Department of Economics, Fordham University, Bronx, NY, United States of America.
Paris School of Economics, Paris, France.
PLoS One. 2024 Jan 2;19(1):e0296154. doi: 10.1371/journal.pone.0296154. eCollection 2024.
Several observational studies from locations around the globe have documented a positive correlation between air pollution and the severity of COVID-19 disease. Observational studies cannot identify the causal link between air quality and the severity of COVID-19 outcomes, and these studies face three key identification challenges: 1) air pollution is not randomly distributed across geographies; 2) air-quality monitoring networks are sparse spatially; and 3) defensive behaviors to mediate exposure to air pollution and COVID-19 are not equally available to all, leading to large measurement error bias when using rate-based COVID-19 outcome measures (e.g., incidence rate or mortality rate). Using a quasi-experimental design, we explore whether traffic-related air pollutants cause people with COVID-19 to suffer more extreme health outcomes in New York City (NYC). When we address the previously overlooked challenges to identification, we do not detect causal impacts of increased chronic concentrations of traffic-related air pollutants on COVID-19 death or hospitalization counts in NYC census tracts.
几项来自全球各地的观察性研究记录了空气污染与 COVID-19 疾病严重程度之间存在正相关关系。观察性研究无法确定空气质量与 COVID-19 结果严重程度之间的因果关系,并且这些研究面临三个关键的识别挑战:1)空气污染在地理上不是随机分布的;2)空气质量监测网络在空间上稀疏;3)防御行为来减轻对空气污染和 COVID-19 的暴露,并非所有人都同样可用,这导致使用基于比率的 COVID-19 结果测量(例如,发病率或死亡率)时存在大量测量误差偏差。我们使用拟实验设计来探索在纽约市(NYC),与交通相关的空气污染物是否会导致 COVID-19 患者的健康结果更加极端。当我们解决以前被忽视的识别挑战时,我们没有发现增加的慢性交通相关空气污染物浓度对 NYC 人口普查区 COVID-19 死亡或住院人数的因果影响。