Skip to main content
Download PDF

Risk perception of COVID-19 among sub-Sahara Africans: a web-based comparative survey of local and diaspora residents

BMC Public Health volume 21, Article number: 1562 (2021) Cite this article

Abstract

Background

Perceived risk towards the coronavirus pandemic is key to improved compliance with public health measures to reduce the infection rates. This study investigated how Sub-Saharan Africans (SSA) living in their respective countries and those in the diaspora perceive their risk of getting infected by the COVID-19 virus as well as the associated factors.

Methods

A web-based cross-sectional survey on 1969 participants aged 18 years and above (55.1% male) was conducted between April 27th and May 17th 2020, corresponding to the mandatory lockdown in most SSA countries. The dependent variable was the perception of risk for contracting COVID-19 scores. Independent variables included demographic characteristics, and COVID-19 related knowledge and attitude scores. Univariate and multiple linear regression analyses identified the factors associated with risk perception towards COVID-19.

Results

Among the respondents, majority were living in SSA (n = 1855, 92.8%) and 143 (7.2%) in the diaspora. There was no significant difference in the mean risk perception scores between the two groups (p = 0.117), however, those aged 18–28 years had lower risk perception scores (p = 0.003) than the older respondents, while those who were employed (p = 0.040) and had higher levels of education (p < 0.001) had significantly higher risk perception scores than other respondents. After adjusting for covariates, multivariable analyses revealed that SSA residents aged 39–48 years (adjusted coefficient, β = 0.06, 95% CI [0.01, 1.19]) and health care sector workers (β = 0.61, 95% CI [0.09, 1.14]) reported a higher perceived risk of COVID-19. Knowledge and attitude scores increased as perceived risk for COVID-19 increased for both SSAs in Africa (β = 1.19, 95% CI [1.05, 1.34] for knowledge; β = 0.63, 95% CI [0.58, 0.69] for attitude) and in Diaspora (β = 1.97, 95% CI [1.16, 2.41] for knowledge; β = 0.30, 95% CI [0.02, 0.58] for attitude).

Conclusions

There is a need to promote preventive measures focusing on increasing people’s knowledge about COVID-19 and encouraging positive attitudes towards the mitigation measures such as vaccines and education. Such interventions should target the younger population, less educated and non-healthcare workers.

Peer Review reports

Introduction

Risk perception refers to people’s subjective assessments of the possibility of outcomes that may follow undesirable events such as disasters and pandemics [1]. The ongoing novel coronavirus SARS-CoV2 (COVID-19) pandemic has caused enormous global mortality and public health devastation [2]. While the 2014 West African Ebola Virus Disease (EVD) pandemic was limited to African countries, and the severe acute respiratory syndrome (SARS) of 2002–03 limited to Asian countries, COVID-19 has been a global and unprecedented ‘black swan’ event [3, 4]. COVID-19 infection is highly contagious, and mortality caused by the virus has exceeded 3.4 million deaths as of 27th of May 2021 ─ more than any of its predecessors [5]. It is, therefore no surprise that countries are in a race towards developing and administering an effective vaccine [6, 7].

In response to the COVID-19 global threat [8], the World Health Organization (WHO) immediately raised awareness of healthcare workers around the world [9]. The WHO has also raised funds globally and developed Strategic Preparedness and Response Plans (SPRP) to support and protect poorer countries with weak healthcare systems [10]. The goal of the SPRP was to control infection, limit transmission, communicate key information, provide early acute care, and minimize disastrous economic and social effects. National governments locked down their populations, stopped the mobility of goods and services, closed all schools and universities, and shut all state and international borders with many employees working from homes [11,12,13,14]. Nonetheless, these mitigating measures’ success depends upon the public’s readiness to comply, which in turn is inspired by their risk perceptions about the pandemic [15].

Globally, devastating pandemics such as COVID-19 can provide valuable opportunities to learn about human risk perception and attendant behavior [16, 17] and how findings from such studies can be used to inform the allocation of resources within such countries and within international multilateral organizations and agencies such as the WHO [18, 19]. Such studies can also provide an evidence base for the formulation of public health and risk policies. Severe outcomes from natural disasters are often influenced by the level and distribution of economic resources and income within the population of a country (or region) [20, 21]. Several seminal bodies of literature highlight the role of resources or the lack of them in societal responses to disasters [22] and show how positive psychology can contribute to community development during disasters [23]. Culture and risk perception are closely linked and cultural beliefs and values may contribute to the success or otherwise of efforts to control the COVID 19 pandemic [24, 25]. As a result of the different cultural exposures of African residents and Africans living in the diaspora (living outside Africa), this comparative analysis will bring to the fore what specific local context risk management strategies should be implemented by SSA governments. For instance, Quinn et al. showed that people’s attachment to their place of residence affected their perceived disaster-related risks [26]. The findings of this web-based cross-sectional study will highlight the implications of the analysis for what we might expect of Africans living in Africa and Africans living outside Africa as well as policy implications in disaster risk management in general. For policymakers tasked with communicating risk, this research would provide a particularly valuable lens through which we can address the emotional underpinnings of adaptation behavior.

