Racial Disparities in Maternal Exposure to Ambient Air Pollution During Pregnancy and Prevalence of Congenital Heart Defects.

Air pollution may be a potential cause of congenital heart defects (CHDs), but racial disparities in this association are unexplored. We conducted a statewide population-based cohort study using North Carolina birth data from 2003-2015 (N=1,225,285) to investigate the relationship between air pollution and CHDs (specifically pulmonary valve atresia/stenosis, Tetralogy of Fallot (TOF), and atrioventricular septal defect (AVSD)). Maternal exposure to particulate matter ≤2.5 micrometers in diameter (PM2.5) and ozone during weeks 3-9 of pregnancy were estimated using the Environmental Protection Agency's Downscaler Model. Single- and co-pollutant log-binomial models were created for the entire population and stratified by race to investigate disparities. Positive associations between PM2.5 and CHDs were observed. An increasing concentration-response association was found for PM2.5 and TOF in adjusted, co-pollutant models (Quartile 4 prevalence ratio: 1.46; 95% CI: 1.06, 2.03). Differences in the effect of PM2.5 on CHD prevalence were seen in some models stratified by race, although clear exposure-prevalence gradients were not evident. Positive associations were also seen in adjusted, co-pollutant models of ozone and AVSD. Study results suggest that prenatal PM2.5 and ozone exposure may increase the prevalence of certain CHDs. A consistent pattern of differences in association by race/ethnicity was not apparent.

Perinatal exposure to polychlorinated biphenyls (PCBs) and child neurodevelopment: A comprehensive systematic review of outcomes and methodological approaches.

BACKGROUND: Polychlorinated biphenyls (PCBs), extensively used in various products, prompt ongoing concern despite reduced exposure since the 1970s. This systematic review explores prenatal PCB and hydroxylated metabolites (OH-PCBs) exposure's association with child neurodevelopment. Encompassing cognitive, motor development, behavior, attention, ADHD, and ASD risks, it also evaluates diverse methodological approaches in studies. METHODS: PubMed, Embase, PsycINFO, and Web of Science databases were searched through August 23, 2023, by predefined search strings. Peer-reviewed studies published in English were included. The inclusion criteria were: (i) PCBs/OH-PCBs measured directly in maternal and cord blood, placenta or breast milk collected in the perinatal period; (ii) outcomes of cognitive development, motor development, attention, behavior, attention-deficit/hyperactivity disorder (ADHD), and autism spectrum disorder (ASD) among children≤18 years old. Quality assessment followed the National Heart, Lung, and Blood Institute's tool. RESULTS: Overall, 87 studies were included in this review. We found evidence for the association between perinatal PCB exposure and adverse cognitive development and attention issues in middle childhood. There appeared to be no or negligible link between perinatal PCB exposure and early childhood motor development or the risk of ADHD/ASD. There was an indication of a sex-specific association with worse cognition and attention scores among boys. Some individual studies suggested a possible association between prenatal exposure to OH-PCBs and neurodevelopmental outcomes. There was significant heterogeneity between the studies in exposure markers, exposure assessment timing, outcome assessment, and statistical analysis. CONCLUSIONS: Significant methodological, clinical and statistical heterogeneity existed in the included studies. Adverse effects on cognitive development and attention were observed in middle childhood. Little or no apparent link on both motor development and risk of ADHD/ASD was observed in early childhood. Inconclusive evidence prevailed regarding other neurodevelopmental aspects due to limited studies. Future research could further explore sex-specific associations and evaluate associations at lower exposure levels post-PCB ban in the US. It should also consider OH-PCB metabolites, co-pollutants, mixtures, and their potential interactions.

Neighborhood environmental vulnerability and pediatric asthma morbidity in US metropolitan areas.

