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Research Article

Perceived risk of infection and death from COVID-19 among community members of low- and middle-income countries: A cross-sectional study

[version 1; peer review: 1 approved, 1 approved with reservations]
Mahir Gachabayov
https://orcid.org/0000-0002-9200-1304
1, Khan Sharun
https://orcid.org/0000-0003-1040-3746
2, Daniel M. Felsenreich3, [...] Firzan Nainu
https://orcid.org/0000-0003-0989-4023
4, Samsul Anwar
https://orcid.org/0000-0003-3165-2151
5, Amanda Yufika6, Youdiil Ophinni
https://orcid.org/0000-0001-9443-1538
7,8, Chika Yamada9, Marhami Fahriani
https://orcid.org/0000-0003-0452-3485
10, Milda Husnah10,11, Rawan Raad
https://orcid.org/0000-0003-4060-3378
12, Namareg ME. Khiri13, Rashed YA. Abdalla
https://orcid.org/0000-0002-7841-4807
14, Rashed Y. Adam15, Mohajer IH. Ismaeil
https://orcid.org/0000-0003-4864-3454
16, Asma Y. Ismail17, Wajdi Kacem18, Zeineb Teyeb
https://orcid.org/0000-0003-2871-9773
19, Khaoula Aloui
https://orcid.org/0000-0002-1594-3429
20, Montacer Hafsi20, Manel Ferjani
https://orcid.org/0000-0003-1039-0500
21, Nasrine Ben Hadj Dahman20, Dalia A. Deeb
https://orcid.org/0000-0002-4434-947X
22, Dina Emad23, Kirellos Said Abbas24, Fatma A. Monib25, Farah S. Sami23, Subramaniam Ramanarayanan
https://orcid.org/0000-0003-2863-6708
26, Suhrud Panchawagh
https://orcid.org/0000-0002-8606-4202
27, Sunil Anandu28, Md Ariful Haque
https://orcid.org/0000-0003-4632-5153
29, Lirane ED. Ferreto30, María FC. Briones31, Rocío BI. Morales
https://orcid.org/0000-0003-3473-4654
31, Sebastián Lazcano-Díaz31, José TO. Aburto
https://orcid.org/0000-0001-8705-4041
31, Jorge ET. Rojas31, Emmanuel O. Balogun32, Hendrix I. Kusuma
https://orcid.org/0000-0003-3555-0136
10,11,33, Cut Meurah Yeni34,35, Niken Asri Utami34,35, Seyi S. Enitan
https://orcid.org/0000-0001-5993-7920
36, Akele R. Yomi
https://orcid.org/0000-0003-2006-0820
37, Abiodun Durosinmi
https://orcid.org/0000-0002-5382-5108
38, Esther N. Adejumo36, Eyiuche D. Ezigbo
https://orcid.org/0000-0001-9397-3706
39, Elham Babadi40, Edris Kakemam41, Irfan Ullah42, Najma I. Malik
https://orcid.org/0000-0002-3521-1014
43, Francesco Rosiello
https://orcid.org/0000-0002-6532-1185
44, Talha B. Emran
https://orcid.org/0000-0003-3188-2272
45, Eva Imelda46, Guilherme W. Wendt
https://orcid.org/0000-0002-9014-6120
47, Morteza Arab-Zozani
https://orcid.org/0000-0001-7223-6707
48, Kuldeep Dhama
https://orcid.org/0000-0001-7469-4752
2,49, Mudatsir Mudatsir
https://orcid.org/0000-0002-5643-9384
10,50,51, Harapan Harapan
https://orcid.org/0000-0001-7630-8413
10,50-52
Mahir Gachabayov
https://orcid.org/0000-0002-9200-1304
1, Khan Sharun
https://orcid.org/0000-0003-1040-3746
2, [...] Daniel M. Felsenreich3, Firzan Nainu
https://orcid.org/0000-0003-0989-4023
4, Samsul Anwar
https://orcid.org/0000-0003-3165-2151
5, Amanda Yufika6, Youdiil Ophinni
https://orcid.org/0000-0001-9443-1538
7,8, Chika Yamada9, Marhami Fahriani
https://orcid.org/0000-0003-0452-3485
10, Milda Husnah10,11, Rawan Raad
https://orcid.org/0000-0003-4060-3378
12, Namareg ME. Khiri13, Rashed YA. Abdalla
https://orcid.org/0000-0002-7841-4807
14, Rashed Y. Adam15, Mohajer IH. Ismaeil
https://orcid.org/0000-0003-4864-3454
16, Asma Y. Ismail17, Wajdi Kacem18, Zeineb Teyeb
https://orcid.org/0000-0003-2871-9773
19, Khaoula Aloui
https://orcid.org/0000-0002-1594-3429
20, Montacer Hafsi20, Manel Ferjani
https://orcid.org/0000-0003-1039-0500
21, Nasrine Ben Hadj Dahman20, Dalia A. Deeb
https://orcid.org/0000-0002-4434-947X
22, Dina Emad23, Kirellos Said Abbas24, Fatma A. Monib25, Farah S. Sami23, Subramaniam Ramanarayanan
https://orcid.org/0000-0003-2863-6708
26, Suhrud Panchawagh
https://orcid.org/0000-0002-8606-4202
27, Sunil Anandu28, Md Ariful Haque
https://orcid.org/0000-0003-4632-5153
29, Lirane ED. Ferreto30, María FC. Briones31, Rocío BI. Morales
https://orcid.org/0000-0003-3473-4654
31, Sebastián Lazcano-Díaz31, José TO. Aburto
https://orcid.org/0000-0001-8705-4041
31, Jorge ET. Rojas31, Emmanuel O. Balogun32, Hendrix I. Kusuma
https://orcid.org/0000-0003-3555-0136
10,11,33, Cut Meurah Yeni34,35, Niken Asri Utami34,35, Seyi S. Enitan
https://orcid.org/0000-0001-5993-7920
36, Akele R. Yomi
https://orcid.org/0000-0003-2006-0820
37, Abiodun Durosinmi
https://orcid.org/0000-0002-5382-5108
38, Esther N. Adejumo36, Eyiuche D. Ezigbo
https://orcid.org/0000-0001-9397-3706
39, Elham Babadi40, Edris Kakemam41, Irfan Ullah42, Najma I. Malik
https://orcid.org/0000-0002-3521-1014
43, Francesco Rosiello
https://orcid.org/0000-0002-6532-1185
44, Talha B. Emran
https://orcid.org/0000-0003-3188-2272
45, Eva Imelda46, Guilherme W. Wendt
https://orcid.org/0000-0002-9014-6120
47, Morteza Arab-Zozani
https://orcid.org/0000-0001-7223-6707
48, Kuldeep Dhama
https://orcid.org/0000-0001-7469-4752
2,49, Mudatsir Mudatsir
https://orcid.org/0000-0002-5643-9384
10,50,51, Harapan Harapan
https://orcid.org/0000-0001-7630-8413
10,50-52
PUBLISHED 22 Mar 2022
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OPEN PEER REVIEW
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This article is included in the Emerging Diseases and Outbreaks gateway.

