Citation: Osuagwu, U.L.; Timothy C.G.;. Langsi, R.; Abu, E.K.; Mashige, K.P.; Ekpenyong, B.; Miner, C.A.; Oloruntoba, R.; Ishaya, T.; Envuladu, E.A.; et al. Differences in Perceived Risk of Contracting SARS-CoV-2 during and after the Lockdown in Sub-Saharan African Countries. Int. J. Environ. Res. Public Health 2021, 18, x. https://doi.org/10.3390/xxxxx

Academic Editor(s): Jimmy T. Efird

Received: 7 October 2021

Accepted: 18 October 2021

Published: date

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Copyright: © 2021 by the authors. Submitted for possible open access publication under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Abstract:

This study investigated risk perception in sub-Sahara Africa during and after the lockdown of contracting and dying of SARS-CoV-2. Two online surveys were conducted one year apart, with participants 18 years and above living in sub-Sahara Africa or the diaspora. Each survey took four weeks. The first survey was taken from 18 April to 16 May 2020, i.e., during the lockdown. The second survey was taken from 14 April to 14 May 2021, i.e., after the lockdown. A cross-sectional study using adopted and modified questionnaires for both surveys were distributed through online platforms. Question about risks perception of contracting and dying of SARS-CoV-2 were asked. The Helsinki declaration was applied, and ethical approvals were obtained. Total responses for both surveys, i.e., both during and after the lockdown, was 4605. The mean age was similar in both surveys (18–28 years). The mean risk perception scores were higher after lockdown by 3.59%. Factors associated with risk perception of COVID-19 were survey period, age group, region of residence, and occupation. Non–health care workers had a lower risk perception of COVID-19. This first comparative study on the level of risk perception of Africans during and after the lockdown shows that one in every three and every four persons in sub-Sahara Africa felt at high risk of contracting COVID-19 and thought they could die from contracting the same, respectively.

Keywords: SARS-CoV-2; sub-Sahara Africa; risks perception

1. Introduction

Since SARS-CoV-2, a beta coronavirus genre more closely linked to the SARS-CoV-1 (79% sequence identity) than to the MERS-CoV (52% identity) [1], was declared a pandemic by WHO in 2020 [2], the virus has infected over 237 million people, with the death of no less than 4,839,000 people, with US being the worst-affected country recording 727,273 deaths, followed by Brazil—599,414 deaths, India—449,883 deaths, Mexico—279,894 deaths, and Russia—212,625 deaths, as of 7 October 2021. In Africa, South Africa—87,981, Tunisia—24,971, Egypt—17,531, and Morocco—14,390 account for the highest number of deaths from COVID-19 in the region [3]. The SARS-CoV-2 infection presents with dry cough, fever, dyspnea, and lung trouble, among other signs [4]. With no effective cure or current drug for the treatment of the infection insight, SARS-CoV-2 continues to be a source of concern across the globe and more so in sub-Saharan Africa considering the poor health care system [5]. The rollout of the vaccines has been anything but smooth due to the mixed messages from the various governments and the difficulty in accessibility for developing countries [6,7]. This increases the mistrust displayed by citizens across the globe and increases the perception of risk in the community [7]

In sub-Saharan Africa (SSA), the impact of SARS-CoV-2 has remained minimal compared to the Americas, Europe, and Asia; however, there has been an increase in COVID-19 deaths across Africa since mid-July 2021.^§ Although the reasons for this are not well understood, researchers have suggested that the demographic age structure of sub-Saharan Africa is the leading factor of the low morbidity and mortality of COVID-19 compared to other regions of the world [8]. Other factors, such as the lack of long-term care facilities, potential cross-protection from previous exposure to circulating coronaviruses, and low testing of SARS-CoV-2, have resulted in an undercounting of deaths and effective government public health responses have contributed to the lower burden of the disease [8]. According to data from the US Centers for Disease Control and Prevention (CDC), 80% of COVID-19-related deaths occur in individuals aged 65 years and older [9], with UK data demonstrating that advanced age is the strongest risk for death and dramatically outweighs the risks associated with any other demographic factor or medical condition [10]. The median age of the SSA population is considerably lower than other regions, with a median age of 18 and only 3.0% of the African population older than 65 years [11,12]