BACKGROUND: Research suggests demographic, economic, residential, and health-related factors influence vulnerability to environmental exposures. Greater environmental vulnerability may exacerbate environmentally related health outcomes. We developed a neighborhood environmental vulnerability index (NEVI) to operationalize environmental vulnerability on a neighborhood level. OBJECTIVE: We explored the relationship between NEVI and pediatric asthma emergency department (ED) visits (2014-19) in 3 US metropolitan areas: Los Angeles County, Calif; Fulton County, Ga; and New York City, NY. METHODS: We performed separate linear regression analyses examining the association between overall NEVI score and domain-specific NEVI scores (demographic, economic, residential, health status) with pediatric asthma ED visits (per 10,000) across each area. RESULTS: Linear regression analyses suggest that higher overall and domain-specific NEVI scores were associated with higher annual pediatric asthma ED visits. Adjusted R2 values suggest that overall NEVI scores explained at least 40% of the variance in pediatric asthma ED visits. Overall NEVI scores explained more of the variance in pediatric asthma ED visits in Fulton County. NEVI scores for the demographic, economic, and health status domains explained more of the variance in pediatric asthma ED visits in each area compared to the NEVI score for the residential domain. CONCLUSION: Greater neighborhood environmental vulnerability was associated with greater pediatric asthma ED visits in each area. The relationship differed in effect size and variance explained across the areas. Future studies can use NEVI to identify populations in need of greater resources to mitigate the severity of environmentally related outcomes, such as pediatric asthma.

Practical Guide to Honest Causal Forests for Identifying Heterogeneous Treatment Effects.

"Heterogeneous treatment effects" is a term which refers to conditional average treatment effects (i.e., CATEs) that vary across population subgroups. Epidemiologists are often interested in estimating such effects because they can help detect populations that may particularly benefit from or be harmed by a treatment. However, standard regression approaches for estimating heterogeneous effects are limited by preexisting hypotheses, test a single effect modifier at a time, and are subject to the multiple-comparisons problem. In this article, we aim to offer a practical guide to honest causal forests, an ensemble tree-based learning method which can discover as well as estimate heterogeneous treatment effects using a data-driven approach. We discuss the fundamentals of tree-based methods, describe how honest causal forests can identify and estimate heterogeneous effects, and demonstrate an implementation of this method using simulated data. Our implementation highlights the steps required to simulate data sets, build honest causal forests, and assess model performance across a variety of simulation scenarios. Overall, this paper is intended for epidemiologists and other population health researchers who lack an extensive background in machine learning yet are interested in utilizing an emerging method for identifying and estimating heterogeneous treatment effects.

Utilizing machine learning and lipidomics to distinguish primary lateral sclerosis from amyotrophic lateral sclerosis.

INTRODUCTION/AIMS: There are currently no imaging or blood diagnostic biomarkers that can differentiate amyotrophic lateral sclerosis (ALS) from primary lateral sclerosis (PLS) patients early in their disease courses. Our objective is to examine whether patients with PLS can be differentiated from ALS reliably by using plasma lipidome profile and supervised machine learning. METHODS: 40 ALS and 28 PLS patients derived from the Multicenter Cohort study of Oxidative Stress (COSMOS) and 28 healthy control volunteers (CTR) were included. ALS, PLS, and CTR were matched by age and sex. Plasma samples were obtained after overnight fasting. Lipids were extracted from the plasma samples and analyzed using liquid chromatography/mass spectrometry to obtain relative concentrations of 392 lipid species. The lipid data were partitioned into training and testing datasets randomly. An elastic net algorithm was trained using cross-validation to classify PLS vs ALS and PLS vs CTR. Final accuracy was evaluated in the testing dataset. RESULTS: The elastic net model trained with labeled PLS and ALS training lipid dataset demonstrated accuracy (number classified correctly/total number), sensitivity, and specificity of 100% in classifying PLS vs ALS in the unlabeled testing lipid dataset. Similarly, the elastic net model trained with labeled PLS and CTR training lipid datasets demonstrated accuracy, sensitivity, and specificity of 88% in classifying PLS vs CTR in the unlabeled testing lipid dataset. DISCUSSION: Our study suggests PLS patients can be accurately distinguished from ALS and CTR by combining lipidome profile and supervised machine learning without clinical information.

