Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta.
Department of Environmental Medicine and Climate Science, Icahn School of Medicine at Mount Sinai, New York.
NEJM Evid. 2024 Dec;3(12):EVIDoa2400182. doi: 10.1056/EVIDoa2400182. Epub 2024 Nov 26.
Fine particulate matter (PM) exposure is adversely linked to atherosclerotic cardiovascular disease (ASCVD). However, most studies focused on PM mass rather than its chemical composition and specific sources. Particulate pollution sources can have distinct, cumulative, and potentially synergistic health impacts. We investigated the associations of source-specific PM exposure with ASCVD mortality in the United States, considering the combined associations and regional variations.
We used data from the Centers for Medicare & Medicaid Services (including data from 65,838,403 participants) from 2000 to 2016. We estimated PM exposure using machine-learning models and attributed components to five source categories. We used Poisson survival models to assess the associations with the source categories.
Higher ASCVD mortality rate (rate ratio [95% confidence interval (CI)] per interquartile range increase) was associated with oil combustion (1.051 [1.049 to 1.052]), industrial pollution (1.054 [1.052 to 1.056]), coal and biomass burning (1.065 [1.062 to 1.067]), and motor vehicle pollution (1.044 [1.042 to 1.046]). These associations persisted even after limiting our sample to ZIP code-years with PM<9 μg/m - the current National Ambient Air Quality Standard. In these areas the observed rate ratio for a one-unit increase in PM mass was 1.028 (95% CI, 1.026 to 1.029).
We found higher ASCVD mortality rate associated with PM, with differential effects across sources. These data highlight the importance of considering local population characteristics and exposure patterns when assessing health risks associated with PM.
细颗粒物（PM）暴露与动脉粥样硬化性心血管疾病（ASCVD）呈负相关。然而，大多数研究都集中在 PM 质量上，而不是其化学成分和特定来源。颗粒物污染源可能具有不同的、累积的、潜在协同的健康影响。我们研究了美国特定污染源 PM 暴露与 ASCVD 死亡率之间的关联，同时考虑了综合关联和区域差异。
我们使用了 2000 年至 2016 年美国医疗保险和医疗补助服务中心（包括来自 65838403 名参与者的数据）的数据。我们使用机器学习模型来估计 PM 暴露量，并将成分归因于五个源类别。我们使用泊松生存模型来评估与源类别之间的关联。
ASCVD 死亡率（每增加一个四分位距的比率比[95%置信区间（CI）]）与石油燃烧（1.051[1.049 至 1.052]）、工业污染（1.054[1.052 至 1.056]）、煤和生物质燃烧（1.065[1.062 至 1.067]）和机动车污染（1.044[1.042 至 1.046]）相关。即使在将我们的样本限制在 PM<9μg/m 的邮政编码-年，这些关联仍然存在，目前的国家环境空气质量标准。在这些地区，PM 质量每增加一个单位的观察到的比率比为 1.028（95%CI，1.026 至 1.029）。
我们发现 ASCVD 死亡率与 PM 呈正相关，不同来源的影响也不同。这些数据强调了在评估与 PM 相关的健康风险时，考虑当地人口特征和暴露模式的重要性。
