Comparison of Long-Term PM2.5 Concentrations from Ground-Based Monitoring, CMAQ Models and Satellite-Derived AOD to Characterize Adverse Cardiovascular Outcomes

摘要

Background: Adverse cardiovascular events have been linked with particulate matter <2.5 pm (PM2.5) exposure obtained primarily from air quality monitors, which rarely co-locate with participant residences. Modeled PM2.5 predictions at finer resolution may more accurately predict residential exposure, though few studies have compared results across different exposure assessment methods. Methods: We utilized a cohort of 5679 patients who had undergone a cardiac catheterization between 2002-2009 and resided in North Carolina. Exposure to PM2.5 for the year prior to catheterization was estimated using data from air quality monitors, Community Multiscale Air Quality (CMAQ) fused models at the census tract and 12km spatial resolutions, and satellite-based models at 10km and 1km resolutions. The coronary artery disease (CAD) index was used to measure severity of CAD, and individuals with an index >23 were considered positive cases with hemodynamically significant lesions. Separate models were constructed for each exposure metric, and logistic regression was used to model odds of having CAD or a recent myocardial infarction (Ml) with each l-pg/m3 increase in PM2.5, adjusting for sex, race, smoking status, socioeconomic status, and urban/rural status. Results: Correlations between exposure metrics ranged from 0.6 to 0.9 and mean annual average PM2.5 levels ranged from 12.3 to 12.8 μg/m3. Interquartile ranges varied across metrics with an IQR of 0.8 for 10km-satellite models and 1.3 for 1km-satellite models. The elevated odds for CAD>23 and Ml ranged across exposure metrics, with a CAD odds ratio of 1.04 (95%CI: 0.99,1.10) for monitored data and 1.13 (95%CI: 1.06,1.21) for 10km-satellite data. Results differed by urban/rural status when using the monitored and census tract-CMAQ metrics. Conclusions: Long-term air pollution exposure was associated with coronary artery disease for both modeled and monitored data. This abstract does not necessarily represent EPA policy.