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Research Article| Volume 102, P118-122, January 2021

Africa’s low COVID-19 mortality rate: A paradox?

Open AccessPublished:October 16, 2020DOI:https://doi.org/10.1016/j.ijid.2020.10.038
Africa’s low COVID-19 mortality rate: A paradox?
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      Highlights

      • •
        Coronavirus disease 2019 (COVID-19), now a pandemic, is caused by severe acute respiratory syndrome-coronavirus 2 (SARS-CoV 2).
      • •
        It is life threatening, mostly leading to death in the elderly and those with comorbidities such as cardiovascular disease and diabetes mellitus.
      • •
        COVID-19 mortality rates are generally lower in Africa than in more advanced regions.
      • •
        The positive predictors of COVID-19 mortality include population mean age, life expectancy, and pre-COVID era ‘65-yr+ mortality %’.
      • •
        Pre-COVID era CVD mortality rate is a negative predictor of COVID-19 mortality rate.

      Abstract

      Background

      COVID-19 continues to spread worldwide, with high numbers of fatalities reported first in China, followed by even higher numbers in Italy, Spain, the UK, the USA, and other advanced countries. Most African countries, even with their less advanced healthcare systems, continue to experience lower COVID-19 mortality rates. This was the case as the pandemic reached its first peak, plateaued, and declined. It is currently rising again in some countries, though not as rapidly as before.
      This study aimed to determine the predictors of COVID-19 mortality rate. This may help explain why Africa’s COVID-19 mortality rate is, ironically, lower than that of more advanced countries with better health systems. This will also assist various governments in balancing their COVID-19 restrictive and socioeconomic measures.

      Methodology

      This was an analytical review, which used pre-COVID-19 era population data and current COVID-19 mortality figures to determine predictors of COVID-19 mortality rates. Pearson’s correlation was used to test the association between some population variables and COVID-19 mortality rates. Next, stepwise multiple regression analysis was used to determine significant predictors of COVID-19 mortality rates.

      Results

      Significant positive predictors of COVID-19 mortality rate included pre-COVID-19 era ‘65-yr+ mortality %’ (R2 = 0.574, B = 2.86, p < 0.001), population mean age (R2 = 0.570, B = 4.77, p = 0.001), and life expectancy (R2 = 0.524, B = 1.67, p = 0.008). Pre-COVID-19 era CVD death rate was a negative predictor of COVID-19 mortality rate (R2 = 0.524, B = −0.584, p = 0.012).

      Conclusion

      Africa’s lower COVID-19 mortality rate is due to the lower population mean age, lower life expectancy, lower pre-COVID-19 era ‘65yr+ mortality rate’, and smaller pool of people surviving and living with cardiovascular diseases.

