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

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. We examined fetal growth using birth weight adjusted for gestational age and 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, racially minoritized background, 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 (ß = -100 g (g); 95% confidence interval (CI): -215, 16), than those with higher educational status (ß = -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 (ß = 23 g; 95% CI: -25, 71) compared to those with higher educational status (ß = -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.

Using Latent Profile Analysis to Identify Associations Between Gestational Chemical Mixtures and Child Neurodevelopment.

BACKGROUND: Unsupervised machine learning techniques have become increasingly popular for studying associations between gestational exposure mixtures and human health. Latent profile analysis is one method that has not been fully explored. METHODS: We estimated associations between gestational chemical mixtures and child neurodevelopment using latent profile analysis. Using data from the Maternal-Infant Research on Environmental Chemicals (MIREC) research platform, a longitudinal cohort of pregnant Canadian women and their children, we generated latent profiles from 27 gestational exposure biomarkers. We then examined the associations between these profiles and child Verbal IQ, Performance IQ, and Full-Scale IQ, measured with the Wechsler Preschool and Primary Scale of Intelligence, Third Edition (WPPSI-III). We validated our findings using k-means clustering. RESULTS: Latent profile analysis detected five latent profiles of exposure: a reference profile containing 61% of the study participants, a high monoethyl phthalate (MEP) profile with moderately low persistent organic pollutants (POPs) containing 26%, a high POP profile containing 6%, a low POP profile containing 4%, and a smoking chemicals profile containing 3%. We observed negative associations between both the smoking chemicals and high MEP profiles and all IQ scores and between the high POP profile and Full-Scale and Verbal IQ scores. We also found a positive association between the low POP profile and Full-Scale and Performance IQ scores. All associations had wide 95% confidence intervals. CONCLUSIONS: Latent profile analysis is a promising technique for identifying patterns of chemical exposure and is worthy of further study for its use in examining complicated exposure mixtures.

Who benefits most from a prenatal HEPA filter air cleaner intervention on childhood cognitive development? The UGAAR randomized controlled trial.

BACKGROUND: Air pollution exposure during pregnancy affects children's brain function. Maternal stress and nutrition, socioeconomic status, and the child's sex may modify this relationship. OBJECTIVE: To identify characteristics of children with the largest increases in full-scale IQ (FSIQ) after their mothers used HEPA filter air cleaners during pregnancy. METHODS: In this randomized controlled trial we randomly assigned women to receive 1-2 air cleaners or no air cleaners during pregnancy. We analyzed maternal hair samples for cortisol and dehydroepiandrosterone (DHEA). When the children were 48 months old, we measured FSIQ with the Wechsler Preschool and Primary Scale of Intelligence. We evaluated ten potential modifiers of the intervention-FSIQ relationship using interaction terms in separate regression models. To account for correlations between modifiers, we also used a single regression model containing main effects and intervention x modifier terms for all potential modifiers. RESULTS: Among 242 mother-child dyads with complete data, the intervention was associated with a 2.3-point increase (95% CI: -1.5, 6.0 points) in mean FSIQ. The intervention improved mean FSIQ among children of mothers in the bottom (5.4 points; 95% CI: -0.8, 11.5) and top (6.1 points; 95% CI: 0.5, 11.8) cortisol tertiles, but not among those whose mothers were in the middle tertile. The largest between-group difference in the intervention's effect was a 7.5-point (95% CI: -0.7, 15.7) larger increase in mean FSIQ among children whose mothers did not take vitamins than among children whose mothers did take vitamins (interaction p-value = 0.07). We also observed larger benefits among children whose mothers did not complete university, and those with lower hair DHEA concentrations, hair cortisol concentrations outside the middle tertile, or more perceived stress. CONCLUSION: The benefits of reducing air pollution during pregnancy on brain development may be greatest for children whose mothers who do not take vitamins, experience more stress, or have less education.

Association Between Gestational Exposure to Toxicants and Autistic Behaviors Using Bayesian Quantile Regression.