Methods

Design and setting of the study

This was an online survey created in Survey monkey to assess the risk perceptions of Africans. The study was conducted between April 27th and May 17th 2020 corresponding to the mandatory lockdown period in most SSA countries. The survey instrument shown in the Supplementary Table, was adapted and developed from the WHO recommended questions [27] and have been used in previous studies [27]. It was not feasible to undertake a conventional Africa-wide community-based sampling survey at this particular period of lockdown and restricted mobility. A one-page project information statement, which doubled as a recruitment poster, was posted/reposted to WhatsApp and Facebook chat groups and individual accounts together with an e-Link to the online survey. The information sheet and poster contained a brief introduction on the background of the study, its objectives, procedures, the voluntary nature of participation, the declaration of anonymity, privacy and confidentiality, as well as instructions for completing the questionnaire.

We also posted the poster and questionnaire on various websites and official accounts of several local organisations and individuals. Survey questionnaires were also sent out by email to selected groups and individuals in all the target countries, relying on the authors’ networks with collaborating academics and local people.

Questionnaire

The questionnaire was divided into three sections, including demographics, knowledge, risk perception, feeling about self-isolation, attitude towards public health practices to mitigate the spread of COVID-19 (compliance) as presented in Table 1. Most of the items on the questionnaire that assessed the respondent’s knowledge of COVID- 19, required mostly a ‘true’ or ‘false’ or a ‘yes’ or ‘no’ response with an additional “Not sure” option. Each question used a binary scale, and a correct answer was assigned 1 point, whereas an incorrect/unsure answer was assigned 0 points. The knowledge score ranged from 0 to 18 points. These items have been validated elsewhere to have an acceptable internal consistency [28]. To reduce unintended bias, we conducted a statistical test using Kuder Richardson correlation coefficient for binary outcomes by creating two dummy variables. One of the dummy variables included ‘Yes’ and ‘Not sure’ and the other dummy variable was the combination of ‘No’ and ‘Unsure’ and the alpha coefficient for the two dummy variables was 0.86, indicating a strong relationship.

Table 1 Survey items for knowledge, attitude and perception towards COVID-19
Full size table

For the risk perception items shown in P1 − P6 of Table 1, each question used a Likert scale with five levels, and the scores ranged from 1 for ‘lowest’ and 5 for ‘highest’ with a maximum score range of 5 to 30 points. We determined the Cronbach’s alpha coefficients of the perception items to be 0.84, which indicated a satisfactory internal consistency of perception items. Questions were asked on “How the respondents felt about self-isolation” (P7 − P12) were classified as “Yes” or “No” The Kuder Richardson Cronbach’s alpha coefficient of the “How the respondents felt about the quarantine items” was 0.74, which indicated an acceptable internal consistency. Respondents were also asked about their attitude towards the public health measures put in place by the respective governments to reduce the spread of the virus in items A1-A8. The Likert scale in items A3-A5 was scored as 0 for ‘lowest’ and 4 for “highest” with the score ranging from 0 to 17 points and the alpha coefficients of the attitude items were 0.73 and demonstrated that the internal consistency of the attitude items was satisfactory.

Characteristics of the participants

Participants were those living in South Africa, Nigeria, Ghana, Kenya, Tanzania and Malawi. Respondents in the diaspora, including those living in the UK, USA, Australia, Canada, New Zealand and Germany, were also included. Recipients were further encouraged to send on or ‘snowball’ the survey questionnaire to other WhatsApp groups and friends that they knew. Eligibility criteria included that respondents had to be of African nationality, aged 18 years or older, able to understand the contents of the poster/questionnaire, and agreed to participate in the study.

Dependent variable

The dependent variable for this study was the perception of risk for contracting COVID-19, which was categorized as continuous. The items utilized to measure the risk perception of COVID-19 are shown in Table 1 (P1-P6). The responses included very high, high, low, very low, and unlikely. The items ranged from 1 (unlikely) to 5 (very high).

Independent variables

These included demographic A) characteristics of the participants, which consists of age, gender, marital status, education, employment status, occupation (if employed), religion, if they lived alone, the number of people living together in the household and place of current residence. B), Knowledge about COVID-19 origin, symptoms and prevention. C), Feeling about the practice of self-isolation during COVID-19 lockdown. D) Attitude towards COVID-19 mitigation measures that included the practice of self-isolation, home quarantine (A1 and A2) as well as compliance questions (A3-A8)(see Table 1).

Sample size determination

The survey assumed a proportion of 50% with 95% confidence and 2.5% margin of error based on a previous study [29]. This is because the main objective of this research was on COVID-19, and there were no previous studies from SSA that examined factors associated with risk perception of 2019-nCoV. An online sample size calculator was used, and we assumed a sample size of approximately 1921, including 20% non-response rate.

Statistical analysis

Scores for risk perception were calculated for each of the independent variables and treated as a continuous variable with mean (±standard deviation) risk scores. The risk perception scores ranged from 1 to 30. Risk scores by independent variables were summarized using a t-test for two categorical groups and a one-way analysis of variance (ANOVA) for more than two categorical groups. Univariate linear regression analyses were conducted to assess the unadjusted coefficients (B) with 95% confidence intervals (CI) among SSA residents and residents in the diaspora. The adjusted coefficients (β) with their 95% confidence intervals obtained from the multiple linear regression model were used to measure the factors associated with the risk perception of COVID-19 among SSA residents and those in the diaspora. Only significant variables in the univariate analysis were used to build the regression model. Knowledge was included in the model because it is strongly related to attitude and practice, while knowledge and attitude have been reported to be associated with practice ([30]). Feeling about the practice of self-isolation during the COVID-19 lockdown would help in identifying individuals who could develop mental health issue during the lockdown because past studies showed that longer duration of separation and restriction of people’s movement due to SARS were associated with poorer mental health [31, 32]. Including attitude towards the mitigation practices in the model would influence action to reduce the spread of the infection. In our linear regression analyses, we checked for homogeneity of variance and multicollinearity, including Variance Inflation Factors (VIF) and the VIF < 4 was considered suitable [33]. All analysis was performed using Stata version 14.1 (StataCorp 2015. College Station, United States of America), and a two-tailed p-value < 0.05 was considered statistically significant.

