4.7 Article

Associations between Long-Term Exposure to Chemical Constituents of Fine Particulate Matter (PM2.5) and Mortality in Medicare Enrollees in the Eastern United States

Journal

ENVIRONMENTAL HEALTH PERSPECTIVES
Volume 123, Issue 5, Pages 467-474

Publisher

US DEPT HEALTH HUMAN SCIENCES PUBLIC HEALTH SCIENCE
DOI: 10.1289/ehp.1307549

Keywords

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Funding

  1. U.S. Environmental Protection Agency [R 834894, RD 83479801, RD 83490001]
  2. Health Effects Institute [4909]
  3. National Institute of Environmental Health Sciences/National Institutes of Health (NIH) [R01 ES019560, R01 ES019955, R01 ES019587, R21 ES022585-01]
  4. National Cancer Institute/NIH [P01 CA134294]
  5. National Research Foundation of Korea [NRF-2011-0013111, 2012K1A3A1A12054839, K21004000001-10A0500-00710]
  6. National Research Foundation of Korea [21A20131412859, 2011-0013111, 2012K1A3A1A12054839] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)

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BACKGROUND: Several epidemiological studies have reported that long-term exposure to fine particulate matter (PM2.5) is associated with higher mortality. Evidence regarding contributions of PM2.5 constituents is inconclusive. OBJECTIVES: We assembled a data set of 12.5 million Medicare enrollees (>= 65 years of age) to determine which PM2.5 constituents are a) associated with mortality controlling for previous-year PM2.5 total mass (main effect); and b) elevated in locations exhibiting stronger associations between previous-year PM2.5 and mortality (effect modification). METHODS: For 518 PM2.5 monitoring locations (eastern United States, 2000-2006), we calculated monthly mortality rates, monthly long-term (previous 1-year average) PM2.5, and 7-year averages (2000-2006) of major PM2.5 constituents [elemental carbon (EC), organic carbon matter (OCM), sulfate (SO42-), silicon (Si), nitrate (NO3-), and sodium (Na)] and community-level variables. We applied a Bayesian hierarchical model to estimate location-specific mortality rates associated with previous-year PM2.5 (model level 1) and identify constituents that contributed to the spatial variability of mortality, and constituents that modified associations between previous-year PM2.5 and mortality (model level 2), controlling for community-level confounders. RESULTS: One-standard deviation (SD) increases in 7-year average EC, Si, and NO3- concentrations were associated with 1.3% [95% posterior interval (PI): 0.3, 2.2], 1.4% (95% PI: 0.6, 2.4), and 1.2% (95% PI: 0.4, 2.1) increases in monthly mortality, controlling for previous-year PM2.5. Associations between previous-year PM2.5 and mortality were stronger in combination with 1-SD increases in SO42- and Na. CONCLUSIONS: Long-term exposures to PM2.5 and several constituents were associated with mortality in the elderly population of the eastern United States. Moreover, some constituents increased the association between long-term exposure to PM2.5 and mortality. These results provide new evidence that chemical composition can partly explain the differential toxicity of PM2.5.

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