The Creation of a Multidomain Neighborhood Environmental Vulnerability Index Across New York City.

Compared to previous studies commonly using a single summary score, we aimed to construct a multidomain neighborhood environmental vulnerability index (NEVI) to characterize the magnitude and variability of area-level factors with the potential to modify the association between environmental pollutants and health effects. Using the Toxicological Prioritization Index framework and data from the 2015-2019 U.S. Census American Community Survey and the 2020 CDC PLACES Project, we quantified census tract-level vulnerability overall and in 4 primary domains (demographic, economic, residential, and health status), 24 subdomains, and 54 distinct area-level features for New York City (NYC). Overall and domain-specific indices were calculated by summing standardized feature values within the subdomains and then aggregating and weighting based on the number of features within each subdomain within equally-weighted primary domains. In citywide comparisons, NEVI was correlated with multiple existing indices, including the Neighborhood Deprivation Index (r = 0.91) and Social Vulnerability Index (r = 0.87) but provided additional information on features contributing to vulnerability. Vulnerability varied spatially across NYC, and hierarchical cluster analysis using subdomain scores revealed six patterns of vulnerability across domains: 1) low in all, 2) primarily low except residential, 3) medium in all, 4) high demographic, economic, and residential 5) high economic, residential, and health status, and 6) high demographic, economic and health status. Created using methods that offer flexibility for theory-based construction, NEVI provided detailed vulnerability metrics across domains that can inform targeted research and public health interventions aimed at reducing the health impacts from environmental exposures across urban centers.

Urinary metabolites of polycyclic aromatic hydrocarbons and short-acting beta agonist or systemic corticosteroid asthma medication use within NHANES.

BACKGROUND: Within cross-sectional studies like the U.S. National Health and Nutritional Examination Survey (NHANES), researchers have observed positive associations between polycyclic aromatic hydrocarbon (PAH) exposure and asthma diagnosis. It is unclear whether similar relationships exist for measures of acute asthma outcomes, including short-term asthma medication use to alleviate symptoms. We examined the relationship between markers of recent PAH exposure and 30-day short-acting beta agonist (SABA) or systemic corticosteroid use, an indicator for recent asthma symptoms. MATERIALS AND METHODS: For 16,550 children and adults across multiple waves of NHANES (2005-2016), we fit quasi-Poisson multivariable regression models to describe the association between urinary 1-hydroxypyrene (a metabolite of PAH) and SABA or systemic corticosteroid use. We assessed for effect modification by age group and asthma controller medication use. All models were adjusted for urinary creatinine, age, female/male designation, race/ethnicity, poverty, insurance coverage, and serum cotinine. RESULTS: After controlling for confounding, an increase of one standard deviation of 1-hydroxypyrene was associated with greater prevalence of recent SABA or systemic corticosteroid use (PR: 1.06, 95% CI: 1.03-1.10). The results were similar among those with ever asthma diagnosis and across urine creatinine dilution methods. We did not observe effect modification by age group (p-interaction = 0.22) or asthma controller medication use (p-interaction = 0.73). CONCLUSION: Markers of recent PAH exposure was positively associated with SABA or systemic corticosteroid use, across various urine dilution adjustment methods. It is important to ensure appropriate temporality between exposures and outcomes in cross-sectional studies.

Early life stress, prenatal secondhand smoke exposure, and the development of internalizing symptoms across childhood.

