Khathutshelo Percy Mashige¹, Uchechukwu Levi Osuagwu^2,1*, Sekar Ulagnathan³, Bernadine N Ekpenyong^4,1, Emmanuel Kwasi Abu⁵, Piwuna Christopher Goson⁶, Raymond Langsi⁷, Obinna Nwaeze⁸, Timothy Chikasirimobi G⁹, Deborah Donald Charwe¹⁰, Richard Oloruntoba11, Chundung Asabe Miner¹², Tanko Ishaya¹³, Godwin O Ovenseri-Ogbomo^1,14, Kingsley Emwinyore Agho^15,1

1. African Vision Research Institute (AVRI), School of Health Sciences, University of KwaZulu-Natal, Westville Campus, Durban, 3629, South Africa
2. Translational Health Research Institute (THRI), Western Sydney University, Campbelltown, New South Wales, Australia*
3. Department of Optometry and Vision Sciences, Queensland University of Technology, Kelvin Groove, Brisbane, Queensland, Australia
4. Department of Public Health, Faculty of Allied Medical Sciences, College of Medical Sciences, University of Calabar, Calabar, Cross River State, Nigeria
5. School of Allied Health Sciences, Department of Optometry and Vision Science, University of Cape Coast, Cape Coast, Ghana
6. Department of Psychiatry, College of Health Sciences, University of Jos, Nigeria
7. University of Bamenda, Bambili, Cameroon
8. County Durham and Darlington National Health Service (NHS) Foundation, United Kingdom
9. Department of Optometry and Vision Sciences, School of Public Health, Biomedical Sciences and Technology, Masinde Muliro University of Science and Technology, Kakamega, Kenya
10. Tanzania Food and Nutrition Center, Dar-es Salaam, Tanzania
11. Supply Chain Management, Curtin Business School, School of Management and Marketing, Curtin University, Bentley, Western Australia, Australia
12. Department of Community Medicine, College of Health Sciences, University of Jos, Jos, Plateau State, Nigeria
13. Department of Computer Science, University of Jos, Jos, Plateau State, Nigeria
14. Department of Optometry, Faculty of Life Sciences, University of Benin, Benin City, Edo State, Nigeria.
15. School of Health Sciences, Western Sydney University, Campbelltown, New South Wales, Australia

Corresponding author: Dr Uchechukwu Levi Osuagwu

Translational Health Research Institute (THRI), School of Medicine, Western Sydney University, Campbelltown, NSW 2560, Australia

Tel +61 401 193 234

Email l.osuagwu@westernsydney.edu.au

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Abstract:

Purpose: The key preventive measures adopted to minimise the spread of the coronavirus disease (COVID-19) had significant health, economic and physical impacts mostly in developing countries. This study evaluated the health, economic and physical impacts of COVID-19 lockdown measures among sub-Saharan African (SSA) population and associated demographic variations.

Methods: 1970 respondents took part in this web-based cross-sectional survey during the mandatory lockdown period in most SSA. The dependent variables were health (COVID-19 infection, hospitalisation), socioeconomic (lost job, closed down business) and physical impacts (separated from family) of COVID-19. Univariate and bivariate logistic regression analyses were used to explore the factors associated with each of the dependent variables by the four sub-regions (Southern, Western, Central and East Africa).

Results: The respondents were aged 34.1 ± 11.5 years (range: 18 – 75 years) and mostly men (1099, 55%). 25.9% (n = 511) reported an impact of COVID-19 pandemic with significant regional variations (p<0.0005, higher proportion were East 36.2% and Southern Africans 30.3%) but no gender (p=0.334) and age group variations (p>0.05). Among Central African respondents, more men than women lost their businesses (45.7% versus 14.3%, p=0.002) and COVID-19 infections (40.0% versus 18.2%, p=0.024) during the study period. Multivariable analysis revealed that respondents from East (Adjusted odds ratio [AOR] 1.95, 95% confidence interval [CI]: 1.42-2.69), Southern (AOR 1.46, 95% CI: 1.09-1.96) and Central Africa (AOR 1.47, 95% CI: 1.06-2.03) reported significantly higher impact of COVID-19. Those who reported family separation during the lockdown were more likely to be older participants (39-48 years, AOR 2.48, 95% CI: 1.11-5.57)

Conclusion: One in four SSA respondents, mostly East and Southern Africans were adversely affected by COVID-19 pandemic during the lockdown. Interventions in high-risk populations are needed to reduce the health, socioeconomic and gender disparities in the impacts of COVID-19.

