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  4. Multi-pollutant exposure profiles associated with term low birth weight in Los Angeles County
 
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Multi-pollutant exposure profiles associated with term low birth weight in Los Angeles County
File(s)
1-s2.0-S0160412016300460-main.pdf (1.53 MB)
Published version
Author(s)
Coker, E
Liverani, S
Ghosh, JK
Jerrett, M
Beckerman, B
more
Type
Journal Article
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.
Date Issued
2016-02-15
Date Acceptance
2016-02-05
Citation
Environment International, 2016, 91, pp.1-13
URI
http://hdl.handle.net/10044/1/40523
DOI
https://www.dx.doi.org/10.1016/j.envint.2016.02.011
ISSN
1873-6750
Publisher
Elsevier
Start Page
1
End Page
13
Journal / Book Title
Environment International
Volume
91
Copyright Statement
© 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license
(http://creativecommons.org/licenses/by/4.0/).
License URL
http://creativecommons.org/licenses/by/4.0/
Subjects
Air pollution
Bayesian
Clustering
Low birth weight
Pollutant profile
Profile regression
Environmental Sciences
MD Multidisciplinary
Publication Status
Published
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