F1000Research F1000Research 2046-1402 F1000 Research Limited London, UK 10.12688/f1000research.109575.2 Research Article Articles Perceived risk of infection and death from COVID-19 among community members of low- and middle-income countries: A cross-sectional study

[version 2; peer review: 2 approved]

 Gachabayov Mahir Validation Writing – Review & Editing https://orcid.org/0000-0002-9200-1304 1 Sharun Khan Writing – Original Draft Preparation Writing – Review & Editing https://orcid.org/0000-0003-1040-3746 2 Felsenreich Daniel M. Data Curation Investigation Validation Writing – Review & Editing 3 Nainu Firzan Data Curation Investigation Validation Writing – Review & Editing https://orcid.org/0000-0003-0989-4023 4 Anwar Samsul Formal Analysis Methodology Resources Software Validation Writing – Original Draft Preparation Writing – Review & Editing https://orcid.org/0000-0003-3165-2151 5 Yufika Amanda Methodology Project Administration Resources Validation Writing – Review & Editing 6 Ophinni Youdiil Validation Writing – Review & Editing https://orcid.org/0000-0001-9443-1538 7 8 Yamada Chika Validation Writing – Review & Editing 9 Fahriani Marhami Data Curation Investigation Validation Writing – Review & Editing https://orcid.org/0000-0003-0452-3485 10 Husnah Milda Data Curation Investigation Validation Writing – Review & Editing 10 11 Raad Rawan Data Curation Investigation Validation Writing – Review & Editing https://orcid.org/0000-0003-4060-3378 12 Khiri Namareg ME. Data Curation Investigation Validation Writing – Review & Editing 13 Abdalla Rashed YA. Data Curation Investigation Validation Writing – Review & Editing https://orcid.org/0000-0002-7841-4807 14 Adam Rashed Y. Data Curation Investigation Validation Writing – Review & Editing 15 Ismaeil Mohajer IH. Data Curation Investigation Validation Writing – Review & Editing https://orcid.org/0000-0003-4864-3454 16 Ismail Asma Y. 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Data Curation Investigation Validation Writing – Review & Editing 25 Sami Farah S. Data Curation Investigation Validation Writing – Review & Editing 23 Ramanarayanan Subramaniam Data Curation Investigation Validation Writing – Review & Editing https://orcid.org/0000-0003-2863-6708 26 Panchawagh Suhrud Data Curation Investigation Validation Writing – Review & Editing https://orcid.org/0000-0002-8606-4202 27 Anandu Sunil Data Curation Investigation Validation Writing – Review & Editing 28 Haque Md Ariful Data Curation Investigation Validation Writing – Review & Editing https://orcid.org/0000-0003-4632-5153 29 Ferreto Lirane ED. Data Curation Investigation Validation Writing – Review & Editing 30 Briones María FC. Data Curation Investigation Validation Writing – Review & Editing 31 Morales Rocío BI. Data Curation Investigation Validation Writing – Review & Editing https://orcid.org/0000-0003-3473-4654 31 Lazcano-Díaz Sebastián Data Curation Investigation Validation Writing – Review & Editing 31 Aburto José TO. Data Curation Investigation Validation Writing – Review & Editing https://orcid.org/0000-0001-8705-4041 31 Rojas Jorge ET. Data Curation Investigation Validation Writing – Review & Editing 31 Balogun Emmanuel O. Data Curation Investigation Validation Writing – Review & Editing 32 Kusuma Hendrix I. Data Curation Investigation Validation Writing – Review & Editing https://orcid.org/0000-0003-3555-0136 10 11 33 Yeni Cut Meurah Data Curation Investigation Validation Writing – Review & Editing 34 35 Utami Niken Asri Data Curation Investigation Validation Writing – Review & Editing 34 35 Enitan Seyi S. Data Curation Investigation Validation Writing – Review & Editing https://orcid.org/0000-0001-5993-7920 36 Yomi Akele R. Data Curation Investigation Validation Writing – Review & Editing https://orcid.org/0000-0003-2006-0820 37 Durosinmi Abiodun Data Curation Investigation Validation Writing – Review & Editing https://orcid.org/0000-0002-5382-5108 38 Adejumo Esther N. Data Curation Investigation Validation Writing – Review & Editing 36 Ezigbo Eyiuche D. Data Curation Investigation Validation Writing – Review & Editing https://orcid.org/0000-0001-9397-3706 39 Babadi Elham Data Curation Investigation Validation Writing – Review & Editing 40 Kakemam Edris Data Curation Investigation Validation Writing – Review & Editing 41 Ullah Irfan Data Curation Investigation Validation Writing – Review & Editing 42 Malik Najma I. Data Curation Investigation Validation Writing – Review & Editing https://orcid.org/0000-0002-3521-1014 43 Rosiello Francesco Data Curation Investigation Validation Writing – Review & Editing https://orcid.org/0000-0002-6532-1185 44 Emran Talha B. Data Curation Investigation Validation Writing – Review & Editing https://orcid.org/0000-0003-3188-2272 45 Imelda Eva Data Curation Investigation Validation Writing – Review & Editing 46 Wendt Guilherme W. Data Curation Investigation Validation Writing – Review & Editing https://orcid.org/0000-0002-9014-6120 47 Arab-Zozani Morteza Data Curation Investigation Validation Writing – Review & Editing https://orcid.org/0000-0001-7223-6707 48 Dhama Kuldeep Data Curation Investigation Validation Writing – Review & Editing https://orcid.org/0000-0001-7469-4752 2 49 Mudatsir Mudatsir Data Curation Investigation Project Administration Validation Writing – Review & Editing https://orcid.org/0000-0002-5643-9384 10 50 