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.

许多研究报告称 PM _2.5与死亡率相关，但这些研究因未测量混杂因素、未使用因果模型、未关注暴露和死亡率的变化而受到批评。最近的研究使用了倾向评分，这是一种因果建模方法，需要假设没有不可测量的混杂因素。

我们使用差异中的差异（一种关注暴露变化的因果建模方法，并通过设计控制未测量的混杂因素）来分析美国医疗保险人群的 PM _2.5和死亡率，并进行了 623、036、820 人年的随访，以及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。我们控制每个集群内的多个随时间变化的混杂因素。另一项单独的分析检查了暴露量始终低于 12 μg/m ³的参与者。我们发现，PM _2.5每增加 1 μg/m ³ ，该年的死亡风险就会增加 3.85 × 10 ^-4 (95% CI 1.95 × 10 ^-4 , 5.76 × 10 ^-4 )。这相当于每年每 1 μg/m ³就有 14,000 人过早死亡。当暴露量限制在12 μg/m ³以下时，死亡风险增加为4.26 × 10 ^-4 (95% CI 1.43 × 10 ^-4 , 7.09 × 10 ^-4 )。使用对忽略的混杂因素具有稳健性的因果建模方法，我们发现 PM _2.5与死亡率增加之间存在关联，包括低于美国和欧盟标准的死亡率。