A national difference in differences analysis of the effect of PM_2.5 on annual death rates

Many studies have reported that PM_2.5 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 PM_2.5 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 PM_2.5 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 PM_2.5 with increased death rates, including below U.S. and E.U. standards.