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Data-driven machine learning methods present an opportunity to simultaneously assess the impact of multiple air pollutants on health outcomes. The goal of this study was to apply a two-stage, data-driven approach to identify associations between air pollutant exposure profiles and children's cognitive skills. Data from 6900 children enrolled in the Early Childhood Longitudinal Study, Birth Cohort, a national study of children born in 2001 and followed through kindergarten, were linked to estimated concentrations of 104 ambient air toxics in the 2002 National Air Toxics Assessment using ZIP code of residence at age 9 months. In the first-stage, 100 regression trees were learned to identify ambient air pollutant exposure profiles most closely associated with scores on a standardized mathematics test administered to children in kindergarten. In the second-stage, the exposure profiles frequently predicting lower math scores were included within linear regression models and adjusted for confounders in order to estimate the magnitude of their effect on math scores. This approach was applied to the full population, and then to the populations living in urban and highly-populated urban areas. Our first-stage results in the full population suggested children with low trichloroethylene exposure had significantly lower math scores. This association was not observed for children living in urban communities, suggesting that confounding related to urbanicity needs to be considered within the first-stage. When restricting our analysis to populations living in urban and highly-populated urban areas, high isophorone levels were found to predict lower math scores. Within adjusted regression models of children in highly-populated urban areas, the estimated effect of higher isophorone exposure on math scores was −1.19 points (95% CI −1.94, −0.44). Similar results were observed for the overall population of urban children. This data-driven, two-stage approach can be applied to other populations, exposures and outcomes to generate hypotheses within high-dimensional exposure data. [ABSTRACT FROM AUTHOR]

Nutrients that regulate methylation processes may modify susceptibility to the effects of air pollutants. Data from the National Birth Defects Prevention Study, 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 post-conception weeks 2-8, were assigned to 6160 non-diabetic mothers of cases and controls using inverse distance-squared weighting of air monitors within 50 km of maternal residence. Intake 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. [ABSTRACT FROM AUTHOR]

Background: Epidemiologic literature suggests that exposure to air pollutants is associated with fetal development. Objectives: We investigated maternal exposures to air pollutants during weeks 2–8 of pregnancy and their associations with congenital heart defects. Methods: Mothers from the National Birth Defects Prevention Study, a nine-state case–control study, were assigned 1-week and 7-week averages of daily maximum concentrations of carbon monoxide, nitrogen dioxide, ozone, and sulfur dioxide and 24-hr measurements of fine and coarse particulate matter using the closest air monitor within 50 km to their residence during early pregnancy. Depending on the pollutant, a maximum of 4,632 live-birth controls and 3,328 live-birth, fetal-death, or electively terminated cases had exposure data. Hierarchical regression models, adjusted for maternal demographics and tobacco and alcohol use, were constructed. Principal component analysis was used to assess these relationships in a multipollutant context. Results: Positive associations were observed between exposure to nitrogen dioxide and coarctation of the aorta and pulmonary valve stenosis. Exposure to fine particulate matter was positively associated with hypoplastic left heart syndrome but inversely associated with atrial septal defects. Examining individual exposure-weeks suggested associations between pollutants and defects that were not observed using the 7-week average. Associations between left ventricular outflow tract obstructions and nitrogen dioxide and between hypoplastic left heart syndrome and particulate matter were supported by findings from the multipollutant analyses, although estimates were attenuated at the highest exposure levels. Conclusions: Using daily maximum pollutant levels and exploring individual exposure-weeks revealed some positive associations between certain pollutants and defects and suggested potential windows of susceptibility during pregnancy. [ABSTRACT FROM AUTHOR]

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. • Rates of preterm birth and low birthweight continue to rise in the United States. • Interdisciplinary data science can address challenges of previous research. • Integrative analysis of complex environmental data can improve translation efforts. [ABSTRACT FROM AUTHOR]

Purpose: Studies suggest exposure to ambient particulate matter less than 2.5 μg/m3 in aerodynamic diameter (PM2.5) may be associated with preterm birth (PTB), but few have evaluated how this is modified by ambient temperature. We investigated the relationship between PM2.5 exposure during pregnancy and PTB in infants without birth defects (1999-2006) and enrolled in the National Birth Defects Prevention Study and how it is modified by concurrent temperature.Methods: PTB was defined as spontaneous or iatrogenic delivery before 37 weeks. Exposure was assigned using inverse distance weighting with up to four monitors within 50 kilometers of maternal residence. To account for state-level variations, a Bayesian two-level hierarchal model was developed.Results: PTB was associated with PM2.5 during the third and fourth months of pregnancy (range: (odds ratio (95% confidence interval) = 1.00 (0.35, 2.15) to 1.49 (0.82, 2.68) and 1.31 (0.56, 2.91) to 1.62 (0.7, 3.32), respectively); no week of exposure conveyed greater risk. Temperature may modify this relationship; higher local average temperatures during pregnancy yielded stronger positive relationships between PM2.5 and PTB compared to nonstratified results.Conclusions: Results add to literature on associations between PM2.5 and PTB, underscoring the importance of considering co-exposures when estimating effects of PM2.5 exposure during pregnancy. [ABSTRACT FROM AUTHOR]