Autism spectrum disorder, which is characterized by impaired social communication and stereotypic behaviors, affects 1%-2% of children. Although prenatal exposure to toxicants has been associated with autistic behaviors, most studies have been focused on shifts in mean behavior scores. We used Bayesian quantile regression to assess the associations between log2-transformed toxicant concentrations and autistic behaviors across the distribution of behaviors. We used data from the Maternal-Infant Research on Environmental Chemicals study, a pan-Canadian cohort (2008-2011). We measured metal, pesticide, polychlorinated biphenyl, phthalate, bisphenol-A, and triclosan concentrations in blood or urine samples collected during the first trimester of pregnancy. Using the Social Responsiveness Scale (SRS), in which higher scores denote more autistic-like behaviors, autistic behaviors were assessed in 478 children aged 3-4 years old. Lead, cadmium, and most phthalate metabolites were associated with mild increases in SRS scores at the 90th percentile of the SRS distribution. Manganese and some pesticides were associated with mild decreases in SRS scores at the 90th percentile of the SRS distribution. We identified several monotonic trends in which associations increased in magnitude from the bottom to the top of the SRS distribution. These results suggest that quantile regression can reveal nuanced relationships and, thus, should be more widely used by epidemiologists.

Prenatal exposure to endocrine disrupting chemical mixtures and infant birth weight: A Bayesian analysis using kernel machine regression.

BACKGROUND: Pregnant women are regularly exposed to a multitude of endocrine disrupting chemicals (EDCs). EDC exposures, both individually and as mixtures, may affect fetal growth. The relationship of EDC mixtures with infant birth weight, however, remains poorly understood. We examined the relations between prenatal exposure to EDC mixtures and infant birth weight. METHODS: We used data from the Maternal-Infant Research on Environmental Chemicals (MIREC) Study, a pan-Canadian cohort of 1857 pregnant women enrolled between 2008 and 2011. We quantified twenty-one chemical concentrations from five EDC classes, including organochlorine compounds (OCs), metals, perfluoroalkyl substances (PFAS), phenols and phthalate metabolites that were detected in >70% of urine or blood samples collected during the first trimester. In our primary analysis, we used Bayesian kernel machine regression (BKMR) models to assess variable importance, explore EDC mixture effects, and identify any interactions among EDCs. Our secondary analysis used traditional linear regression to compare the results with those of BKMR and to quantify the changes in mean birth weight in relation to prenatal EDC exposures. RESULTS: We found evidence that mixtures of OCs and metals were associated with monotonic decreases in mean birth weight across the whole range of exposure. trans-Nonachlor from the OC mixture and lead (Pb) from the metal mixture had the greatest impact on birth weight. Our linear regression analysis corroborated the BKMR results and found that a 2-fold increase in trans-nonachlor and Pb concentrations reduced mean birth weight by -38 g (95% confidence interval (CI): -67, -10) and -39 g (95% CI: -69, -9), respectively. A sex-specific association for OC mixture was observed among female infants. PFAS, phenols and phthalates were not associated with birth weight. No interactions were observed among the EDCs. CONCLUSIONS: Using BKMR, we observed that both OC and metal mixtures were associated with decreased birth weight in the MIREC Study. trans-Nonachlor from the OC mixture and Pb from the metal mixture contributed most to the adverse effects.

Portable HEPA filter air cleaner use during pregnancy and children's behavior problem scores: a secondary analysis of the UGAAR randomized controlled trial.

BACKGROUND: Developmental exposure to particulate matter (PM) air pollution may impair children's behaviors. Our objectives were to quantify the impact of reducing indoor PM using portable HEPA filter air cleaners during pregnancy on behavioral problems in children and to assess associations between indoor fine PM (PM2.5) concentrations during pregnancy and children's behavior. METHODS: This is a secondary analysis of a single-blind parallel-group randomized controlled trial in which we randomly assigned 540 non-smoking pregnant women to receive 1 or 2 HEPA filter air cleaners or no air cleaners. We administered the Behavior Assessment System for Children (BASC-3) to caregivers when children were a mean age of 23 months, and again at a mean age of 48 months. Primary outcomes were the four BASC-3 composite scales: externalizing problems, internalizing problems, adaptive skills, and the behavioral symptoms index. We imputed missing data using multiple imputation with chained equations. The primary analysis was by intention-to-treat. In a secondary analysis, we evaluated associations between BASC-3 composite indices and modeled trimester-specific PM2.5 concentrations inside residences. RESULTS: We enrolled participants at a median of 11 weeks gestation. After excluding miscarriages, still births and neonatal deaths, our analysis included 478 children (233 control and 245 intervention). We observed no differences in the mean BASC-3 scores between treatment groups. An interquartile increase (20.1 µg/m3) in first trimester PM2.5 concentration was associated with higher externalizing problem scores (2.4 units, 95% CI: 0.7, 4.1), higher internalizing problem scores (2.4 units, 95% CI: 0.7, 4.0), lower adaptive skills scores (-1.5 units, 95% CI: -3.0, 0.0), and higher behavior symptoms index scores (2.3 units, 95% CI: 0.7, 3.9). Third trimester PM2.5 concentrations were also associated with some behavioral indices at age 4, but effect estimates were smaller. No significant associations were observed with PM2.5 concentrations during the second trimester or for any of the BASC indices when children were 2 years old. CONCLUSION: We found no benefit of reducing indoor particulate air pollution during pregnancy on parent-reported behaviors in children. Associations between indoor PM2.5 concentrations in the first trimester and behavioral scores among 4-year old children suggest that it may be necessary to intervene early in pregnancy to protect children, but these exploratory findings should be interpreted cautiously. TRIAL REGISTRATION: ClinicalTrials.gov: NCT01741051.