51 Harapan Harapan Conceptualization Data Curation Formal Analysis Funding Acquisition Investigation Methodology Resources Software Supervision Validation Visualization Writing – Original Draft Preparation Writing – Review & Editing https://orcid.org/0000-0001-7630-8413 a 10 50 51 52 Department of Abdominal Surgery, Vladimir City Emergency Hospital, Vladimir, 600014, Russian Federation Division of Surgery, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, Uttar Pradesh, 243122, India Division of General Surgery, Department of Surgery, Medical University of Vienna, Vienna, 1090, Austria Department of Pharmacy, Faculty of Pharmacy, Hasanuddin University, Makassar, 90245, Indonesia Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh, 23111, Indonesia Department of Family Medicine, School of Medicine, Universitas Syiah Kuala, Banda Aceh, 23111, Indonesia Ragon Institute of MGH, MIT and Harvard, Cambridge, 02139, USA Laboratory of Host Defense, WPI Immunology Frontier Research Center (IFReC), Osaka University, Osaka, 565-0874, Japan Department of Environmental Coexistence, Center for Southeast Asian Studies, Kyoto University, Kyoto, 606-8304, Japan Medical Research Unit, School of Medicine, Universitas Syiah Kuala, Banda Aceh, 23111, Indonesia Master Program of Biology, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, BAnda Aceh, 23111, Indonesia Faculty of Medicine and General Surgery, Sudan University of Science and Technology, Khartoum, 407, Sudan Faculty of Medicine, University of Khartoum, Omdurman, 11111, Sudan Faculty of Medicine, University of Bahri, Khartoum, 11111, Sudan Omdurman Teaching Hospital, Khartoum, 00000, Sudan Faculty of Medicine, Alzaiem Alazhari University, Khartoum, 13311, Sudan Department of Internal Medicine, Faculty of Medicine, Sudan International University, Khartoum, 12769, Sudan Department of Emergency Medicine, Faculty of Medicine of Tunis, University Tunis el Manar, Tunis, 2074, Tunisia Department of Internal Medicine, Faculty of Medicine of Tunis, University of Tunis El Manar, Tunis, 2074, Tunisia Faculty of Medicine of Tunis, University of Tunis El Manar, Tunis, 2074, Tunisia Faculty of Dental Medicine, University of Monastir, Monastir, 4180, Tunisia Faculty of Medicine, Zagazig University, El-sharkia, 44519, Egypt Faculty of Medicine, Ain Shams University Nasr City, Cairo, 1181, Egypt Faculty of Medicine, Alexandria University, Alexandria, 21131, Egypt Faculty of Medicine, Assiut University, Assiut, 71515, Egypt Department of Public Health Dentistry, Indira Gandhi Institute of Dental Sciences, Nellikuzhi, Kothamangalam, Kerala, 686691, India Department of General Medicine, Smt. KashibaiNavale Medical College and General Hospital, Pune, Maharashtra, 411041, India Division of Veterinary Parasitology, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, Uttar Pradesh, 243122, India Department of Orthopedic Surgery, Yan’an Hospital Affiliated to Kunming Medical University, Kunming, Yunnan, 650000, China Department of Public Health and Postgraduate Program in Applied Health Sciences, Faculty of Medicine, Western Paraná State University, Francisco Beltrão, 85601-970, Brazil Faculty of Medicine, University of La Frontera, Temuco, 4781218, Chile Department of Biochemistry, Faculty of Life Sciences, Ahmadu Bello University, Zaria, Kaduna State, 2222, Nigeria Department of Biology Education, Faculty of Tarbiyah and Teacher Training, Universitas Islam Negeri Ar-Raniry, Banda Aceh, 23111, Indonesia Department of Obstetrics and Gynecology, School of Medicine, Universitas Syiah Kuala, Banda Aceh, 23111, Indonesia Department of Obstetrics and Gynecology, Dr. Zainoel Abidin Hospital, Banda Aceh, 24415, Indonesia Department of Medical Laboratory Science, Babcock University, Ilishan-Remo, Ogun State, 121103, Nigeria Afe Babalola University, Ado Ekiti, 360102, Nigeria Covenant University Medical Center, Ijebu Ode, Ogun State, 120101, Nigeria Department of Medical Laboratory Sciences, Faculty of Health Sciences & Technology, Afe Babalola University, Nsukka, Enugu State, 40006, Nigeria Research Fellow, Mayo Clinic, Rochester, 14604, USA Tabriz Health Services Management Research Center, Tabriz University of Medical Sciences, Tabriz, 516599001, Iran Department of Internal Medicine, Kabir Medical College, Gandhara University, Peshawar, 25000, Pakistan Department of Psychology, University of Sargodha, Sargodha, 40100, Pakistan Department of Public Health and Infectious Disease, Sapienza-University of Rome, Rome, 00185, Italy Department of Pharmacy, BGC Trust University Bangladesh, Chittagong, 4381, Bangladesh Department of Ophthalmology, School of Medicine, Universitas Syiah Kuala, Banda Aceh, 23111, Indonesia Department of Life Sciences, Faculty of Medicine, Western Paraná State University, Francisco Beltrão, 85601-970, Brazil Social Determinants of Health Research Center, Birjand University of Medical Sciences, Birjand, 97, Iran Division of Pathology, ICAR-Indian Veterinary Research Institute,, Izatnagar, Bareilly, Uttar Pradesh, 243122, India Tropical Disease Centre, School of Medicine, Universitas Syiah Kuala, Banda Aceh, 23111, Indonesia Department of Microbiology, School of Medicine, Universitas Syiah Kuala, Banda Aceh, 23111, Indonesia Tsunami and Disaster Mitigation Research Centre, Universitas Syiah Kuala, Banda Aceh, 231111, Indonesia harapan@unsyiah.ac.id