BACKGROUND: Prior findings relating secondhand tobacco smoke (SHS) exposure and internalizing problems, characterized by heightened anxiety and depression symptoms, have been equivocal; effects of SHS on neurodevelopment may depend on the presence of other neurotoxicants. Early life stress (ELS) is a known risk factor for internalizing symptoms and is also often concurrent with SHS exposure. To date the interactive effects of ELS and SHS on children's internalizing symptoms are unknown. We hypothesize that children with higher exposure to both prenatal SHS and ELS will have the most internalizing symptoms during the preschool period and the slowest reductions in symptoms over time. METHODS: The present study leveraged a prospective, longitudinal birth cohort of 564 Black and Latinx mothers and their children, recruited between 1998 and 2006. Cotinine extracted from cord and maternal blood at birth served as a biomarker of prenatal SHS exposure. Parent-reported Child Behavior Checklist (CBCL) scores were examined at four timepoints between preschool and eleven years-old. ELS exposure was measured as a composite of six domains of maternal stress reported at child age five. Latent growth models examined associations between SHS, ELS, and their interaction term with trajectories of children's internalizing symptoms. In follow-up analyses, weighted quintile sum regression examined contributions of components of the ELS mixture to children's internalizing symptoms at each time point. RESULTS: ELS interacted with SHS exposure such that higher levels of ELS and SHS exposure were associated with more internalizing symptoms during the preschool period (ß = 0.14, p = 0.03). The interaction between ELS and SHS was also associated with a less negative rate of change in internalizing symptoms over time (ß=-0.02, p = 0.01). Weighted quintile sum regression revealed significant contributions of maternal demoralization and other components of the stress mixture to children's internalizing problems at each age point (e.g., age 11 WQS ß = 0.26, p < 0.01). CONCLUSIONS: Our results suggest that prior inconsistencies in studies of SHS on behavior may derive from unmeasured factors that also influence behavior and co-occur with exposure, specifically maternal stress during children's early life. Findings point to modifiable targets for personalized prevention.

Early intervention and special education in New York City: Patterns of service use and disparities affecting children of colour.

BACKGROUND: Compliance with the requirements of the Individuals with Disabilities Education Act (IDEA) in the United States is monitored through review of cross-sectional reports from three discrete, age-defined programmes (early intervention [EI], early childhood special education [ECSE)] and school-age special education [SE]) to promote the timely, efficient and effective delivery of appropriate services to all eligible children. Analysis of longitudinal data is required to discern how children use services across programmes to provide the necessary context for IDEA oversight and to identify areas for programme or policy interventions to reduce barriers to service use and promote equity. METHODS: We applied sequence analysis to a data linkage across five public record systems among 15 626 New York City children born in 1998 who had records from birth through third grade. RESULTS: Five predominant patterns of service use were identified: (1) multiple therapies across EI/ECSE/SE (13%), (2) EI without transition to Department of Education schools or services (24%), (3) EI and intermittent ECSE/SE (16%), (4) older entry into EI and both speech and occupational therapy throughout ECSE/SE (9%) and (5) limited EI use and mostly speech therapy in ECSE/SE (38%). Each pattern had distinct demographics (e.g., pattern 2 was disproportionately White and from low poverty neighbourhoods; pattern 4 was disproportionately male and Black; pattern 5 was disproportionately Latino) and academic outcomes (e.g., pattern 1 had largest proportion in a SE school and not tested in third grade; pattern 3 had third grade tests scores that were similar to overall citywide mean scores). CONCLUSIONS: The differences in demographic profiles across the five patterns of service use illustrate the systemic inequities in the delivery of these important services. Delayed entry and limited use of EI services among children of colour underscore the need for equity goals to increase early referral and optimize service use.

Prescription Opioid Laws and Opioid Dispensing in US Counties: Identifying Salient Law Provisions With Machine Learning.

BACKGROUND: Hundreds of laws aimed at reducing inappropriate prescription opioid dispensing have been implemented in the United States, yet heterogeneity in provisions and their simultaneous implementation have complicated evaluation of impacts. We apply a hypothesis-generating, multistage, machine-learning approach to identify salient law provisions and combinations associated with dispensing rates to test in future research. METHODS: Using 162 prescription opioid law provisions capturing prescription drug monitoring program (PDMP) access, reporting and administration features, pain management clinic provisions, and prescription opioid limits, we used regularization approaches and random forest models to identify laws most predictive of county-level and high-dose dispensing. We stratified analyses by overdose epidemic phases-the prescription opioid phase (2006-2009), heroin phase (2010-2012), and fentanyl phase (2013-2016)-to further explore pattern shifts over time. RESULTS: PDMP patient data access provisions most consistently predicted high-dispensing and high-dose dispensing counties. Pain management clinic-related provisions did not generally predict dispensing measures in the prescription opioid phase but became more discriminant of high dispensing and high-dose dispensing counties over time, especially in the fentanyl period. Predictive performance across models was poor, suggesting prescription opioid laws alone do not strongly predict dispensing. CONCLUSIONS: Our systematic analysis of 162 law provisions identified patient data access and several pain management clinic provisions as predictive of county prescription opioid dispensing patterns. Future research employing other types of study designs is needed to test these provisions' causal relationships with inappropriate dispensing and to examine potential interactions between PDMP access and pain management clinic provisions. See video abstract at, http://links.lww.com/EDE/B861.