Keywords: Job loss, infections, hospitalisation, family separation, lockdown, coronavirus infection, Africa.

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Introduction

The coronavirus disease (COVID-19) pandemic has had significant health and economic impacts largely as a consequence of the key preventive measures adopted by most countries to minimise the spread of the virus^{1, 2}. These measures include partial or complete lockdowns of economies resulting in temporary closures of airports, businesses, schools and social services. The pandemic was a profound shock to societies and economies, and underscores society’s reliance on women both on the front line and at home3. The pandemic simultaneously exposed structural inequalities in health economy, security and social protection, and intensified gender inequality in Africa with many women having to work harder than before while earning even less than they normally would^{4, 5}. In China, women make up more than 90% of health-care workers in Hubei province⁶ and in Africa, about 70% of nurses are women7; highlighting the gendered nature of the health workforce and the increased risk that female health workers are exposed to⁸. No policies and public health efforts have yet addressed the gendered impacts of disease outbreaks globally,⁹ and this lack of action continues even in the response to coronavirus disease (COVID-19), worse still among SSA countries.

As noted in India where a majority of residents in rural areas depend on foreign remittances, in the month immediately after the lockdown announcement, weekly household local income dropped by 88% compared to the long-term average with another 63% reduction in foreign remittances.10 The loss of jobs and income and the inability to access hospital services during the lockdown was also linked to psychosocial distress among respondents in India.¹¹ In a South African study on the economic impacts of the pandemic in the hotel industry¹², researchers reported that 99.7% of the hotels were negatively impacted by the COVID-19 pandemic. This included 67% that reported a decline in income, 30.2% stated they expected that 90-100% of their staff would lose their jobs during the lockdown and many of the hotels were at risk of bankruptcy (62.8%) and permanent business closure¹². These studies highlighted the need for micro survey data showing the economic impacts of COVID-19 lockdowns on poor and vulnerable households living in other developing countries¹⁰. However, no study has examined the differential effects of COVID-19 impact by the different sub-regions.

SSA countries face many health and economic challenges, and the impact of COVID-19 in this region could be higher than that in developed economies^{13, 14}. Although SSA countries were the last to register COVID-19 cases15, the region has been reporting high infection rates while other regions¹⁶, which have trade links with SSA such as China, have started to flatten out with economic stimulus and investment plans underway. In addition, while the rest of the world is emerging from the global slowdown and reopening businesses^{17, 18}, the trend in SSA economies seems to veer towards a deeper recession with further possibilities of production and trade related constraints if infection rate continues to rise¹⁹.

In addition, demographic, political, cultural and health issues in the sub-regions of SSA are different, which implies that the impact of COVID-19 may be different¹⁹. For example, over nine percent of Mauritius’ population was 60 years and older in 2005, making it the oldest country in SSA while other Southern African countries such as South Africa and Lesotho had approximately eight percent of their populations aged 60 and older. Countries such as Benin, Burundi, Kenya, Mauritania, Rwanda, Uganda, and Zambia, had the older population accounting for less than four percent of the total population²⁰. In terms of HIV infections, Southern African countries of Botswana and Swaziland have the highest rates, while West Africa has been relatively less affected by HIV infection compared to other regions of SSA²¹. Furthermore, data from the World Health Organisation has shown that strata with lower socioeconomic status are more prone to the dangers of COVID-19²².

The poor economic resilience and differences in socio-demographic variables among different sub-regions of SSA exposes the region to greater risks of serious negative impacts from the COVID-19 pandemic. For instance, in Ethiopia where there was no direct restrictions imposed on the agriculture sector (the primary means of livelihood for most people) during the pandemic, researchers projected that the sector could face a 4.7% loss in output due to its linkages with the rest of the economy.²³ Therefore, this study was carried out to investigate the health, economic and physical impacts of COVID-19 and their associations with variables such as gender, age and region of origin, among respondents living in SSA countries. Recognising these impacts and their associations with gender and regional variations is an important step to understanding the primary and secondary effects of a health emergency on different individuals and communities, and for creating effective, equitable policies and interventions²⁴.

Respondents were from sub-Saharan African countries including those living abroad and in their countries of origin with origin from Ghana, Cameroun (only distributed to the English-speaking regions), Nigeria, South Africa, Tanzania, Kenya, Uganda etc. Respondents who were 18 years and older, and able to provide online consent were considered for the survey.

Study design

A cross-sectional descriptive study was conducted over a period of one month (April 18 to May 18, 2020) corresponding to the period of mandatory lockdown and restriction of movement in most of the countries surveyed. The data were obtained electronically via survey monkey with an e-link of the structured synchronised questionnaire posted on Facebook and WhatsApp which were commonly used by the locals in the participating countries and was sent via emails by the researchers to facilitate responses. Online survey was the only feasible way of reaching people as we could not perform nationwide community-based sample survey during this period.

