A hierarchical modeling approach to estimate regional acute health effects of particulate matter sources.
Stat Med
; 36(9): 1461-1475, 2017 04 30.
Article
em En
| MEDLINE
| ID: mdl-28098412
ABSTRACT
Exposure to particulate matter (PM) air pollution has been associated with a range of adverse health outcomes, including cardiovascular disease hospitalizations and other clinical parameters. Determining which sources of PM, such as traffic or industry, are most associated with adverse health outcomes could help guide future recommendations aimed at reducing harmful pollution exposure for susceptible individuals. Information obtained from multisite studies, which is generally more precise than information from a single location, is critical to understanding how PM impacts health and to informing local strategies for reducing individual-level PM exposure. However, few methods exist to perform multisite studies of PM sources, which are not generally directly observed, and adverse health outcomes. We developed SHared Across a REgion (SHARE), a hierarchical modeling approach that facilitates reproducible, multisite epidemiologic studies of PM sources. SHARE is a two-stage approach that first summarizes information about PM sources across multiple sites. Then, this information is used to determine how community-level (i.e., county-level or city-level) health effects of PM sources should be pooled to estimate regional-level health effects. SHARE is a type of population value decomposition that aims to separate out regional-level features from site-level data. Unlike previous approaches for multisite epidemiologic studies of PM sources, the SHARE approach allows the specific PM sources identified to vary by site. Using data from 2000 to 2010 for 63 northeastern US counties, we estimated regional-level health effects associated with short-term exposure to major types of PM sources. We found that PM from secondary sulfate, traffic, and metals sources was most associated with cardiovascular disease hospitalizations. Copyright © 2017 John Wiley & Sons, Ltd.
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Texto completo:
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Bases de dados:
MEDLINE
Assunto principal:
Modelos Estatísticos
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Exposição por Inalação
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Material Particulado
Tipo de estudo:
Clinical_trials
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Guideline
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Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Stat Med
Ano de publicação:
2017
Tipo de documento:
Article
País de afiliação:
Estados Unidos