Disease-specific incident glomerulonephritis displays geographic clustering in under-serviced rural areas of British Columbia, Canada.

There is little known about geographic variability in the incidence of glomerular disease and its potential implications for care delivery. To evaluate this, we performed a population-level cohort study using a provincial renal pathology database (2000-2012) to capture all incident cases of glomerulonephritis in British Columbia, Canada. This included 401 patients with membranous nephropathy (MN), 824 patients with IgA nephropathy (IgAN), 385 patients with focal segmental glomerulosclerosis (FSGS), 397 patients with lupus nephritis (LN) and 399 patients with ANCA-related glomerulonephritis (ANCA-GN). Geographic clusters were identified using Bayesian spatial models to estimate the incidence of each disease in 74 regions compared to the mean incidence in the entire province (incidence rate ratio, [IRR]), adjusted for region-level age, sex and race. The proportion of overall variability in incidence attributed to inter-regional differences varied by disease: 18% in MN, 81% in IgAN, 18% in FSGS, 59% in ANCA-GN, and 89% in LN. Except for LN, clustering was not explained by demographics. All IgAN and LN clusters were in urban regions close to nephrology centers, whereas ANCA-GN, MN and FSGS clustered mainly in rural regions. All ANCA-GN clusters were rural with median population density 1.2 persons/km2 and driving distances of 10-676 km to the nearest nephrology center. Thus, we found significant geographic clustering in the incidence of different glomerular diseases. MN, FSGS and ANCA-GN clustered in sparsely populated regions with limited access to care, underscoring the importance of regional variability in glomerular diseases to inform health services delivery.

Factors associated with delayed and late ART initiation among people living with HIV in BC: results from the engage study.

We examined correlates of late and delayed initiation of antiretroviral therapy (ART) in British Columbia, Canada. From December 2013 to December 2015 we recruited treatment-naïve people living with HIV who initiated ART within the previous year. 'Late initiation' was defined as CD4 cell count ≤500 cells/µL at ART initiation and 'delayed initiation' as ≥1 year between HIV diagnosis and initiation. Multivariable logistic regression assessed independent correlates of late and delayed initiation. Of 87 participants, 44 (51%) initiated late and 22 (26%) delayed initiation. Delayed initiation was positively associated with older age (adjusted odds ratio [AOR]: 1.06 per year, 95% confidence interval [95% CI]: 1.01-1.12) and inversely associated with wanting to start ART at diagnosis (AOR: 0.06, 95% CI: 0.02-0.21). Variables associated with late initiation were older age (AOR: 1.09 per year, 95% CI: 1.03-1.15) and medical reason(s) for initiation (AOR: 5.00, 95% CI: 1.41-17.86). Late initiation was less likely among those with greater perceived ART efficacy (AOR 0.94, 95% CI: 0.90-0.98) and history of incarceration (AOR: 0.12, 95% CI: 0.03-0.56). Disparities in timing of initiation were observed for age, perceived ART efficacy, and history of incarceration. Enhanced health services that address these factors may facilitate earlier treatment initiation.

Coal smoke, gestational cadmium exposure, and fetal growth.