This article is included in the Japan Institutional Gateway gateway.

This article is included in the Sociology of Health gateway.

Abstract

Background: Risk perceptions of coronavirus disease 2019 (COVID-19) are considered important as they impact community health behaviors. The aim of this study was to determine the perceived risk of infection and death due to COVID-19 and to assess the factors associated with such risk perceptions among community members in low- and middle-income countries (LMICs) in Africa, Asia, and South America.
Methods: An online cross-sectional study was conducted in 10 LMICs in Africa, Asia, and South America from February to May 2021. A questionnaire was utilized to assess the perceived risk of infection and death from COVID-19 and its plausible determinants. A logistic regression model was used to identify the factors associated with such risk perceptions.
Results: A total of 1,646 responses were included in the analysis of the perceived risk of becoming infected and dying from COVID-19. Our data suggested that 36.4% of participants had a high perceived risk of COVID-19 infection, while only 22.4% had a perceived risk of dying from COVID-19. Being a woman, working in healthcare-related sectors, contracting pulmonary disease, knowing people in the immediate social environment who are or have been infected with COVID-19, as well as seeing or reading about individuals infected with COVID-19 on social media or TV were all associated with a higher perceived risk of becoming infected with COVID-19. In addition, being a woman, elderly, having heart disease and pulmonary disease, knowing people in the immediate social environment who are or have been infected with COVID-19, and seeing or reading about individuals infected with COVID-19 on social media or TV had a higher perceived risk of dying from COVID-19.
Conclusions: The perceived risk of infection and death due to COVID-19 are relatively low among respondents; this suggests the need to conduct health campaigns to disseminate knowledge and information on the ongoing pandemic.

Keywords

COVID-19, perceived risk, online cross-sectional study, preventive measure, determinants

Corresponding author: Harapan Harapan
Competing interests: No competing interests were disclosed.
Grant information: This work was supported by Universitas Syiah Kuala, Ministry of Education, Culture, Research and Technology, H-Index Scheme 2021 (169/UN11/SPK/PNBP/2021), granted to Harapan Harapan.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Copyright:  © 2022 Gachabayov M et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
How to cite: Gachabayov M, Sharun K, Felsenreich DM et al. Perceived risk of infection and death from COVID-19 among community members of low- and middle-income countries: A cross-sectional study [version 1; peer review: 1 approved, 1 approved with reservations]. F1000Research 2022, 11:345 (https://doi.org/10.12688/f1000research.109575.1) First published: 22 Mar 2022, 11:345 (https://doi.org/10.12688/f1000research.109575.1) Latest published: 07 Sep 2022, 11:345 (https://doi.org/10.12688/f1000research.109575.2)

