Long-term exposure to PM2.5 major components and mortality in the southeastern United States.

2022 
Abstract Background Long-term exposure to fine particulate matter (PM2.5) mass has been associated with adverse health effects. However, the health effects of PM2.5 components have been less studied. There is a pressing need to better understand the relative contribution of components of PM2.5, which can lay the scientific basis for designing effective policies and targeted interventions. Methods We conducted a population-based cohort study, comprising all Medicare enrollees aged 65 or older in the southeastern United States from 2000 to 2016, to explore the associations between long-term exposure to PM2.5 major components and all-cause mortality among the elderly. Based on well-validated prediction models, we estimated ZIP code-level annual mean concentrations for five major PM2.5 components, including black carbon (BC), nitrate (NIT), organic matter (OM), sulfate (SO4), and soil particles. Data were analyzed using Cox proportional hazards models, adjusting for potential confounders. Results The cohort comprised 13,590,387 Medicare enrollees and a total of 107,191,652 person-years. In single-component models, all five major PM2.5 components were significantly associated with elevated all-cause mortality. The hazard ratios (HR) per interquartile range (IQR) increase in exposure were 1.027 (95% CI: 1.025–1.030), 1.012 (95% CI: 1.010–1.013), 1.018 (95% CI: 1.017–1.020), 1.021 (95% CI: 1.017–1.024), and 1.004 (95% CI: 1.003–1.006) for BC, NIT, OM, SO4, and soil particles, respectively. While the effect estimate of soil component was statistically significant, it is much smaller than those of combustion-related components. Conclusion Our study provides epidemiological evidence that long-term exposure to major PM2.5 components is significantly associated with elevated mortality.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    64
    References
    0
    Citations
    NaN
    KQI
    []