Results

Demographics of respondents in Africa and in the diaspora

Of the 1969 respondents (55.1% male and 44.9% female) that completed the survey, the majority were living in SSA (n = 1855, 92.8%) and 143 (7.2%) in the diaspora. The percentage distribution of the respondents by country of residence for local residents and those in diaspora has been presented as a Supplementary figure. The majority of the local respondents lived in Ghana (28.2%), followed by Nigeria (26.7%) and South Africa (21.7%), while many of those in diaspora were from the USA (19.6%), then UK (18.2%) and Australia (15.4%). Figure 1 presents the mean scores (out of 30) and the 95% CI of risk perception scores towards COVID-19 based on respondents region of residence. There was no significant difference in the mean risk perception scores between the two groups (p = 0.117). Table 2 shows the demographics of SSA in Africa and in the diaspora with their mean (standard deviation) scores for perceived risk towards COVID-19. Compared to residents in Africa, those living in the diaspora were younger, more often female, and less often married.

Fig. 1
figure 1

Mean scores (/30) of risk perception towards COVID-19 among Sub-Saharan Africans living locally (Africa) and in the diaspora. Error bars are 95% confidence intervals of mean scores

Full size image
Table 2 Demographics of Sub-Saharan Africans living in Africa and in the diaspora with their mean (standard deviation) scores for the perceived risk of contracting COVID-19
Full size table

Perception of overall COVID-19-associated risk

For those in SSA, the risk perception score was significantly lower in the 18–28 years age group (p = 0.003, Table 2) than in older age groups. Again, employment (p = 0.040) and a higher level of education (p < 0.001, Table 2) were significantly associated with higher risk perception scores than being unemployed and having a lower education, respectively. There was no significant difference in the risk perception scores based on gender, marital status, religion, occupation, and the number of people living together among SSA residents. The risk perception score did not significantly differ across the sociodemographic characteristics of the participants living in the diaspora.

Among those living in SSA and those in the diaspora, the mean scores for risk perception was similar between those who either practiced or did not practice self-isolation and home quarantine. Similarly, no significant differences in risk perception were observed between participants who reported being anxious, bored, frustrated, angry compared to those who did not report any of these symptoms in the two groups.

Table 3 shows the unadjusted and adjusted coefficients for factors associated with risk perception of COVID-19 among Africans residing in SSA. In contrast, Table 4 shows the same information for those living in the diaspora. Among the local SSA residents, working in the health care sector (adjusted coefficient, β = 0.61, 95% CI [0.09, 1.14]) was associated with high-risk perception towards COVID-19, as well as knowledge (β = 1.19, 95% CI [1.05, 1.34]) and attitude (β = 0.63, 95% CI [0.58, 0.69]) towards COVID-19 mitigation measures (Table 3). Although, unemployment (B = − 0.78, 95% CI [− 5.53, − 0.04]) and lower levels of education (primary/secondary education, B = − 2.19, 95% CI [− 3.32, − 1.05]) were significantly associated with lower risk perception towards COVID-19 in the univariate analysis, the significance was lost after adjusting for other potential confounding factors.

Table 3 Unadjusted and adjusted coefficients for factors associated with perceived risk of contracting Coronavirus diseases (COVID-19) among SSAs living in African countries
Full size table
Table 4 Unadjusted and adjusted coefficients and 95% confidence intervals (CI) of factors associated with perceived risk of contracting Coronavirus diseases (COVID-19) among SSAs living in the diaspora
Full size table

From Table 4, it can be seen that, among SSAs in the diaspora, knowledge (β = 1.79, 95% CI [1.16, 2.41]) and attitude (β = 0.30, 95% CI [0.02, 0.58]) were similarly associated with a high-risk perception of COVID-19. However, there was no significant association between the demographic variables and the risk perception scores in this group.

Discussion

This study found comparable high-risk perception scores among residents living in SSA and those in the diaspora, which were associated with an increase in knowledge of COVID-19 and attitude towards the mitigation measures. Health care workers resident in SSA had higher risk perception scores compared to their counterpart non-healthcare workers. Although having lower education and not working during the pandemic were associated with lower risk perception of COVID-19 among local residents, this association was nullified after adjusting for other demographic variables.