Survey questionnaire

The questionnaire included a brief overview of the context, purpose, procedures, nature of participation, privacy and confidentiality statements and notes to be filled out. The items were developed based on COVID-19 knowledge questionnaire guidelines of the World Health Organization (WHO) for clinical and community management of COVID-19. A sample of the questionnaire has been reported previously²⁵ including the details of initial pilot. However, for the purpose of this study, the section utilised in the analysis is shown in Supplementary Table.

Outcome variables

The main outcome variable was any impact of COVID-19, which was coded as ‘1’, if the participants reported that they or any family member was affected by COVID-19 and ‘0’ if they did not report any impact of COVID-19.  The secondary outcomes were five items on the impact of COVID-19 including whether or not the participant or a family member a) lost their job, b) closed down business, c) contracted COVID-19, d) hospitalised due to COVID-19, and e) were completely separated from their families during the lockdown period.

Independent variables

The independent variables included the demographic characteristics of the participants: age (divided into four age groups including 18-28, 29-38, 29-48 and 49+years based on the distribution), region of origin (West, East, Southern and Central Africa), religion (Christian and others), educational (Postgraduate including masters and PhD, undergraduate University degree, primary/secondary school), marital (married/de facto and others including widowed, divorced, separated, and single), employment and occupational status (working in healthcare and non-healthcare sectors).

Data analysis

Demographic and the outcome variables were summarised as counts and percentages for categorical variables and two-way frequency table was used to obtain the proportion estimates for each sub-region. The level of association between gender and the impact of COVID-19 was tested using Fisher’s exact test by sub-region. Having any impact of COVID-19 was considered as ‘yes’ if participants reported any of the following outcomes: lost job, closed business, contracted COVID-19, hospitalised and separated from family, and ‘no’ if they did not  report any of the five impacts.

In the univariate and bivariate analyses, Odds ratios with 95% confidence intervals were calculated in order to assess the unadjusted risk of independent variables on the independent variables. Following the univariate logistic regression analysis, variables with a P-value <0.20 were retained and used to build a multivariable logistic regression model which examined the factors associated with the impacts of COVID-19 pandemic. We conducted bivariate logistic regressions to determine factors associated with ‘any impact of COVID-19’, and for each of the five impacts of COVID-19 during the pandemic at P <0.05. The significant factors in the univariate analysis were added to the regression model. The odds ratios with 95% confidence intervals were also calculated to assess the adjusted factors. All statistical analyses were performed using the Statistical Package for Social Sciences (SPSS, version 26, IBM, Armonk, NY, USA).

Ethical considerations

Ethical approval for the study was sought and obtained from the Human Research Ethics Committee of the Cross River State Ministry of Health (CRSMOH/HRP/HREC/2020/117). The study was carried out in accordance with the Helsinki Declaration for Human Research. The confidentiality of participants was assured in that no identifying information was obtained from participants. Participants were required to answer a ‘yes’ or ‘no’ to the consent question during survey completion to indicate their willingness to participate in this study.

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Results

Of the 2032 completed surveys, data for 1970 (96.9%) participants from West Africa (56.1%), Southern Africa (20.4%), Central Africa (12.7%) and East Africa (10.8%) who responded to the items on the impact of COVID-19 were analysed. The respondents were aged 34.1 ± 11.5 years (mean ±standard deviation; range: 18 – 75 years) and mostly men (1099, 55%). Other demographic details showing the participants’ characteristics by sub-regions are provided in Table 1.

Figure 2. Percentage breakdown of the impacts of COVID-19 (lost jobs, closed down businesses, contracted COVID-19, hospitalized due to COVID-19 and physical separation from families) during the lockdown in the sub-Saharan African regions by gender: West Africa (A), East Africa (B), Southern Africa (C) and Central African (D) regions (n=1970). *Indicates a significant difference in the impact of COVID-19 between men and women (p < 0.05, independent t-test).

Impact of COVID-19 according to region

Figure 3 shows the percentage breakdown of respondents who experienced any impact of COVID-19 in each sub-region during the pandemic. There were statistically significant differences in the proportion of respondents who reported any impact of COVID-19 compared with those who did not report any impact of the pandemic within the sub-regions (p<0.001). East (36.2%) and Southern African (30.3%) respondents were more likely to report any impact of the pandemic compared with those from the other sub-regions.