Background

Exposure to fine particulate matter (<2.5 mm in aerodynamic diameter, PM_2.5) pollution, even at low concentrations is associated with increased mortality. Estimates of the magnitude of the effect of particulate air pollution on mortality are generally done on a coarse spatial scale, such as 0.5°, and may fail to capture small spatial differences in exposure and baseline rates, which can bias results and impede the ability to consider environmental justice. We estimated the burden of mortality attributable to long-term exposure to ambient PM_2.5 among adults in the Continental United States on a 1 km scale, in order to provide information for decision makers setting health priorities.

Methods

We conducted a health impact assessment for 2015 using a model predicting U.S. PM_2.5 concentrations at a spatial resolution of 1 km cells. We applied a concentration-response curve from a recently published meta-analysis of long-term PM_2.5 mortality association which incorporates new findings at high and low PM_2.5 concentrations. We computed the change in deaths in each grid cell, based on its grid cell population, Zip code level baseline mortality rates, and exposure under two scenarios; a decrease of PM_2.5 exposure levels by 40% and a decrease of PM_2.5 exposure levels to the county minimum PM_2.5 concentrations.

Results

We estimated the deaths would fall by 104,786 (95% CI 57,016–135,726) and 112,040 (95% CI 63,261-159,116) attributable to 40% reduction and reduction to the county minimum PM_2.5 concentrations_, respectively. The greatest mortality impact due to 40% reduction in PM_2.5 was observed in California with; 11,621 (95% CI; 7156-15,989) and Texas with; 9616 (95% CI; 5798–13,352) excess deaths attributable to annual mean PM_2.5 concentrations of 9.54 and 9.12 μg m⁻³, respectively. Within city analyses showed substantial heterogeneity in risk. The estimated Attributable fraction (AF %) in locations with high PM_2.5 levels was 8.6% (95% CI 5.4–11.7) compared to the overall AF% of 4.9% (95% CI; 2.9–6.8). In comparison, results using county average PM_2.5 were smaller than the estimates from the 1 km PM_2.5 datasets. Similarly, estimates using county-level mortality rates were smaller than estimates based on Zip-code level mortality rates.

Conclusions

Our study provides evidence of major health benefits expected from reducing PM_2.5 exposure, even in regions with relatively low PM_2.5 concentrations. Spatial characteristics of exposure and baseline mortality (e.g., accuracy, scales, and variations) in disease burden studies can significantly impact the results.

中文翻译：

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使用高时空分辨率模型估计美国大陆长期暴露于 PM2.5 导致的超额死亡率

背景

暴露于细颗粒物（空气动力学直径小于 2.5 毫米，PM _2.5）污染，即使在低浓度下也会增加死亡率。对颗粒物空气污染对死亡率影响程度的估计通常是在粗略的空间尺度上进行的，例如 0.5°，并且可能无法捕捉到暴露率和基线率的微小空间差异，这可能会使结果产生偏差并阻碍评估的能力。考虑环境正义。我们在 1 公里范围内估计了美国大陆成年人因长期暴露于环境 PM _2.5导致的死亡率负担，以便为制定健康优先事项的决策者提供信息。

方法

_{我们使用预测美国 PM 2.5}浓度的模型对 2015 年的健康影响进行了评估，其空间分辨率为 1 公里单元格。我们应用了最近发表的长期 PM _2.5死亡率关联的荟萃分析中的浓度响应曲线，该荟萃分析结合了高和低 PM _2.5浓度的新发现。我们根据网格单元的数量、邮政编码级别的基线死亡率和两种情况下的暴露情况，计算了每个网格单元中死亡人数的变化；PM _2.5暴露水平降低 40%，并将 PM _2.5暴露水平降低至县最低 PM _2.5浓度。

结果

我们估计死亡人数将分别下降 104,786 人（95% CI 57,016–135,726）和 112,040 人（95% CI 63,261-159,116），这可归因于县最低 PM _2.5浓度_分别降低 40% 和降低。由于 PM _2.5降低 40%，对死亡率的影响最大的是加利福尼亚州；11,621 (95% CI; 7156-15,989) 和德克萨斯州；_{年平均 PM 2.5}浓度分别为 9.54 和 9.12 μg m ^-3导致 9616 (95% CI; 5798–13,352) 额外死亡。城市内部分析显示风险存在显着异质性。高 PM _{2.5地区的估计归因分数 (AF %)}AF 水平为 8.6%（95% CI 5.4–11.7），而总体 AF% 为 4.9%（95% CI；2.9–6.8）。相比之下，使用县平均 PM _2.5的结果小于 1 公里 PM _2.5数据集的估计值。同样，使用县级死亡率的估计值小于基于邮政编码级死亡率的估计值。

结论

_{我们的研究提供了减少 PM 2.5}暴露对健康有益的证据，即使在 PM _2.5浓度相对较低的地区也是如此。疾病负担研究中暴露和基线死亡率的空间特征（例如，准确性、规模和变化）可以显着影响结果。