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.

Identifying critical windows of prenatal phenol, paraben, and pesticide exposure and child neurodevelopment: Findings from a prospective cohort study.

BACKGROUND: This study aimed to investigate how exposure to a mixture of endocrine disrupting chemicals (EDCs) during two points in pregnancy affects early childhood neurodevelopment. METHODS: We analyzed publicly-available data from a high-risk cohort of mothers and their children (2007-2014) that measured six EDCs including methyl-, ethyl- and propyl parabens (MEPB, ETPB, PRPB), Bisphenol-A (BPA), 3,5,6-trichloro-2-pyridinol (TCPy), 3-phenoxybenzoic acid (3-PBA) in prenatal urine samples during the second and third trimesters. Neurodevelopmental scores were assessed using Mullen Scales of Early Learning (MSEL) at age 3. We used mean field variational Bayes for lagged kernel machine regression (MFVB-LKMR) to investigate the association between trimester-specific co-exposure to the six EDCs and MSEL scores at age 3, stratified by sex. RESULTS: The analysis included 130 children. For females, the relationship between BPA and 3PBA with MSEL score varied between the two trimesters. In the second trimester, effect estimates for BPA were null but inversely correlated with MSEL score in the third trimester. 3PBA had a negative relationship with MSEL in the second trimester and positive correlation in the third trimester. For males, effect estimates for all EDCs were in opposing directions across trimesters. MFVB-LKMR analysis identified significant two-way interaction between EDCs for MSEL scores in both trimesters. For example, in females, the MSEL scores associated with increased exposure to TCPy were 1.75 units (95%credible interval -0.04, -3.47) lower in the 2nd trimester and 4.61 (95%CI -3.39, -5.84) lower in the third trimester when PRPB was fixed at the 75th percentile compared to when PRPB was fixed at the 25th percentile. CONCLUSION: Our study provides evidence that timing of EDC exposure within the prenatal period may impact neurodevelopmental outcomes in children. More of these varying effects were identified among females. Future research is needed to explore EDC mixtures and the timing of exposure during pregnancy to enhance our understanding of how these chemicals impact child health.

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.

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.

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.

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.

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.

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.

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.

Community-level exposomics: a population-centered approach to address public health concerns.

Environmental factors affecting health and vulnerability far outweigh genetics in accounting for disparities in health status and longevity in US communities. The concept of the exposome, the totality of exposure from conception onwards, provides a paradigm for researchers to investigate the complex role of the environment on the health of individuals. We propose a complementary framework, community-level exposomics, for population-level exposome assessment. The goal is to bring the exposome paradigm to research and practice on the health of populations, defined by various axes including geographic, social, and occupational. This framework includes the integration of community-level measures of the built, natural and social environments, environmental pollution-derived from conventional and community science approaches, internal markers of exposure that can be measured at the population-level and early responses associated with health status that can be tracked using population-based monitoring. Primary challenges to the implementation of the proposed framework include needed advancements in population-level measurement, lack of existing models with the capability to produce interpretable and actionable evidence and the ethical considerations of labeling geographically-bound populations by exposomic profiles. To address these challenges, we propose a set of recommendations that begin with greater engagement with and empowerment of affected communities and targeted investment in community-based solutions. Applications to urban settings and disaster epidemiology are discussed as examples for implementation.

The effects of the historical practice of residential redlining in the United States on recent temporal trends of air pollution near New York City schools.

BACKGROUND: In the 1930's the United States (US) sponsored Home Owners' Loan Corporation (HOLC) created maps that determined risk for mortgage lending based on the racial and ethnic composition of neighborhoods leading to disinvestment in "redlined" or highest risk neighborhoods. This historical practice has perpetuated racial and economic segregation, and health disparities, that persist today. Interventions near schools where children spend large portions of the day, could impact large groups of children but schools are an often-overlooked environment for exposure. Despite a declining trend of ambient pollution in New York City (NYC) between 1998 and 2012, little is known about differences in air quality improvement near schools by historical redlining neighborhood status. Our objective was to examine if recent temporal trends of air pollution near NYC public schools differed in historically redlined neighborhoods. METHODS: We examined annual average street-level concentrations of combustion-related air pollutants (black carbon (BC), particulate matter (PM2.5), nitrogen dioxide (NO2), and nitric oxide (NO)), within a 250-m radius around schools using NYC Community Air Survey land-use regression models (n = 1,462). Year of monitoring, historical redlining (binary), and summer ozone were included in multivariable linear regression using generalized estimating equation models. Average annual percent change (APC) in pollutant concentration was calculated. Models were further stratified by historical redlining and a multiplicative interaction term (year of monitoring × historical redlining) was used to assess effect modification. RESULTS: Overall, there was a decreasing trend of BC (APC = -4.40%), PM2.5 (-3.92%), NO2 (-2.76%), and NO (-6.20%) during the 10-year period. A smaller reduction of BC, PM2.5 and NO was observed in redlined neighborhoods (n = 722), compared to others (n = 740): BC (APC: -4.11% vs -4.69%; Pinteraction < 0.01), PM2.5 (-3.82% vs -4.11%; Pinteraction < 0.01), and NO (-5.73% vs -6.67%; Pinteraction < 0.01). Temporal trends of NO2 did not differ by historical redlining (Pinteraction = 0.60). CONCLUSIONS: Despite significant reductions in annual average pollution concentrations across NYC, schools in historically redlined neighborhoods, compared to others, experienced smaller decrease in pollution, highlighting a potential ongoing ramification of the discriminatory practice.