No competing interests were disclosed.

 7 9 2022 2022 11 345 5 9 2022 Copyright: © 2022 Gachabayov M et al. 2022 This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.

 COVID-19 perceived risk online cross-sectional study preventive measure determinants Universitas Syiah Kuala, Ministry of Education, Culture, Research and Technology, H-Index Scheme 2021 169/UN11/SPK/PNBP/2021 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. Amendments from Version 1

In the revised manuscript, we have provided more detail of information about the sampling technique and we provided explanation of the healthcare-related job under sub-heading Explanatory variables. We have revised some texts to avoid the confusion based on the suggestions of the reviewer. We also have deleted some texts to avoid the repetitive. We have added some other limitations of our study.

 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 society 1 – 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 study 23 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. This study used nonprobability sampling technique, snowball sampling method, to recruit the respondents. The invited potential respondents were also requested to share the invitation to their friends or phone contacts. The survey 22 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 study 11 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 adequate (a score of more than 50%). Each question was analyzed separately.

Explanatory variables

Age, gender, residency, 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. Healthcare-related job included doctor, dentist, nurse, physical therapist, nutritionist, pharmacist, paramedic, and laboratory staff. 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 1 22 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 37.5% were part of a family with a monthly household income of less than $500. 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.

Logistic regression showing factors associated with perceived risk of COVID-19 infection (n=1,649).
Variable n (%) High perceived risk Unadjusted Adjusted
n (%) OR (95% CI) p-value OR (95% CI) p-value
Country
Pakistan (R) 263 (15.9) 52 (19.8) 1 1
Brazil 107 (6.5) 40 (37.4) 2.42 (1.48 – 3.98) <0.001 2.32 (1.13 – 4.77) 0.022
Chile 106 (6.4) 23 (21.7) 1.12 (0.65 – 1.95) 0.677 1.40 (0.66 – 2.97) 0.379
Egypt 98 (6.0) 50 (51.0) 4.23 (2.57 – 6.96) <0.001 2.35 (1.36 – 4.07) 0.002
India 339 (20.6) 149 (44.0) 3.18 (2.19 – 4.61) <0.001 4.28 (2.33 – 7.86) <0.001
Iran 141 (8.6) 53 (37.6) 2.44 (1.55 – 3.86) <0.001 1.30 (0.75 – 2.24) 0.353
Nigeria 161 (9.8) 28 (17.4) 0.85 (0.51 – 1.42) 0.543 1.00 (0.50 – 2.00) 0.995
Bangladesh 131 (7.9) 54 (41.2) 2.85 (1.79 – 4.52) <0.001 2.86 (1.68 – 4.87) <0.001
Sudan 174 (10.6) 76 (43.7) 3.15 (2.05 – 4.82) <0.001 1.61 (0.97 – 2.67) 0.066
Tunisia 129 (7.8) 75 (58.1) 5.64 (3.55 – 8.96) <0.001 3.66 (2.12 – 6.32) <0.001
Age group (year)
<20 (R) 280 (17.0) 63 (22.5) 1 1
21-30 926 (56.2) 373 (40.3) 2.32 (1.70 – 3.17) <0.001 1.50 (1.03 – 2.18) 0.035
31-40 270 (16.4) 115 (42.6) 2.56 (1.77 – 3.70) <0.001 1.69 (1.02 – 2.82) 0.043
41-50 119 (7.2) 33 (27.7) 1.32 (0.81 – 2.16) 0.264 0.90 (0.48 – 1.69) 0.745
>51 54 (3.3) 16 (29.6) 1.45 (0.76 – 2.77) 0.261 0.71 (0.31 – 1.63) 0.423
Sex
Male (R) 692 (42.0) 234 (33.8) 1 1
Female 957 (58.0) 366 (38.2) 1.21 (0.99 – 1.49) 0.065 1.40 (1.10 – 1.78) 0.006
Residency
Rural (R) 314 (19.0) 116 (36.9) 1
