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. 2023 Jan;9(1):e12727.
doi: 10.1016/j.heliyon.2022.e12727. Epub 2022 Dec 29.

Main modulators of COVID-19 epidemic in sub-Saharan Africa

Boris-Enock Zinsou  1 , Diane Letourneur  2 , Joël Siko  1 , Raïssa Muriel de Souza  3   4 , Frejus Adjagba  5 , Pascal Pineau  6
Affiliations

Main modulators of COVID-19 epidemic in sub-Saharan Africa

Boris-Enock Zinsou et al. Heliyon. 2023 Jan.

Abstract

Background: The coronavirus disease 2019 (COVID-19) pandemic is responsible for an important global death toll from which sub-Saharan Africa (SSA) seems mostly protected. The reasons explaining this situation are still poorly understood.

Methods: We analyzed the correlation between reported COVID-19 data between February 14, 2020 and May 18, 2021, and demographic, socioeconomic, climatic, diagnostic data, and comorbidities in 47 SSA countries. Different databases including the WHO data center, Our World in Data, and the World Bank were used.

Findings: As of May 17, 2021, SSA reported 2% of COVID-19 cases and 2.9% of deaths, with the southern region being the most affected with 56.4% of cases and 75.0% of deaths. COVID-19 mortality was positively correlated with medical variables (national obesity rate, diabetes prevalence, cancer incidence, and cardiovascular disease mortality rate), socioeconomic characteristics (international tourism, per capita health expenditure, human development index, HDI, and years of schooling), and health system variables (nurse density, number of COVID-19 tests per capita), but negatively correlated with the population under 15 years of age and the malaria index.

Interpretation: Our study suggests that higher economic status fits with high COVID-19 mortality in SSA. In this regard, it represents primarily a disease of modern and wealthy societies, and can therefore be considered as an exception among infectious diseases that historically affected more severely underserved populations living in low- and middle-income countries. However, it should be made clear that observed correlations do not imply inevitably causation and that additional studies are necessary to confirm our observations.

Keywords: COVID-19; Countries; Mortality; Risk factors; SSA, Sub-Saharan Africa; Sub-Saharan Africa.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Heuristic approach employed to assess the importance of a collection of relevant variables on mortality from COVID-19 in 47 SSA countries.
Figure 2
Figure 2
Epidemiological situation of COVID-19 in SSA in May 2021. A: cumulative number of cases per country; B: cumulative number (%) of cases per region; C: cumulative number of deaths per country; D: cumulative number (%) of deaths per region; E: COVID-19 case fatality rate per country; F: case fatality rate (%) per region. Panels of six maps in B, D, and F correspond to insular countries around the continent.
Figure 3
Figure 3
Dot plots corresponding to the correlation between COVID-19 mortality and health system or demographic indicators. A- Correlation of COVID-19 mortality rate with the number of PCR tests performed per inhabitant in each SSA country. B-Correlation of COVID-19 mortality rate in SSA countries with nurse density. C- Correlation of COVID-19 mortality rate in SSA with the universal health service coverage. D-Correlation of COVID-19 mortality rate in SSA with the percentage of the population in SSA under 15 years old.
Figure 4
Figure 4
Dot plots corresponding to the correlation between COVID-19 mortality and diseases. A-E Correlations with noncommunicable diseases, F–I Correlations with infectious diseases. Correlation of COVID-19 mortality rate in SSA A- Obesity rate (2016) in the adult population, B- Percentage of population with high blood glucose >7 mmol/l in 2014, C-Percentage of deaths related to cardiovascular diseases in 2017, D-Cancer incidence rate in 2020, E-Number of newborns with sickle cell disease in 2010, F-Number of malaria cases in 2017, G-Percentage of deaths from communicable diseases, H-Mortality rate attributed to unsafe water, unsafe sanitation and poor hygiene in 2016, I-Death rate from pneumonia in 2017.
Figure 5
Figure 5
Dot plots corresponding to the correlation between COVID-19 mortality and economic indicators. A-Health expenditure/capita, B-Human development index, C-School life expectancy, D-Socio-demographic index, E-GDP per capita and F-International tourism.
Figure 6
Figure 6
Most important parameters influencing COVID-19 mortality and ranked according to their Spearman r coefficient. Associated p-values are displayed in the bar-chart below. Colored spearman coefficient bars correspond to parameters remaining significant after Bonferroni correction for multiple tests.
Figure 7
Figure 7
PCA of parameters correlated with the deaths rates from COVID-19. The 1st dimension separates West, Middle and East regions from Southern and Insular nations. The 2d dimension separates Southern and Insular regions. A-PCA coloration according to geography. B-The same PCA with color code corresponding to the mortality rate.
Figure 8
Figure 8
A-Matrix ranking SSA countries according to the most important parameters for COVID-19 mortality. Colors of the heatmap are given according to the rank of the country for each feature. B-COVID-19 mortality in SSA countries grouped in quartiles according to their synthetic score in the ranking. C-Dot correlating mortality and ranking in the matrix.
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