Introduction

The coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has had catastrophic effects on the economy, health, and society1–3 and is associated with long-term health problem.4,5 The risk perceptions of COVID-19 are considered important as they influence health behaviors in the community.6 According to one study, accessing the media to obtain information on COVID-19 is linked to an increase in COVID-19 perceived risk and severity.7 In addition, a study found that the willingness to receive COVID-19 vaccination was related to the perceived risk of becoming infected with SARS-CoV-2.8 Therefore, providing reliable information on the risks of infection and possible adverse health outcomes (including death) could directly influence an individual’s risk perceptions. This, in turn, may affect their decisions to select suitable preventive measures.9

Several studies have analyzed risk perceptions during the ongoing pandemic to learn more about their correlates and predictors.6,7,9,10 In general, the findings from these studies suggest that risk perceptions of SARS-CoV-2 infection are predictive of the adoption of different protective behaviors.6,9 Furthermore, strategic interventions aimed at increasing the risk perceptions of individuals can help promote desired protective behaviors.6 In addition, perceived risks are also considered as important drivers for the adoption of COVID-19 control measures implemented by the government and other responsible agencies.10

Studies that assess risk perceptions and the associated determining factors will provide policymakers with critical information that will help to design population-wide health measures to combat the ongoing pandemic. In addition, long-term COVID-19 risk perception studies, especially in low- and middle-income countries (LMICs), will help to develop efficient preventive strategies within the population.9 The present study was designed to determine the perceived risk of becoming infected and dying as a result of COVID-19, as well as to assess the factors associated with such risk perceptions in the community members of LMICs in Africa, Asia, and South America.

Methods

Study design and setting

We conducted an online cross-sectional study23 in 10 LMICs in Africa, Asia, and South America between February and May 2021. The survey link, hosted by SurveyMonkey, was shared on Twitter, Facebook, and WhatsApp. The survey22 consisted of three sections: an introduction page with study information and an informed consent page where respondents had to provide consent to participate, and the main survey, which asked respondents about their demographic background, previous health conditions, perceived risk of infection and death due to COVID-19, and several possible determinants. The survey included an informed consent page that included the benefits of the study, risks and discomforts and information that this study was completely voluntary. All respondents provided consent to participate by clicking “Agree” before the next page could be opened. It took approximately 15 minutes to complete all the questions. Before being utilized in the study, the questions in the questionnaire were evaluated and their validity was confirmed. Most of the questions were adopted from a previous study11 and during the validity assessment, each question was assessed by experts in the fields of virology and public health. Changes were made to the questionnaire based on the validity assessment. No further reliability or validity tests in the field were made. The inclusion criteria to be eligible in this survey were those who were aged over 18 years old and able to read and understand English in the 10 studied countries. We sought a 95% confidence level and a 5% margin of error to recruit the minimal sample size with a conservative assumption that 50% of respondents having good perceived risk of COVID-19. We employed a convenience sampling approach, a non-probability sampling method, to recruit the samples. We received 1,849 responses during the study of which 203 respondents were excluded due to incomplete information.

The Institutional Review Board of Universitas Syiah Kuala & Zainoel Abidin Hospital (129/EA/FK-RSUDZA/2021) approved the study protocol and it was registered with the Indonesian National Health Research and Development Ethics Commission (1171012P).

Study variables

Response variable

The response variable in this study was the perceived risk of COVID-19. To assess the perceived risk, the respondents were asked two questions: (1) “What do you think are the chances that you will get COVID-19 in the next month?” and (2) “What do you think is your risk of dying from COVID-19 if infected?” A slide bar was provided to the respondents, with which they could move the percentage between 0% and 100%. The perceived risk score was then classified into low (a score equal to or less than 50%) and high (a score of more than 50%). Each question was analyzed separately.

Explanatory variables

Age, gender, urbanicity, monthly household income in USD, religion, occupation sector (healthcare- and non-healthcare-related), type of occupation, and the presence of COVID-19 comorbidities based on self-reports, such as hypertension, diabetes, heart disease, and pulmonary disease were all included as explanatory variables. In addition, the respondents were asked whether they knew anyone in their immediate social environment who were or had been infected with COVID-19. Respondents were also questioned about their exposure to information (have seen or read) of individuals infected with COVID-19 on TV or social media.

Statistical analysis

Logistic regression analysis was used to assess the factors associated with the perceived risk of contracting COVID-19 and the factors associated with the perceived risk of dying from COVID-19 if infected. The unadjusted logistic regressions, crude odds ratio (OR), and 95% confidence interval (CI) of each plausible factor were calculated separately in the first step. Factors with p-values of less than 0.25 in the univariate analysis were included in the adjusted analysis, where the adjusted OR (aOR) was calculated. The analyses were conducted using SPSS software version 24 (SPSS Inc., Chicago, IL, USA) (SPSS, RRID:SCR_019096).

Results

Demographic characteristics

Table 122 presents the characteristics of the 1,646 responses that were analyzed. India and Pakistan had the highest number of respondents. More than half of the participants were between the ages of 21 and 30, and 58% of the total respondents were female. The majority (81%) of the respondents lived in urban areas, and 65% were part of a family with a monthly household income of less than $2,000. Respondents with hypertension, diabetes, heart disease, and pulmonary disease made up 5.9%, 3.5%, 3.3%, and 5.5% of the study participants, respectively. Almost 70% of the respondents knew someone who had been infected with COVID-19 in their immediate social environment, and more than 92.7% had seen or read about individuals infected with COVID-19 on social media or TV.