BACKGROUND: Gestational cadmium exposure may impair fetal growth. Coal smoke has largely been unexplored as a source of cadmium exposure. We investigated the relationship between gestational cadmium exposure and fetal growth, and assessed coal smoke as a potential source of airborne cadmium, among non-smoking pregnant women in Ulaanbaatar, Mongolia, where coal combustion in home heating stoves is a major source of outdoor and indoor air pollution. METHODS: This observational study was nested within the Ulaanbaatar Gestation and Air Pollution Research (UGAAR) study, a randomized controlled trial of portable high efficiency particulate air (HEPA) filter air cleaner use during pregnancy, fetal growth, and early childhood development. We measured third trimester blood cadmium concentrations in 374 out of 465 participants who had a live birth. We used multiple linear and logistic regression to assess the relationships between log2-transformed maternal blood cadmium concentrations and birth weight, length, head circumference, ponderal index, low birth weight, small for gestational age, and preterm birth in crude and adjusted models. We also evaluated the relationships between log2-transformed blood cadmium concentrations and the density of coal-burning stoves within 5000 m of each participant's apartment as a proxy of coal smoke emissions from home heating stoves. RESULTS: The median (25th,75th percentile) blood cadmium concentration was 0.20 (0.15, 0.29) µg/L. A doubling of blood cadmium was associated with a 95 g (95% CI: 34, 155 g) reduction in birth weight in adjusted models. An interquartile range increase in coal stove density (from 3.4 to 4.9 gers/hectare) surrounding participants' apartments was associated with a 12.2% (95% CI: 0.3, 25.6%) increase in blood cadmium concentrations. CONCLUSIONS: Gestational cadmium exposure was associated with reduced birth weight. In settings where coal is a widely used fuel, cadmium may play a role in the putative association between air pollution and impaired fetal growth.

Very low-level prenatal mercury exposure and behaviors in children: the HOME Study.

BACKGROUND: Mercury is toxic to the developing brain, but the lowest concentration associated with the development of behavior problems is unclear. The purpose of this study was to examine the association between very low-level mercury exposure during fetal development and behavior problems in children. METHODS: We used data from 389 mothers and children in a prospective pregnancy and birth cohort study. We defined mean prenatal mercury concentration as the mean of total whole blood mercury concentrations in maternal samples collected at 16- and 26-weeks of gestation, delivery, and neonatal cord blood samples. We assessed parent-reported child behavior up to five times from two to 8 years of age using the Behavioral Assessment System for Children (BASC-2). At 8 years of age, we assessed self-reported child anxiety using the Spence Children's Anxiety Scale (SCAS). We used multiple linear mixed models and linear regression models to estimate the association between mean prenatal mercury concentrations and child behavior and anxiety, respectively. RESULTS: The median prenatal total blood mercury concentrations was 0.67 µg/L. Overall, we did not find statistically significant associations between mean prenatal mercury concentrations and behavior problems scores, but a 2-fold increase in mercury concentrations at 16-weeks gestation was associated with 0.83 point (95% CI: 0.05, 1.62) higher BASC-2 anxiety scores. Maternal and cord blood mercury concentrations at delivery were associated with parent-reported anxiety at 8 years. CONCLUSION: We found limited evidence of an association between very-low level prenatal mercury exposure and behaviors in children, with an exception of anxiety.

A comparison of Bayesian and Monte Carlo sensitivity analysis for unmeasured confounding.

Bias from unmeasured confounding is a persistent concern in observational studies, and sensitivity analysis has been proposed as a solution. In the recent years, probabilistic sensitivity analysis using either Monte Carlo sensitivity analysis (MCSA) or Bayesian sensitivity analysis (BSA) has emerged as a practical analytic strategy when there are multiple bias parameters inputs. BSA uses Bayes theorem to formally combine evidence from the prior distribution and the data. In contrast, MCSA samples bias parameters directly from the prior distribution. Intuitively, one would think that BSA and MCSA ought to give similar results. Both methods use similar models and the same (prior) probability distributions for the bias parameters. In this paper, we illustrate the surprising finding that BSA and MCSA can give very different results. Specifically, we demonstrate that MCSA can give inaccurate uncertainty assessments (e.g. 95% intervals) that do not reflect the data's influence on uncertainty about unmeasured confounding. Using a data example from epidemiology and simulation studies, we show that certain combinations of data and prior distributions can result in dramatic prior-to-posterior changes in uncertainty about the bias parameters. This occurs because the application of Bayes theorem in a non-identifiable model can sometimes rule out certain patterns of unmeasured confounding that are not compatible with the data. Consequently, the MCSA approach may give 95% intervals that are either too wide or too narrow and that do not have 95% frequentist coverage probability. Based on our findings, we recommend that analysts use BSA for probabilistic sensitivity analysis. Copyright © 2017 John Wiley & Sons, Ltd.