The finding that older individuals felt at greater risk of COVID-19 was in line with past studies showing that older individuals have significantly higher COVID-19 related severe complications and deaths than young individuals [34]. Public awareness of this information may explain the finding of lower risk perception for contracting the infection among younger respondents in SSA. As highlighted by Dillard et al. [35], having a perceived low risk of infection can make young people become less compliant to public health measures. This can in turn lead to higher COVID-19 infection [35], and ultimately passing the infection to the population more susceptible to COVID-19 related complications since young people were shown to be more likely to transmit the virus than others [36]. In line with these findings, some countries took stringent steps to limit the young population from transmitting COVID-19 infection to the older population [37,38,39,40] but recorded mixed success [40,41,42]. Rapid and proactive outreach programs targeted at young people in Australia and Canada might explain why the risk perception was similar between younger and older participants living in the diaspora in this study [43]. Such directed programs and policies should be implemented within the vulnerable groups in our local populations.

Studies have reported a high perceived risk of COVID-19 among African health care workers [44,45,46] but did not compare between health and non-health care workers. In a cross-sectional study conducted on 350 Ghanaians during the early stage of the COVID-19 outbreak, there was no difference in risk perception scores between healthcare and non-healthcare workers [47] but healthcare workers reported higher mean scores than non-healthcare workers. The higher mean score recorded among healthcare worker in this study may be attributed to the fact that healthcare workers had to work even if their individual risk perception would want to make them to comply with risk mitigation measures such as isolation [46, 48]. In this study, high-risk perception for contracting COVID-19 was associated with working in the health care sector, but this was only significant in the SSA residents. Firsthand experience with the virus is often linked to high-risk perception [49], and higher knowledge of the disease among health care workers than the non-health care workers might explain their higher perception of risk for contracting the infection. The lack of proper training on protective measures reported in previous studies by health workers in SSA countries [46] may explain the significant association found among local health care workers but not among those living in the diaspora. Again, the implementation of targeted policies may as well account for the lack of association among respondents living abroad.

In this study, knowledge about COVID-19 and a positive attitude towards the mitigation measures were associated with a high-risk perception of contracting the disease, both in SSA and in the diaspora. Similar findings have been reported in Ethiopia [50], where individuals who perceived a higher risk were more likely to adopt protective measures, which in turn influences the probability of infection [50, 51]. However, the prevalence of misinformation about COVID-19 among SSA respondents [52], together with the psychological stress caused by themisinformation about COVID-19 due to the poor knowledge about the disease [28], are potential sources of reduced risk perception in this sub region. These would lead to increased transmissions and mortality. Hence, accurate information about the pandemic using the trusted media platforms can help in proper risk judgement and adoption of public health measures to control the spread of infection [28, 53].

COVID-19 related morbidity and mortality vary disproportionately based on sociodemographic characteristics, for instance, males and older people have high mortality due to COVID-19 compared to females and the young population [54]. Individuals' behaviours towards safety measures have been linked to their level of the perceived risk of disease [35]. Adopting public health measures such as the use of a nose mask in public areas and frequent hand sanitization can lead to successful control of air-borne infectious diseases like COVID-19 [53]. Therefore, public health strategies for successful control of COVID-19 among SSAs may be beneficial by targeting the sub-population identified in this study. This includes, the unemployed, non-healthcare workers, the younger population and those with lower education.

This study was limited by several factors: 1), the assessed risk perception and comparison of the perception scores from SSA residents in and outside Africa may be limited by the fact that those who felt they were at risk of COVID-19 infection were more likely to respond to recommended health behaviours [55]; 2), findings from this study cannot be generalized to the entire SSA regions; 3), it was an online survey made available only in English language thus restricting respondents without access to the internet or where internet penetration remains relatively low and some from French-speaking SSA nations [56]; 4), the survey items were self-administered and some of the questions for example, those on compliance require subjective responses, and has no answer that can be verified. If a respondent reported good behaviour but did not practice it, there is no way we can independently verify their responses. Despite these limitations, this study from SSA region provided insight into the role of residence in mitigating the factors that influence risk perception of COVID-19 among SSAs during the pandemic. The study used a robust analysis to control for potential confounders during the analysis in order to reduce the issue of bias.

Conclusions

In summary, this study explored the factors associated with the risk perception of contracting COVID-19 among SSAs, particularly looking at the role of residence in peoples’ level of risk perception. The findings indicate that health communication and education strategies designed to promote the adoption of preventive behaviours among SSAs should focus on increasing knowledge about the disease and encouraging a positive attitude towards the mitigation measures. In addition, such programmes will benefit from targeting the unemployed, less educated, healthcare workers and the younger population, for optimum outcome. These findings can be helpful in policy implications in disaster risk management, including the control of COVID-19, particularly in English speaking countries in the SSA region.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

COVID-19:

Coronavirus SARS-CoV2

SSA:

Sub-Sahara Africa

CI:

Confidence intervals

EVD:

Ebola Virus Disease

EPPM:

Extended parallel process model

WHO:

World Health Organization

SPRP:

Strategic Preparedness and Response Plans

ANOVA:

Analysis of variance

References

  1. Paek H-J, Hove T. Risk perceptions and risk characteristics. Oxford research encyclopedia of communication; 2017. https://0-doi-org.brum.beds.ac.uk/10.1093/acrefore/9780190228613.013.283.

    Book  Google Scholar 

  2. Anderson RM, Heesterbeek H, Klinkenberg D, Hollingsworth TD. How will country-based mitigation measures influence the course of the COVID-19 epidemic? Lancet. 2020;395(10228):931–4. https://0-doi-org.brum.beds.ac.uk/10.1016/S0140-6736(20)30567-5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Zito RR. Bounding the “Black Swan” Probability. In: Mathematical Foundations of System Safety Engineering. Tucson: Springer Charm; 2020. p. 51–62. https://0-doi-org.brum.beds.ac.uk/10.1007/978-3-030-26241-9_3.