      Keywords

      Introduction

      Coronavirus disease 2019 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (
      • World Health Organization
      Naming the coronavirus disease (COVID-19) and the virus that causes it.
      World Health Organization (WHO), 2020
      ). The outbreak started in Wuhan, China, in December 2019 (
      • Huang C.
      • Wang Y.
      • Li X.
      • Ren L.
      • Zhao J.
      • Hu Y.
      • et al.
      Novel coronavirus — China.
      World Health Organization (WHO), 2020
      ,
      • Huang C.
      • Wang Y.
      • Li X.
      • Ren L.
      • Zhao J.
      • Hu Y.
      • et al.
      Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China.
      Lancet. 2020; 395 (PMC 7159299. PMID 31986264): 497-506https://doi.org/10.1016/s0140-6736(20)30183-5
      ). It was declared a Public Health Emergency of International Concern by the World Health Organization (WHO) on January 30, 2020 and a pandemic on March 11, 2020 (
      • World Health Organization
      Statement on the second meeting of the International Health Regulations (2005) Emergency Committee regarding the outbreak of novel coronavirus (2019-nCoV).
      World Health Organization (WHO), 2020
      ,
      • World Health Organization
      WHO Director-General’s opening remarks at the media briefing on COVID-19–11 March 2020.
      World Health Organization, 2020
      ).
      The primary mode of spread of SARS-CoV-2 is via close contact between persons, through small droplets generated by sneezing, coughing, and talking (
      • World Health Organization
      WHO Director-General’s opening remarks at the media briefing on COVID-19–11 March 2020.
      World Health Organization, 2020
      ,
      • World Health Organization
      Q&A on coronaviruses (COVID-19).
      World Health Organization (WHO), 2020
      ,
      • Centers for Disease Control and Prevention
      How COVID-19 spreads.
      Centers for Disease Control and Prevention (CDC), 2020
      ). The incubation period is around 5 days, with a range of 2–14 days. Common symptoms include fever, cough, fatigue, shortness of breath, and anosmia, and can be complicated by pneumonia and acute respiratory distress syndrome (
      • Centers for Disease Control and Prevention
      How COVID-19 spreads.
      Centers for Disease Control and Prevention (CDC), 2020
      ,
      • Hopkins C.
      • Kumar N.
      Loss of sense of smell as marker of COVID-19 infection.
      Ear, Nose and Throat Surgery Body of the United Kingdom, 2020
      ,
      • Centers for Disease Control and Prevention
      Coronavirus disease 2019 (COVID-19) — symptoms.
      US Centers for Disease Control and Prevention (CDC), 2020
      ,
      • Centers for Disease Control and Prevention
      Interim clinical guidance for management of patients with confirmed coronavirus disease (COVID-19).
      US Centers for Disease Control and Prevention (CDC), 2020
      ).
      Recommended measures for preventing spread include hand hygiene, social distancing, wearing a face mask in public, and isolation of suspects. Other measures include travel restrictions, lockdowns, and facility closures (
      • Centers for Disease Control and Prevention
      Coronavirus disease 2019 (COVID-19) — symptoms.
      US Centers for Disease Control and Prevention (CDC), 2020
      ,
      • Centers for Disease Control and Prevention
      Interim clinical guidance for management of patients with confirmed coronavirus disease (COVID-19).
      US Centers for Disease Control and Prevention (CDC), 2020
      ,
      • Velavan T.P.
      • Meyer C.G.
      The COVID-19 epidemic.
      Trop Med Int Health. 2020; 25 (PMC 7169770. PMID 32052514): 278-280https://doi.org/10.1111/tmi.13383
      ).
      During the early stages of the COVID-19 pandemic, when China, followed by Italy, Spain, and the USA, were severely hit by fatalities, it was widely speculated that Africa would be worst hit when the virus eventually reached the continent. The logic of this prediction lay in the fact that most of Africa is underdeveloped, constantly struggling with inadequate healthcare facilities and gloomy health indices. When the virus finally found its way to Africa, some fatalities followed, but the expected catastrophe never occurred, even when the pandemic reached its first peak, plateaued, and then declined. It is currently rising again in some countries, though not as rapidly as before.
      This paradox formed the basis of this study, which aimed to look objectively at the possible factors affecting COVID-19 mortality rate, in an attempt to explain why Africa’s COVID-19 mortality rate is, ironically, lower than that of more advanced countries with better healthcare systems. This will also enable heads of governments of advanced and underdeveloped countries to prioritize preventive measures that best suit their overall risk and pattern of COVID-19 fatalities, thereby ensuring a healthy balance between lockdown/restrictive measures and socioeconomic policies.

      Methodology

      This was an analytical review, which used population data and current COVID-19 mortality figures to attempt to answer why COVID-19 mortality in Africa is, ironically, lower than in more advanced countries.
      In light of the considerable challenge of attempting to analyze data from all countries in the world, sample countries for analysis were selected to represent certain criteria in order to have ample representation of sociodemographic and mortality patterns (pre-COVID-19 and COVID-19) for the different world regions. These criteria included differences in population pyramids, median age, life expectancy, socioeconomic/technological advancement, pre-COVID-19 era mortality rates, and COVID-19 mortality rates. Additionally, the countries selected had reached their peaks of infection rates and/or case fatality rates (CFR), and were in the plateau or decline phase.
      Current population data, for example population mean age, mean life expectancy, and pre-COVID-19 era mortality rates for the general population and for the > 65 years age group, were collated for selected countries. Additionally, data on diabetes prevalence and cardiovascular disease (CVD) mortality were obtained. Subsequently, the most up-to-date COVID-19 morbidity and mortality data were compiled and tabulated (
      • Roser Max
      • Ritchie Hannah
      • Ortiz-Ospina Esteban
      • Hasell Joe
      Coronavirus pandemic (COVID-19).
      Published online at OurWorldInData.org, 2020
      ,
      • United Nations, Department of Economic and Social Affairs, Population Division
      World population prospects 2019: wallchart.
      2019
      ,
      • Beltekian D.
      • Gavrilov D.
      • Giattino C.
      • Hasell J.
      • Macdonald B.
      • Mathieu E.
      • et al.
      COVID-19 data.
      Our World in Data, 2020
      ,
      • World Population Review
      Death rate by country 2020.
      2020
      ,
      • Worldometer
      Population live updates (2020).
      2020
      ,
      • Worldometer
      COVID-19 coronavirus pandemic live updates (2020).
      2020
      ). IBM-SPSS version 23 (IBM Corp; Armonk, NY, USA) was used for statistical analysis. Pearson’s correlation was used to test the association between some population variables and COVID-19 mortality rates.
      Stepwise multiple regression analysis was used to determine significant predictors of COVID-19 mortality rates. COVID-19 mortality rate (deaths per million population) was used as a dependent variable in the regression analysis because it was more reliable than case fatality rate (CFR), since the latter depends heavily on COVID-19 testing capability, which is highly variable and resource-based.

