Abstract：
Numerous studies have demonstrated that short-term exposure to particulate matter less than 10 μm (PM10) is positively associated with the COVID-19 incidence. However, no study has investigated the spatiotemporal pattern in this association, which plays important roles in identifying high-susceptibility regions and stages of epidemic. In this work, taking the 49 native states in America as an example, we used an advanced strategy to investigate this issue. First, time-series generalized additive model (GAM) were independently constructed to obtain the state-specific associations between short-term exposure to PM10 and the daily COVID-19 cases from 1 April 2020 to 31 December 2021. Then, a Leroux-prior-based conditional autoregression (LCAR) was used to spatially smoothen the associations. Third, the temporal variation of association and the reasons underlying the spatiotemporal heterogeneity were investigated by incorporating the time-varying GAM into LCAR. Results showed that PM10 was adversely associated with COVID-19 incidence in all the states. On average, a 10 μg/m3 increase of PM10 was associated with a 7.38% (95% CI 5.20-9.64%) increase in COVID-19 cases. A substantial spatial heterogeneity was observed, with strong associations in the middle and northeastern regions and weak associations in the western regions. The temporal trend of association presented a U shape, with the strongest association in the end of 2021. The vaccination rate was examined as a significant effect modifier. Our study provided the first evidence about the spatiotemporal pattern in PM10-COVID-19 associations and suggested that air pollution deserves more attention in the post-pandemic era and in the middle and northeastern regions in America for COVID-19 control and prevention.
摘要：
许多研究表明，短期暴露于小于10μm的颗粒物（PM10）与COVID-19的发病率呈正相关。然而,没有研究调查这种关联的时空模式，这在确定高易感区域和流行阶段中起着重要作用。在这项工作中,以美国的49个州为例，我们使用了先进的策略来调查这个问题。首先,我们独立构建了时间序列广义加性模型(GAM),以获得2020年4月1日至2021年12月31日PM10短期暴露与每日COVID-19病例之间的状态特异性关联.然后，使用基于Leroux先验的条件自回归(LCAR)对关联进行空间平滑。第三，通过将时变GAM纳入LCAR,研究了关联的时间变化和时空异质性的原因.结果显示，在所有州，PM10与COVID-19的发病率呈负相关。平均而言,PM10增加10μg/m3与COVID-19病例增加7.38%（95%CI5.20-9.64%）相关。观察到很大的空间异质性，中部和东北部地区协会强，西部地区协会弱。关联的时间趋势呈现U形，与2021年底最强的关联。将疫苗接种率作为显著的效应调节剂进行检查。我们的研究提供了有关PM10-COVID-19关联的时空模式的第一个证据，并表明在大流行后时代以及美国中部和东北地区的空气污染值得更多关注，以控制和预防COVID-19。
