A national difference in differences analysis of the effect of PM2.5 on annual death rates.

Many studies have reported that PM2.5 was associated with mortality, but these were criticized for unmeasured confounding, not using causal modeling, and not focusing on changes in exposure and mortality rates. Recent studies have used propensity scores, a causal modeling approach that requires the assumption of no unmeasured confounders. We used differences in differences, a causal modeling approach that focuses on exposure changes, and controls for unmeasured confounders by design to analyze PM2.5 and mortality in the U.S. Medicare population, with 623, 036, 820 person-years of follow-up, and 29, 481, 444 deaths. We expanded the approach by clustering ZIP codes into 32 groups based on racial, behavioral and socioeconomic characteristics, and analyzing each cluster separately. We controlled for multiple time varying confounders within each cluster. A separate analysis examined participants whose exposure was always below 12 µg/m3. We found an increase of 1 µg/m3 in PM2.5 produced an increased risk of dying in that year of 3.85 × 10-4 (95% CI 1.95 × 10-4, 5.76 × 10-4). This corresponds to 14,000 early deaths per year per 1 µg/m3. When restricted to exposures below 12 µg/m3, the increased mortality risk was 4.26 × 10-4 (95% CI 1.43 × 10-4, 7.09 × 10-4). Using a causal modeling approach robust to omitted confounders, we found associations of PM2.5 with increased death rates, including below U.S. and E.U. standards.

A self-controlled approach to survival analysis, with application to air pollution and mortality.

BACKGROUND: Many studies have reported that long-term air pollution exposure is associated with increased mortality rates. These investigations have been criticized for failure to control for omitted, generally personal, confounders. Study designs that are robust to such confounders can address this issue. METHODS: We used a self-controlled design for survival analysis. We stratified on each person in the Medicare cohort between 2000 and 2015 who died, and examined whether PM2.5, O3 and NO2 exposures predicted in which follow-up period the death occurred. We used conditional logistic regression stratified on person and controlled for nonlinear terms in calendar year and age. By design slowly varying covariates such as smoking history, BMI, diabetes and other pre-existing conditions, usual alcohol consumption, sex, race, socioeconomic status, and green space were controlled by matching each person to themselves. RESULTS: There were 6,452,618 deaths in the study population in the study period. We observed a 5.37% increase in the mortality rate (95% CI 4.67%, 6.08%) for every 5 µg/m3 increase in PM2.5, a 1.98% (95% CI 1.61%, 2.36%) increase for 5 ppb increment in O3, and a 2.10% decrease (95% CI 1.88%, 2.33%) for a 5 ppb increase in NO2. When restricted to persons whose PM2.5 exposure never exceeded 12 µg/m3 in any year between 2000 and 2015, the effect size increased for PM2.5 (12.71% (11.30, 14.15)), and the signs of O3 and NO2 reversed (-0.26% (-0.88, 0.35) for O3 and 1.77% increase (1.40, 2.13) for NO2). Effect sizes were larger for Blacks (e.g. 7.71% (5.46, 10.02) for PM2.5). CONCLUSION: There is strong evidence that the association between annual exposure to PM2.5 and mortality is not confounded by individual or neighborhood covariates, and continues below the standard. The effects of O3 and NO2 are difficult to disentangle.

Estimating the Effects of PM2.5 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 PM2.5 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 PM2.5 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 PM2.5 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 PM2.5 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 PM2.5 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 PM2.5 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 PM2.5 on the distribution of age at death using causal modeling techniques to control for confounding. We find that reducing PM2.5 concentrations below the 2012 U.S. annual standard would substantially increase life expectancy in the Medicare population. https://doi.org/10.1289/EHP3130.