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

• Type of Material: Article
• Authors: Mike Z He, Columbia University; Vivian Do, Columbia University; Siliang Liu, Columbia University; Patrick L Kinney, Boston University; Arlene M Fiore, Columbia University; Xiaomeng Jin, University of California Berkeley; Nicholas DeFelice, Icahn School of Medicine At Mount Sinai; Jianzhao Bi, University of Washington; Yang Liu, Emory University; Tabassum Z Insaf, New York State Department of Health; Marianthi-Anna Kioumourtzoglou, Columbia University
• Language: English
• Date: 2021-08-23
• Publisher: BMC
• Publication Version: Final Published Version
• Copyright Statement: © The Author(s) 2021
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1186/s12940-021-00782-3
• Title of Journal or Parent Work: ENVIRONMENTAL HEALTH
• Volume: 20
• Issue: 1
• Start Page: 93
• End Page: 93
• Grant/Funding Information: This study was partially supported by the National Institute of Environmental Health Sciences (NIEHS) Individual Fellowship Grant (F31 ES029372), Institutional Research Training Grant (T32 ES023770), Research Project Grant (R01 ES030616) and Center Core Grant (P30 ES009089), the New York State Energy Research and Development Authority (Grant number: 91268), NASA Health and Air Quality Applied Sciences Team (HAQAST, Grant NNX16AQ20G), and NASA Applied Sciences Program (Grant NNX16AQ28G), the Columbia Global Policy Initiative Faculty Grant, and the Columbia Weatherhead East Asian Institute Sasakawa Young Leaders Fellowship Fund.
• Keywords: Environmental Sciences; Particulate matter; Science & Technology; LUNG-FUNCTION; LAND-USE REGRESSION; SPATIAL VARIABILITY; HOSPITAL ADMISSIONS; AIR-POLLUTION; NITROGEN-DIOXIDE; Environmental Sciences & Ecology; FINE PARTICULATE MATTER; Public, Environmental & Occupational Health; Cardiovascular morbidity; OZONE EXPOSURE; MEASUREMENT ERROR; Life Sciences & Biomedicine; AMBIENT PM2.5; Exposure assessment
• Research Categories: Environmental Sciences; Chemistry, General

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.