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PM2.5 and Mortality in 207 US Cities: Modification by Temperature and City Characteristics

Kioumourtzoglou, Marianthi-Anna; Schwartz, Joel; James, Peter; Dominici, Francesca; Zanobetti, Antonella

doi: 10.1097/EDE.0000000000000422
Air Pollution

Background: The reported estimated effects between long-term PM2.5 exposures and mortality vary spatially. We assessed whether community-level variables, including socioeconomic status indicators and temperature, modify this association.

Methods: We used data from >35 million Medicare enrollees from 207 US cities (2000–2010). For each city, we calculated annual PM2.5 averages, measured at ambient central monitoring sites. We used a variation of a causal modeling approach and fitted city-specific Cox models, which we then pooled using a random effects meta-regression. In this second stage, we assessed whether temperature and city-level variables, including smoking and obesity rates, poverty, education and greenness, modify the long-term PM2.5–mortality association.

Results: We found an association between long-term PM2.5 and survival (hazard ratio = 1.2; 95% confidence interval [CI]: 1.1, 1.3 per 10 μg/m3 increase in the annual PM2.5 average concentrations). We observed elevated estimates in the Southeastern, South and Northwestern US (hazard ratio = 1.9; 95% CI: 1.7, 2.2, and 1.4; 95% CI: 1.2, 1.7, and 1.4; 95% CI: 1.1, 1.9, respectively). We observed a higher association between long-term PM2.5 exposure and mortality in warmer cities. Furthermore, we observed increasing estimates with increasing obesity rates, %residents and families in poverty, %black residents and %population without a high school degree, and lower effects with increasing median household income and %white residents.

Conclusions: To the best of our knowledge, this is the first study to assess modification by temperature and community-level characteristics on the long-term PM2.5–survival association. Our findings suggest that living in cities with high temperatures and low socio economic status (SES) is associated with higher effect estimates.

Supplemental Digital Content is available in the text.

From the Departments of aEnvironmental Health, bEpidemiology, & cBiostatistics, Harvard T.H. Chan School of Public Health, Boston, MA.

Submitted 7 January 2015; accepted 20 November 2015.

This publication was developed under a STAR Research Assistance Agreement No. RD-834900 and was also made possible by USEPA STAR Fellowship Assistance Agreement No. FP-9172890-01, Grant RD 83479801, and R834894 awarded by the US Environmental Protection Agency. 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 Grants HEI 4909, NIH T32 ES007069, NHLBI T32 HL 098048, NIH R01 ES019560, R21 ES020152, R21 ES024012, and NIEHS R01 ES019955, R01 ES024332, R21 ES022585-01, and P30 ES000002.

The authors report no conflicts of interest.

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Correspondence: Marianthi-Anna Kioumourtzoglou, Harvard T.H. Chan School of Public Health, 401 Park Drive Landmark Building, 3rd Floor East, Boston, MA 02215. E-mail:

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