      Results

      Pre-COVID-19 era population/mortality data and current COVID-19 mortality data from 23 countries were used for the analysis (
      • Roser Max
      • Ritchie Hannah
      • Ortiz-Ospina Esteban
      • Hasell Joe
      Coronavirus pandemic (COVID-19).
      Published online at OurWorldInData.org, 2020
      ,
      • United Nations, Department of Economic and Social Affairs, Population Division
      World population prospects 2019: wallchart.
      2019
      ,
      • Beltekian D.
      • Gavrilov D.
      • Giattino C.
      • Hasell J.
      • Macdonald B.
      • Mathieu E.
      • et al.
      COVID-19 data.
      Our World in Data, 2020
      ,
      • World Population Review
      Death rate by country 2020.
      2020
      ,
      • Worldometer
      Population live updates (2020).
      2020
      ,
      • Worldometer
      COVID-19 coronavirus pandemic live updates (2020).
      2020
      ).
      Countries with the highest mean age (years), and life expectancy (years) included Italy (47.9, 83.5), Spain (45.5, 83.6), the UK (40.8, 81.3), and the USA (38.3, 78.9). These countries also had 65-yr+ deaths comprising the highest percentage of total deaths in the pre-COVID-19 era (65-yr+ mortality %). Countries with the lowest mean age and life expectancy were Benin Republic (18.8, 61.8), Burkina Faso (17.6, 61.6), Chad (16.7, 54.2), Cote d’Ivoire (18.7, 57.8), Cameroon (18.8, 59.3), Niger (15.1, 62.4), and Nigeria (18.1, 54.7). Accordingly, the 65-yr+ mortality % is lowest among these countries (Table 1) (
      • Roser Max
      • Ritchie Hannah
      • Ortiz-Ospina Esteban
      • Hasell Joe
      Coronavirus pandemic (COVID-19).
      Published online at OurWorldInData.org, 2020
      ,
      • United Nations, Department of Economic and Social Affairs, Population Division
      World population prospects 2019: wallchart.
      2019
      ,
      • Beltekian D.
      • Gavrilov D.
      • Giattino C.
      • Hasell J.
      • Macdonald B.
      • Mathieu E.
      • et al.
      COVID-19 data.
      Our World in Data, 2020
      ,
      • World Population Review
      Death rate by country 2020.
      2020
      ,
      • Worldometer
      Population live updates (2020).
      2020
      ,
      • Worldometer
      COVID-19 coronavirus pandemic live updates (2020).
      2020
      ).
      Table 1Population and mortality data for selected countries.
      Pre-COVID-19 population dataCOVID-19 morbidity/mortality
      CountryPopulationMean age (yrs)LE (yrs)Mortality rate/100065-yr+ mortality %CVD deaths/10 0000DM prevalence (%)Total casesTotal deathsDeaths/millionCFR (%)
      Italy60 461 82847.983.510.6891134.8258 13635 430586.014.37
      Spain46 754 78345.583.69.186997.17386 05428 838616.810.98
      UK67 886 00440.881.39.4851224.28324 60141 423610.215.42
      US331 000 00038.378.98.87415110.795 668 105176 362532.83.87
      China1 440 000 00038.776.97.3722629.789 6544 7113.275.44
      India1 380 000 00028.269.77.34928210.43 044 94056 70641.092.55
      Japan126 000 00048.284.610.790795.761 7471 1769.304.20
      Canada37 742 15741.482.47.8821067.37124 6299 071240.38.08
      Australia25 499 88137.983.46.6831085.0724 60248519.021.04
      Benin12 123 19818.861.88.7232360.992 115393.221.91
      Burkina Faso20 903 27817.661.67.9212692.421 320552.635.11
      Chad16 425 85916.754.212.0142816.1982764.638.47
      Cote d'Ivoire26 378 27518.757.89.9223042.4217 3741134.280.64
      Cameroon26 545 86418.859.39.1222457.218 76240815.372.31
      DRC89 561 4041760.79.3203196.19 8102502.792.34
      Ghana31 072 94521.164.17.2302985.043 5052618.40.53
      Madagascar27 691 01919.667.05.9314063.914 2771786.430.87
      Niger24 206 63615.162.48202382.421 172692.856.26
      Nigeria206 000 00018.154.712191812.4251 9059974.842.21
      Senegal16 743 93018.767.95.6352412.4212 85026615.891.84
      Uganda45 741 00016.463.46.4192132.51 848190.4150
      Qatar2 881 06031.980.21.24117716.52116 76519366.990.14
      Saudi Arabia34 813 86731.975.13.55226017.72306 3703 619103.90.97
      World7 790 000 00030.972.68572338.5123 243 621805 740103.44.28
      LE = life expectancy, 65-yr+ mortality % = percentage of total deaths who were >65 yrs old in the pre-COVID-19 era, CVD = cardiovascular disease, DM = diabetes mellitus, CFR = case fatality rate.
      Data sources:
      12. Max Roser, Hannah Ritchie, Esteban Ortiz-Ospina and Joe Hasell (2020). Coronavirus pandemic (COVID-19). Published online at OurWorldInData.org. Retrieved from: ‘https://ourworldindata.org/coronavirus’ [Online Resource]. Accessed on August 23, 2020.
      13. United Nations, Department of Economic and Social Affairs, Population Division (2019). World Population Prospects 2019: Wallchart. Available at https://population.un.org/wpp/ Accessed on August 23, 2020.
      14. Beltekian D, Gavrilov D, Giattino C, Hasell J, Macdonald B, Mathieu E, Esteban Ortiz-Ospina, Hannah Ritchie, Max Roser. COVID-19 data. Our World in Data 2020. Available at https://covid.ourworldindata.org/data/owid-covid-data.csv. Accessed on August 23, 2020.
      15. World Population Review (2020) available at https://worldpopulationreview.com/countries/death-rate-by-country/ Accessed on August 23, 2020.
      16. Worldometer. Population live updates (2020). Available at https://www.worldometers.info/coronavirus/ Accessed on August 23, 2020.
      17. Worldometer. COVID-19 coronavirus pandemic live updates (2020). Available at https://www.worldometers.info/population/ Accessed on August 23, 2020.
      COVID-19 deaths per million population (mortality rate) was significantly directly correlated with population mean age (r = 0.66, p <  0.001), life expectancy (r = 0.58, p =  0.003), and pre-COVID-era 65yr+ mortality % (r = 0.66, p <  0.001), but inversely correlated with pre-COVID-19 era CVD death (r = −0.59, p =  0.002). COVID-19 CFR was also directly correlated with mean age, life expectancy, and pre-COVID-19 era 65yr+ mortality %, though with lower strength than COVID-19 mortality rate. CFR was also inversely correlated with pre-COVID-19 CVD deaths (Table 2).
      Table 2Pearson’s correlation of COVID-19 mortality rates and pre-COVID-19 era population and mortality data.
      Mean ageLEMortality rate65-yr+ mortality %CVD deathsDM prevalenceDeaths per millionCFR
      Mean ageR10.9390.0230.978−0.7550.3910.6580.573
      p0.0000.9140.0000.0000.0590.0000.003
      LER0.9391−0.2670.944−0.7050.4360.5780.425
      p0.0000.2070.0000.0000.0330.0030.039
      Mortality rateR0.023−0.26710.024−0.178−0.5470.2600.499
      p0.9140.2070.9130.4050.0060.2190.013
      65yr+ mortality %R0.9780.9440.0241−0.7410.3030.6590.571
      p0.0000.0000.9130.0000.1500.0000.004
      CVD deathsR−0.755−0.705−0.178−0.7411−0.078−0.592−0.526
      p0.0000.0000.4050.0000.7190.0020.008
      DM prevalenceR0.3910.436−0.5470.303−0.07810.030-0.093
      p0.0590.0330.0060.1500.7190.8890.665
      Deaths per millionR0.6580.5780.2600.659−0.5920.03010.823
      p0.0000.0030.2190.0000.0020.8890.000
      CFRR0.5730.4250.4990.571−0.526−0.0930.8231
      p0.0030.0390.0130.0040.0080.6650.000
      R = correlation coefficient, p = level of significance, LE = life expectancy, 65-yr+ mortality % = percentage of total deaths who were >65 yrs old in the pre-COVID-19 era, CVD = cardiovascular diseases, DM = diabetes mellitus, CFR = case fatality rate.
      Significant positive predictors of COVID-19 mortality rate included pre-COVID-19 era 65yr+ mortality % (R2 = 0.574, B = 2.86, p <  0.001), population mean age (R2 = 0.570, B = 4.77, p =  0.001), and life expectancy (R2 = 0.524, B = 1.67, p =  0.008). Pre-COVID-19 era CVD death rate was a negative predictor of COVID-19 mortality rate (R2 = 0.524, B = –0.584, p =  0.012) (Table 3).
      Table 3Linear regression showing predictors of COVID-19 mortality rate.
      Independent variablesR2Unstandardized coefficientsStandardized coefficientstp95.0% confidence interval for B
      BSEBetaLower boundUpper bound
      65-yr+ mortality %0.5742.8580.6310.6954.5280.0001.5494.167
      Mean age0.5704.7651.2040.6453.9590.0012.2697.261
      Life expectancy0.5241.6660.5740.5272.9050.0080.4772.856
      CVD deaths0.524−0.5840.213−0.605−2.7450.012−1.027−0.142
      Mortality rate0.4291.5070.9290.2160.7900.4390.2852.729
      DM prevalence0.2541.2070.8220.2410.8880.3850.2552.806
      Dependent variable: COVID-19 mortality rate (deaths per million). 65-yr+ mortality % = percentage of total deaths who were >65 yrs old in the pre-COVID-19 era, R2 = variation in dependent variable that can be explained by the independent variable, beta = regression coefficient, SE = standard error, p = level of significance, CVD = cardiovascular diseases, DM = diabetes mellitus.