Multi-Level Socioenvironmental Contributors to Childhood Asthma in New York City: a Cluster Analysis.

Childhood asthma exacerbation remains the leading cause of pediatric emergency department visits and hospitalizations and disproportionately affects Latinx and Black children, compared to non-Latinx White children in NYC. Environmental exposures and socioeconomic factors may jointly contribute to childhood asthma exacerbations; however, they are often studied separately. To better investigate the multiple contributors to disparities in childhood asthma, we compiled data on various individual and neighborhood level socioeconomic and environmental factors, including education, race/ethnicity, income disparities, gentrification, housing characteristics, built environment, and structural racism, from the NYC Department of Health's KIDS 2017 survey and the US Census' American Community Survey. We applied cluster analysis and logistic regression to first identify the predominant patterns of social and environmental factors experienced by children in NYC and then estimate whether children experiencing specific patterns are more likely to experience asthma exacerbations. We found that housing and built environment characteristics, such as density and age of buildings, were the predominant features to differentiate the socio-environmental patterns observed in New York City. Children living in neighborhoods with greater proportions of rental housing, high-density buildings, and older buildings were more likely to experience asthma exacerbations than other children. These findings add to the literature about childhood asthma in urban environments, and can assist efforts to target actionable policies and practices that promote health equity related to childhood asthma.

Identifying environmental exposure profiles associated with timing of menarche: A two-step machine learning approach to examine multiple environmental exposures.

BACKGROUND: Variation in the timing of menarche has been linked with adverse health outcomes in later life. There is evidence that exposure to hormonally active agents (or endocrine disrupting chemicals; EDCs) during childhood may play a role in accelerating or delaying menarche. The goal of this study was to generate hypotheses on the relationship between exposure to multiple EDCs and timing of menarche by applying a two-stage machine learning approach. METHODS: We used data from the National Health and Nutrition Examination Survey (NHANES) for years 2005-2008. Data were analyzed for 229 female participants 12-16 years of age who had blood and urine biomarker measures of 41 environmental exposures, all with >70% above limit of detection, in seven classes of chemicals. We modeled risk for earlier menarche (<12 years of age vs older) with exposure biomarkers. We applied a two-stage approach consisting of a random forest (RF) to identify important exposure combinations associated with timing of menarche followed by multivariable modified Poisson regression to quantify associations between exposure profiles ("combinations") and timing of menarche. RESULTS: RF identified urinary concentrations of monoethylhexyl phthalate (MEHP) as the most important feature in partitioning girls into homogenous subgroups followed by bisphenol A (BPA) and 2,4-dichlorophenol (2,4-DCP). In this first stage, we identified 11 distinct exposure biomarker profiles, containing five different classes of EDCs associated with earlier menarche. MEHP appeared in all 11 exposure biomarker profiles and phenols appeared in five. Using these profiles in the second-stage of analysis, we found a relationship between lower MEHP and earlier menarche (MEHP ≤ 2.36 ng/mL vs >2.36 ng/mL: adjusted PR = 1.36, 95% CI: 1.02, 1.80). Combinations of lower MEHP with benzophenone-3, 2,4-DCP, and BPA had similar associations with earlier menarche, though slightly weaker in those smaller subgroups. For girls not having lower MEHP, exposure profiles included other biomarkers (BPA, enterodiol, monobenzyl phthalate, triclosan, and 1-hydroxypyrene); these showed largely null associations in the second-stage analysis. Adjustment for covariates did not materially change the estimates or CIs of these models. We observed weak or null effect estimates for some exposure biomarker profiles and relevant profiles consisted of no more than two EDCs, possibly due to small sample sizes in subgroups. CONCLUSION: A two-stage approach incorporating machine learning was able to identify interpretable combinations of biomarkers in relation to timing of menarche; these should be further explored in prospective studies. Machine learning methods can serve as a valuable tool to identify patterns within data and generate hypotheses that can be investigated within future, targeted analyses.

