Previous nationwide studies have reported links between long-term concentrations of fine particulate matter (PM2.5) and COVID-19 infection and mortality rates. In order to translate these results to the state level, we use Bayesian hierarchical models to explore potential links between long-term PM2.5 concentrations and census tract-level rates of COVID-19 outcomes (infections, hospitalizations, and deaths) in Colorado. We explicitly consider how the uncertainty in PM2.5 estimates affects our results by comparing four different PM2.5 surfaces from academic and governmental organizations. After controlling for 20 census tract-level covariates, we find that our results depend heavily on the choice of PM2.5 surface. Using PM2.5 estimates from the United States EPA, we find that a 1 μg/m increase in long-term PM2.5 concentrations is associated with a statistically significant 26% increase in the relative risk of hospitalizations and a 34% increase in mortality. Results for all other surfaces and outcomes were not statistically significant. At the same time, we find a clear association between communities of color and COVID-19 outcomes at the Colorado census tract level that is minimally affected by the choice of PM2.5 surface. A per-interquartile range (IQR) increase in the percent of non-African American people of color was associated with a 31%, 43%, and 56% increase in the relative risk of infection, hospitalization, and mortality respectively, while a per-IQR increase in the proportion of non-Hispanic African Americans was associated with a 4% and 7% increase in the relative risk of infections and hospitalizations. The current disagreement among the different PM2.5 estimates is a key factor limiting our ability to link environmental exposures and health outcomes at the census tract level. These results have strong implications for the implementation of an equitable public health response during the crisis and suggest targeted areas for additional air monitoring in Colorado.

中文翻译：

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科罗拉多州人口普查区的长期空气污染和与 COVID-19 相关的其他风险因素

此前的全国性研究报告了细颗粒物 (PM2.5) 的长期浓度与 COVID-19 感染和死亡率之间的联系。为了将这些结果转化为州一级，我们使用贝叶斯分层模型来探索科罗拉多州长期 PM2.5 浓度与人口普查区层面的 COVID-19 结果（感染、住院和死亡）率之间的潜在联系。通过比较来自学术和政府组织的四种不同的 PM2.5 表面，我们明确考虑了 PM2.5 估计值的不确定性如何影响我们的结果。在控制了 20 个人口普查区域级协变量后，我们发现我们的结果在很大程度上取决于 PM2.5 表面的选择。使用美国 EPA 的 PM2.5 估计值，我们发现长期 PM2.5 浓度每增加 1 μg/m3 与住院相对风险增加 26% 和死亡率增加 34% 具有统计显着性相关。所有其他表面和结果的结果没有统计学意义。与此同时，我们发现科罗拉多州人口普查区层面的有色人种社区与 COVID-19 结果之间存在明显关联，而 PM2.5 表面选择的影响极小。非非裔美国有色人种百分比的每四分位数范围 (IQR) 增加分别与感染、住院和死亡率的相对风险增加 31%、43% 和 56% 相关，而每四分位数范围 (IQR) 增加与感染、住院和死亡的相对风险分别增加-非西班牙裔非裔美国人比例的 IQR 增加与感染和住院相对风险增加 4% 和 7% 相关。当前不同 PM2.5 估计值之间的分歧是限制我们在人口普查区层面将环境暴露与健康结果联系起来的能力的一个关键因素。这些结果对危机期间实施公平的公共卫生应对措施具有重大影响，并建议科罗拉多州进行额外空气监测的目标区域。