  4. McMaster M, Nettleton C, Tom C, Xu B, Cao C, Qiao P. Risk management: rethinking fashion supply chain management for multinational corporations in light of the COVID-19 outbreak. J Risk Financ Manage. 2020;13(8):173. https://0-doi-org.brum.beds.ac.uk/10.3390/jrfm13080173.

    Article  Google Scholar 

  5. Roser M, Ritchie H, Ortiz-Ospina E, Hasell J. Coronavirus pandemic (COVID-19): Our world in data; 2020. url: https://ourworldindata.org/coronavirus (cited on page 4). 2020.

  6. Pereira M, Paixão E, Trajman A, De Souza RA, Da Natividade MS, Pescarini JM, et al. The need for fast-track, high-quality and low-cost studies about the role of the BCG vaccine in the fight against COVID-19. Respir Res. 2020;21:1–3.

    Article  Google Scholar 

  7. Henley J, Connolly K, Jones S. European and US experts question UK's fast-track of COVID vaccine: The Guardian. 4 December 2020. https://www.theguardian.com/world/2020/dec/03/europe-us-experts-question-uk-fast-track-covid-vaccine.

  8. World Health Organization. (‎2020)‎. Coronavirus disease 2019 (‎COVID-19)‎: situation report, 73. World Health Organization. https://apps.who.int/iris/handle/10665/331686.

  9. Ferioli M, Cisternino C, Leo V, Pisani L, Palange P, Nava S. Protecting healthcare workers from SARS-CoV-2 infection: practical indications. Eur Respir Rev. 2020;29(155):200068. https://0-doi-org.brum.beds.ac.uk/10.1183/16000617.0068-2020.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Organization WH. Coronavirus disease 2019 (COVID-19) strategic preparedness and response plan: Accelerating readiness in the Eastern Mediterranean Region: February 2020: World Health Organization. Regional Office for the Eastern Mediterranean. 2020.

    Google Scholar 

  11. Ogolodom MP, Mbaba AN, Alazigha N, Erondu OF, Egbe NO, Golden I, et al. Knowledge, Attitudes and Fears of HealthCare Workers towards the Corona Virus Disease (COVID-19) Pandemic in South-South, Nigeria. Health Sci J. 2020;2020(1):1–10 Sp Iss 1: 002.

    Google Scholar 

  12. Di Gennaro F, Pizzol D, Marotta C, Antunes M, Racalbuto V, Veronese N, et al. Coronavirus diseases (COVID-19) current status and future perspectives: a narrative review. Int J Environ Res Public Health. 2020;17(8). https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17082690.

  13. Xiao Y, Torok ME. Taking the right measures to control COVID-19. Lancet Infect Dis. 2020;20(5):523–4. https://0-doi-org.brum.beds.ac.uk/10.1016/s1473-3099(20)30152-3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Law S, Leung AW, Xu C. Severe acute respiratory syndrome (SARS) and coronavirus disease-2019 (COVID-19): from causes to preventions in Hong Kong. Int J Infect Dis. 2020;94:156–63. https://0-doi-org.brum.beds.ac.uk/10.1016/j.ijid.2020.03.059.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Jahangiry L, Bakhtari F, Sohrabi Z, Reihani P, Samei S, Ponnet K, et al. Risk perception related to COVID-19 among the Iranian general population: an application of the extended parallel process model. BMC Public Health. 2020;20:1–8.

    Article  Google Scholar 

  16. Brown P. Studying COVID-19 in light of critical approaches to risk and uncertainty: research pathways, conceptual tools, and some magic from Mary Douglas: Taylor & Francis; 2020.

  17. Barnes SJ. Information management research and practice in the post-COVID-19 world. Int J Inf Manag. 2020;55:102175. https://0-doi-org.brum.beds.ac.uk/10.1016/j.ijinfomgt.2020.102175.

    Article  Google Scholar 

  18. Marron JM, Joffe S, Jagsi R, Spence RA, Hlubocky FJ. Ethics and resource scarcity: ASCO recommendations for the oncology community during the COVID-19 pandemic. J Clin Oncol. 2020;38(19):2201–5. https://0-doi-org.brum.beds.ac.uk/10.1200/JCO.20.00960.

    Article  CAS  PubMed  Google Scholar 

  19. Farrell TW, Francis L, Brown T, Ferrante LE, Widera E, Rhodes R, et al. Rationing limited health care resources in the COVID-19 era and beyond: ethical considerations regarding older adults. J Am Geriatr Soc. 2020;68(6):1143–9. https://0-doi-org.brum.beds.ac.uk/10.1111/jgs.16539.

    Article  PubMed  Google Scholar 

  20. Tang R, Wu J, Ye M, Liu W. Impact of economic development levels and disaster types on the short-term macroeconomic consequences of natural Hazard-induced disasters in China. Int J Disaster Risk Sci. 2019;10(3):371–85. https://0-doi-org.brum.beds.ac.uk/10.1007/s13753-019-00234-0.