Interdisciplinary data science to advance environmental health research and improve birth outcomes.

Rates of preterm birth and low birthweight continue to rise in the United States and pose a significant public health problem. Although a variety of environmental exposures are known to contribute to these and other adverse birth outcomes, there has been a limited success in developing policies to prevent these outcomes. A better characterization of the complexities between multiple exposures and their biological responses can provide the evidence needed to inform public health policy and strengthen preventative population-level interventions. In order to achieve this, we encourage the establishment of an interdisciplinary data science framework that integrates epidemiology, toxicology and bioinformatics with biomarker-based research to better define how population-level exposures contribute to these adverse birth outcomes. The proposed interdisciplinary research framework would 1) facilitate data-driven analyses using existing data from health registries and environmental monitoring programs; 2) develop novel algorithms with the ability to predict which exposures are driving, in this case, adverse birth outcomes in the context of simultaneous exposures; and 3) refine biomarker-based research, ultimately leading to new policies and interventions to reduce the incidence of adverse birth outcomes.

Asthma among hospitalized patients with COVID-19 and related outcomes.

BACKGROUND: Several underlying conditions have been associated with severe acute respiratory syndrome coronavirus 2 illness, but it remains unclear whether underlying asthma is associated with worse coronavirus disease 2019 (COVID-19) outcomes. OBJECTIVE: Given the high prevalence of asthma in the New York City area, our objective was to determine whether underlying asthma was associated with poor outcomes among hospitalized patients with severe COVID-19 compared with patients without asthma. METHODS: Electronic heath records were reviewed for 1298 sequential patients 65 years or younger without chronic obstructive pulmonary disease who were admitted to our hospital system with a confirmed positive severe acute respiratory syndrome coronavirus 2 test result. RESULTS: The overall prevalence of asthma among all hospitalized patients with COVID-19 was 12.6%, yet a higher prevalence (23.6%) was observed in the subset of 55 patients younger than 21 years. There was no significant difference in hospital length of stay, need for intubation, length of intubation, tracheostomy tube placement, hospital readmission, or mortality between patients with and without asthma. Observations between patients with and without asthma were similar when stratified by obesity, other comorbid conditions (ie, hypertension, hyperlipidemia, and diabetes), use of controller asthma medication, and absolute eosinophil count. CONCLUSIONS: Among hospitalized patients 65 years or younger with severe COVID-19, asthma diagnosis was not associated with worse outcomes, regardless of age, obesity, or other high-risk comorbidities. Future population-based studies are needed to investigate the risk of developing COVID-19 among patients with asthma once universal testing becomes readily available.

Birth characteristics of children who used early intervention and special education services in New York City.

BACKGROUND: Early intervention (EI) and special education (SE) are beneficial for children with developmental disabilities and/or delays and their families, yet there are disparities in service use. We sought to identify the birth characteristics that predict EI/SE service use patterns. METHODS: We conducted a retrospective cohort study using linked administrative data from five sources for all children born in 1998 to New York City resident mothers. Multinomial regression was used to identify birth characteristics that predicted predominant patterns of service use. RESULTS: Children with service use patterns characterized by late or limited/no EI use were more likely to be first-born children and have Black or Latina mothers. Children born with a gestational age ≤31 weeks were more likely to enter services early. Early term gestational age was associated with patterns of service use common to children with pervasive developmental delay, and maternal obesity was associated with the initiation of speech therapy at the time of entry into school. CONCLUSIONS: Maternal racial disparities existed for patterns of EI/SE service use. Specific birth characteristics, such as parity and gestational age, may be useful to better identify children who are at risk for suboptimal EI use.