Air pollution, children's academic achievement and the potential mediating role of preterm birth.

INTRODUCTION: Previous research has observed relationships between higher prenatal exposure to air pollutants and neurodevelopmental and academic outcomes later in childhood. Identifying intermediate outcomes mediating this relationship would inform prevention and intervention efforts. We aimed to investigate if previously observed associations between prenatal exposure to common urban air pollutants, diesel and perchloroethylene, and performance on third grade standardized tests were mediated through increased risk of preterm birth. METHODS: Data from the 1994-1998 birth cohorts within the New York City Longitudinal Study of Early Development were included in this analysis. Exposure was determined by linking the mother's residence at the time of delivery to the U.S. EPA's 1996 National Air Toxic Assessment of estimated ambient concentrations of diesel and perchloroethylene. Children's third grade standardized math and language tests were used as the markers for academic achievement. Missing data on covariates were imputed, while participants with missing information on gestational age and test scores were excluded. Linear regression models and causal mediation analysis were used to examine potential mediation by preterm birth. RESULTS: In total, 187,723 and 196,122 participants were included in language and math analyses, respectively. Children with exposure to the fourth quartile of diesel or perchloroethylene had approximately 0.03 (95%CI: 0.02, 0.04) lower math z-scores when compared to individuals with exposure in the first quartile, although there was no consistent decreasing trend in math z-scores over increasing quartiles of diesel or perchloroethylene. We did not find evidence of mediation by preterm birth or exposure-mediator interaction in our models. CONCLUSION: We did not find evidence that observed relationships between exposure to common urban air pollutants and test z-scores in childhood were mediated through an increased risk of preterm birth. This suggests other pathways between early exposure to air pollution and neurodevelopment should be investigated with causal mediation approaches.

Receipt of Early Intervention Services Before Age 3 Years and Performance on Third-Grade Standardized Tests Among Children Exposed to Lead.

IMPORTANCE: Research has shown that early intervention programs can improve academic outcomes of children with developmental delays. It has been suggested that similar programs may combat the deleterious effects of lead on children's neurodevelopment. However, to our knowledge, there are no published studies examining this possibility. OBJECTIVE: The objective of this study was to estimate the association between receipt of early intervention services and third-grade standardized test scores among children exposed to lead before age 3 years. DESIGN, SETTING, AND PARTICIPANTS: Cohort study including children born in New York City, New York, from 1994 to 1998 within an administrative data linkage of birth, lead monitoring, early intervention, and education data systems. Participants had a blood lead level of 4 µg/dL or greater at any point before age 3 years and later attended public school in New York City. EXPOSURES: Any use of early intervention services from birth through age 3 years. MAIN OUTCOMES AND MEASURES: Children who did or did not receive early intervention services were matched using propensity scores. Linear and log-binomial regression were used to estimate the association between receipt of early intervention services before age 3 years and standardized test scores in math and English-language arts in third grade. RESULTS: There were 2986 children exposed to lead who received early intervention services before age 36 months. Of these children, 2757 were propensity score-matched to 8160 children who did not receive services. Children who received early intervention services did 7% (95% CI, 3%-12%) of an SD better on math and 10% (95% CI, 5%-14%) of an SD better on English-language arts tests than children who did not receive services. In addition, children who received services were 14% (95% CI, 9%-19%) and 16% (95% CI, 9%-23%) more likely to meet test-based standards in math and English-language arts, respectively, than children who did not receive services. These associations became larger in magnitude when analyses were restricted to children with higher blood lead levels. CONCLUSIONS AND RELEVANCE: By leveraging existing public health data, this study found evidence that receipt of early intervention services may benefit the academic performance of children exposed to lead early in life.

Modeling complex effects of exposure to particulate matter and extreme heat during pregnancy on congenital heart defects: A U.S. population-based case-control study in the National Birth Defects Prevention Study.

