Fine particulate matter and cardiovascular disease: Comparison of assessment methods for long-term exposure.

Journal Article (Journal Article)

BACKGROUND: Adverse cardiovascular events have been linked with 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; however 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 NC. Exposure to PM2.5 for the year prior to catheterization was estimated using data from air quality monitors (AQS), Community Multiscale Air Quality (CMAQ) fused models at the census tract and 12km spatial resolutions, and satellite-based models at 10km and 1km resolutions. Case status was either a coronary artery disease (CAD) index >23 or a recent myocardial infarction (MI). Logistic regression was used to model odds of having CAD or an MI with each 1-unit (μg/m3) increase in PM2.5, adjusting for sex, race, smoking status, socioeconomic status, and urban/rural status. RESULTS: We found that the elevated odds for CAD>23 and MI were nearly equivalent for all exposure assessment methods. One difference was that data from AQS and the census tract CMAQ showed a rural/urban difference in relative risk, which was not apparent with the satellite or 12km-CMAQ models. CONCLUSIONS: Long-term air pollution exposure was associated with coronary artery disease for both modeled and monitored data.

Full Text

Duke Authors

Cited Authors

  • McGuinn, LA; Ward-Caviness, C; Neas, LM; Schneider, A; Di, Q; Chudnovsky, A; Schwartz, J; Koutrakis, P; Russell, AG; Garcia, V; Kraus, WE; Hauser, ER; Cascio, W; Diaz-Sanchez, D; Devlin, RB

Published Date

  • November 2017

Published In

Volume / Issue

  • 159 /

Start / End Page

  • 16 - 23

PubMed ID

  • 28763730

Pubmed Central ID

  • PMC6100751

Electronic International Standard Serial Number (EISSN)

  • 1096-0953

Digital Object Identifier (DOI)

  • 10.1016/j.envres.2017.07.041


  • eng

Conference Location

  • Netherlands