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.

许多研究报告说，PM _2.5与死亡率有关，但由于无法衡量的混淆，不使用因果模型，不关注暴露和死亡率变化而受到批评。最近的研究使用倾向评分，这是一种因果建模方法，不需要假设任何无法测量的混杂因素。

我们使用差异中的差异，针对暴露变化的因果模型方法，以及通过设计对未测混杂因素的控制来分析美国Medicare人群的PM _2.5和死亡率，随访时间为623、036、820人年， 29，481，444人死亡。我们根据种族，行为和社会经济特征将邮政编码分为32个组，并分别分析每个组，从而扩展了该方法。我们控制了每个集群中的多个时变混杂因素。另一项分析检查了参与者的暴露始终低于12μg/ m ³。我们发现，当年PM _2.5浓度每升高1μg/ m ³，死亡风险就会增加3.85×10 ^-4。（95％CI 1.95×10 ^-4，5.76×10 ^-4）。这相当于每年每1μg/ m ³死亡14,000 。当受限于曝光低于12微克/米³，增加的死亡风险是4.26×10 ^-4（95％CI 1.43×10 ^-4，7.09×10 ^-4）。使用对遗漏的混杂因素鲁棒的因果建模方法，我们发现PM _2.5与死亡率增加相关，包括低于美国和欧盟标准。