Estimating the Effects of [Formula: see text] on Life Expectancy Using Causal Modeling Methods

BACKGROUND: Many cohort studies have reported associations between [Formula: see text] 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 [Formula: see text] 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 [Formula: see text] 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 [Formula: see text] 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 [Formula: see text] exposure of [Formula: see text] (the 2012 National Ambient Air Quality Standard) was 0.89 y less (95% CI: 0.88, 0.91) than estimated for a counterfactual [Formula: see text] exposure of [Formula: see text]. 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 [Formula: see text] at [Formula: see text] compared with 20.1% if exposed to an annual average of [Formula: see text]. CONCLUSIONS: We believe that this is the first study to directly estimate the effect of [Formula: see text] on the distribution of age at death using causal modeling techniques to control for confounding. We find that reducing [Formula: see text] concentrations below the 2012 U.S. annual standard would substantially increase life expectancy in the Medicare population. https://doi.org/10.1289/EHP3130