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如何将大流行事件的影响降到最低:COVID-19 危机的教训。

How to Minimize the Impact of Pandemic Events: Lessons From the COVID-19 Crisis.

机构信息

Dipartimento di Scienze Chimiche, Università di Padova, Padova, Italy.

Independent Researcher.

出版信息

Int J Health Policy Manag. 2020 Nov 1;9(11):469-474. doi: 10.34172/ijhpm.2020.115.

Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the current pandemic of coronavirus disease 2019 (COVID-19). This pandemic is characterized by a high variability in death rate (defined as the ratio between the number of deaths and the total number of infected people) across world countries. Several possible explanations have been proposed, but it is not clear whether this variability is due to a single predominant factor or instead to multiple causes. Here we addressed this issue using multivariable regression analysis to test the impact of the following factors: the hospital stress (defined as the ratio between the number of infected cases and the total number of hospital beds), the population median age, and the quality of the National Health System (NHS). For this analysis, we chose countries of the world with over 3000 infected cases as of April 1, 2020. Hospital stress was found to be the crucial factor in explaining the variability of death rate, while the others had negligible relevance. Different procedures for quantifying cases of infection and death for COVID-19 could affect the variability in death rate across countries. We therefore applied the same statistical approach to Italy, which is divided into 20 Regions that share the same protocol for counting the outcomes of this pandemic. Correlation between hospital stress and death rate was even stronger than that observed for countries of the world. Based on our findings and the historical trend for the availability of hospital beds, we propose guidelines for policy-makers to properly manage future pandemics.

摘要

严重急性呼吸系统综合症冠状病毒 2 型(SARS-CoV-2)是导致目前的 2019 年冠状病毒病(COVID-19)大流行的原因。这场大流行的特点是世界各国的死亡率(定义为死亡人数与感染总人数的比率)存在很大差异。已经提出了几种可能的解释,但尚不清楚这种可变性是由于单一主要因素还是由于多种原因造成的。在这里,我们使用多元回归分析来解决这个问题,以检验以下因素的影响:医院压力(定义为感染病例数与医院总床位数的比率)、人口中位数年龄和国家卫生系统(NHS)的质量。为此分析,我们选择了截至 2020 年 4 月 1 日感染人数超过 3000 人的世界各国。研究发现,医院压力是解释死亡率差异的关键因素,而其他因素则相关性不大。不同的量化 COVID-19 感染和死亡病例的程序可能会影响各国死亡率的差异。因此,我们将相同的统计方法应用于意大利,意大利分为 20 个地区,这些地区采用相同的方案来计算这种大流行的结果。医院压力与死亡率之间的相关性甚至强于世界各国的相关性。根据我们的研究结果和医院床位供应的历史趋势,我们为决策者提出了指导方针,以妥善管理未来的大流行。

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