    Article  CAS  Google Scholar 

  21. De Juan A, Pierskalla J, Schwarz E. Natural disasters, aid distribution, and social conflict–Micro-level evidence from the 2015 earthquake in Nepal. World Dev. 2020;126:104715. https://0-doi-org.brum.beds.ac.uk/10.1016/j.worlddev.2019.104715.

    Article  Google Scholar 

  22. Haeffele S, Storr VH, editors. Government Responses to Crisis: Mercatus Studies in Political and Social Economy. London: Springer Nature ; 2020. p. 1-145.

  23. Morgado AM. Disasters, individuals, and communities: can positive psychology contribute to community development after disaster? Community Dev. 2020:1–14.

  24. Appleby-Arnold S, Brockdorff N, Jakovljev I, Zdravković S. Applying cultural values to encourage disaster preparedness: lessons from a low-hazard country. Int J Disaster Risk Reduct. 2018;31:37–44. https://0-doi-org.brum.beds.ac.uk/10.1016/j.ijdrr.2018.04.015.

    Article  Google Scholar 

  25. Rathod S. Rapid response: impact of culture on response to COVID 19. BMJ. 2020;369:m1556. https://0-doi-org.brum.beds.ac.uk/10.1136/bmj.m1556.

    Article  Google Scholar 

  26. Quinn T, Bousquet F, Guerbois C, Sougrati E, Tabutaud M. The dynamic relationship between sense of place and risk perception in landscapes of mobility. Ecol Soc. 2018;23(2). https://0-doi-org.brum.beds.ac.uk/10.5751/ES-10004-230239.

  27. Abir T, Kalimullah NA, Osuagwu UL, Yazdani DMN-A, Al Mamun A, Husain T et al. Factors associated with perception of risk and knowledge of contracting the novel COVID-19 among adults in Bangladesh: analysis of online surveys. 2020.

    Google Scholar 

  28. Zhong Y, Liu W, Lee T-Y, Zhao H, Ji J. Risk perception, knowledge, information sources and emotional states among COVID-19 patients in Wuhan, China. Nurs Outlook. 2020;69(1):13–21. https://0-doi-org.brum.beds.ac.uk/10.1016/j.outlook.2020.08.005.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Abir T, Kalimullah NA, Osuagwu UL, Yazdani DMN-A, Mamun AA, Husain T, et al. 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.

    Article  CAS  Google Scholar 

  30. Li Z-H, Zhang X-R, Zhong W-F, Song W-Q, Wang Z-H, Chen Q, et al. Knowledge, attitudes, and practices related to coronavirus disease 2019 during the outbreak among workers in China: a large cross-sectional study. PLoS Negl Trop Dis. 2020;14(9):e0008584. https://0-doi-org.brum.beds.ac.uk/10.1371/journal.pntd.0008584.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Hawryluck L, Gold WL, Robinson S, Pogorski S, Galea S, Styra R. SARS control and psychological effects of quarantine, Toronto,Canada. Emerg Infect Dis. 2004;10(7):1206–12. https://0-doi-org.brum.beds.ac.uk/10.3201/eid1007.030703.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Reynolds DL, Garay J, Deamond S, Moran MK, Gold W, Styra R. Understanding, compliance and psychological impact of the SARS quarantine experience. Epidemiol Infect. 2008;136(7):997–1007. https://0-doi-org.brum.beds.ac.uk/10.1017/S0950268807009156.

    Article  CAS  PubMed  Google Scholar 

  33. Vatcheva KP, Lee M, McCormick JB, Rahbar MH. Multicollinearity in regression analyses conducted in epidemiologic studies. Epidemiol (Sunnyvale, Calif). 2016;6(2):227.

    Google Scholar 

  34. Drefahl S, Wallace M, Mussino E, Aradhya S, Kolk M, Brandén M, et al. A population-based cohort study of socio-demographic risk factors for COVID-19 deaths in Sweden. Nat Commun. 2020;11(1):5097. https://0-doi-org.brum.beds.ac.uk/10.1038/s41467-020-18926-3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Dillard AJ, Ferrer RA, Welch JD. Associations between narrative transportation, risk perception and behaviour intentions following narrative messages about skin cancer. Psychol Health. 2018;33(5):573–93. https://0-doi-org.brum.beds.ac.uk/10.1080/08870446.2017.1380811.

    Article  PubMed  Google Scholar 

  36. Huang L, Zhang X, Zhang X, Wei Z, Zhang L, Xu J, et al. Rapid asymptomatic transmission of COVID-19 during the incubation period demonstrating strong infectivity in a cluster of youngsters aged 16-23 years outside Wuhan and characteristics of young patients with COVID-19: a prospective contact-tracing study. J Infect. 2020;80(6):e1–e13. https://0-doi-org.brum.beds.ac.uk/10.1016/j.jinf.2020.03.006.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Organization WH. COVID-19 global risk communication and community engagement strategy, December 2020–May 2021: interim guidance, 23 December 2020: World Health Organization; 2020.

  38. Hasmuk K, Sallehuddin H, Tan MP, Cheah WK, Ibrahim R, Chai ST. The long term care COVID-19 situation in Malaysia. International Long-Term Care Policy Network; 2020.

    Google Scholar 

  39. Karim W, Haque A, Anis Z, Ulfy MA. The movement control order (mco) for covid-19 crisis and its impact on tourism and hospitality sector in Malaysia. Int Tour Hosp J. 2020;3:1–7.

