Multi-pollutant exposure profiles associated with term low birth weight in Los Angeles County. (May 2016)

• Record Type: Journal Article
• Authors: Coker, Eric; Liverani, Silvia; Ghosh, Jo Kay; Jerrett, Michael; Beckerman, Bernardo; Li, Arthur; Ritz, Beate; Molitor, John
• Is Part Of: Environment international. Volume 91(2016:Jun.)
• Page Start: 1
• Page End: 13
• Publication Date: 2016-05
• Subjects: Air pollution -- Bayesian -- Clustering -- Low birth weight -- Pollutant profile -- Profile regression; Environmental protection -- Periodicals; Environmental health -- Periodicals; Environmental monitoring -- Periodicals; Environmental Monitoring -- Periodicals; Environnement -- Protection -- Périodiques; Hygiène du milieu -- Périodiques; Environnement -- Surveillance -- Périodiques; Environmental health; Environmental monitoring; Environmental protection; Periodicals; 333.705
• DOI: 10.1016/j.envint.2016.02.011
• Languages: English
• ISSNs: 0160-4120

Abstract

Abstract: Research indicates that multiple outdoor air pollutants and adverse neighborhood conditions are spatially correlated. Yet health risks associated with concurrent exposure to air pollution mixtures and clustered neighborhood factors remain underexplored. Statistical models to assess the health effects from pollutant mixtures remain limited, due to problems of collinearity between pollutants and area-level covariates, and increases in covariate dimensionality. Here we identify pollutant exposure profiles and neighborhood contextual profiles within Los Angeles (LA) County. We then relate these profiles with term low birth weight (TLBW). We used land use regression to estimate NO2, NO, and PM2.5 concentrations averaged over census block groups to generate pollutant exposure profile clusters and census block group-level contextual profile clusters, using a Bayesian profile regression method. Pollutant profile cluster risk estimation was implemented using a multilevel hierarchical model, adjusting for individual-level covariates, contextual profile cluster random effects, and modeling of spatially structured and unstructured residual error. Our analysis found 13 clusters of pollutant exposure profiles. Correlations between study pollutants varied widely across the 13 pollutant clusters. Pollutant clusters with elevated NO2, NO, and PM2.5 concentrations exhibited increased log odds of TLBW, and those with low PM2.5, NO2, and NO concentrations showed lower log odds of TLBW. The spatial patterning of pollutant cluster effects on TLBW, combined with between-pollutant correlations within pollutant clusters, imply that traffic-related primary pollutants influence pollutant cluster TLBW risks. Furthermore, contextual clusters with the greatest log odds of TLBW had more adverse neighborhood socioeconomic, demographic, and housing conditions. Our data indicate that, while the spatial patterning of high-risk multiple pollutant clusters largely overlaps with adverse contextual neighborhood cluster, both contribute to TLBW while controlling for the other. Graphical abstract: Spatial distribution of pollutant profile cluster random effect posterior probabilities. Probabilities close to 1 are indicative of pollutant profile clusters with the highest certainty for effects on term low birth weight. Pollutant profile clusters along major highways and within LA County's urban core impart the highest effect size and highest probability of effects. Highlights: Pollutant profiles across LA County reveal distinct localized spatial patterns. Pollutant profile effects on term low birth weight (TLBW) may be non-linear. Profiles reflective of primary traffic emissions displayed highest TLBW risk. High risk contextual profiles and high risk pollutant profiles overlap spatially. Profile regression shows potential for investigation of multipollutant health risks.