South Africa has one of the highest infection and deaths rates due to COVID-19 in Africa. In addition, countries have already implemented the recommended public health regulations, such as strict, partial, or full lockdown procedures [4]; social distancing; mask-wearing in public places; and vaccination rolled out in the majority of the SSA countries. Countries that embarked on total lockdown were avoiding any national resurgence. Wide-scale domestic, foreign, and religious events have been cancelled for fear of SARS-Cov-2 outbreak as they were considered super-spreaders of the virus [13,14]. Such actions have an enormous socio-economic impact on the country [15], and the shutdown has upstretched fears of economic repercussions [16]. Due to this pandemic, everything about human life, including exports and imports of goods, business, infrastructural development, agriculture, and education, seem to have stopped, and these have a direct and indirect negative effect on the economy [17] given the already weak economy of some SSA countries and the resultant drawback risks.

In South Africa, a study showed that a higher perceived risk of COVID-19 infection was associated with greater depressive symptoms and, with such high rates of severe mental illness coupled with the low availability of mental healthcare amidst COVID-19 in the region, there is a need for studies to understand if the change in time has any effect on the level of risk perception for targeted intervention, including the need for immediate and accessible psychological resources [18]. In our recent study conducted during the early lockdown, SSA displayed high individual risk perception scores, which was greater in older participants and those working in health care sectors after adjusting for covariates [19]. It is unclear whether similar risk perceptions and associated factors remain after participants have grown in their knowledge of the disease spread and the commencement of the vaccine rollout in most SSA countries. The current study aims to investigate the individual perception of risk for contracting SARS-Cov-2 and the associated factors by comparing the data obtained during lockdown with those obtained in the post-lockdown period in SSA. The findings of this study will provide an understanding of the population at higher risk for which can be used to implement emergency policies to counter the spread of SARS-Cov-2.

2. Materials and methods

2.1. Data collection

An e-link to the survey was disseminated via emails and posted on social media platforms (Facebook and WhatsApp) and was available for four weeks at each period (during the lockdown and after lockdown). Of the 4605 participants for both the first and second survey, 4572 provided responses on their place of origin and out of these numbers, and 4551 mentioned their countries in SSA and were included in the analysis. Internal procedures during the data collection explained the small difference in the number of participants between the two surveys (n = 2,001, vs. n = 2,550). Table 1  provides details about the sociodemographic characteristics of the participants and health status.^# The questionnaire included a brief overview of the context, purpose, procedures, nature of participation, privacy and confidentiality statements, and notes to be filled out [20].

Table 1. Sociodemographic characteristics of respondents in both surveys.

In order to further reduce bias, this online survey used a Likert scale with provisions for neutral responses so that the answers were not influenced in one way or another. The participants did not receive any incentives; their responses were voluntary and anonymized. Testing for the internal validity of the survey items, the Cronbach’s alpha coefficient score ranged from 0.70 and 0.74, indicating satisfactory consistency.

2.2. Measures

The questionnaire collected data on sociodemographic variables (Table 1), self-assessment of risks about COVID-19, and if they think the public health authorities in their country are doing enough to contain the virus, whether they or any of their close relative was affected by COVID-19, and whether or not they think COVID-19 is real. Other questions relating to knowledge of COVID-19, habits during lockdown, and attitudes towards the infection were included in survey 1, while questions related to knowledge and attitude towards COVID vaccination were included in one of the two surveys. Those questions that were not in both surveys are not included in the current analysis, but the interested reader on these topics is referred to the published articles for a description of items and responses.

2.3. Assessment of risks about COVID-19

Self-assessments of risks about COVID-19 were measured with two items which was common in both surveys. The first item concerned the perception of the risk of being infected by COVID-19 (Q1: “Please rate your risk of being infected with the Coronavirus (COVID-19)”), and the second item was the self-assessment of the risk of dying from the infection (Q2: “Please rate your risk of dying from the Coronavirus (COVID-19) infection”). Each question used a Likert scale with five levels [21]. The scores for each item ranged from 0 (lowest) to 4 (highest). The perceived risk towards COVID-19 score ranged from 0–8 points.

