95% CI: -24, 6).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur findings suggest that maternal hardships can modify the associations of gestational exposure to some OCs and metals with infant birth weight.\u003c/p\u003e","manuscriptTitle":"Gestational exposure to organochlorine compounds and metals and infant birth weight: Effect modification by maternal hardships","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-18 17:36:08","doi":"10.21203/rs.3.rs-4244204/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-05-12T12:42:18+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-10T19:10:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-03T19:06:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"0f6dd141-9e24-49fb-8f86-0b737d10c2d4","date":"2024-04-17T15:58:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"37737f4d-d7a2-4281-84a3-ed73f1d08bd9","date":"2024-04-16T13:44:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"11de2134-b716-44d9-9660-540ccba14d7f","date":"2024-04-15T14:58:55+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-10T19:34:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-10T01:31:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-10T01:31:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Health","date":"2024-04-09T23:22:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"enhe","sideBox":"Learn more about [Environmental Health](http://ehjournal.biomedcentral.com)","snPcode":"12940","submissionUrl":"https://submission.nature.com/new-submission/12940/3","title":"Environmental Health","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c5623f10-e643-4b62-ba9f-01eb645af977","owner":[],"postedDate":"April 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-07-02T00:20:18+00:00","versionOfRecord":{"articleIdentity":"rs-4244204","link":"https://doi.org/10.1186/s12940-024-01095-x","journal":{"identity":"environmental-health","isVorOnly":false,"title":"Environmental Health"},"publishedOn":"2024-07-01 00:20:18","publishedOnDateReadable":"July 1st, 2024"},"versionCreatedAt":"2024-04-18 17:36:08","video":"","vorDoi":"10.1186/s12940-024-01095-x","vorDoiUrl":"https://doi.org/10.1186/s12940-024-01095-x","workflowStages":[]},"version":"v1","identity":"rs-4244204","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4244204","identity":"rs-4244204","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}