Urban 1335 (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) 1 1
500-999 289 (17.5) 110 (38.1) 1.05 (0.79 – 1.40) 0.734 1.02 (0.74 – 1.40) 0.921
1,000-1,999 192 (11.6) 72 (37.5) 1.03 (0.73 – 1.43) 0.879 0.92 (0.63 – 1.35) 0.665
2,000-2,999 148 (9.0) 63 (42.6) 1.27 (0.88 – 1.83) 0.202 1.29 (0.85 – 1.98) 0.236
3,000-4,999 128 (7.8) 39 (30.5) 0.75 (0.50 – 1.13) 0.169 0.79 (0.50 – 1.26) 0.319
5,000-7,999 100 (6.1) 36 (36.0) 0.96 (0.62 – 1.49) 0.864 1.00 (0.60 – 1.64) 0.984
≥8,000 174 (10.6) 52 (29.9) 0.73 (0.51 – 1.05) 0.088 0.67 (0.43 – 1.05) 0.080
Religion
Islam (R) 915 (55.5) 354 (38.7) 1 1
Christian/Protestant/Methodist/Lutheran/Baptist 179 (10.9) 46 (25.7) 0.55 (0.38 – 0.79) 0.001 0.65 (0.38 – 1.11) 0.117
Catholic 127 (7.7) 41 (32.3) 0.76 (0.51 – 1.12) 0.164 0.68 (0.37 – 1.25) 0.213
Hindu 239 (14.5) 92 (38.5) 0.99 (0.74 – 1.33) 0.956 0.44 (0.26 – 0.74) 0.002
Atheist or agnostic 87 (5.3) 25 (28.7) 0.64 (0.39 – 1.04) 0.069 0.50 (0.27 – 0.93) 0.028
Others 102 (6.2) 42 (41.2) 1.11 (0.73 – 1.68) 0.625 0.84 (0.49 – 1.43) 0.510
Healthcare-related job
No (R) 908 (55.1) 261 (28.7) 1 1
Yes 741 (44.9) 339 (45.7) 2.09 (1.71 – 2.56) <0.001 1.48 (1.16 – 1.89) 0.002
Have hypertension
No a (R) 1102 (66.8) 404 (36.7) 1
Yes b 97 (5.9) 34 (35.1) 0.93 (0.60 – 1.44) 0.752
Do not know 450 (27.3) 162 (36.0) 0.97 (0.77 – 1.22) 0.806
Have diabetes
No a (R) 1190 (72.2) 447 (37.6) 1 1
Yes b 58 (3.5) 19 (32.8) 0.81 (0.46 – 1.42) 0.461 0.79 (0.40 – 1.55) 0.487
Do not know 401 (24.3) 134 (33.4) 0.83 (0.66 – 1.06) 0.136 0.79 (0.56 – 1.13) 0.200
Have heart disease
No a (R) 1093 (66.3) 395 (36.1) 1 1
Yes b 55 (3.3) 26 (47.3) 1.58 (0.92 – 2.73) 0.097 1.59 (0.82 – 3.07) 0.166
Do not know 501 (30.4) 179 (35.7) 0.98 (0.79 – 1.23) 0.874 1.07 (0.71 – 1.59) 0.755
Have pulmonary disease
No a (R) 1044 (63.3) 371 (35.5) 1 1
Yes b 90 (5.5) 51 (56.7) 2.37 (1.53 – 3.67) <0.001 2.44 (1.48 – 4.05) 0.001
Do not know 515 (31.2) 178 (34.6) 0.96 (0.77 – 1.20) 0.705 1.22 (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) 1 1
Yes 1141 (69.2) 495 (43.4) 2.94 (2.30 – 3.76) <0.001 2.05 (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) 1 1
Yes 1528 (92.7) 573 (37.5) 2.09 (1.34 – 3.24) <0.001 1.72 (1.06 – 2.77) 0.027
Occupation
Self-employed (R) 155 (9.4) 42 (27.1) 1 1
Employed for wages 417 (25.3) 177 (42.4) 1.98 (1.33 – 2.97) <0.001 2.22 (1.43 – 3.45) <0.001
Out of work for less 1 year AND more than 1 year 73 (4.4) 23 (31.5) 1.24 (0.67 – 2.27) 0.492 1.49 (0.77 – 2.89) 0.241
Homemaker 34 (2.1) 12 (35.3) 1.47 (0.67 – 3.23) 0.340 1.65 (0.68 – 4.00) 0.272
Student 948 (57.5) 330 (34.8) 1.44 (0.98 – 2.10) 0.061 2.05 (1.31 – 3.22) 0.002
Retired or unable to work 22 (1.3) 16 (72.7) 7.18 (2.63 – 19.56) <0.001 11.36 (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) 1 1
I work more hours 300 (18.2) 126 (42.0) 1.37 (1.05 – 1.78) 0.019 1.17 (0.85 – 1.59) 0.336
I work fewer hours 286 (17.3) 107 (37.4) 1.13 (0.86 – 1.48) 0.378 1.02 (0.75 – 1.39) 0.900
I was let go from my job 54 (3.3) 18 (33.3) 0.95 (0.53 – 1.69) 0.850 0.90 (0.46 – 1.74) 0.753

COVID-19, coronavirus disease 2019; OR, odds ratio; CI, confidence interval; R, reference group.

Have been tested or examined by a doctor but negative.

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 adequate perceived risk of being infected with COVID-19. The unadjusted and adjusted 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). Gender was only associated significantly with perceived risk in adjusted logistic regression.