2.4. Ethical consideration

This cross-sectional study was approved by the Human Research Ethics Committee of the Cross River State Ministry of Health, Nigeria (CRSMOH/RP/REC/2020/116) for the first survey, and by the Humanities and Social Sciences Research Ethics Committee (HSSREC 00002504/2021) of the University of KwaZulu-Natal, Durban, South Africa for the second survey. The study adhered to the principles of the Helsinki declaration (as modified in Fortaleza 2013) for research involving human subjects [22]. Prior to the study, an explanation detailing the nature and purpose of the study was provided to all participants using an online preamble. Informed consent was obtained from the participants who were required to answer either a ‘yes’ or ‘no’ to a question on whether or not they were willing to participate in the survey voluntarily. The confidentiality of participants’ responses was assured, and anonymity was maintained. To ensure that only one response per respondent was included in the study per survey, participants were instructed not to take part in the survey at both periods more than once, and, during analysis, we also restricted the data by IP address of the participants.

2.5. Statistical analysis

Continuous variables were summarized using descriptive statistics, including the number of observations used in the calculation (n), mean, and standard deviation (SD), while categorical variables were summarized as counts and percentages of each category for all demographic characteristics for during and post lockdown. To profile the risk of being infected by COVID-19 and the risk of dying from the infection, the Chi-square test was used to determine their prevalence. Each demographic characteristic was compared with a t-test for 2 groups, and one-way analysis of variance (ANOVA) for 3 or more groups. Simple linear regression analysis was used to report the unadjusted coefficient and retained those variables with p value <  0.20 in order to build a multiple linear regression analysis. For multiple linear regression, an elimination procedure was applied to remove non-significant variables (p > 0.05). All analyses were performed using ‘SVY’ commands in STATA/MP V.13.0 (Stata Corp, College Station, TX, USA).

3. Result

3.1. Descriptive statistics

The descriptive statistics of the sociodemographic variables are presented in Table 1,^§§ showing the summary of responses from those who participated in the survey during the lockdown and after the lockdown periods. Total responses were a combination of both survey responses. The mean age of the respondents 34.4 ± 11.7 years was similar in both surveys (34.1 ± 11.6 and 34.6 ± 11.8 years, during and post-lockdown respectively). Table 1 shows that most of the respondents  were in the 18–28 years age group (38%, n = 1697). There was an almost equal representation of male and female respondents. Most respondents (55.7%, n = 2522) were not married, about half of them (52.9%, n = 2383) completed post-secondary education, and many were employed (66.9%, n = 3001) and worked in a non-healthcare sector (40.5%, n = 1602) at the time of this study. Furthermore, 89.7% of the respondents (n = 4042) were Christians.

Figure 1a and b presents the percentage of responses for the items that make up the dependent variable:) the risk of becoming infected with COVID-19 and the risk of dying from COVID-19 infection, respectively. For each item, the proportion from both surveys who felt either at high or very high risk of contracting the infection was 39.9%, and about a quarter thought they were at risk of dying from the infection. Compared with during lockdown, significantly more respondents felt at high risk [17.12%; 95%CI 16.05–18.24% versus 9.27% 95%CI 8.46–10.15] and very high risk [7.21%, 95%CI 6.49–9.00% versus 5.34%, 95%CI 4.72–6.03%] of becoming infected from COVID-19 post-lockdown. Similarly, 11.76% [95%CI 10.85–12.72%] of respondents felt at high risk of dying from COVID-19 infection after the lockdown compared with 4.92% [95%CI 4.33–5.59%] during the lockdown.

