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Long-term Exposure to PM2.5 and Mortality Among Older Adults in the Southeastern US

Wang, Yan; Shi, Liuhua; Lee, Mihye; Liu, Pengfei; Di, Qian; Zanobetti, Antonella; Schwartz, Joel D.

doi: 10.1097/EDE.0000000000000614
Air Pollution

Background: Little is known about what factors modify the effect of long-term exposure to PM2.5 on mortality, in part because in most previous studies certain groups such as rural residents and individuals with lower socioeconomic status (SES) are under-represented.

Methods: We studied 13.1 million Medicare beneficiaries (age ≥65) residing in seven southeastern US states during 2000–2013 with 95 million person-years of follow-up. We predicted annual average of PM2.5 in each zip code tabulation area (ZCTA) using a hybrid spatiotemporal model. We fit Cox proportional hazards models to estimate the association between long-term PM2.5 and mortality. We tested effect modification by individual-level covariates (race, sex, eligibility for both Medicare and Medicaid, and medical history), neighborhood-level covariates (urbanicity, percentage below poverty level, lower education, median income, and median home value), mean summer temperature, and mass fraction of 11 PM2.5 components.

Results: The hazard ratio (HR) for death was 1.021 (95% confidence interval: 1.019, 1.022) per 1 μg m−3 increase in annual PM2.5. The HR decreased with age. It was higher among males, non-whites, dual-eligible individuals, and beneficiaries with previous hospital admissions. It was higher in neighborhoods with lower SES or higher urbanicity. The HR increased with mean summer temperature. The risk associated with PM2.5 increased with relative concentration of elemental carbon, vanadium, copper, calcium, and iron and decreased with nitrate, organic carbon, and sulfate.

Conclusions: Associations between long-term PM2.5 exposure and death were modified by individual-level, neighborhood-level variables, temperature, and chemical compositions.

Supplemental Digital Content is available in the text.

From the aDepartment of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA; and bJohn A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA.

Submitted 31 January 2016; accepted 13 December 2016.

This publication was made possible by USEPA grant RD-83587201. 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. This publication was also made possible by National Institutes of Environmental Health Sciences (NIEHS) Grant ES-000002, R01 ES024332-01, R21 ES024012, and P50 MD010428-01.

The authors report no conflicts of interest.

Because the Medicare dataset was used under a Data User Agreement, the dataset and code are not available for replication.

Supplemental digital content is available through direct URL citations in the HTML and PDF versions of this article (

Correspondence: Yan Wang, Department of Environmental Health, Harvard T.H. Chan School of Public Health, 401 Park Drive, P.O. Box 15677, Boston, MA. E-mail:

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