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, only 22.4% (370/1,649) had a high (adequate) perceived risk of dying from COVID-19. In the adjusted logistic regression, country, age group, gender, religion, type of job, having 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).

Logistic regression showing factors associated with perceived risk of dying from COVID-19 if infected (n=1,649).
Variable n (%) High perceived risk Unadjusted Adjusted
n (%) OR (95% CI) p-value OR (95% CI) p-value
Country
Pakistan (R) 263 (15.9) 65 (24.7) 1 1
Brazil 107 (6.5) 25 (23.4) 0.93 (0.55 – 1.58) 0.784 0.88 (0.40 – 1.92) 0.745
Chile 106 (6.4) 16 (15.1) 0.54 (0.30 – 0.99) 0.045 0.70 (0.30 – 1.60) 0.392
Egypt 98 (6.0) 26 (26.5) 1.10 (0.65 – 1.87) 0.724 1.00 (0.56 – 1.80) 0.990
India 339 (20.6) 56 (16.5) 0.60 (0.40 – 0.90) 0.013 0.93 (0.49 – 1.78) 0.823
Iran 141 (8.6) 32 (22.7) 0.89 (0.55 – 1.45) 0.651 0.63 (0.35 – 1.13) 0.117
Nigeria 161 (9.8) 25 (15.5) 0.56 (0.34 – 0.93) 0.026 0.83 (0.40 – 1.73) 0.622
Bangladesh 131 (7.9) 47 (35.9) 1.70 (1.08 – 2.68) 0.021 2.10 (1.23 – 3.58) 0.006
Sudan 174 (10.6) 44 (25.3) 1.03 (0.66 – 1.60) 0.892 0.74 (0.44 – 1.25) 0.260
Tunisia 129 (7.8) 34 (26.4) 1.09 (0.67 – 1.77) 0.725 0.80 (0.45 – 1.44) 0.462
Age group (year)
<20 (R) 280 (17.0) 54 (19.3) 1 1
21-30 926 (56.2) 207 (22.4) 1.21 (0.86 – 1.68) 0.275 1.23 (0.83 – 1.85) 0.305
31-40 270 (16.4) 68 (25.2) 1.41 (0.94 – 2.11) 0.097 1.57 (0.90 – 2.75) 0.116
41-50 119 (7.2) 22 (18.5) 0.95 (0.55 – 1.65) 0.853 1.20 (0.60 – 2.40) 0.613
>51 54 (3.3) 19 (35.2) 2.27 (1.21 – 4.28) 0.011 2.70 (1.21 – 5.99) 0.015
Gender
Male (R) 692 (42.0) 137 (19.8) 1 1
Female 957 (58.0) 233 (24.3) 1.30 (1.03 – 1.65) 0.029 1.58 (1.20 – 2.08) 0.001
Residency
Rural (R) 314 (19.0) 70 (22.3) 1
Urban 1335 (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) 1 1
500-999 289 (17.5) 66 (22.8) 0.80 (0.58 – 1.11) 0.179 0.80 (0.56 – 1.14) 0.212
1,000-1,999 192 (11.6) 36 (18.8) 0.62 (0.42 – 0.93) 0.022 0.58 (0.38 – 0.90) 0.015
2,000-2,999 148 (9.0) 36 (24.3) 0.87 (0.57 – 1.32) 0.504 0.94 (0.59 – 1.49) 0.777
3,000-4,999 128 (7.8) 23 (18.0) 0.59 (0.36 – 0.96) 0.034 0.62 (0.36 – 1.05) 0.076
5,000-7,999 100 (6.1) 21 (21.0) 0.72 (0.43 – 1.20) 0.205 0.73 (0.42 – 1.28) 0.267
≥8,000 174 (10.6) 21 (12.1) 0.37 (0.23 – 0.61) <0.001 0.34 (0.19 – 0.60) <0.001
Religion
Islam (R) 915 (55.5) 242 (26.4) 1 1
