Long-Term Exposure To PM2.5 And Mortality In 207 Us Cities: Modification By Population Characteristics

Abstract

Introduction: Although the association between long-term PM2.5 exposures and mortality has been consistently reported in the literature, these estimates vary spatially. We conducted a study to assess whether community-level variables, including socioeconomic status (SES) indicators and temperature modify the association between long term exposure to PM2.5 and mortality. Methods: We used data from =35 million Medicare enrollees living in 207 U.S. cities (2000–2010). For each city, we calculated annual PM2.5 averages, measured at ambient central monitoring sites, as the exposure of interest. We used a variation of a causal modeling approach, fitting Cox models, separately within each city, and we pooled the city-specific effects 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, %black residents, % developed land etc. modify the association between long term exposure to PM2.5 and mortality. Results: We found a significant association between long-term PM2.5 effects and survival (HR = 1.19; 95%CI: 1.11–1.28 and 1.17; 95%CI: 1.05–1.31 per 10 μg/m3 increase in the annual and 2-year PM2.5 average concentrations). We observed significant effect modification by temperature, with higher effect estimates in warmer cities. Furthermore, we observed increasing effects with increasing obesity rates, percent of residents and families in poverty, proportion of black residents and percent of the population without a high school degree, and lower effects with increasing median household income and proportion of white residents. Conclusions: To our knowledge, this is the first study to assess modification by temperature and community-level characteristics on the association between long-term PM2.5 exposures and survival. Our findings suggest that living in cities with higher temperatures and communities with low SES is associated with higher effect estimates.