BACKGROUND: Many cohort studies have reported associations between PM 2:5 and the hazard of dying, but few have used formal causal modeling methods, estimated marginal effects, or directly modeled the loss of life expectancy. OBJECTIVE: Our goal was to directly estimate the effect of PM 2:5 on the distribution of life span using causal modeling techniques. METHODS: We derived nonparametric estimates of the distribution of life expectancy as a function of PM 2:5 using data from 16,965,154

more »

... e beneficiaries in the Northeastern and mid-Atlantic region states (129,341,959 person-years of follow-up and 6,334,905 deaths). We fit separate inverse probability-weighted logistic regressions for each year of age to estimate the risk of dying at that age given the average PM 2:5 concentration at each subject's residence ZIP code in the same year, and we used Monte Carlo simulations to estimate confidence intervals. RESULTS: The estimated mean age at death for a population with an annual average PM 2:5 exposure of 12 lg=m 3 (the 2012 National Ambient Air Quality Standard) was 0.89 y less (95% CI: 0.88, 0.91) than estimated for a counterfactual PM 2:5 exposure of 7:5 lg=m 3 . In comparison, life expectancy at 65 y of age increased by 0.9 y between 2004 and 2013 in the United States. We estimated that 23.5% of the Medicare population would die before 76 y of age if exposed to PM 2:5 at 12 lg=m 3 compared with 20.1% if exposed to an annual average of 7:5 lg=m 3 . CONCLUSIONS: We believe that this is the first study to directly estimate the effect of PM 2:5 on the distribution of age at death using causal modeling techniques to control for confounding. We find that reducing PM 2:5 concentrations below the 2012 U.S. annual standard would substantially increase life expectancy in the Medicare population. https://doi.