Notes

^§ https://www.afro.who.int/news/covid-19-deaths-africa-surge-more-40-over-previous-week

^# https://www.frontiersin.org/articles/10.3389/fpsyg.2021.619145/full

^§§ https://www.frontiersin.org/articles/10.3389/fpsyg.2021.619145/full

1. Xia, S.; Liu, M.; Wang, C.; Xu, W.; Lan, Q.; Feng, S.; Qi, F.; Bao, L.; Du, L.; Liu, S. Inhibition of SARS-CoV-2 (previously 2019-nCoV) infection by a highly potent pan-coronavirus fusion inhibitor targeting its spike protein that harbors a high capacity to mediate membrane fusion. Cell Res. 2020, 30, 343–355. [Google Scholar] [CrossRef] [Green Version]

2. Olshaker, M.; Osterholm, M.T. Deadliest Enemy: Our War Against Killer Germs; Hachette: London, UK, 2017. [Google Scholar]

3. COVID-19 Coronavirus Pandemic. Available online: https://www.worldometers.info/ (accessed on 7 October 2021).

4. Chang, S.L.; Harding, N.; Zachreson, C.; Cliff, O.M.; Prokopenko, M. Modelling transmission and control of the COVID-19 pandemic in Australia. Nat. Commun. 2020, 11, 1–13. [Google Scholar] [CrossRef]

5. WHO. Maintaining Essential Health Services: Operational Guidance for the COVID-19 Context: Interim Guidance, 1 June 2020; World Health Organization: Geneva, Switzerland, 2020. [Google Scholar]

6. Schwartz, J.L. Evaluating and deploying COVID-19 vaccines—The importance of transparency, scientific integrity, and public trust. N. Engl. J. Med. 2020, 383, 1703–1705. [Google Scholar] [CrossRef] [PubMed]

7. OECD.org. Enhancing Public Trust in COVID-19 Vaccination: The Role of Governments. 2021. Available online: https://www.oecd.org/coronavirus/policy-responses/enhancing-public-trust-in-covid-19-vaccination-the-role-of-governments-eae0ec5a/ (accessed on 15 September 2021).

8. Adams, J.; MacKenzie, M.J.; Amegah, A.K.; Ezeh, A.; Gadanya, M.A.; Omigbodun, A.; Sarki, A.M.; Thistle, P.; Ziraba, A.K.; Stranges, S. The Conundrum of Low COVID-19 Mortality Burden in sub-Saharan Africa: Myth or Reality? Glob. Health: Sci. Pract. 2021, 9, 433–443. [Google Scholar]

9. CDC. Older Adults Risks and Vaccine Information Center for Disease Control. 2021. Available online: https://www.cdc.gov/aging/covid19/covid19-older-adults.html (accessed on 15 September 2021).

10. Williamson, E.J.; Walker, A.J.; Bhaskaran, K.; Bacon, S.; Bates, C.; Morton, C.E.; Curtis, H.J.; Mehrkar, A.; Evans, D.; Inglesby, P. Factors associated with COVID-19-related death using OpenSAFELY. Nature 2020, 584, 430–436. [Google Scholar] [CrossRef] [PubMed]

11. Gates, B.; Gates, M. Our 2019 Annual Letter—Compact 2025 Resources—IFPRI Knowledge Collections. 2019. Available online: https://ebrary.ifpri.org/digital/collection/p15738coll16/id/1082/ (accessed on 15 September 2021).

12. Adamchak, D.J. Population aging in sub-Saharan Africa: The effects of development on the elderly. Popul. Environ. 1989, 10, 162–176. [Google Scholar] [CrossRef]

13. Jaja, I.F.; Anyanwu, M.U.; Iwu Jaja, C.-J. Social distancing: How religion, culture and burial ceremony undermine the effort to curb COVID-19 in South Africa. Emerg. Microbes Infect. 2020, 9, 1077–1079. [Google Scholar] [CrossRef]

14. Levin, J. The faith community and the SARS-CoV-2 outbreak: Part of the problem or part of the solution? J. Relig. Health 2020, 59, 2215–2228. [Google Scholar] [CrossRef]

15. Buheji, M. Stopping future COVID-19 like pandemics from the Source-A Socio-Economic Perspective. Am. J. Econ 2020, 10, 115–125. [Google Scholar] [CrossRef]

16. Khan, N.; Fahad, S.; Faisal, S.; Naushad, M. Quarantine role in the control of corona virus in the world and its impact on the world economy. SSRN Electron. J. 2020. [Google Scholar] [CrossRef]

17. Moti, U.G.; Ter Goon, D. Novel Coronavirus Disease: A delicate balancing act between health and the economy. Pak. J. Med Sci. 2020, 36, S134. [Google Scholar] [CrossRef]