Obesity Paradox in Intracerebral Hemorrhage.

Background and Purpose- Although obesity is an established risk factor for cardiovascular disease and stroke, studies have shown evidence of an obesity paradox-a protective effect of obesity in patients who already have these disease states. Data on the obesity paradox in intracerebral hemorrhage is limited. Methods- Clinical data for adult intracerebral hemorrhage patients were extracted from the National Inpatient Sample between 2007 and 2014. Multivariable logistic regression analyzed the association of body habitus with in-hospital mortality, discharge disposition, length of stay, tracheostomy or gastrostomy placement, and ventriculoperitoneal shunt placement. Results- There were 99 212 patients who were eligible. Patients with both obesity (OR=0.69; 95% CI=0.62-0.76; P<0.001) and morbid obesity (OR=0.85; 95% CI=0.74-0.97; P=0.02) were associated with decreased odds of in-hospital mortality. Morbid obesity was significantly associated with increased odds of a tracheostomy or gastrostomy placement (OR=1.42; 1.20-1.69; P<0.001) and decreased odds of a routine discharge disposition (OR=0.84; 0.74-0.97; P=0.014). Conclusions- Obesity and morbid obesity appear to protect against mortality in intracerebral hemorrhage.

Combined association of BTEX and material hardship on ADHD-suggestive behaviours among a nationally representative sample of US children.

BACKGROUND: Previous research shows that environmental and social factors contribute to the development of attention-deficit/hyperactivity disorder (ADHD). OBJECTIVE: To determine the relationship between early-life exposure to common ambient air pollutants (benzene, toluene, ethylbenzene, and xylene, also known as BTEX), household material hardship (a measure of socio-economic status), and ADHD-suggestive behaviours in kindergarten-age children. METHODS: Pollutant exposure estimated from the 2002 National Air Toxics Assessment at each child's residential ZIP code at enrolment was linked to the Early Childhood Longitudinal Study Birth Cohort (n = 4650). Material hardship was assigned as a composite score of access to food, health care, and housing. Kindergarten teachers rated children's behaviours and activity in the classroom using a five-point Likert scale. Children with summary scores in the bottom decile were classified as displaying ADHD-suggestive behaviours. Logistic regression models were constructed to estimate the association between both BTEX exposure and material hardship on ADHD-suggestive behaviours. RESULTS: The odds of displaying ADHD-suggestive behaviours were greater in children with combined high-level exposure to BTEX and in those experiencing material hardship (odds ratio 1.54, 95% confidence interval [CI] 1.12, 2.11, and OR 2.12, 95% CI 1.25, 3.59, respectively), adjusting for covariates. These associations were stronger when restricting the study population to urban areas. There was no evidence of interaction between early life BTEX exposure and material hardship, although the effects of BTEX exposure were slightly greater in magnitude among those with higher material hardship scores. CONCLUSIONS: Children exposed to air toxics, material hardship, or both early in life are more likely to display signs of ADHD-suggestive behaviours as assessed by their kindergarten teachers. The associations between exposures to air pollution and to socio-economic hardship were observed in all children but were particularly strong in those living in urban areas.

Maternal exposure to outdoor air pollution and congenital limb deficiencies in the National Birth Defects Prevention Study.