      Discussion

      The aim of this study was to attempt to explain the seemingly low COVID-19 mortality rate in low-income, less-developed African countries. This would only be possible if we looked into possible determinants of COVID-19 mortality rate. Population data showed that the median ages and life expectancies of most African countries are lower than those in other parts of the world (Table 1). This has given rise to a population pyramid consisting of a predominantly young population in Africa, while the Western and other developed nations have a predominantly older population. This disparity may be explained by the high birth rates in less-developed African countries, and the more advanced healthcare systems in developed nations, allowing their citizens to live longer. Moreover, Africa, with its less advanced healthcare systems, continues to be troubled with communicable as well as non-communicable diseases, leading to a lower life expectancy.
      In the same vein, mortality data showed that the ‘65-yr+ mortality %’ is clearly higher in the West and in other developed nations than in Africa. This is obviously because a much higher proportion of the population in the West and other industrialized nations tend to live beyond 65 years of age compared with the African population (Table 1). This high ‘65-yr+ mortality %’ in Italy, Spain, the UK, the USA, China, Japan, etc. may also be reflected in the COVID-19 mortality rate, since elderly persons are most susceptible to COVID-19 death (Table 1).
      On further analysis, there was a significant positive correlation between COVID-19 mortality rate and population mean age, life expectancy, and pre-COVID-19 ‘65-yr+ mortality %’. This provides further evidence that a predominantly older population is more susceptible to COVID-19 deaths. Ironically, a significant negative correlation was noted between COVID-19 mortality and pre-COVID-19 era cardiovascular disease (CVD) deaths. This may be explained by the fact that advanced countries, such as the USA, Italy, Spain, Japan, and China, with their better healthcare facilities, were able to slow down CVD deaths prior to COVID-19 by maintaining a larger pool of persons surviving and living with moderate and even severe CVD. Those with CVD are highly susceptible to COVID-19 deaths. On the other hand, less advanced nations such as Nigeria, Chad, Benin Republic, Niger, and Ivory Coast, with their less advanced healthcare systems, will most likely have had their pool of persons with CVD trimmed by pre-COVID-19 era CVD deaths.
      Although diabetes mellitus (DM) has generally been identified as a risk factor for COVID-19 death, no significant correlation was found between COVID-19 mortality rate and DM prevalence. This may be explained by the effects of confounders, such as poor glycemic control, age of those with DM, duration of DM, prevalence of complications of DM, level of DM care, and pre-COVID-19 era mortality due to DM.
      Similarly, COVID-19 case fatality rate (CFR) was positively correlated with population mean age, life expectancy, and pre-COVID-19 era ‘65yr+ mortality%’, and negatively correlated with pre-COVID-19 era CVD mortality rate. However, COVID-19 mortality rate showed stronger correlations with these variables than COVID-19 CFR. This may be due to the fact that COVID-19 mortality rate, with the population as denominator, is a more reliable index of mortality than COVID-19 CFR, whose denominator is total confirmed cases of COVID-19, which is dependent on the testing capacity and resources of a facility or country.
      Finally, multiple stepwise regression analysis identified pre-COVID-19 era ‘65-yr+ mortality %’ as the strongest predictor of COVID-19 death. This further strengthens the evidence for vulnerability of the elderly to COVID-19 death. Other significant predictors identified were population mean age and life expectancy. Of course, these are factors that contribute to pre-COVID-19 era ‘65-yr+ mortality %’, as well as increasing the proportion of elderly persons susceptible to COVID-19 death. Pre-COVID-19 era CVD death rate was identified as a negative or inverse predictor of COVID-19 mortality rate. This also strengthens the above argument — that advanced healthcare is associated with lower pre-COVID-19 era CVD mortality rate, leaving a larger pool of persons surviving and living with CVD who eventually become susceptible to COVID-19 death.
      One limitation of this study was the non-inclusion of all the countries of the world in the analysis, but the effect of this confounding factor was minimized by the selection of countries from different categories, in terms of socioeconomic levels, COVID-19 mortality rates, and demographic characteristics. Differences in the testing capacities of different countries was another confounding factor; however, its effect was minimized by using the COVID-19 mortality rate as the dependent variable and not case fatality rate. This is because the denominator for COVID-19 mortality rate — the population — is more stable than the denominator for case fatality rate, i.e. total confirmed cases, which is dependent on the COVID-19 testing capacity of any given nation.

