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非洲的2019冠状病毒病:对2020年1月至6月21个非洲国家的探索性横断面分析

COVID-19 in Africa: An Explorative Cross-Sectional Analysis of Twenty-One African Countries From January to June 2020.

作者信息

Awoyemi Toluwalase, Adenipekun Ayokunle, Chima-Kalu Roseline, Adedayo Olubukola, Obarombi Joshua, Bello Oluwamayowa, Bello Oluwaseun, Adamu Danladi

机构信息

Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, GBR.

Emergency Medicine, Health Education England West Midlands, Birmingham, GBR.

出版信息

Cureus. 2022 May 6;14(5):e24767. doi: 10.7759/cureus.24767. eCollection 2022 May.

DOI:10.7759/cureus.24767
PMID:35686270
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9170426/
Abstract

INTRODUCTION

Africa has surprisingly recorded better gains in containing the coronavirus spread than countries with the better health indices, such as the USA and UK. The low rate of coronavirus disease 2019 (COVID-19) cases and death in Africa represents a puzzle with different biological and social theories such as low COVID-19 testing capacity, substantial young population, few old people, favourable climate, genetic admixture, infectious disease antibodies, and sound community health care systems proposed. We aimed to understand the COVID-19 preventive measures in a group of twenty-one systematically selected African countries that may explain the low burden of COVID-19 in Africa.

METHODS

Data (COVID-19, health, socioeconomic, and demographics indices) of twenty-one systemically selected African countries were retrieved from the various official country and multilateral organization sources such as Worldbank, and the United nations development Programme (UNDP). The extracted data were analyzed in three large groups: international travel restrictions, physical and social distancing, and movement restrictions (lockdown measures; curfews, partial or/and national lockdowns). Data cleaning, analysis (including Pearson correlation), and visualization were done with Microsoft Excel and Graph Pad Prism version 9 (https://www.graphpad.com/).

RESULT

Southern Africa had the greatest number of cases and deaths within the period studied compared to East Africa, which was the least COVID-19 affected sub-region (in terms of COVID-19 cases and deaths). We observed that coronary artery disease death rate was highly correlated with COVID-19 death density (number of COVID-19 deaths/total population) and similarly observed a correlation between the number of cases and deaths and number of in-country arrivals, pandemic preparedness (health security index), COVID-19 containment, and health index (not correlated with deaths). Finally, we noted that the most effective preventive strategy was the 'use of a face mask'.

CONCLUSION

Africa had fewer COVID-19 cases and COVID-19 related deaths. Our data shows that the rapidity and stringency of COVID-19 preventive measures and government policies, and the low level of tourism in Africa compared to other countries (i.e., low COVID-19 seeding rate) may have been contributory to these favorable statistics. We hope these findings impact how the preparedness for pandemics can be enhanced to decrease the burden of preventable deaths and morbidity.

摘要

引言

令人惊讶的是,与美国和英国等健康指数较好的国家相比,非洲在控制新冠病毒传播方面取得了更好的成效。非洲2019冠状病毒病(COVID-19)的低病例率和死亡率是一个谜,人们提出了不同的生物学和社会学理论来解释,比如COVID-19检测能力低、年轻人口众多、老年人较少、气候适宜、基因混合、传染病抗体以及完善的社区医疗保健系统等。我们旨在了解21个经系统挑选的非洲国家采取的COVID-19预防措施,这些措施可能解释了非洲COVID-19负担较低的原因。

方法

从世界银行和联合国开发计划署(UNDP)等各种官方国家和多边组织来源检索了21个经系统挑选的非洲国家的数据(COVID-19、健康、社会经济和人口统计指数)。提取的数据被分为三大类进行分析:国际旅行限制、物理和社交距离以及行动限制(封锁措施;宵禁、部分或全国性封锁)。使用Microsoft Excel和Graph Pad Prism 9版本(https://www.graphpad.com/)进行数据清理、分析(包括Pearson相关性分析)和可视化。

结果

在研究期间,与受COVID-19影响最小的东非次区域(就COVID-19病例和死亡而言)相比,南部非洲的病例和死亡人数最多。我们观察到冠心病死亡率与COVID-19死亡密度(COVID-19死亡人数/总人口)高度相关,同样观察到病例和死亡人数与国内入境人数、大流行防范(卫生安全指数)、COVID-19遏制情况以及健康指数(与死亡情况不相关)之间存在相关性。最后,我们注意到最有效的预防策略是“佩戴口罩”。

结论

非洲的COVID-19病例和与COVID-19相关的死亡人数较少。我们的数据表明,COVID-19预防措施和政府政策的迅速性和严格性,以及与其他国家相比非洲较低的旅游水平(即较低的COVID-19传播率)可能是这些良好统计数据的促成因素。我们希望这些发现能对如何加强大流行防范以减轻可预防死亡和发病负担产生影响。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a9e/9170426/1898bb0a567d/cureus-0014-00000024767-i08.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a9e/9170426/5dc99531dd74/cureus-0014-00000024767-i06.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a9e/9170426/dda37576dcad/cureus-0014-00000024767-i07.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a9e/9170426/1898bb0a567d/cureus-0014-00000024767-i08.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a9e/9170426/e61c55d3ea6d/cureus-0014-00000024767-i01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a9e/9170426/76275ae2bdff/cureus-0014-00000024767-i02.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a9e/9170426/8ada380d5f82/cureus-0014-00000024767-i03.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a9e/9170426/8dee3803b1b1/cureus-0014-00000024767-i04.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a9e/9170426/415dfbd1b684/cureus-0014-00000024767-i05.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a9e/9170426/5dc99531dd74/cureus-0014-00000024767-i06.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a9e/9170426/dda37576dcad/cureus-0014-00000024767-i07.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a9e/9170426/1898bb0a567d/cureus-0014-00000024767-i08.jpg

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