ArticleEnvironmental pollution (Barking, Essex : 1987)2017
Using machine learning to identify air pollution exposure profiles associated with early cognitive skills among U.S. children.
Article in Environmental pollution (Barking, Essex : 1987), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it, 60 citations in OpenAlex.
- Air Pollution and Cognitive Impairment across the Life Course in Humans: A Systematic Review with Specific Focus on Income Level of Study Area.International journal of environmental research and public health · 2022Pooled it
- A systematic review of data mining and machine learning for air pollution epidemiology.BMC public health · 2017Pooled it
- Air Pollution Exposure, Prefrontal Connectivity, and Emotional Behavior in Early Adolescence.Research report (Health Effects Institute) · 2025Article
- Prenatal metal exposures and childhood gut microbial signatures are associated with depression score in late childhood.The Science of the total environment · 2024Article
- A pruned feed-forward neural network (pruned-FNN) approach to measure air pollution exposure.Environmental monitoring and assessment · 2023Article
- Disparities in Toxic Chemical Exposures and Associated Neurodevelopmental Outcomes: A Scoping Review and Systematic Evidence Map of the Epidemiological Literature.Environmental health perspectives · 2023Article
- Cumulative Neurotoxicological Air Pollution Exposure Is Associated with Lower Reading Improvement and Diminished Benefits of Literacy Interventions for Urban Elementary Students of Color.Journal of urban health : bulletin of the New York Academy of Medicine · 2023Article
- Long-term exposure to particulate matter was associated with increased dementia risk using both traditional approaches and novel machine learning methods.Scientific reports · 2022Article
- Machine learning-driven identification of early-life air toxic combinations associated with childhood asthma outcomes.The Journal of clinical investigation · 2021Article
- Prescription Opioid Laws and Opioid Dispensing in US Counties: Identifying Salient Law Provisions With Machine Learning.Epidemiology (Cambridge, Mass.) · 2021Article
- Inequalities in Exposure to Ambient Air Neurotoxicants and Disparities in Markers of Neurodevelopment in Children by Maternal Nativity Status.International journal of environmental research and public health · 2021Article
- Reflection on modern methods: good practices for applied statistical learning in epidemiology.International journal of epidemiology · 2021Article
- Identifying environmental exposure profiles associated with timing of menarche: A two-step machine learning approach to examine multiple environmental exposures.Environmental research · 2021Article
- What about the environment? Leveraging multi-omic datasets to characterize the environment's role in human health.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2021Article
- Prenatal PMNeurotoxicology · 2020Article
- Machine Learning Within Studies of Early-Life Environmental Exposures and Child Health: Review of the Current Literature and Discussion of Next Steps.Current environmental health reports · 2020Review
- Hazardous air pollutants and telomere length in the Sister Study.Environmental epidemiology (Philadelphia, Pa.) · 2019Article
- AI in Health: State of the Art, Challenges, and Future Directions.Yearbook of medical informatics · 2019Review
- Complex Mixtures, Complex Analyses: an Emphasis on Interpretable Results.Current environmental health reports · 2019Review
- The Combined Influence of Air Pollution and Home Learning Environment on Early Cognitive Skills in Children.International journal of environmental research and public health · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.