Short-term PM(2.5) and cardiovascular admissions in NY State: assessing sensitivity to exposure model choice

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 (PM(2.5)) spatio-temporal predictions (2002–2012). We employed overdispersed Poisson models to investigate the relationship between daily PM(2.5) and CVD, adjusting for potential confounders, separately for each state-wide PM(2.5) dataset. RESULTS: For all PM(2.5) datasets, we observed positive associations between PM(2.5) 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/m(3) increase in daily PM(2.5). 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 PM(2.5) and CVD admissions, regardless of model choice. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12940-021-00782-3.