Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA.
Sci Adv. 2020 Nov 4;6(45). doi: 10.1126/sciadv.abd4049. Print 2020 Nov.
Assessing whether long-term exposure to air pollution increases the severity of COVID-19 health outcomes, including death, is an important public health objective. Limitations in COVID-19 data availability and quality remain obstacles to conducting conclusive studies on this topic. At present, publicly available COVID-19 outcome data for representative populations are available only as area-level counts. Therefore, studies of long-term exposure to air pollution and COVID-19 outcomes using these data must use an ecological regression analysis, which precludes controlling for individual-level COVID-19 risk factors. We describe these challenges in the context of one of the first preliminary investigations of this question in the United States, where we found that higher historical PM exposures are positively associated with higher county-level COVID-19 mortality rates after accounting for many area-level confounders. Motivated by this study, we lay the groundwork for future research on this important topic, describe the challenges, and outline promising directions and opportunities.
评估长期暴露于空气污染是否会增加 COVID-19 健康结果(包括死亡)的严重程度,是一个重要的公共卫生目标。COVID-19 数据的可用性和质量限制仍然是开展这一主题结论性研究的障碍。目前,代表性人群的 COVID-19 结果的公开数据仅以区域水平的计数形式存在。因此,使用这些数据研究长期暴露于空气污染与 COVID-19 结果的关系必须采用生态回归分析,这使得无法控制个体层面的 COVID-19 风险因素。我们在美国首次对此问题进行初步调查之一的背景下描述了这些挑战,我们发现,在考虑了许多区域水平的混杂因素后,较高的历史 PM 暴露与更高的县级 COVID-19 死亡率呈正相关。受这项研究的启发,我们为这一重要主题的未来研究奠定了基础,描述了挑战,并概述了有希望的方向和机会。