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Children’s Lead Exposure: A Multimedia Modeling Analysis to Guide Public Health Decision-Making

BACKGROUND: Drinking water and other sources for lead are the subject of public health concerns around the Flint, Michigan, drinking water and East Chicago, Indiana, lead in soil crises. In 2015, the U.S. Environmental Protection Agency (EPA)’s National Drinking Water Advisory Council (NDWAC) recomm...

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Autores principales: Zartarian, Valerie, Xue, Jianping, Tornero-Velez, Rogelio, Brown, James
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Environmental Health Perspectives 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5915183/
https://www.ncbi.nlm.nih.gov/pubmed/28934096
http://dx.doi.org/10.1289/EHP1605
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author Zartarian, Valerie
Xue, Jianping
Tornero-Velez, Rogelio
Brown, James
author_facet Zartarian, Valerie
Xue, Jianping
Tornero-Velez, Rogelio
Brown, James
author_sort Zartarian, Valerie
collection PubMed
description BACKGROUND: Drinking water and other sources for lead are the subject of public health concerns around the Flint, Michigan, drinking water and East Chicago, Indiana, lead in soil crises. In 2015, the U.S. Environmental Protection Agency (EPA)’s National Drinking Water Advisory Council (NDWAC) recommended establishment of a “health-based, household action level” for lead in drinking water based on children’s exposure. OBJECTIVES: The primary objective was to develop a coupled exposure–dose modeling approach that can be used to determine what drinking water lead concentrations keep children’s blood lead levels (BLLs) below specified values, considering exposures from water, soil, dust, food, and air. Related objectives were to evaluate the coupled model estimates using real-world blood lead data, to quantify relative contributions by the various media, and to identify key model inputs. METHODS: A modeling approach using the EPA’s Stochastic Human Exposure and Dose Simulation (SHEDS)-Multimedia and Integrated Exposure Uptake and Biokinetic (IEUBK) models was developed using available data. This analysis for the U.S. population of young children probabilistically simulated multimedia exposures and estimated relative contributions of media to BLLs across all population percentiles for several age groups. RESULTS: Modeled BLLs compared well with nationally representative BLLs (0–23% relative error). Analyses revealed relative importance of soil and dust ingestion exposure pathways and associated Pb intake rates; water ingestion was also a main pathway, especially for infants. CONCLUSIONS: This methodology advances scientific understanding of the relationship between lead concentrations in drinking water and BLLs in children. It can guide national health-based benchmarks for lead and related community public health decisions. https://doi.org/10.1289/EHP1605
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spelling pubmed-59151832018-04-25 Children’s Lead Exposure: A Multimedia Modeling Analysis to Guide Public Health Decision-Making Zartarian, Valerie Xue, Jianping Tornero-Velez, Rogelio Brown, James Environ Health Perspect Research BACKGROUND: Drinking water and other sources for lead are the subject of public health concerns around the Flint, Michigan, drinking water and East Chicago, Indiana, lead in soil crises. In 2015, the U.S. Environmental Protection Agency (EPA)’s National Drinking Water Advisory Council (NDWAC) recommended establishment of a “health-based, household action level” for lead in drinking water based on children’s exposure. OBJECTIVES: The primary objective was to develop a coupled exposure–dose modeling approach that can be used to determine what drinking water lead concentrations keep children’s blood lead levels (BLLs) below specified values, considering exposures from water, soil, dust, food, and air. Related objectives were to evaluate the coupled model estimates using real-world blood lead data, to quantify relative contributions by the various media, and to identify key model inputs. METHODS: A modeling approach using the EPA’s Stochastic Human Exposure and Dose Simulation (SHEDS)-Multimedia and Integrated Exposure Uptake and Biokinetic (IEUBK) models was developed using available data. This analysis for the U.S. population of young children probabilistically simulated multimedia exposures and estimated relative contributions of media to BLLs across all population percentiles for several age groups. RESULTS: Modeled BLLs compared well with nationally representative BLLs (0–23% relative error). Analyses revealed relative importance of soil and dust ingestion exposure pathways and associated Pb intake rates; water ingestion was also a main pathway, especially for infants. CONCLUSIONS: This methodology advances scientific understanding of the relationship between lead concentrations in drinking water and BLLs in children. It can guide national health-based benchmarks for lead and related community public health decisions. https://doi.org/10.1289/EHP1605 Environmental Health Perspectives 2017-09-12 /pmc/articles/PMC5915183/ /pubmed/28934096 http://dx.doi.org/10.1289/EHP1605 Text en EHP is an open-access journal published with support from the National Institute of Environmental Health Sciences, National Institutes of Health. All content is public domain unless otherwise noted.
spellingShingle Research
Zartarian, Valerie
Xue, Jianping
Tornero-Velez, Rogelio
Brown, James
Children’s Lead Exposure: A Multimedia Modeling Analysis to Guide Public Health Decision-Making
title Children’s Lead Exposure: A Multimedia Modeling Analysis to Guide Public Health Decision-Making
title_full Children’s Lead Exposure: A Multimedia Modeling Analysis to Guide Public Health Decision-Making
title_fullStr Children’s Lead Exposure: A Multimedia Modeling Analysis to Guide Public Health Decision-Making
title_full_unstemmed Children’s Lead Exposure: A Multimedia Modeling Analysis to Guide Public Health Decision-Making
title_short Children’s Lead Exposure: A Multimedia Modeling Analysis to Guide Public Health Decision-Making
title_sort children’s lead exposure: a multimedia modeling analysis to guide public health decision-making
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5915183/
https://www.ncbi.nlm.nih.gov/pubmed/28934096
http://dx.doi.org/10.1289/EHP1605
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