Table 1. Logistic regression showing factors associated with perceived risk of COVID-19 infection (n=1,649).

Variablen (%)High perceived riskUnadjustedAdjusted
n (%)OR (95% CI)p-valueOR (95% CI)p-value
Country
Pakistan (R)263 (15.9)52 (19.8)11
Brazil107 (6.5)40 (37.4)2.42 (1.48 – 3.98)<0.0012.32 (1.13 – 4.77)0.022
Chile106 (6.4)23 (21.7)1.12 (0.65 – 1.95)0.6771.40 (0.66 – 2.97)0.379
Egypt98 (6.0)50 (51.0)4.23 (2.57 – 6.96)<0.0012.35 (1.36 – 4.07)0.002
India339 (20.6)149 (44.0)3.18 (2.19 – 4.61)<0.0014.28 (2.33 – 7.86)<0.001
Iran141 (8.6)53 (37.6)2.44 (1.55 – 3.86)<0.0011.30 (0.75 – 2.24)0.353
Nigeria161 (9.8)28 (17.4)0.85 (0.51 – 1.42)0.5431.00 (0.50 – 2.00)0.995
Bangladesh131 (7.9)54 (41.2)2.85 (1.79 – 4.52)<0.0012.86 (1.68 – 4.87)<0.001
Sudan174 (10.6)76 (43.7)3.15 (2.05 – 4.82)<0.0011.61 (0.97 – 2.67)0.066
Tunisia129 (7.8)75 (58.1)5.64 (3.55 – 8.96)<0.0013.66 (2.12 – 6.32)<0.001
Age group (year)
<20 (R)280 (17.0)63 (22.5)11
21-30926 (56.2)373 (40.3)2.32 (1.70 – 3.17)<0.0011.50 (1.03 – 2.18)0.035
31-40270 (16.4)115 (42.6)2.56 (1.77 – 3.70)<0.0011.69 (1.02 – 2.82)0.043
41-50119 (7.2)33 (27.7)1.32 (0.81 – 2.16)0.2640.90 (0.48 – 1.69)0.745
>5154 (3.3)16 (29.6)1.45 (0.76 – 2.77)0.2610.71 (0.31 – 1.63)0.423
Sex
Male (R)692 (42.0)234 (33.8)11
Female957 (58.0)366 (38.2)1.21 (0.99 – 1.49)0.0651.40 (1.10 – 1.78)0.006
Urbanicity
Rural (R)314 (19.0)116 (36.9)1
Urban1335 (81.0)484 (36.3)0.97 (0.75 – 1.25)0.820
Monthly household income (USD)
<500 (R)618 (37.5)228 (36.9)11
500-999289 (17.5)110 (38.1)1.05 (0.79 – 1.40)0.7341.02 (0.74 – 1.40)0.921
1,000-1,999192 (11.6)72 (37.5)1.03 (0.73 – 1.43)0.8790.92 (0.63 – 1.35)0.665
2,000-2,999148 (9.0)63 (42.6)1.27 (0.88 – 1.83)0.2021.29 (0.85 – 1.98)0.236
3,000-4,999128 (7.8)39 (30.5)0.75 (0.50 – 1.13)0.1690.79 (0.50 – 1.26)0.319
5,000-7,999100 (6.1)36 (36.0)0.96 (0.62 – 1.49)0.8641.00 (0.60 – 1.64)0.984
≥8,000174 (10.6)52 (29.9)0.73 (0.51 – 1.05)0.0880.67 (0.43 – 1.05)0.080
Religion
Islam (R)915 (55.5)354 (38.7)11
Christian/Protestant/Methodist/Lutheran/Baptist179 (10.9)46 (25.7)0.55 (0.38 – 0.79)0.0010.65 (0.38 – 1.11)0.117
Catholic127 (7.7)41 (32.3)0.76 (0.51 – 1.12)0.1640.68 (0.37 – 1.25)0.213
Hindu239 (14.5)92 (38.5)0.99 (0.74 – 1.33)0.9560.44 (0.26 – 0.74)0.002
Atheist or agnostic87 (5.3)25 (28.7)0.64 (0.39 – 1.04)0.0690.50 (0.27 – 0.93)0.028
Others102 (6.2)42 (41.2)1.11 (0.73 – 1.68)0.6250.84 (0.49 – 1.43)0.510
Healthcare-related job
No (R)908 (55.1)261 (28.7)11
Yes741 (44.9)339 (45.7)2.09 (1.71 – 2.56)<0.0011.48 (1.16 – 1.89)0.002
Have hypertension
Noa(R)1102 (66.8)404 (36.7)1
Yes b97 (5.9)34 (35.1)0.93 (0.60 – 1.44)0.752
Do not know450 (27.3)162 (36.0)0.97 (0.77 – 1.22)0.806
Have diabetes
Noa(R)1190 (72.2)447 (37.6)11
Yes b58 (3.5)19 (32.8)0.81 (0.46 – 1.42)0.4610.79 (0.40 – 1.55)0.487
Do not know401 (24.3)134 (33.4)0.83 (0.66 – 1.06)0.1360.79 (0.56 – 1.13)0.200
Have heart disease
Noa(R)1093 (66.3)395 (36.1)11
Yes b55 (3.3)26 (47.3)1.58 (0.92 – 2.73)0.0971.59 (0.82 – 3.07)0.166
Do not know501 (30.4)179 (35.7)0.98 (0.79 – 1.23)0.8741.07 (0.71 – 1.59)0.755
Have pulmonary disease
No a(R)1044 (63.3)371 (35.5)11
Yes b90 (5.5)51 (56.7)2.37 (1.53 – 3.67)<0.0012.44 (1.48 – 4.05)0.001
Do not know515 (31.2)178 (34.6)0.96 (0.77 – 1.20)0.7051.22 (0.82 – 1.82)0.335
Know people in immediate social environment who are or have been infected with COVID-19
No (R)508 (30.8)105 (20.7)11
Yes1141 (69.2)495 (43.4)2.94 (2.30 – 3.76)<0.0012.05 (1.55 – 2.70)<0.001
Have you seen or read about individuals infected with the COVID-19 on social media or TV?
No (R)121 (7.3)27 (22.3)11
Yes1528 (92.7)573 (37.5)2.09 (1.34 – 3.24)<0.0011.72 (1.06 – 2.77)0.027
Occupation
Self-employed (R)155 (9.4)42 (27.1)11
Employed for wages417 (25.3)177 (42.4)1.98 (1.33 – 2.97)<0.0012.22 (1.43 – 3.45)<0.001
Out of work for less 1 year AND more than 1 year73 (4.4)23 (31.5)1.24 (0.67 – 2.27)0.4921.49 (0.77 – 2.89)0.241
Homemaker34 (2.1)12 (35.3)1.47 (0.67 – 3.23)0.3401.65 (0.68 – 4.00)0.272
Student948 (57.5)330 (34.8)1.44 (0.98 – 2.10)0.0612.05 (1.31 – 3.22)0.002
Retired or unable to work22 (1.3)16 (72.7)7.18 (2.63 – 19.56)<0.00111.36 (3.81 – 33.87)<0.001
Has how much your work changed as a result of the COVID-19 pandemic?
No change and not applicable (not working) (R)1009 (61.2)349 (34.6)11
I work more hours300 (18.2)126 (42.0)1.37 (1.05 – 1.78)0.0191.17 (0.85 – 1.59)0.336
I work fewer hours286 (17.3)107 (37.4)1.13 (0.86 – 1.48)0.3781.02 (0.75 – 1.39)0.900
I was let go from my job54 (3.3)18 (33.3)0.95 (0.53 – 1.69)0.8500.90 (0.46 – 1.74)0.753