BACKGROUND: Congenital limb deficiencies (CLDs) are a relatively common group of birth defects whose etiology is mostly unknown. Recent studies suggest maternal air pollution exposure as a potential risk factor. AIM: To investigate the relationship between ambient air pollution exposure during early pregnancy and offspring CLDs. METHODS: The study population was identified from the National Birth Defects Prevention Study, a population-based multi-center case-control study, and consisted of 615 CLD cases and 5,701 controls with due dates during 1997 through 2006. Daily averages and/or maxima of six criteria air pollutants (particulate matter <2.5 µm [PM2.5], particulate matter <10 µm [PM10], nitrogen dioxide [NO2], sulfur dioxide [SO2], carbon monoxide [CO], and ozone [O3]) were averaged over gestational weeks 2-8, as well as for individual weeks during this period, using data from EPA air monitors nearest to the maternal address. Logistic regression was used to estimate odds ratios (aORs) and 95% confidence intervals (CIs) adjusted for maternal age, race/ethnicity, education, and study center. We estimated aORs for any CLD and CLD subtypes (i.e., transverse, longitudinal, and preaxial). Potential confounding by co-pollutant was assessed by adjusting for one additional air pollutant. Using the single pollutant model, we further investigated effect measure modification by body mass index, cigarette smoking, and folic acid use. Sensitivity analyses were conducted restricting to those with a residence closer to an air monitor. RESULTS: We observed near-null aORs for CLDs per interquartile range (IQR) increase in PM10, PM2.5, and O3. However, weekly averages of the daily average NO2 and SO2, and daily max NO2, SO2, and CO concentrations were associated with increased odds of CLDs. The crude ORs ranged from 1.03 to 1.12 per IQR increase in these air pollution concentrations, and consistently elevated aORs were observed for CO. Stronger associations were observed for SO2 and O3 in subtype analysis (preaxial). In co-pollutant adjusted models, associations with CO remained elevated (aORs: 1.02-1.30); but aORs for SO2 and NO2 became near-null. The aORs for CO remained elevated among mothers who lived within 20 km of an air monitor. The aORs varied by maternal BMI, smoking status, and folic acid use. CONCLUSION: We observed modest associations between CLDs and air pollution exposures during pregnancy, including CO, SO2, and NO2, though replication through further epidemiologic research is warranted.

Maternal Exposure to Nitrogen Dioxide, Intake of Methyl Nutrients, and Congenital Heart Defects in Offspring.

Nutrients that regulate methylation processes may modify susceptibility to the effects of air pollutants. Data from the National Birth Defects Prevention Study (United States, 1997-2006) were used to estimate associations between maternal exposure to nitrogen dioxide (NO2), dietary intake of methyl nutrients, and the odds of congenital heart defects in offspring. NO2 concentrations, a marker of traffic-related air pollution, averaged across postconception weeks 2-8, were assigned to 6,160 nondiabetic mothers of cases and controls using inverse distance-squared weighting of air monitors within 50 km of maternal residences. Intakes of choline, folate, methionine, and vitamins B6 and B12 were assessed using a food frequency questionnaire. Hierarchical regression models, which accounted for similarities across defects, were constructed, and relative excess risks due to interaction were calculated. Relative to women with the lowest NO2 exposure and high methionine intake, women with the highest NO2 exposure and lowest methionine intake had the greatest odds of offspring with a perimembranous ventricular septal defect (odds ratio = 3.23, 95% confidence interval: 1.74, 6.01; relative excess risk due to interaction = 2.15, 95% confidence interval: 0.39, 3.92). Considerable departure from additivity was not observed for other defects. These results provide modest evidence of interaction between nutrition and NO2 exposure during pregnancy.

Toward Greater Implementation of the Exposome Research Paradigm within Environmental Epidemiology.

Investigating a single environmental exposure in isolation does not reflect the actual human exposure circumstance nor does it capture the multifactorial etiology of health and disease. The exposome, defined as the totality of environmental exposures from conception onward, may advance our understanding of environmental contributors to disease by more fully assessing the multitude of human exposures across the life course. Implementation into studies of human health has been limited, in part owing to theoretical and practical challenges including a lack of infrastructure to support comprehensive exposure assessment, difficulty in differentiating physiologic variation from environmentally induced changes, and the need for study designs and analytic methods that accommodate specific aspects of the exposome, such as high-dimensional exposure data and multiple windows of susceptibility. Recommendations for greater data sharing and coordination, methods development, and acknowledgment and minimization of multiple types of measurement error are offered to encourage researchers to embark on exposome research to promote the environmental health and well-being of all populations.