Christian/Protestant/Methodist/Lutheran/Baptist 179 (10.9) 24 (13.4) 0.43 (0.27 – 0.68) <0.001 0.54 (0.29 – 1.00) 0.051
Catholic 127 (7.7) 28 (22.0) 0.79 (0.50 – 1.23) 0.290 0.96 (0.50 – 1.85) 0.902
Hindu 239 (14.5) 32 (13.4) 0.43 (0.29 – 0.64) <0.001 0.44 (0.24 – 0.81) 0.008
Atheist or agnostic 87 (5.3) 18 (20.7) 0.73 (0.42 – 1.24) 0.243 0.93 (0.47 – 1.84) 0.840
Others 102 (6.2) 26 (25.5) 0.95 (0.60 – 1.52) 0.835 1.05 (0.58 – 1.90) 0.862
Healthcare-related job
No (R) 908 (55.1) 198 (21.8) 1
Yes 741 (44.9) 172 (23.2) 1.08 (0.86 – 1.37) 0.496
Have hypertension
No a (R) 1102 (66.8) 227 (20.6) 1 1
Yes b 97 (5.9) 32 (33.0) 1.90 (1.21 – 2.97) 0.005 1.29 (0.75 – 2.22) 0.365
Do not know 450 (27.3) 111 (24.7) 1.26 (0.97 – 1.64) 0.078 1.38 (0.96 – 1.99) 0.087
Have diabetes
No a (R) 1190 (72.2) 266 (22.4) 1
Yes b 58 (3.5) 16 (27.6) 1.32 (0.73 – 2.39) 0.353
Do not know 401 (24.3) 88 (21.9) 0.98 (0.74 – 1.28) 0.865
Have heart disease
No a (R) 1093 (66.3) 233 (21.3) 1 1
Yes b 55 (3.3) 24 (43.6) 2.86 (1.65 – 4.96) <0.001 2.35 (1.20 – 4.60) 0.013
Do not know 501 (30.4) 113 (22.6) 1.08 (0.83 – 1.39) 0.578 1.03 (0.67 – 1.58) 0.901
Have pulmonary disease
No a (R) 1044 (63.3) 215 (20.6) 1 1
Yes b 90 (5.5) 42 (46.7) 3.37 (2.17 – 5.24) <0.001 2.88 (1.73 – 4.78) <0.001
Do not know 515 (31.2) 113 (21.9) 1.08 (0.84 – 1.40) 0.539 0.88 (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) 1 1
Yes 1141 (69.2) 277 (24.3) 1.43 (1.10 – 1.86) 0.007 1.51 (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) 1 1
Yes 1528 (92.7) 337 (22.1) 0.76 (0.50 – 1.15) 0.187 0.70 (0.44 – 1.10) 0.122
Occupation
Self-employed (R) 155 (9.4) 35 (22.6) 1 1
Employed for wages 417 (25.3) 103 (24.7) 1.13 (0.73 – 1.74) 0.599 1.12 (0.69 – 1.81) 0.643
Out of work for less 1 year AND more than 1 year 73 (4.4) 23 (31.5) 1.58 (0.85 – 2.94) 0.150 1.64(0.83 – 3.25) 0.153
Homemaker 34 (2.1) 12 (35.3) 1.87 (0.84 – 4.15) 0.124 1.52 (0.63 – 3.68) 0.351
Student 948 (57.5) 192 (20.3) 0.87 (0.58 – 1.31) 0.507 0.92 (0.56 – 1.49) 0.720
Retired or unable to work 22 (1.3) 5 (22.7) 1.01 (0.35 – 2.93) 0.988 0.70 (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) 1 1
I work more hours 300 (18.2) 80 (26.7) 1.44 (1.07 – 1.95) 0.016 1.39 (0.99 – 1.96) 0.056
I work fewer hours 286 (17.3) 71 (24.8) 1.31 (0.96 – 1.79) 0.086 1.25 (0.89 – 1.75) 0.192
I was let go from my job 54 (3.3) 16 (29.6) 1.67 (0.91 – 3.06) 0.095 1.26 (0.65 – 2.46) 0.497