a Have been tested or examined by a doctor but negative.

b Have been diagnosed by a doctor.

Perceived risk of becoming infected with COVID-19 and the associated determinants

Of the 1,646 respondents, 36.4% (600/1,646) had a high perceived risk of being infected with COVID-19. The unadjusted logistic regression suggested that the country, age group, religion, type of job, having pulmonary disease, knowing people in the immediate social environment who were or had been infected with COVID-19, seeing or reading about individuals infected with COVID-19 on social media or TV, and type of occupation were associated with a perceived risk of contracting COVID-19 (Table 1). In the adjusted logistic regression, only country, age group, gender, religion, type of job, those with pulmonary disease, knowing people in the immediate social environment who were or had been infected with COVID-19, seeing or reading about individuals infected with COVID-19 on social media or TV, and type of occupation were associated with a perceived risk of contracting COVID-19.

Females had a greater perceived risk of contracting COVID-19 compared to males (aOR: 1.40; 95% CI: 1.10–1.78, p=0.006) (Table 1). Those working in healthcare-related sectors had almost 1.48 times higher odds of a higher perceived risk of contracting COVID-19 compared to those working in non-healthcare sectors (aOR: 1.48; 95% CI: 1.16–1.89, p=0.002). Our study also found that people with COVID-19 comorbidities, in particular pulmonary disease, had a higher perceived risk compared to those with no pulmonary disease (aOR: 2.44; 95% CI: 1.48–4.05). Participants who knew someone from their immediate social environment who were or had been infected with COVID-19, as well as those who had seen or read about COVID-19 cases on social media or TV, had 2.05 (95% CI: 1.55–2.70) and 1.72 (95% CI: 1.06–2.77) times higher odds of perceived risk of becoming infected with COVID-19 compared to those who did not know and had never seen or read about COVID-19 cases, respectively. Compared to self-employed participants, those who were employed for wages, students, and those who were retired (unable to work) had higher odds of contracting COVID-19 (aOR: 2.22, 2.05, and 11.36, respectively) (Table 1).

