Autor: |
Mike Z. He, Vivian Do, Siliang Liu, Patrick L. Kinney, Arlene M. Fiore, Xiaomeng Jin, Nicholas DeFelice, Jianzhao Bi, Yang Liu, Tabassum Z. Insaf, Marianthi-Anna Kioumourtzoglou |
Jazyk: |
angličtina |
Rok vydání: |
2021 |
Předmět: |
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Zdroj: |
Environmental Health, Vol 20, Iss 1, Pp 1-11 (2021) |
Druh dokumentu: |
article |
ISSN: |
1476-069X |
DOI: |
10.1186/s12940-021-00782-3 |
Popis: |
Abstract Background Air pollution health studies have been increasingly using prediction models for exposure assessment even in areas without monitoring stations. To date, most studies have assumed that a single exposure model is correct, but estimated effects may be sensitive to the choice of exposure model. Methods We obtained county-level daily cardiovascular (CVD) admissions from the New York (NY) Statewide Planning and Resources Cooperative System (SPARCS) and four sets of fine particulate matter (PM2.5) spatio-temporal predictions (2002–2012). We employed overdispersed Poisson models to investigate the relationship between daily PM2.5 and CVD, adjusting for potential confounders, separately for each state-wide PM2.5 dataset. Results For all PM2.5 datasets, we observed positive associations between PM2.5 and CVD. Across the modeled exposure estimates, effect estimates ranged from 0.23% (95%CI: -0.06, 0.53%) to 0.88% (95%CI: 0.68, 1.08%) per 10 µg/m3 increase in daily PM2.5. We observed the highest estimates using monitored concentrations 0.96% (95%CI: 0.62, 1.30%) for the subset of counties where these data were available. Conclusions Effect estimates varied by a factor of almost four across methods to model exposures, likely due to varying degrees of exposure measurement error. Nonetheless, we observed a consistently harmful association between PM2.5 and CVD admissions, regardless of model choice. |
Databáze: |
Directory of Open Access Journals |
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