RESUMO
BACKGROUND: Inhaled air pollutants are environmental determinants of health with negative impacts on human health. Air pollution has been linked to the incidence and progression of disease, with its effects unequally distributed across the population. Children compared to adults are a highly vulnerable group and suffer disproportionately from systemic environmental inequities exacerbated by social determinants. OBJECTIVE: To explore air pollution cluster patterns among 6- to 19-year-olds from the 2015-2016 National Health and Nutrition Examination Survey (NHANES) and examine chemical cluster associations with social vulnerability. METHODS: NHANES data was extracted for 697 children and adolescents. Social vulnerability characteristics from questionnaires were assembled to construct a modified social vulnerability index (SVI). Thirty-four air pollutant exposure chemicals were measured in urine and available from the laboratory sub-sample A data. K-means clustering classified the sample into three groups: low, medium, and high chemical exposure groups. Logistic regression was used to examine associations between high chemical group membership and SVI after adjusting for age, biological sex, and BMI. Complex survey analysis was conducted using SAS v9.4 to reflect population effects. RESULTS: Air pollution clusters revealed significant differences in mean concentrations between groups for 31 analytes with minimal distinction in mixture profiles. SVI scores differed significantly between the three groups (P = .002), and with each point increase in their SVI, the odds of a child being assigned to the highest-chemical exposure group increased by 11.55% (95% CI: 1.02-1.31), after adjustment. CONCLUSION: Unsupervised clustering of environmental sub-sample specimens from NHANES provides an innovative, multi-pollutant model that can be used to explore exposure patterns in this population. Utilizing the modified SVI allows for the identification of children that may be highly susceptible to air pollution. It is imperative to interpret the research findings in light of historical structural and discriminatory inequalities to develop beneficial and sustainable solutions.