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 6,900 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.

Machine Learning for Epidemiological Analysis in The Industrial Area for a Sustainable Life

“…The effect of multiple air pollutants on health issues using a two-stage approach were implemented in [26]. Ambient air pollution exposure was identified using a CaRT model in stage 1.…”

Review of recent researches on air pollution effects on human cognitive functions

“…Children who live in regions with higher Isophorone had lower math scores, while exposure to Trichloroethylene indicated no relation with math scores [6]. Jennifer Ailshire and colleagues conducted a study entitled "neighborhood social stressors, fine particulate matter air pollution, and cognitive function among older U.S. adults" in USC Leonard Davis School of Gerontology, University of Southern California, Los Angeles to investigate the cognitive function and effect of air pollution [7]. There have been numerous studies on the relationship between environmental air pollution and adults' cognitive function.…”

“…Twoway regression was used in this study to determine the relationship between various data categories, Chi-square tests were employed to analyze cognitive differences between individuals, and stress level in their living places then the relevant model was illustrated. Research findings indicated more strong relationship between PM2.5 particulates and cognitive mistakes among elders living in stressful areas so that these individuals had higher sensitivity to dangerous environmental factors and stressful factors [7]. Q.Gao and colleagues conducted a study entitled "particulate matter air pollution associated with hospital admissions for mental disorders: A time-series study in Beijing, China" [8].…”

Artificial intelligence and pediatrics: A synthetic mini review

“…Using the thematic analysis ( Figure 3) we identified six themes: AI in brain mapping applications include prediction of child brain maturity based on fMRI [14], brain functional connectivity in preterm infants [15], classifying individuals at high-risk for psychosis based on functional brain activity [16], prediction of pediatric unipolar depression [17], analysis of resting-state brain function for attentiondeficit/hyperactivity disorder, predicting the language outcomes following cochlear implantation [18] and similar; AI use in pattern recognition are used for seizure prediction in children with epilepsy [19], visualization of complex data [20], predicting neurodevelopment [21] , identifying motor abnormalities [22], analysing EMG, ECG and other signal [23], image analysis, segmentation [24], etc;…”