18. Kim, A.W.; Nyengerai, T.; Mendenhall, E. Evaluating the mental health impacts of the COVID-19 pandemic: Perceived risk of COVID-19 infection and childhood trauma predict adult depressive symptoms in urban South Africa. Psychol. Med. 2020, 1–13. [Google Scholar] [CrossRef] [PubMed]

19. Abu, E.K.; Oloruntoba, R.; Osuagwu, U.L.; Bhattarai, D.; Miner, C.A.; Goson, P.C.; Langsi, R.; Nwaeze, O.; Chikasirimobi, T.G.; Ovenseri-Ogbomo, G.O. Risk perception of COVID-19 among sub-Sahara Africans: A web-based comparative survey of local and diaspora residents. BMC Public Health 2021, 21, 1–13. [Google Scholar] [CrossRef]

20. Ovenseri-Ogbomo, G.O.; Ishaya, T.; Osuagwu, U.L.; Abu, E.K.; Nwaeze, O.; Oloruntoba, R.; Ekpenyong, B.; Mashige, K.P.; Chikasirimobi, T.G.; Langsi, R. Factors associated with the myth about 5G network during COVID-19 pandemic in sub-Saharan Africa. J. Glob. Health Rep. 2020. [Google Scholar] [CrossRef]

21. Maurer, T.J.; Andrews, K.D. Traditional, Likert, and simplified measures of self-efficacy. Educ. Psychol. Meas. 2000, 60, 965–973. [Google Scholar] [CrossRef]

22. Association, W.M. World Medical Association Declaration of Helsinki: Ethical principles for medical research involving human subjects. JAMA 2013, 310, 2191–2194. [Google Scholar]

23. McGough, J.J.; Faraone, S.V. Estimating the size of treatment effects: Moving beyond p values. Psychiatry 2009, 6, 21. [Google Scholar] [PubMed]

24. Emslie, G.J.; Rush, A.J.; Weinberg, W.A.; Kowatch, R.A.; Hughes, C.W.; Carmody, T.; Rintelmann, J. A double-blind, randomized, placebo-controlled trial of fluoxetine in children and adolescents with depression. Arch. Gen. Psychiatry 1997, 54, 1031–1037. [Google Scholar] [CrossRef]

25. Ferrer, R.A.; Klein, W.M. Risk perceptions and health behavior. Curr. Opin. Psychol. 2015, 5, 85–89. [Google Scholar] [CrossRef] [PubMed] [Green Version]

26. Attema, A.E.; L’Haridon, O.; Raude, J.; Seror, V.; The COCONEL Group. Beliefs and Risk Perceptions About COVID-19: Evidence From Two Successive French Representative Surveys During Lockdown. Front. Psychol. 2021, 12, 619145. [Google Scholar] [CrossRef]

27. Viscusi, W.K. Are individuals Bayesian decision makers? Am. Econ. Rev. 1985, 75, 381–385. [Google Scholar]

28. Wouters, O.J.; Shadlen, K.C.; Salcher-Konrad, M.; Pollard, A.J.; Larson, H.J.; Teerawattananon, Y.; Jit, M. Challenges in ensuring global access to COVID-19 vaccines: Production, affordability, allocation, and deployment. Lancet 2021, 397, 1023–1034. [Google Scholar] [CrossRef]

29. Haidere, M.F.; Ratan, Z.A.; Nowroz, S.; Zaman, S.B.; Jung, Y.-J.; Hosseinzadeh, H.; Cho, J.Y. COVID-19 vaccine: Critical questions with complicated answers. Biomol. Ther. 2021, 29, 1–10. [Google Scholar] [CrossRef] [PubMed]

30. COVID-19 Perceptions, Stigma and Impact; Ministry of Health: Nairobi, Kenya, 2020.

31. Clark, A.; Jit, M.; Warren-Gash, C.; Guthrie, B.; Wang, H.H.; Mercer, S.W.; Sanderson, C.; McKee, M.; Troeger, C.; Ong, K.L. Global, regional, and national estimates of the population at increased risk of severe COVID-19 due to underlying health conditions in 2020: A modelling study. Lancet Glob. Health 2020, 8, e1003–e1017. [Google Scholar] [CrossRef]