Gestational exposure to endocrine disrupting chemicals in relation to infant birth weight: a Bayesian analysis of the HOME Study.

BACKGROUND: Pregnant women are exposed to a mixture of endocrine disrupting chemicals (EDCs). Gestational EDC exposures may be associated with changes in fetal growth that elevates the risk for poor health later in life, but few studies have examined the health effects of simultaneous exposure to multiple chemicals. This study aimed to examine the association of gestational exposure to five chemical classes of potential EDCs: phthalates and bisphenol A, perfluoroalkyl substances (PFAS), polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs), and organochlorine pesticides (OCPs) with infant birth weight. METHODS: Using data from the Health Outcomes and Measures of Environment (HOME) Study, we examined 272 pregnant women enrolled between 2003-2006. EDC concentrations were quantified in blood and urine samples collected at 16 and 26 weeks gestation. We used Bayesian Hierarchical Linear Models (BHLM) to examine the associations between newborn birth weight and 53 EDCs, 2 organochlorine pesticides (OPPs) and 2 heavy metals. RESULTS: For a 10-fold increase in chemical concentration, the mean differences in birth weights (95% credible intervals (CI)) were 1 g (-20, 23) for phthalates, -11 g (-52, 34) for PFAS, 0.2 g (-9, 10) for PCBs, -4 g (-30, 22) for PBDEs, and 7 g (-25, 40) for OCPs. CONCLUSION: Gestational exposure to phthalates, PFAS, PCBs, PBDEs, OCPs or OPPs had null or small associations with birth weight. Gestational OPP, Pb, and PFAS exposures were most strongly associated with lower birth weight.

Examining the relationship between health-related need and the receipt of care by participants experiencing homelessness and mental illness.

BACKGROUND: People experiencing homelessness and mental illness face multiple barriers to care. The goal of this study was to examine the association between health service use and indicators of need among individuals experiencing homelessness and mental illness in Vancouver, Canada. We hypothesized that those with more severe mental illness would access greater levels of primary and specialist health services than those with less severe mental illness. METHODS: Participants met criteria for homelessness and current mental disorder using standardized criteria (n = 497). Interviews assessed current health status and involvement with a variety of health services including specialist, general practice, and emergency services. The 80th percentile was used to differentiate 'low health service use' and 'high health service use'. Using multivariate logistic regression analysis, we analyzed associations between predisposing, enabling and need-related factors with levels of primary and specialist health service use. RESULTS: Twenty-one percent of participants had high primary care use, and 12% had high use of specialist services. Factors significantly (p ≤ 0.05) associated with high primary care use were: multiple physical illnesses [AOR 2.74 (1.12, 6.70]; poor general health [AOR 1.68 (1.01, 2.81)]; having a regular family physician [AOR 2.27 (1.27, 4.07)]; and negative social relationships [AOR 1.74 (1.01, 2.99)]. Conversely, having a more severe mental disorder (e.g. psychotic disorder) was significantly associated with lower odds of high service use [AOR 0.59 (0.35, 0.97)]. For specialist care, recent history of psychiatric hospitalization [AOR 2.53 (1.35, 4.75)] and major depressive episode [AOR 1.98 (1.11, 3.56)] were associated with high use, while having a blood borne infectious disease (i.e., HIV, HCV, HBV) was associated with lower odds of high service use. CONCLUSIONS: Contrary to our hypotheses, we found that individuals with greater assessed need, including more severe mental disorders, and blood-borne infectious diseases had significantly lower odds of being high health service users than those with lower assessed needs. Our findings reveal an important gap between levels of need and service involvement for individuals who are both homeless and mentally ill and have implications for health service reform in relation to the unmet and complex needs of a marginalized sub-population. ( TRIAL REGISTRATION: ISRCTN57595077 and ISRCTN66721740).

Statin use and fracture risk: can we quantify the healthy-user effect?

