Original Article | Published:

Acute effect of fine particulate matter on mortality in three Southeastern states from 2007–2011

Journal of Exposure Science and Environmental Epidemiology volume 26, pages 173179 (2016) | Download Citation

Abstract

Epidemiologic studies on acute effects of air pollution have generally been limited to larger cities, leaving questions about rural populations behind. Recently, we had developed a spatiotemporal model to predict daily PM2.5 level at a 1 km2 using satellite aerosol optical depth (AOD) data. Based on the results from the model, we applied a case-crossover study to evaluate the acute effect of PM2.5 on mortality in North Carolina, South Carolina, and Georgia between 2007 and 2011. Mortality data were acquired from the Departments of Public Health in the States and modeled PM2.5 exposures were assigned to the zip code of residence of each decedent. We performed various stratified analyses by age, sex, race, education, cause of death, residence, and environmental protection agency (EPA) standards. We also compared results of analyses using our modeled PM2.5 levels and those imputed daily from the nearest monitoring station. 848,270 non-accidental death records were analyzed and we found each 10 μg/m3 increase in PM2.5 (mean lag 0 and lag 1) was associated with a 1.56% (1.19 and 1.94) increase in daily deaths. Cardiovascular disease (2.32%, 1.57–3.07) showed the highest effect estimate. Blacks (2.19%, 1.43–2.96) and persons with education ≤8 year (3.13%, 2.08–4.19) were the most vulnerable populations. The effect of PM2.5 on mortality still exists in zip code areas that meet the PM2.5 EPA annual standard (2.06%, 1.97–2.15). The effect of PM2.5 below both EPA daily and annual standards was 2.08% (95% confidence interval=1.99–2.17). Our results showed more power and suggested that the PM2.5 effects on rural populations have been underestimated due to selection bias and information bias. We have demonstrated that our AOD-based exposure models can be successfully applied to epidemiologic studies. This will add new study populations in rural areas, and will confer more generalizability to conclusions from such studies.

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Acknowledgements

This publication was made possible by USEPA grant RD 83479801. Its contents are solely the responsibility of the grantee and do not necessarily represent the official views of the USEPA. Further, USEPA does not endorse the purchase of any commercial products or services mentioned in the publication.

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Affiliations

  1. Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts, USA

    • Mihye Lee
    • , Petros Koutrakis
    •  & Joel Schwartz
  2. Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA

    • Brent Coull
  3. Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Beer Sheva, Israel

    • Itai Kloog

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The authors declare no conflict of interest.

Corresponding author

Correspondence to Mihye Lee.

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DOI

https://doi.org/10.1038/jes.2015.47