Short-term PM2.5 and cardiovascular admissions in NY State: assessing sensitivity to exposure model choice

Environmental Health

Xiaomeng Jin¹  Arlene M. Fiore²  Jianzhao Bi³  Siliang Liu⁴  Marianthi-Anna Kioumourtzoglou⁴  Vivian Do⁴  Mike Z. He⁵  Patrick L. Kinney⁶  Nicholas DeFelice⁷  Yang Liu⁸  Tabassum Z. Insaf⁹ 

[1] Department of Chemistry, University of California, Berkeley, Berkeley, CA, USA;Department of Earth and Environmental Sciences, Columbia University, New York, NY, USA;Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY, USA;Department of Environmental & Occupational Health Sciences, University of Washington School of Public Health, Seattle, WA, USA;Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY, USA;Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY, USA;Department of Environmental Medicine and Public Health, Icahn School of Medicine At Mount Sinai, One Gustave L. Levy Place, Box 1057, 10029, New York, NY, USA;Department of Environmental Health, Boston University School of Public Health, Boston, MA, USA;Department of Environmental Medicine and Public Health, Icahn School of Medicine At Mount Sinai, One Gustave L. Levy Place, Box 1057, 10029, New York, NY, USA;Gangarosa Department of Environmental Health, Emory University, Rollins School of Public Health, Atlanta, GA, USA;New York State Department of Health, Albany, NY, USA;School of Public Health, University At Albany, Rensselaer, NY, USA;

关键词: Particulate matter;    Exposure assessment;    Cardiovascular morbidity;   

DOI  :  10.1186/s12940-021-00782-3

来源: Springer

【 摘 要 】

BackgroundAir 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.MethodsWe 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.ResultsFor 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.ConclusionsEffect 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.

【 授权许可】

CC BY