BACKGROUND: Numerous observational studies show that statin use is associated with lower risk of osteoporotic fractures. However, a causal relationship is not supported by data from randomized trials. Unmeasured confounding is implicated as a likely culprit for the controversy because of failure to measure and adjust for patient-level tendencies to engage in healthy behaviors. However, an alternative explanation is selection bias because of the inclusion of prevalent users of statins in the analysis. The relative importance of either bias has not been investigated in a quantitative sensitivity analysis. METHODS: We conducted a systematic review to summarize the pattern of association between statin use and fracture risk in observational studies. Our objective was to quantify the magnitude of unmeasured confounding and selection bias in a sensitivity analysis. RESULTS: In 17 published studies, the pooled relative risk for the association between current use of statins and fracture risk was 0.75 (95% confidence interval = 0.66-0.85). Upon adjustment for individual-level use of preventative health services, the pooled relative risk shifted by less than 5% on the log scale. However, a sensitivity analysis for selection bias revealed that moderate levels of bias could eliminate the association between statins and fracture risk. CONCLUSIONS: It appears that confounding from unmeasured variables cannot explain the protective association between statins and fractures that has been observed in the literature.

Hierarchical priors for bias parameters in Bayesian sensitivity analysis for unmeasured confounding.

Recent years have witnessed new innovation in Bayesian techniques to adjust for unmeasured confounding. A challenge with existing methods is that the user is often required to elicit prior distributions for high-dimensional parameters that model competing bias scenarios. This can render the methods unwieldy. In this paper, we propose a novel methodology to adjust for unmeasured confounding that derives default priors for bias parameters for observational studies with binary covariates. The confounding effects of measured and unmeasured variables are treated as exchangeable within a Bayesian framework. We model the joint distribution of covariates by using a log-linear model with pairwise interaction terms. Hierarchical priors constrain the magnitude and direction of bias parameters. An appealing property of the method is that the conditional distribution of the unmeasured confounder follows a logistic model, giving a simple equivalence with previously proposed methods. We apply the method in a data example from pharmacoepidemiology and explore the impact of different priors for bias parameters on the analysis results.

Sensitivity analysis for live birth bias in the Ulaanbaatar Gestation and Air Pollution Research study.

The Ulaanbaatar Gestation and Air Pollution Research (UGAAR) study is a randomized controlled trial of the effects of portable high efficiency particulate air (HEPA) filter air cleaner use during pregnancy on fetal growth and child health outcomes. A challenge with the study is that the outcome variables cannot be measured in the absence of a live birth. Thus pregnancy loss is a competing event for the outcome variables that can obscure the intervention-outcome relationship. This phenomenon is called "live birth bias" in the epidemiological literature, and it is an example of selection bias due to adjustment for variables affected by previous exposure. Methods: In this investigation, we reanalyzed data from the UGAAR study and examined the impacts of the intervention on three health outcomes: preterm birth (PTB), birth weight, and full-scale IQ (FSIQ) measured with the Wechsler Preschool and Primary Scale of Intelligence III when children were four years old, while accounting for live birth bias. Specifically, we used a novel multiple imputation approach to simulate scenarios in which pregnancy losses had instead been born alive and experienced a range of health outcomes. Results: After accounting for live birth bias, the association between the intervention and PTB diminished. Additionally, the magnitude of intervention effect on birth weight and FSIQ increased. FSIQ was less sensitive to live birth bias than birth weight. Conclusion: We introduced a novel analysis approach to examine the role of live birth bias, and the findings will be useful in environmental epidemiology studies of birth cohorts.

Portable HEPA filter air cleaner use during pregnancy and children's autistic behaviors at four years of age: The UGAAR randomized controlled trial.

BACKGROUND: Developmental exposure to airborne particulate matter (PM) may increase children's risk of developing autism spectrum disorder. We quantified the impact of reducing PM exposure during pregnancy on the development of autistic traits in children. We also assessed associations between indoor fine PM (PM2.5) concentrations during pregnancy and autistic traits. METHODS: In this parallel-group randomized controlled trial, we randomized 540 non-smoking pregnant women to receive HEPA filter air cleaners or to a control group, which did not receive air cleaners. We administered the Social Responsiveness Scale (SRS-2) to caregivers when children were a median of 48 months (range: 48 to 51 months). Our primary outcome was the SRS-2 total T-score. We imputed missing data using multiple imputation with chained equations and our primary analysis was by intention to treat. In secondary analyses, we estimated associations between full pregnancy and trimester-specific indoor PM2.5 concentrations and T-scores. RESULTS: We enrolled participants at a median of 11 weeks' gestation. Our analysis included 478 children (233 control, 245 intervention). The intervention reduced average indoor PM2.5 concentrations by 29 % (95 % CI: 21, 37 %). The mean SRS-2 total T-score was 0.5 units lower (95 % CI: -2.5, 1.5) among intervention participants, with evidence of larger benefits for children at the high end of the T-score distribution. An interquartile range (9.6 µg/m3) increase in indoor PM2.5 during pregnancy was associated with 1.8-unit (95 % CI: 0.3, 3.2) increase in mean SRS-2 total T-score. Effect estimates for PM2.5 concentrations by trimester were smaller and confidence intervals spanned no effect. CONCLUSION: Reducing indoor PM during pregnancy had little impact on mean autism-related behavior scores in children. However, indoor PM2.5 concentrations during pregnancy were associated with higher scores. Exposure to particulate matter during pregnancy may influence the development of autistic traits in childhood. TRIAL REGISTRATION: ClinicalTrials.gov: NCT01741051.