COVID-19, coronavirus disease 2019; OR, odds ratio; CI, confidence interval; R, reference group.

Have been tested or examined by a doctor but negative.

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, the percentages of participants who had adequate perceived risk of COVID-19 infection and perceived risk of dying from COVID-19 were relatively low, only 36.4% and 22.4%, respectively. These are 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. In addition, since the survey was conducted in English, this could be source of bias because some of the individuals in those studied countries might unable to read and understand English.

 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.

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J. 2021;1(3):e48. 10.52225/narra.v1i3.48 Fahriani M Ilmawan M Fajar JK : Persistence of long COVID symptoms in COVID-19 survivors worldwide and its potential pathogenesis-a systematic review and meta-analysis. Narra. J. 2021;1(2):e36. 10.52225/narraj.v1i2.36 Herbec A Brown J Jackson SE : Perceived risk factors for severe Covid-19 symptoms and their association with health behaviours: Findings from the HEBECO study. Acta Psychol. 2021;222:103458. 10.1016/j.actpsy.2021.103458 Curtis AF Rodgers M Miller MB : Impact of Sex on COVID-19 Media Exposure, Anxiety, Perceived Risk, and Severity in Middle-Aged and Older Adults. J. Aging Health. 2021;34:51–59. 10.1177/08982643211025383 Dolu I Turhan Z Yalniz DH : COVID-19 Vaccine Acceptance is associated with Vaccine Hesitancy, Perceived Risk and Previous Vaccination Experiences. Disaster Med. Public Health Prep. 2021 Dec 23:1–9.Epub 2021/12/24 34937599 10.1017/dmp.2021.370 Boonsaeng T Carpio CE Guerrero P : Perceived risks of infection, hospitalization, and death from COVID-19 at the Equator: Ecuador and Kenya. Disaster Med. Public Health Prep. 2021;1–6. 34392868 10.1017/dmp.2021.268 Siegrist M Luchsinger L Bearth A : The Impact of trust and risk perception on the acceptance of measures to reduce COVID-19 cases. Risk Anal. 2021;41(5):787–800. 33438218 10.1111/risa.13675 Zhang F Shih SF Harapan H : Changes in COVID-19 risk perceptions: methods of an internet survey conducted in six countries. BMC. Res. Notes. 2021 Nov 25;14(1):428. 34823587 10.1186/s13104-021-05846-8 PMC8613724 Elharake JA Shafiq M McFadden SM : The Association of COVID-19 Risk Perception, County Death Rates, and Voluntary Health Behaviors among U.S. Adult Population. J. Infect. Dis. 2021 Mar 10.Epub 20210310. eng. 33693810 PMC7989350 Ahuja M Mamudu HM Weierbach FM : Correction: Perceptions of risk for COVID-19 among individuals with chronic diseases and stakeholders in Central Appalachia. Humanit. Soc. Sci. Commun. 2021;8(1):279.Epub 20211116. eng. 34826319 10.1057/s41599-021-00974-9 PMC8593630 Birhanu Z Ambelu A Fufa D : Risk perceptions and attitudinal responses to COVID-19 pandemic: an online survey in Ethiopia. BMC Public Health. 2021 May 25;21(1):981.Epub 20210525. eng. 34034694 10.1186/s12889-021-10939-x PMC8148408 Shahin MAH Hussien RM : Risk perception regarding the COVID-19 outbreak among the general population: a comparative Middle East survey. Middle East Current Psychiatry. 2020 2020/12/22;27(1):71. 10.1186/s43045-020-00080-7 Mya KS Aye SM Hlaing WA : Awareness, perceived risk and protective behaviours of Myanmar adults on COVID-19. 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PLoS One. 2021;16(4):e0249022.Epub 20210415. eng. 33857159 10.1371/journal.pone.0249022 PMC8049252 Clark A Jit M Warren-Gash C : 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 Aug;8(8):e1003–e1017.Epub 20200615. eng. 32553130 10.1016/S2214-109X(20)30264-3 PMC7295519 Harapan H : Perceived risk of infection and death from COVID-19 among community members of low- and middle-income countries. figshare. Journal Contribution. 2022. 10.6084/m9.figshare.19128134.v5 Harapan H : STROBE checklist for Perceived risk of infection and death from COVID-19 among community members of low- and middle-income countries. figshare. Journal Contribution. 2022. 10.6084/m9.figshare.19128332.v1 10.5256/f1000research.138139.r149780 Reviewer response for version 2 Al-Mekhlafi Hesham M 1 2 Referee https://orcid.org/0000-0003-2582-7410 Medical Research Centre, Jazan University, Jazan, Saudi Arabia Department of Parasitology, Faculty of Medicine and Health Sciences, Sana’a University, Sana’a, Yemen

Competing interests: No competing interests were disclosed.

 12 9 2022 Copyright: © 2022 Al-Mekhlafi HM 2022 This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. recommendation approve

The authors have adequately addressed my comments.

Is the work clearly and accurately presented and does it cite the current literature?

Yes

If applicable, is the statistical analysis and its interpretation appropriate?

Yes

Are all the source data underlying the results available to ensure full reproducibility?

Yes

Is the study design appropriate and is the work technically sound?

Yes

Are the conclusions drawn adequately supported by the results?

Yes

Are sufficient details of methods and analysis provided to allow replication by others?

Partly

Reviewer Expertise:

Infectious Diseasis, Epidemiology, Public Health, Biostatistics

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

 10.5256/f1000research.138139.r149781 Reviewer response for version 2 Triyono Erwin Astha 1 Referee https://orcid.org/0000-0002-8648-9796 Department of Internal Medicine, Faculty of Medicine, Universitas Airlangga, Surabaya, Indonesia

Competing interests: No competing interests were disclosed.

 8 9 2022 Copyright: © 2022 Triyono EA 2022 This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. recommendation approve

Thanks for your response. Revised article is enough.

Is the work clearly and accurately presented and does it cite the current literature?

Yes

If applicable, is the statistical analysis and its interpretation appropriate?

Yes

Are all the source data underlying the results available to ensure full reproducibility?

Yes

Is the study design appropriate and is the work technically sound?

Yes

Are the conclusions drawn adequately supported by the results?

Yes

Are sufficient details of methods and analysis provided to allow replication by others?

Yes

Reviewer Expertise:

Tropical and Infectious Disease, Internal Medicine, Problem Health

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

 10.5256/f1000research.121089.r146559 Reviewer response for version 1 Triyono Erwin Astha 1 Referee https://orcid.org/0000-0002-8648-9796 Department of Internal Medicine, Faculty of Medicine, Universitas Airlangga, Surabaya, Indonesia

Competing interests: No competing interests were disclosed.

 9 8 2022 Copyright: © 2022 Triyono EA 2022 This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. recommendation approve

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 learned from this article is that Covid-19 poses challenges to a country's health system. These challenges can be related to medical problems and non-medical problems (social problems). Non-medical problems (social problems) arise due to disinformation or hoax issues that make it difficult for medical/scientific information to be conveyed to the public in its entirety. This researcher is very helpful in describing the obstacles, especially in the non-medical aspect (social problems) in handling Covid-19 in a country.

However, there are some comments for improvement:

Need to explain the sampling method used, how to determine 10 countries?

The researcher explained that the limitations of the survey conducted could bias the results, especially because of the possibility that this survey might have excluded individuals belonging to lower socioeconomic classes, those with lower educational qualifications, and those who are illiterate. Can it be written in the conclusion to be a suggestion for further research?

