NYU Center for Data Science and Stern School of Business, New York University, New York, NY, United States.
True Health Initiative, Hamden, CT, United States.
Front Public Health. 2022 Oct 19;10:1010011. doi: 10.3389/fpubh.2022.1010011. eCollection 2022.
As of January 2022, the COVID-19 pandemic was on-going, affecting populations worldwide. The potential risks of the Omicron variant (and future variants) still remain an area of active investigation. Thus, the ultimate human toll of SARS-CoV-2, and, by extension, the variations in that toll among diverse populations, remain unresolved. Nonetheless, an extensive literature on causal factors in the observed patterns of COVID-19 morbidity and cause-specific mortality has emerged-particularly at the aggregate level of analysis. This article explores potential pitfalls in the attribution of COVID outcomes to specific factors in isolation by examining a diverse set of potential factors and their interactions.
We sourced published data to establish a global database of COVID-19 outcomes for 68 countries and augmented these with an array of potential explanatory covariates from a diverse set of sources. We sought population-level aggregate factors from both health- and (traditionally) non-health domains, including: (a) Population biomarkers (b) Demographics and infrastructure (c) Socioeconomics (d) Policy responses at the country-level. We analyzed these data using (OLS) regression and more flexible non-parametric methods such as recursive partitioning, that are useful in examining both potential joint factor contributions to variations in pandemic outcomes, and the identification of possible interactions among covariates across these domains.
Using the national obesity rates of 68 countries as an illustrative predictor covariate of COVID-19 outcomes, we observed marked inconsistencies in apparent outcomes by population. Importantly, we also documented important variations in outcomes, based on interactions of health factors with covariates in other domains that are traditionally not related to biomarkers. Finally, our results suggest that single-factor explanations of population-level COVID-19 outcomes (e.g., obesity vs. cause-specific mortality) appear to be confounded substantially by other factors.
CONCLUSIONS/IMPLICATIONS: Our methods and findings suggest that a full understanding of the toll of the COVID-19 pandemic, as would be central to preparing for similar future events, requires analysis within and among diverse variable domains, and within and among diverse populations. While this may seem apparent, the bulk of the recent literature on the pandemic has focused on one or a few of these drivers in isolation. Hypothesis generation and testing related to pandemic outcomes will benefit from accommodating the nuance of covariate interactions, in an epidemiologic context. Finally, our results add to the literature on the ecological fallacy: the attempt to infer individual drivers and outcomes from the study of population-level aggregates.
截至 2022 年 1 月，COVID-19 大流行仍在继续，影响着全世界的人口。奥密克戎变异株（和未来的变异株）的潜在风险仍然是一个活跃的研究领域。因此，SARS-CoV-2 的最终人类代价，以及由此产生的不同人群中这种代价的变化，仍然没有得到解决。尽管如此，关于 COVID-19 发病率和特定原因死亡率观察模式中的因果因素的大量文献已经出现——特别是在分析的总体水平上。本文通过检查一系列潜在的因素及其相互作用，探讨了将 COVID 结果归因于特定因素时可能出现的陷阱。
我们从已发表的数据中获取了 68 个国家 COVID-19 结果的全球数据库，并从各种来源的一系列潜在解释性协变量中对其进行了扩充。我们从健康和（传统上）非健康领域寻找人群水平的综合因素，包括：（a）人群生物标志物（b）人口统计学和基础设施（c）社会经济学（d）国家层面的政策反应。我们使用（OLS）回归和更灵活的非参数方法，如递归分区，分析这些数据，这些方法在检查大流行结果变化的潜在联合因素贡献以及识别这些领域中协变量之间的可能相互作用方面非常有用。
使用 68 个国家的全国肥胖率作为 COVID-19 结果的一个说明性预测协变量，我们观察到人群中明显的结果不一致。重要的是，我们还记录了基于健康因素与其他传统上与生物标志物无关的领域中的协变量的相互作用的结果变化。最后，我们的结果表明，人群水平 COVID-19 结果的单因素解释（例如，肥胖与特定原因死亡率）似乎受到其他因素的严重混淆。
结论/意义：我们的方法和结果表明，要全面了解 COVID-19 大流行的代价，这对于为类似的未来事件做准备至关重要，需要在不同的变量领域内和之间进行分析，以及在不同的人群中进行分析。虽然这似乎是显而易见的，但最近关于大流行的大量文献都集中在这些驱动因素中的一个或几个因素上。与大流行结果相关的假设生成和检验将受益于在流行病学背景下考虑协变量相互作用的细微差别。最后，我们的结果增加了关于生态谬误的文献：试图从人群水平的总体研究中推断个体驱动因素和结果。