Portable HEPA Filter Air Cleaner Use during Pregnancy and Children's Cognitive Performance at Four Years of Age: The UGAAR Randomized Controlled Trial.

BACKGROUND: Developmental exposure to air pollution is associated with diminished cognitive abilities in observational studies, but no randomized controlled trial has examined the effect of reducing air pollution on cognition in children. OBJECTIVES: We sought to quantify the impact of reducing exposure to particulate matter (PM) during pregnancy on children's cognitive performance at 4 y of age. METHODS: In this single-blind, parallel-group, randomized controlled trial in Ulaanbaatar, Mongolia, we randomly assigned 540 nonsmoking pregnant women (268 intervention and 272 control) to receive 1-2 portable high-efficiency particulate air (HEPA) filter air cleaners or no air cleaners. The air cleaners were used from a median of 11 wk gestation until the end of pregnancy. The primary outcome was full-scale intelligence quotient (FSIQ) assessed using the Wechsler Preschool and Primary Scale of Intelligence, Fourth Edition (WPPSI-IV) when children were a median of 48 months old. We imputed missing outcome data using multiple imputation with chained equations, and our primary analysis was by intention to treat. RESULTS: After excluding known miscarriages, stillbirths, neonatal deaths, and medical conditions that impeded cognitive testing and imputation, 475 (233 control and 242 intervention) children were included in our analyses. In an unadjusted analysis, the mean FSIQ of children who were randomly assigned to the intervention group was 2.5 points [95% confidence interval (CI): -0.4, 5.4 points] higher than that of children in the control group. After adjustment to account for an imbalance in preterm birth between groups, the effect estimate increased to 2.8 points (95% CI: -0.1, 5.7). CONCLUSIONS: Reducing PM air pollution during pregnancy may improve cognitive performance in childhood. https://doi.org/10.1289/EHP10302.

Effects of gestational exposures to chemical mixtures on birth weight using Bayesian factor analysis in the Health Outcome and Measures of Environment (HOME) Study.

Studying the effects of gestational exposures to chemical mixtures on infant birth weight is inconclusive due to several challenges. One of the challenges is which statistical methods to rely on. Bayesian factor analysis (BFA), which has not been utilized for chemical mixtures, has advantages in variance reduction and model interpretation. METHODS: We analyzed data from a cohort of 384 pregnant women and their newborns using urinary biomarkers of phthalates, phenols, and organophosphate pesticides (OPs) and serum biomarkers of polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs), perfluoroalkyl substances (PFAS), and organochlorine pesticides (OCPs). We examined the association between exposure to chemical mixtures and birth weight using BFA and compared with multiple linear regression (MLR) and Bayesian kernel regression models (BKMR). RESULTS: For BFA, a 10-fold increase in the concentrations of PCB and PFAS mixtures was associated with an 81 g (95% confidence intervals [CI] = -132 to -31 g) and 57 g (95% CI = -105 to -10 g) reduction in birth weight, respectively. BKMR results confirmed the direction of effect. However, the 95% credible intervals all contained the null. For single-pollutant MLR, a 10-fold increases in the concentrations of multiple chemicals were associated with reduced birth weight, yet the 95% CI all contained the null. Variance inflation from MLR was apparent for models that adjusted for copollutants, resulting in less precise confidence intervals. CONCLUSION: We demonstrated the merits of BFA on mixture analysis in terms of precision and interpretation compared with MLR and BKMR. We also identified the association between exposure to PCBs and PFAS and lower birth weight.