Perceived risk of dying from COVID-19 and associated determinants

Of the total respondents, 22.4% (370/1,649) had a perceived risk of dying from COVID-19, which was classified as high. In the unadjusted analysis, the level of perceived risk was associated with country, age group, gender, monthly household income, religion, having hypertension, heart disease, and pulmonary disease, and knowing or not knowing someone in the immediate social environment who were or had been infected with COVID-19 to varying degrees (Table 2). In the adjusted logistic regression, country, age group, gender, religion, type of job, pulmonary disease, knowing people in the immediate social environment who were or had been infected with COVID-19, had seen or read about individuals infected with COVID-19 on social media or TV, and type of occupation were associated with a perceived risk of dying from COVID-19 (Table 2).

Table 2. Logistic regression showing factors associated with perceived risk of dying from COVID-19 if infected (n=1,649).

Variablen (%)High perceived riskUnadjustedAdjusted
n (%)OR (95% CI)p-valueOR (95% CI)p-value
Country
Pakistan (R)263 (15.9)65 (24.7)11
Brazil107 (6.5)25 (23.4)0.93 (0.55 – 1.58)0.7840.88 (0.40 – 1.92)0.745
Chile106 (6.4)16 (15.1)0.54 (0.30 – 0.99)0.0450.70 (0.30 – 1.60)0.392
Egypt98 (6.0)26 (26.5)1.10 (0.65 – 1.87)0.7241.00 (0.56 – 1.80)0.990
India339 (20.6)56 (16.5)0.60 (0.40 – 0.90)0.0130.93 (0.49 – 1.78)0.823
Iran141 (8.6)32 (22.7)0.89 (0.55 – 1.45)0.6510.63 (0.35 – 1.13)0.117
Nigeria161 (9.8)25 (15.5)0.56 (0.34 – 0.93)0.0260.83 (0.40 – 1.73)0.622
Bangladesh131 (7.9)47 (35.9)1.70 (1.08 – 2.68)0.0212.10 (1.23 – 3.58)0.006
Sudan174 (10.6)44 (25.3)1.03 (0.66 – 1.60)0.8920.74 (0.44 – 1.25)0.260
Tunisia129 (7.8)34 (26.4)1.09 (0.67 – 1.77)0.7250.80 (0.45 – 1.44)0.462
Age group (year)
<20 (R)280 (17.0)54 (19.3)11
21-30926 (56.2)207 (22.4)1.21 (0.86 – 1.68)0.2751.23 (0.83 – 1.85)0.305
31-40270 (16.4)68 (25.2)1.41 (0.94 – 2.11)0.0971.57 (0.90 – 2.75)0.116
41-50119 (7.2)22 (18.5)0.95 (0.55 – 1.65)0.8531.20 (0.60 – 2.40)0.613
>5154 (3.3)19 (35.2)2.27 (1.21 – 4.28)0.0112.70 (1.21 – 5.99)0.015
Gender
Male (R)692 (42.0)137 (19.8)11
Female957 (58.0)233 (24.3)1.30 (1.03 – 1.65)0.0291.58 (1.20 – 2.08)0.001
Urbanicity
Rural (R)314 (19.0)70 (22.3)1
Urban1335 (81.0)300 (22.5)1.01 (0.75 – 1.36)0.945
Monthly household income (USD)
<500 (R)618 (37.5)167 (27.0)11
500-999289 (17.5)66 (22.8)0.80 (0.58 – 1.11)0.1790.80 (0.56 – 1.14)0.212
1,000-1,999192 (11.6)36 (18.8)0.62 (0.42 – 0.93)0.0220.58 (0.38 – 0.90)0.015
2,000-2,999148 (9.0)36 (24.3)0.87 (0.57 – 1.32)0.5040.94 (0.59 – 1.49)0.777
3,000-4,999128 (7.8)23 (18.0)0.59 (0.36 – 0.96)0.0340.62 (0.36 – 1.05)0.076
5,000-7,999100 (6.1)21 (21.0)0.72 (0.43 – 1.20)0.2050.73 (0.42 – 1.28)0.267
≥8,000174 (10.6)21 (12.1)0.37 (0.23 – 0.61)<0.0010.34 (0.19 – 0.60)<0.001
Religion
Islam (R)915 (55.5)242 (26.4)11
Christian/Protestant/Methodist/Lutheran/Baptist179 (10.9)24 (13.4)0.43 (0.27 – 0.68)<0.0010.54 (0.29 – 1.00)0.051
Catholic127 (7.7)28 (22.0)0.79 (0.50 – 1.23)0.2900.96 (0.50 – 1.85)0.902
Hindu239 (14.5)32 (13.4)0.43 (0.29 – 0.64)<0.0010.44 (0.24 – 0.81)0.008
Atheist or agnostic87 (5.3)18 (20.7)0.73 (0.42 – 1.24)0.2430.93 (0.47 – 1.84)0.840
Others102 (6.2)26 (25.5)0.95 (0.60 – 1.52)0.8351.05 (0.58 – 1.90)0.862
Healthcare-related job
No (R)908 (55.1)198 (21.8)1
Yes741 (44.9)172 (23.2)1.08 (0.86 – 1.37)0.496
Have hypertension
Noa(R)1102 (66.8)227 (20.6)11
Yes b97 (5.9)32 (33.0)1.90 (1.21 – 2.97)0.0051.29 (0.75 – 2.22)0.365
Do not know450 (27.3)111 (24.7)1.26 (0.97 – 1.64)0.0781.38 (0.96 – 1.99)0.087
Have diabetes
Noa(R)1190 (72.2)266 (22.4)1
Yes b58 (3.5)16 (27.6)1.32 (0.73 – 2.39)0.353
Do not know401 (24.3)88 (21.9)0.98 (0.74 – 1.28)0.865
Have heart disease
Noa(R)1093 (66.3)233 (21.3)11
Yes b55 (3.3)24 (43.6)2.86 (1.65 – 4.96)<0.0012.35 (1.20 – 4.60)0.013
Do not know501 (30.4)113 (22.6)1.08 (0.83 – 1.39)0.5781.03 (0.67 – 1.58)0.901
Have pulmonary disease
Noa(R)1044 (63.3)215 (20.6)11
Yesb90 (5.5)42 (46.7)3.37 (2.17 – 5.24)<0.0012.88 (1.73 – 4.78)<0.001
Do not know515 (31.2)113 (21.9)1.08 (0.84 – 1.40)0.5390.88 (0.57 – 1.37)0.578
Know people in immediate social environment who are or have been infected with COVID-19
No (R)508 (30.8)93 (18.3)11
Yes1141 (69.2)277 (24.3)1.43 (1.10 – 1.86)0.0071.51 (1.11 – 2.05)0.008
Have you seen or read about individuals infected with the COVID-19 on social media or TV?
No (R)121 (7.3)33 (27.3)11
Yes1528 (92.7)337 (22.1)0.76 (0.50 – 1.15)0.1870.70 (0.44 – 1.10)0.122
Occupation
Self-employed (R)155 (9.4)35 (22.6)11
Employed for wages417 (25.3)103 (24.7)1.13 (0.73 – 1.74)0.5991.12 (0.69 – 1.81)0.643
Out of work for less 1 year AND more than 1 year73 (4.4)23 (31.5)1.58 (0.85 – 2.94)0.1501.64(0.83 – 3.25)0.153
Homemaker34 (2.1)12 (35.3)1.87 (0.84 – 4.15)0.1241.52 (0.63 – 3.68)0.351
Student948 (57.5)192 (20.3)0.87 (0.58 – 1.31)0.5070.92 (0.56 – 1.49)0.720
Retired or unable to work22 (1.3)5 (22.7)1.01 (0.35 – 2.93)0.9880.70 (0.21 – 2.31)0.558
Has how much your work changed as a result of the COVID-19 pandemic?
No change or not applicable (not working) (R)1009 (61.2)203 (20.1)11
I work more hours300 (18.2)80 (26.7)1.44 (1.07 – 1.95)0.0161.39 (0.99 – 1.96)0.056
I work fewer hours286 (17.3)71 (24.8)1.31 (0.96 – 1.79)0.0861.25 (0.89 – 1.75)0.192
I was let go from my job54 (3.3)16 (29.6)1.67 (0.91 – 3.06)0.0951.26 (0.65 – 2.46)0.497

