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Using machine learning to identify air pollution exposure profiles associated with early cognitive skills among U.S. children.

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.

SUBMITTER: Stingone JA

PROVIDER: S-EPMC5595640 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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Using machine learning to identify air pollution exposure profiles associated with early cognitive skills among U.S. children.

Stingone Jeanette A JA Pandey Om P OP Claudio Luz L Pandey Gaurav G

Environmental pollution (Barking, Essex : 1987) 20170718

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 estimat ...[more]

PMID: 28732336

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Project description:BackgroundElucidating associations between exposures to ambient air pollutants and profiles of cognitive performance may provide insight into neurotoxic effects on the aging brain.ObjectiveWe examined associations between empirically derived profiles of cognitive performance and residential concentrations of particulate matter of aerodynamic diameter < 2.5 (PM2.5) and nitrogen dioxide (NO2) in older women.MethodWomen (N = 2,142) from the Women's Health Initiative Study of Cognitive Aging completed a neuropsychological assessment measuring attention, visuospatial, language, and episodic memory abilities. Average yearly concentrations of PM2.5 and NO2 were estimated at the participant's addresses for the 3 years prior to the assessment. Latent profile structural equation models identified subgroups of women exhibiting similar profiles across tests. Multinomial regressions examined associations between exposures and latent profile classification, controlling for covariates.ResultFive latent profiles were identified: low performance across multiple domains (poor multi-domain; n = 282;13%), relatively poor verbal episodic memory (poor memory; n = 216; 10%), average performance across all domains (average multi-domain; n = 974; 45%), superior memory (n = 381; 18%), and superior attention (n = 332; 15%). Using women with average cognitive ability as the referent, higher PM2.5 (per interquartile range [IQR] = 3.64μg/m3) was associated with greater odds of being classified in the poor memory (OR = 1.29; 95% Confidence Interval [CI] = 1.10-1.52) or superior attention (OR = 1.30; 95% CI = 1.10-1.53) profiles. NO2 (per IQR = 9.86 ppb) was associated with higher odds of being classified in the poor memory (OR = 1.38; 95% CI = 1.17-1.63) and lower odds of being classified with superior memory (OR = 0.81; 95% CI = 0.67-0.97).ConclusionExposure to PM2.5 and NO2 are associated with patterns of cognitive performance characterized by worse verbal episodic memory relative to performance in other domains.

Project description:BackgroundChronic exposure to particulate air pollution may accelerate cognitive decline in older adults, although data on this association are limited. Our objective was to examine long-term exposure to particulate matter (PM) air pollution, both coarse ([PM 2.5-10 μm in diameter [PM(2.5-10)]) and fine (PM <2.5 μm in diameter [PM(2.5)]), in relation to cognitive decline.MethodsThe study population comprised the Nurses' Health Study Cognitive Cohort, which included 19,409 US women aged 70 to 81 years. We used geographic information system-based spatiotemporal smoothing models to estimate recent (1 month) and long-term (7-14 years) exposures to PM(2.5-10), and PM(2.5) preceding baseline cognitive testing (1995-2001) of participants residing in the contiguous United States. We used generalized estimating equation regression to estimate differences in the rate of cognitive decline across levels of PM(2.5-10) and PM(2.5) exposures. The main outcome measure was cognition, via validated telephone assessments, administered 3 times at approximately 2-year intervals, including tests of general cognition, verbal memory, category fluency, working memory, and attention.ResultsHigher levels of long-term exposure to both PM(2.5-10) and PM(2.5) were associated with significantly faster cognitive decline. Two-year decline on a global score was 0.020 (95% CI, -0.032 to -0.008) standard units worse per 10 μg/m(3) increment in PM(2.5-10) exposure and 0.018 (95% CI, -0.035 to -0.002) units worse per 10 μg/m(3) increment in PM(2.5) exposure. These differences in cognitive trajectory were similar to those between women in our cohort who were approximately 2 years apart in age, indicating that the effect of a 10-μg/m(3) increment in long-term PM exposure is cognitively equivalent to aging by approximately 2 years.ConclusionLong-term exposure to PM(2.5-10) and PM(2.5) at levels typically experienced by many individuals in the United States is associated with significantly worse cognitive decline in older women.

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Using machine learning to identify air pollution exposure profiles associated with early cognitive skills among U.S. children.

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Using machine learning to identify air pollution exposure profiles associated with early cognitive skills among U.S. children.

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