Background A growing body of evidence has reported positive associations between long-term exposure to air pollution and poor COVID-19 outcomes. Inconsistent findings have been reported for short-term air pollution, mostly from ecological study designs. Using individual-level data, we studied the association between short-term variation in air pollutants [nitrogen dioxide (NO2), particulate matter with a diameter of <2.5 µm (PM2.5) and a diameter of <10 µm (PM10) and ozone (O3)] and hospital admission among individuals diagnosed with COVID-19. Methods The COVAIR-CAT (Air pollution in relation to COVID-19 morbidity and mortality: a large population-based cohort study in Catalonia, Spain) cohort is a large population-based cohort in Catalonia, Spain including 240 902 individuals diagnosed with COVID-19 in the primary care system from 1 March until 31 December 2020. Our outcome was hospitalization within 30 days of COVID-19 diagnosis. We used individual residential address to assign daily air-pollution exposure, estimated using machine-learning methods for spatiotemporal prediction. For each pandemic wave, we fitted Cox proportional-hazards models accounting for non-linear-distributed lagged exposure over the previous 7 days. Results Results differed considerably by pandemic wave. During the second wave, an interquartile-range increase in cumulative weekly exposure to air pollution (lag0_7) was associated with a 12% increase (95% CI: 4% to 20%) in COVID-19 hospitalizations for NO2, 8% (95% CI: 1% to 16%) for PM2.5 and 9% (95% CI: 3% to 15%) for PM10. We observed consistent positive associations for same-day (lag0) exposure, whereas lag-specific associations beyond lag0 were generally not statistically significant. Conclusions Our study suggests positive associations between NO2, PM2.5 and PM10 and hospitalization risk among individuals diagnosed with COVID-19 during the second wave. Cumulative hazard ratios were largely driven by exposure on the same day as hospitalization.

中文翻译：

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加泰罗尼亚 COVID-19 后短期暴露于空气污染和入院：COVAIR-CAT 研究

背景 越来越多的证据表明，长期暴露于空气污染与 COVID-19 不良结果之间存在正相关关系。关于短期空气污染的报告结果不一致，主要来自生态研究设计。利用个体层面的数据，我们研究了空气污染物[二氧化氮（NO2）、直径<2.5微米的颗粒物（PM2.5）和直径<10微米（PM10）的短期变化之间的关联。 ）和臭氧 (O3)] 以及诊断为 COVID-19 的个体入院情况。方法 COVAIR-CAT（与 COVID-19 发病率和死亡率相关的空气污染：西班牙加泰罗尼亚的一项基于人口的大型队列研究）队列是西班牙加泰罗尼亚的一个基于人口的大型队列，包括 240 902 名被诊断患有 COVID-19 的个体。 2020 年 3 月 1 日至 12 月 31 日期间，初级保健系统中有 19 名患者。我们的结果是在诊断出 COVID-19 后 30 天内住院。我们使用个人居住地址来分配每日空气污染暴露量，并使用机器学习方法进行时空预测进行估计。对于每一波大流行浪潮，我们都拟合了 Cox 比例风险模型，考虑了过去 7 天的非线性分布滞后暴露。结果 大流行浪潮的结果差异很大。在第二波期间，每周累积接触空气污染的四分位范围增加 (lag0_7) 与二氧化氮住院治疗的 COVID-19 增加 12%（95% CI：4% 至 20%）相关，8%（95 PM2.5 的 % CI：1% 至 16%），PM10 的 % CI：9%（95% CI：3% 至 15%）。我们观察到当日 (lag0) 暴露存在一致的正相关性，而超过 lag0 的滞后特定关联通常不具有统计显着性。结论 我们的研究表明，第二波期间诊断为 COVID-19 的个体中，NO2、PM2.5 和 PM10 与住院风险呈正相关。累积风险比主要是由住院当天的暴露造成的。