Abstract
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.
Highlights
The association between air pollution and adverse health is one of the most well-researched topics in epidemiology, with studies spanning different pollutants [1,2,3], timescale of exposure [4,5,6], and outcomes of interest [7,8,9]
Our study aims to address this critical knowledge gap by assessing the sensitivity of fine particle (PM2.5; particles with aerodynamic diameter ≤ 2.5 μm) health effect estimates to the choices of different models for exposure assessment in a time series setting
We focus on the association between daily PM2.5 concentrations and cardiovascular disease (CVD)-related hospitalizations in New York
Summary
The association between air pollution and adverse health is one of the most well-researched topics in epidemiology, with studies spanning different pollutants [1,2,3], timescale of exposure [4,5,6], and outcomes of interest [7,8,9]. Time-series studies in air pollution epidemiology have primarily utilized data from monitoring stations for exposure assignment. Bell et al [11] noted that because the location of monitor systems depends on regulatory compliance and not solely on population density, depending on the pollutant, monitor data are not necessarily best suited for public health research. Air pollution health studies have been increasingly using prediction models for exposure assessment even in areas without monitoring stations. Most studies have assumed that a single exposure model is cor‐ rect, but estimated effects may be sensitive to the choice of exposure model
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