Estimating the effects of PM_2.5 on life expectancy using causal modeling methods

Background: Many cohort studies have reported associations between PM2.5 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 Medicare 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 μg/m³ (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 μg/m³. 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 μg/m³ compared with 20.1% if exposed to an annual average of 7.5 μg/m³. 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.