相关性还是因果关系?解读 PM2.5 空气污染与 COVID-19 在全美蔓延之间的关系

Mohammad Maniat, Hosein Habibi, Elham Manshoorinia, Parisa Raufi, Payam Marous, Masoud Omraninaini
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摘要

许多研究探讨了空气污染(尤其是 PM2.5)与大流行期间 COVID-19 病例发生率之间的潜在联系。虽然有几项研究表明两者之间存在很强的相关性,但由于相关性并不意味着因果关系,因此建议谨慎从事。为了解决这个问题,我们为期两年的观察性研究采用了一种全面的方法,利用了大量样本,并借鉴了全美的时间和空间数据,超越了以往仅限于特定地点的研究的局限性。通过严格的相关性和回归分析,我们控制了潜在的混杂因素。空气污染数据是我们研究的重要组成部分,这些数据来自美国环境保护局(EPA)。此外,COVID-19 案例数据来自约翰霍普金斯大学系统科学与工程中心 (CSSE),为我们的分析提供了一个强大且广受认可的数据集。值得注意的是,COVID-19 病例与人口数量之间存在明显的空间相关性(r=0.98,p 值<0.01),这一点已被多元回归分析所证实,表明人口数量会产生混杂影响。需要强调的是,相关性并不自动意味着直接的因果关系。此外,为了尽量减少人口的影响,我们采用了比率(COVID-19 病例/国家人口),表明 COVID-19 病例的比率与 PM2.5 和人口无关。此外,COVID-19 感染率与人口密度无关,这意味着人口对感染的影响更可能是由于概率而非直接原因。总之,尽管许多研究报告称空气污染与 COVID-19 病例之间存在相关性,但由于人口密度等混杂因素的影响,有必要进行进一步调查,以确定明确的因果关系。总之,尽管许多研究报告称空气污染与 COVID-19 病例之间存在相关性,但仍需进一步调查人口密度等混杂因素的影响,以确定明确的因果关系。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Correlation or Causation: Unraveling the Relationship between PM2.5 Air Pollution and COVID-19 Spread Across the United States
Numerous studies have examined the potential connection between air pollution, particularly PM2.5, and the incidence of COVID-19 cases during the pandemic. While several studies have demonstrated a strong correlation, caution is advised as correlation does not imply causation. To address this concern, our two-year observational study employs a comprehensive approach that utilizes a large sample size and draws on temporal and spatial data across the United States, surpassing the limitations of previous studies restricted to specific locations. Through rigorous correlation and regression analyses, we control for potential confounding factors. Air pollution data, a crucial component of our study, has been sourced from the United States Environmental Protection Agency (EPA). Additionally, COVID-19 case data is extracted from the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, providing a robust and widely recognized dataset for our analyses. Notably, a significant spatial correlation exists between COVID-19 cases and population size (r=0.98, p-value <0.01), as confirmed by multivariate regression analysis, suggesting a confounding influence of population. It is crucial to emphasize that correlation does not automatically imply a direct cause-and-effect relationship. Moreover, to minimize the impact of population, we employ rates (COVID-19 cases/population of States), demonstrating that the rate of COVID-19 cases is independent of PM2.5 and population. Additionally, the rate of COVID-19 infection is not correlated with population density, implying the population's influence on infection is more likely due to probability rather than being a direct cause. In summary, while many studies report a correlation between air pollution and COVID-19 cases, the influence of confounding factors like population density necessitates further investigation to establish a definitive causal relationship. In conclusion, while many studies report a correlation between air pollution and COVID-19 cases, the influence of confounding factors like population density necessitates further investigation to establish a definitive causal relationship. 
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