    Google Scholar 

  40. Cousins S. Experts criticise Australia's aged care failings over COVID-19. Lancet. 2020;396(10259):1322–3. https://0-doi-org.brum.beds.ac.uk/10.1016/S0140-6736(20)32206-6.

    Article  PubMed Central  Google Scholar 

  41. Hsu AT, Lane N, Sinha SK, Dunning J, Dhuper M, Kahiel Z. Impact of COVID-19 on residents of Canada’s long-term care homes–ongoing challenges and policy response. Int Long Term Care Policy Netw. 2020;17:1–8.

  42. Mustaffa N, Lee S-Y, Mohd Nawi SN, Che Rahim MJ, Chee YC, Muhd Besari A, et al. COVID-19 in the elderly: A Malaysian perspective. J Glob Health. 2020;10:020370. https://0-doi-org.brum.beds.ac.uk/10.7189/jogh.10.020370.

    Article  PubMed  PubMed Central  Google Scholar 

  43. DDG/P Office and OECD. The impact of the COVID-19 pandemic on jobs and incomes in G20 economies. Saudi Arabia: International Labour Organization (ILO); 2020. p. 1-46.

  44. Girma S, Agenagnew L, Beressa G, Tesfaye Y, Alenko A. Risk perception and precautionary health behavior toward COVID-19 among health professionals working in selected public university hospitals in Ethiopia. PLoS One. 2020;15:e0241101.

    Article  CAS  Google Scholar 

  45. Abdel Wahed WY, Hefzy EM, Ahmed MI, Hamed NS. Assessment of knowledge, attitudes, and perception of health care workers regarding COVID-19, a cross-sectional study from Egypt. J Community Health. 2020;45(6):1242–51. https://0-doi-org.brum.beds.ac.uk/10.1007/s10900-020-00882-0.

    Article  PubMed  Google Scholar 

  46. Ekpenyong B, Obinwanne CJ, Ovenseri-Ogbomo G, Ahaiwe K, Lewis OO, Echendu DC, et al. 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(14):5141. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17145141.

    Article  CAS  PubMed Central  Google Scholar 

  47. Serwaa D, Lamptey E, Appiah AB, Senkyire EK, Ameyaw JK. Knowledge, risk perception and preparedness towards coronavirus disease-2019 (COVID-19) outbreak among Ghanaians: a quick online cross-sectional survey. Pan Afr Med J. 2020;35(Supp 2). https://0-doi-org.brum.beds.ac.uk/10.11604/pamj.supp.2020.35.2.22630.

  48. Koh D, Lim MK, Chia SE, Ko SM, Qian F, Ng V, et al. Risk perception and impact of severe acute respiratory syndrome (SARS) on work and personal lives of healthcare Workers in Singapore What can we learn? Med Care. 2005;43(7):676–82. https://0-doi-org.brum.beds.ac.uk/10.1097/01.mlr.0000167181.36730.cc.

    Article  PubMed  Google Scholar 

  49. Dryhurst S, Schneider CR, Kerr J, Freeman ALJ, Recchia G, van der Bles AM, et al. Risk perceptions of COVID-19 around the world. J Risk Res. 2020;23(7-8):994–1006. https://0-doi-org.brum.beds.ac.uk/10.1080/13669877.2020.1758193.

    Article  Google Scholar 

  50. Asefa A, Qanche Q, Hailemariam S, Dhuguma T, Nigussie T. Risk perception towards COVID-19 and its associated factors among waiters in selected towns of Southwest Ethiopia. Risk Manag Healthc Policy. 2020;13:2601–10. https://0-doi-org.brum.beds.ac.uk/10.2147/RMHP.S276257.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Herrera-Diestra JL, Meyers LA. Local risk perception enhances epidemic control. PLoS One. 2019;14(12):e0225576. https://0-doi-org.brum.beds.ac.uk/10.1371/journal.pone.0225576.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Ovenseri-Ogbomo G, Ishaya T, Osuagwu UL, Abu EK, Nwaeze O, Oloruntoba R, et al. Factors associated with the myth about 5G network during COVID-19 pandemic in sub-Saharan Africa. J Glob Health Rep. 2020;4:1–13. https://0-doi-org.brum.beds.ac.uk/10.29392/001c.17606.

    Article  Google Scholar 

  53. Lau JT, Tsui H, Lau M, Yang X. SARS transmission, risk factors, and prevention in Hong Kong. Emerg Infect Dis. 2004;10(4):587–92. https://0-doi-org.brum.beds.ac.uk/10.3201/eid1004.030628.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Williamson EJ, Walker AJ, Bhaskaran K, Bacon S, Bates C, Morton CE, et al. Factors associated with COVID-19-related death using OpenSAFELY. Nature. 2020;584(7821):430–6. https://0-doi-org.brum.beds.ac.uk/10.1038/s41586-020-2521-4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Ferrer RA, Klein WM. Risk perceptions and health behavior. Curr Opin Psychol. 2015;5:85–9. https://0-doi-org.brum.beds.ac.uk/10.1016/j.copsyc.2015.03.012.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Hjort J, Poulsen J. The arrival of fast internet and employment in Africa. Am Econ Rev. 2019;109(3):1032–79. https://0-doi-org.brum.beds.ac.uk/10.1257/aer.20161385.