      Conclusion

      Africa’s low COVID-19 mortality rate may be due to the paucity of factors identified as significant predictors of COVID-19 mortality rate. These factors include pre-COVID-19 era ‘65-yr+ mortality %’, population mean age, and life expectancy. Pre-COVID-19 era CVD mortality rate was identified as a negative predictor of COVID-19 mortality rate. The low COVID-19 mortality rate in Africa may also be responsible for the false belief by many in Africa that COVID-19 is unreal.

      Recommendation

      Countries with high pre-COVID-19 era ‘65-yr+ mortality %’, high population mean age, high life expectancy, and large pool of persons surviving and living with CVD — especially Western countries and other advanced countries — are able to redirect their resources appropriately, and balance their restrictive/lockdown measures and socioeconomic policies towards keeping the elderly and persons with comorbidities like CVD safe from COVID-19 infection. Likewise, less advanced countries should balance their COVID-19 lockdown and socioeconomic policies in line with the preponderance of identified risk factors for COVID-19 mortality rate. In summary, there should be no ‘one solution for all’ when it comes to balancing COVID-19 restrictive and socioeconomic policies.

      What is already known on this topic

      • •
        Coronavirus disease 2019 (COVID-19) is a pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV 2).
      • •
        It is life threatening and leads to death mostly in the elderly and those with comorbidities such as cardiovascular disease and diabetes mellitus.

      What this study adds

      • •
        COVID-19 mortality rates are generally lower in Africa than in more advanced nations, whether at the peak or in the plateau or decline phases of infection rates and case fatality rates for the countries analyzed.
      • •
        The positive predictors of COVID-19 mortality rate include population mean age, life expectancy, and pre-COVID-19 era ‘65-yr+ mortality %’.
      • •
        Pre-COVID-19 era CVD mortality rate is a negative predictor of COVID-19 mortality rate.

      Author’s contribution

      Y. Lawal was the sole author involved in the conceptualization, data collection, and writing of this article.

      Ethical approval

      Not applicable.

      Funding

      Funding for this work was strictly personal.

      Competing interests

      There were no conflicts of interest in the writing of this article.

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      Article info

      Publication history

      Published online: October 16, 2020
      Accepted: October 14, 2020
      Received in revised form: October 10, 2020
      Received: August 8, 2020

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      DOI: https://doi.org/10.1016/j.ijid.2020.10.038

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