a Have been tested or examined by a doctor but negative.

b Have been diagnosed by a doctor.

Our data suggested that the elderly group (those over the age of 51) had a higher perceived risk of dying from COVID-19, if infected, by 2.7 times (95% CI: 1.21–5.99) compared to the youngest age group (those under the age of 20) (Table 2). Compared to male participants, female participants had 1.5 times higher odds of perceived risk of dying from COVID-19 (aOR: 1.58; 95% CI: 1.20–2.08, p=0.001). Participants who had heart disease and pulmonary disease had a 2.3- and 2.8-fold chance of having a perceived risk of dying from COVID-19 compared to those who did not have such comorbidities (aOR: 2.35; 95% CI: 1.20–4.60 and aOR: 2.88; 95% CI: 1.73–4.78, respectively). Our study also found that participants who knew someone from their immediate social environment who were or had been infected with COVID-19 had higher odds of perceiving the risk of dying from COVID-19 with an aOR: 1.51; 95% CI: 1.11–2.05.

Discussion

An individual’s perception of COVID-19-associated risks plays a major role in the ongoing pandemic.12 At the community level, perceived risk contributes to increased public participation in preventive measures devised by governments and other health organizations. In addition, it encourages voluntary health behaviors that are beneficial in controlling the spread of SARC-CoV-2.12,13

According to our findings, 36.4% of participants had a high perceived risk of COVID-19 infection, while only 22.4% had a high perceived risk of dying from COVID-19. This is lower compared to the other studies.14–16 In a cross-sectional study conducted in Myanmar, the COVID-19-associated risk perception level was reported to be moderate to high.16 However, a study conducted in Hong Kong reported high levels of perceived risk among participants.17 In another study, higher risk perception was found to be associated with spending more time at home. Furthermore, higher risk perception was also observed in individuals living in counties with higher mortality rates.12 Therefore, governments and other health agencies should focus on increasing the COVID-19 risk perception of the public to enhance voluntary health behaviors. Our study found that female participants had a higher perceived risk of being infected and dying from COVID-19 compared to their male counterparts. Several studies have reported similar findings. Men are less concerned about the potential health consequences associated with COVID-19 and, therefore, express a lower perceived risk of infection.18