    Article  Google Scholar 

Download references

Acknowledgements

Not Applicable.

Funding

No funding was obtained for this study.

Author information

Authors and Affiliations

  1. Department of Optometry and Vision Science, School of Allied Health Sciences, University of Cape Coast, 00233, Cape Coast, Ghana

    Emmanuel Kwasi Abu

  2. School of Management and Marketing, Faculty of Business and Law, Curtin University, Bentley, WA, 6151, Australia

    Richard Oloruntoba

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

    Uchechukwu Levi Osuagwu

  4. Discipline of Optometry, School of Health Sciences, African Vision Research Institute (AVRI), University of KwaZulu-Natal, Westville Campus, Durban, 3629, South Africa

    Uchechukwu Levi Osuagwu, Godwin O. Ovenseri-Ogbomo, Bernadine N. Ekpenyong, Khathutshelo Percy Mashige & Kingsley Emwinyore Agho

  5. Faculty of Health, School of Medicine, Deakin University, Geelong, Victoria, Australia

    Dipesh Bhattarai

  6. Department of Community Medicine, College of Health Sciences, University of Jos, Jos, Nigeria

    Chundung Asabe Miner

  7. Department of Psychiatry, College of Health Sciences, University of Jos, Jos, Nigeria

    Piwuna Christopher Goson

  8. Health Division, University of Bamenda Bambili, Bamenda, Cameroon

    Raymond Langsi

  9. County Durham and Darlington National Health Service (NHS) Foundation, Darlington, DL3 0PD, UK

    Obinna Nwaeze

  10. Department of Optometry and Vision Sciences, School of Public Health, Biomedical Sciences and Technology, Masinde Muliro University of Science and Technology, Kakamega, 50100, Kenya

    Timothy G. Chikasirimobi

  11. Department of Optometry, Center for Health Sciences, University of Highlands and Islands, Old Perth Road, IV2 3JH, Scotland

    Godwin O. Ovenseri-Ogbomo

  12. Department of Optometry, Faculty of Life Sciences, University of Benin, Benin City, Nigeria

    Godwin O. Ovenseri-Ogbomo

  13. Department of Public Health, Faculty of Allied Medical Sciences, College of Medical Sciences, University of Calabar, Calabar, Cross River State, Nigeria

    Bernadine N. Ekpenyong

  14. Tanzania Food and Nutrition Center, P. O. Box 977, Dar-es Salaam, Tanzania

    Deborah Donald Charwe

  15. Department of Computer Science, University of Jos, Jos, Nigeria

    Tanko Ishaya

  16. School of Health Sciences, Western Sydney University, Campbelltown, NSW, 2560, Australia

    Kingsley Emwinyore Agho

Authors
  1. Emmanuel Kwasi Abu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Richard Oloruntoba
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Uchechukwu Levi Osuagwu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dipesh Bhattarai
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chundung Asabe Miner
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Piwuna Christopher Goson
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Raymond Langsi
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Obinna Nwaeze
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Timothy G. Chikasirimobi
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Godwin O. Ovenseri-Ogbomo
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Bernadine N. Ekpenyong
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Deborah Donald Charwe
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Khathutshelo Percy Mashige
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Tanko Ishaya
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Kingsley Emwinyore Agho
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

K.E.A, U.L.O, R.O. conceptualized the study. K.E.A., E.K.A., B.N.E., K.P.M., P.C.G., U.L.O., G.O.O., O.N., R.O., T.I., T.C.G., D.D.C., C.A.M., R.L. and D.B. were involved in data collection and interpretation of the data. K.E.A., D.B. and U.L.O performed the formal analysis of the data. E.K.A., D.B., R.O. and U.L.O. drafted the original manuscript. K.E.A., G.O.O., E.K.A., B.N.E., K.P.M., P.C.G., O.N., R.O., T.I., T.C.G, D.D.C., C.A.M, R.L. and D.B. reviewed and edited the manuscript. K.E.A., T.I., D.D.C., K.P.M. and U.L.O. supervised the project. The author(s) read and approved the final manuscript.

Corresponding author

Correspondence to Uchechukwu Levi Osuagwu.

Ethics declarations

Ethics approval and consent to participate

The Human Research Ethics Committee of the Cross River State Ministry of Health, Nigeria approved this study (#: CRSMOH/RP/REC/2020/116). Written informed consent was obtained from all respondents before participation, by asking respondents to voluntarily answer either a ‘yes’ or ‘no’ to the question inquiring whether they agreed to participate in the survey. Respondents could only proceed to complete the survey if they answered ‘yes’ to this question. All protocols are carried out in accordance in accordance with the Helsinki Declaration for Human Research.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: Supplementary Figure

. Percentage distribution of respondents by country of residence for local and diaspora residents.

Additional file 2: Supplementary Table

. Sample of Survey items with response options.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Abu, E.K., Oloruntoba, R., Osuagwu, U.L. et al. Risk perception of COVID-19 among sub-Sahara Africans: a web-based comparative survey of local and diaspora residents. BMC Public Health 21, 1562 (2021). https://0-doi-org.brum.beds.ac.uk/10.1186/s12889-021-11600-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://0-doi-org.brum.beds.ac.uk/10.1186/s12889-021-11600-3

Keywords

BMC Public Health

ISSN: 1471-2458

Contact us