BACKGROUND/OBJECTIVE: Research suggests gestational exposure to particulate matter ≤2.5 µm (PM2.5) and extreme heat may independently increase risk of birth defects. We investigated whether duration of gestational extreme heat exposure modifies associations between PM2.5 exposure and specific congenital heart defects (CHDs). We also explored nonlinear exposure-outcome relationships. METHODS: We identified CHD case children (n = 2824) and non-malformed live-birth control children (n = 4033) from pregnancies ending between 1999 and 2007 in the National Birth Defects Prevention Study, a U.S. population-based multicenter case-control study. We assigned mothers 6-week averages of PM2.5 exposure during the cardiac critical period (postconceptional weeks 3-8) using the closest monitor within 50 km of maternal residence. We assigned a count of extreme heat days (EHDs, days above the 90th percentile of daily maximum temperature for year, season, and weather station) during this period using the closest weather station. Using generalized additive models, we explored logit-nonlinear exposure-outcome relationships, concluding logistic models were reasonable. We estimated joint effects of PM2.5 and EHDs on six CHDs using logistic regression models adjusted for mean dewpoint and maternal age, education, and race/ethnicity. We assessed multiplicative and additive effect modification. RESULTS: Conditional on the highest observed EHD count (15) and at least one critical period day during spring/summer, each 5 µg/m3 increase in average PM2.5 exposure was significantly associated with perimembranous ventricular septal defects (VSDpm; OR: 1.54 [95% CI: 1.01, 2.41]). High EHD counts (8+) in the same population were positively, but non-significantly, associated with both overall septal defects and VSDpm. Null or inverse associations were observed for lower EHD counts. Multiplicative and additive effect modification estimates were consistently positive in all septal models. CONCLUSIONS: Results provide limited evidence that duration of extreme heat exposure modifies the PM2.5-septal defects relationship. Future research with enhanced exposure assessment and modeling techniques could clarify these relationships.

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.

Inequalities in Exposure to Ambient Air Neurotoxicants and Disparities in Markers of Neurodevelopment in Children by Maternal Nativity Status.

Exposure levels to environmental pollutants vary significantly among different populations. These inequities in exposure to hazardous air pollutants (HAP) among different populations can contribute to disparities in neurodevelopmental outcomes. The aim of this study was to determine if exposure to HAP varies by maternal nativity status, a demographic marker often overlooked in the study of health disparities. We also assessed if those inequalities in exposure levels are associated with neurodevelopmental measures in young children. To do this, we obtained data from the Early Childhood Longitudinal Study-Birth cohort (ECLS-B), a nationally representative sample of children born in the U.S. in the year 2001 (n = 4750). Bayley's Short Form-Research Edition (BSF-R) was used to measure cognitive development at 2 years of age. Using residential location at nine months of age, participants were assigned exposures to ten HAPs identified as potentially neurotoxic. Linear regression models were used to assess the joint effect of maternal nativity status and HAP exposure on neurodevelopment. Results showed inequities in exposure levels to ten different HAPs among the populations, as approximately 32% of children of foreign-born mothers were exposed to high levels of HAPs, compared to 21% of children born to U.S.-born mothers. Adjusting for socioeconomic factors, both isophorone exposure (a marker of industrial pollution) (-0.04, 95% CI, -0.12, 0.04) and maternal nativity status (-0.17, 95% CI, -0.27, -0.06) were independently associated with lower standardized BSF-R mental scores in children. Interaction between nativity status and isophorone was not statistically significant, but the change in mental scores associated with isophorone exposure was greater in children of foreign-born mothers compared to children of U.S.-born mothers (-0.12, vs. -0.03, p = 0.2). In conclusion, exposure to HAPs within the highest quartile was more commonly found among children of foreign-born mothers as compared to children of US-born mothers, indicating inequities in pollutant exposure by nativity status within urban populations. Exposures associated with nativity status may negatively contribute to children's neurodevelopment.

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.

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.

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.

The Semantic Data Dictionary - An Approach for Describing and Annotating Data.

It is common practice for data providers to include text descriptions for each column when publishing datasets in the form of data dictionaries. While these documents are useful in helping an end-user properly interpret the meaning of a column in a dataset, existing data dictionaries typically are not machine-readable and do not follow a common specification standard. We introduce the Semantic Data Dictionary, a specification that formalizes the assignment of a semantic representation of data, enabling standardization and harmonization across diverse datasets. In this paper, we present our Semantic Data Dictionary work in the context of our work with biomedical data; however, the approach can and has been used in a wide range of domains. The rendition of data in this form helps promote improved discovery, interoperability, reuse, traceability, and reproducibility. We present the associated research and describe how the Semantic Data Dictionary can help address existing limitations in the related literature. We discuss our approach, present an example by annotating portions of the publicly available National Health and Nutrition Examination Survey dataset, present modeling challenges, and describe the use of this approach in sponsored research, including our work on a large NIH-funded exposure and health data portal and in the RPI-IBM collaborative Health Empowerment by Analytics, Learning, and Semantics project. We evaluate this work in comparison with traditional data dictionaries, mapping languages, and data integration tools.