In an online survey conducted in Ethiopia, perceived risk was found to be dependent on age, occupation, educational status, and place of residence.14 Our study found that participants working in healthcare-related sectors had a higher perceived risk of contracting COVID-19 compared to those working in non-healthcare sectors. Previous studies have reported that the perceived risk of becoming infected is higher among healthcare professionals.19 The high perceived risk among individuals working in healthcare-related sectors can be attributed to their better knowledge of COVID-19 than the general population.20 In addition, being a woman, having pulmonary disease, knowing people in the immediate social environment who were or have been infected with COVID-19, and seeing or reading about individuals infected with COVID-19 on social media or TV were also associated with a higher perceived risk of being infected with COVID-19.

It has been already established that individuals with chronic diseases (such as heart diseases, chronic obstructive pulmonary disease, hypertension, diabetes, and cancer) are at an increased risk for adverse outcomes with COVID-19.13,21 In our study, individuals with pulmonary disease had a higher perceived risk of COVID-19 infection than healthy individuals. Furthermore, knowing people in the immediate social environment who were or have been infected with COVID-19, as well as seeing or reading about individuals infected with COVID-19 on social media or TV, were associated with a higher perceived risk. Similarly, being an elderly individual with heart disease and pulmonary disease, knowing people in the immediate social environment who were or had been infected with COVID-19, as well as seeing or reading about individuals infected with COVID-19 on social media or TV, also had a higher perceived risk of death due to COVID-19.

Being an online survey, there are a few limitations that can lead to bias in this study. For example, this survey might have excluded individuals belonging to lower socioeconomic classes, those with lower educational qualifications, and those who are illiterate. In addition, variation in Internet access might have contributed to selection bias across the countries evaluated in this study. Furthermore, the participants may also respond in a manner that may cause a social desirability bias.

Conclusions

Our data suggests that there is a low perceived risk of becoming infected and dying due to COVID-19 among community members in certain LMICs. Factors such as age group, sex, religion, type of job, health status, etc., modify the perceived risk. These determinants can be used to alter and influence community health behaviors. Governments and other health agencies can use the data from this study to target susceptible groups within the community by conducting health campaigns to disseminate knowledge and information on the ongoing pandemic.

Data availability

Underlying data

Figshare: Perceived risk of infection and death from COVID-19 among community members of low- and middle-income countries. https://doi.org/10.6084/m9.figshare.19128134.22

The project contains the following underlying data:

  • - Perceived Risk Master Data.xlsx

  • - Questionnaire of Perceived risk of infection and death from COVID-19 among community members of low- and middle-income countries.pdf

Reporting guidelines

Figshare: STROBE checklist for ‘Perceived risk of infection and death from COVID-19 among community members of low- and middle-income countries’. https://doi.org/10.6084/m9.figshare.19128332.23

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

Acknowledgements

We would like to thank Narra Studio Jurnal Indonesia for their assistance during the manuscript preparation.

References

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Gachabayov M, Sharun K, Felsenreich DM et al. Perceived risk of infection and death from COVID-19 among community members of low- and middle-income countries: A cross-sectional study [version 1; peer review: 1 approved, 1 approved with reservations]. F1000Research 2022, 11:345 (https://doi.org/10.12688/f1000research.109575.1)
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Erwin Astha Triyono, Department of Internal Medicine, Faculty of Medicine, Universitas Airlangga, Surabaya, Indonesia 
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A well-written article by collecting data taken from an online survey of several countries to analyze the perceived risk of infection and death due to Covid-19. Overall, this article is interesting and provides many references for its readers. What I ... Continue reading
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Hesham M Al-Mekhlafi, Medical Research Centre, Jazan University, Jazan, Saudi Arabia;  Department of Parasitology, Faculty of Medicine and Health Sciences, Sana’a University, Sana’a, Yemen 
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Title: Perceived risk of infection and death from COVID-19 among community members of low- and middle-income countries: A cross-sectional study

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Al-Mekhlafi HM. Reviewer Report For: Perceived risk of infection and death from COVID-19 among community members of low- and middle-income countries: A cross-sectional study [version 1; peer review: 1 approved, 1 approved with reservations]. F1000Research 2022, 11:345 (https://doi.org/10.5256/f1000research.121089.r130137)
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    Thank you for reviewing our manuscript. The comments and suggestions have improved the quality of our current manuscript significantly.

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    Harapan Harapan, Tropical Disease Centre, School of Medicine, Universitas Syiah Kuala, Banda Aceh, 23111, Indonesia
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    Thank you for reviewing our manuscript. The comments and suggestions have improved the quality of our current manuscript significantly.

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  1. Hesham M Al-Mekhlafi, Jazan University, Jazan, Saudi Arabia; Sana’a University, Sana’a, Yemen
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