32. Drefahl, S.; Wallace, M.; Mussino, E.; Aradhya, S.; Kolk, M.; Brandén, M.; Malmberg, B.; Andersson, G. A population-based cohort study of socio-demographic risk factors for COVID-19 deaths in Sweden. Nat. Commun. 2020, 11, 1–7. [Google Scholar] [CrossRef]

33. Dillard, A.J.; Ferrer, R.A.; Welch, J.D. Associations between narrative transportation, risk perception and behaviour intentions following narrative messages about skin cancer. Psychol. Health 2018, 33, 573–593. [Google Scholar] [CrossRef] [PubMed]

34. Franzen, A.; Wöhner, F. Coronavirus risk perception and compliance with social distancing measures in a sample of young adults: Evidence from Switzerland. PLoS ONE 2021, 16, e0247447. [Google Scholar] [CrossRef]

35. Flaxman, A.D.; Henning, D.J.; Duber, H.C. The relative incidence of COVID-19 in healthcare workers versus non-healthcare workers: Evidence from a web-based survey of Facebook users in the United States. Gates Open Res. 2020, 4, 174. [Google Scholar] [CrossRef]

36. Menon, A.; Klein, E.J.; Kollars, K.; Kleinhenz, A.L. Medical students are not essential workers: Examining institutional responsibility during the COVID-19 pandemic. Acad. Med. 2020, 95, 1149–1151. [Google Scholar] [CrossRef]

37. Ekpenyong, B.; Obinwanne, C.J.; Ovenseri-Ogbomo, G.; Ahaiwe, K.; Lewis, O.O.; Echendu, D.C.; Osuagwu, U.L. Assessment of knowledge, practice and guidelines towards the Novel COVID-19 among eye care practitioners in Nigeria—A survey-based study. Int. J. Environ. Res. Public Health 2020, 17, 5141. [Google Scholar] [CrossRef]

38. Allain-Dupré, D.; Chatry, I.; Michalun, V.; Moisio, A. The Territorial Impact of COVID-19: Managing the Crisis Across Levels of Government; OECD: Paris, France, 2020. [Google Scholar]

39. Ning, L.; Niu, J.; Bi, X.; Yang, C.; Liu, Z.; Wu, Q.; Ning, N.; Liang, L.; Liu, A.; Hao, Y. The impacts of knowledge, risk perception, emotion and information on citizens’ protective behaviors during the outbreak of COVID-19: A cross-sectional study in China. BMC Public Health 2020, 20, 1–12. [Google Scholar] [CrossRef] [PubMed]

40. Biddlestone, M.; Green, R.; Douglas, K.M. Cultural orientation, power, belief in conspiracy theories, and intentions to reduce the spread of COVID-19. Br. J. Soc. Psychol. 2020, 59, 663–673. [Google Scholar] [CrossRef] [PubMed]

41. Knief, U.; Forstmeier, W. Violating the normality assumption may be the lesser of two evils. Behav. Res. Methods 2021, 1–15. [Google Scholar] [CrossRef]

42. Osuagwu, U.L.; Miner, C.A.; Bhattarai, D.; Mashige, K.P.; Oloruntoba, R.; Abu, E.K.; Ekpenyong, B.; Chikasirimobi, T.G.; Goson, P.C.; Ovenseri-Ogbomo, G.O. Misinformation About COVID-19 in Sub-Saharan Africa: Evidence from a Cross-Sectional Survey. Health Secur. 2021, 19, 44–56. [Google Scholar] [CrossRef]

43. Abir, T.; Kalimullah, N.A.; Osuagwu, U.L.; Yazdani, D.M.N.-A.; Mamun, A.A.; Husain, T.; Basak, P.; Permarupan, P.Y.; Agho, K.E. Factors Associated with the Perception of Risk and Knowledge of Contracting the SARS-Cov-2 among Adults in Bangladesh: Analysis of Online Surveys. Int. J. Environ. Res. Public Health 2020, 17, 5252. [Google Scholar] [CrossRef] [PubMed]