Is the work clearly and accurately presented and does it cite the current literature?

Yes

If applicable, is the statistical analysis and its interpretation appropriate?

Yes

Are all the source data underlying the results available to ensure full reproducibility?

Yes

Is the study design appropriate and is the work technically sound?

Yes

Are the conclusions drawn adequately supported by the results?

Yes

Are sufficient details of methods and analysis provided to allow replication by others?

Yes

Reviewer Expertise:

Tropical and Infectious Disease, Internal Medicine, Problem Health

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

Harapan Harapan Syiah Kuala University, Indonesia

Competing interests: We do not have conflict of interest in this study.

 31 8 2022

Dear reviewer,

Thank you for reviewing our manuscript. The comments and suggestions have improved the quality of our current manuscript significantly.

1. Need to explain the sampling method used, how to determine 10 countries?

RESPONSE: Thank you for the question. Those countries were selected to cover of those in LMICs in three continents that had limited information of COVID-19. Also based on the availability of the collaborators.

Information about the sampling technique. In the revised manuscript: “This study used nonprobability sampling technique, snowball sampling method, to recruit the respondents. The invited potential respondents were also requested to share the invitation to their friends or phone contacts.”

2. The researcher explained that the limitations of the survey conducted could bias the results, especially because of the possibility that this survey might have excluded individuals belonging to lower socioeconomic classes, those with lower educational qualifications, and those who are illiterate. Can it be written in the conclusion to be a suggestion for further research?

RESPONSE: Thank you for the suggestion. However, we feel this is not part of the conclusion of the study and we would like to provide the actual results of the study within the conclusion. 

Thank you for the reviewing our manuscript.

 10.5256/f1000research.121089.r130137 Reviewer response for version 1 Al-Mekhlafi Hesham M 1 2 Referee https://orcid.org/0000-0003-2582-7410 Medical Research Centre, Jazan University, Jazan, Saudi Arabia Department of Parasitology, Faculty of Medicine and Health Sciences, Sana’a University, Sana’a, Yemen

Competing interests: No competing interests were disclosed.

 24 5 2022 Copyright: © 2022 Al-Mekhlafi HM 2022 This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. recommendation approve-with-reservations

Ref. No : F1KR00CDE F1R-VER121089-R

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

This manuscript describes a cross-sectional online survey on the perceived risk of contracting and dying from COVID-19 among 1,646 respondents from 10 LMICs. The study found relatively low levels of perceived risk of infection and death, and identified some demographic, socio-economic and health factors that significantly associated with high perceived levels. Although, the topic is not novel as many studies on the same topic are available from different countries; however, data at the regional or multinational level are always interesting and appreciated.

Overall, the manuscript is interesting, well-written and straightforward. Some comments are provided below.

SPECIFIC COMMENTS

Study design and setting: What is the rationale of selecting the 10 countries included in the study? What was the selection strategy?

How was the selection for enrollment process? The questionnaire link was shared on Twitter, Facebook, and WhatsApp. More important information is needed about this process. How to ensure that the link has reached the targeted population? How WhatsApp numbers were selected, etc.?

Study design and setting: Other study design elements also need to be clearly described in the manuscript for a better understanding of the studied population and what they represent. For example, what is being considered a healthcare-related job in this study?

What is the rationale of choosing Pakistan as a reference group for country variable?

Page 5: “able to read and understand English in the 10 studied countries.” Could this be source of bias, in respect to the aim of helping in the design of population-wide preventive measures?

Page 13, discussion, 3 rd paragraph: working in non-healthcare sectors was the only variable explained while the rest were repeated as results. Why did females have higher perceived risk than males?

Page 13, discussion: Some significant variables were omitted in discussion and not explained, e.g. religion, type of occupation. What about country as a predictor? Any explanation?

Page 5: “which 203 respondents were excluded due to incomplete information.” An advantage of online questionnaire is that it can be designed as a respondent cannot proceed with incomplete responses!

Page 6: “65% were part of a family with a monthly household income of less than $2,000”. The tables show that 37.5% of the respondents had a monthly household income of less than $500. I think the later is better representative the studied population and it should be indicated.   

Page 6, Results, Perceived risk of becoming infected with COVID-19 and the associated determinants, first paragraph: The variables found significant by the unadjusted model were also found significant in the adjusted model. The sentences can be rephrased without repeating the list of variables, especially with the variables and related statistics are mentioned in the next paragraph. Gender was found significant only in the adjusted model and this can be indicated.

Urbanicity or residency?

Page 6, results: “…… 22.4% (370/1,649) had a perceived risk of dying from COVID-19, which was classified as high”. This can be rephrased to avoid confusion with a statement in page 3, conclusion “are relatively low”.

Page 6, results: “….. knowing or not knowing someone …”, please rephrase.

Page 6, results, perceived risk of becoming infected…: again, the sentences can be rephrased without repeating the list of variables, especially with the variables and related statistics are mentioned in the next paragraph in page 13.

Page 13, discussion: “This is lower compared to the other studies”. Before this comparison, it should be indicated that the overall reported rates were relatively low.

Is the work clearly and accurately presented and does it cite the current literature?

Yes

If applicable, is the statistical analysis and its interpretation appropriate?

Yes

Are all the source data underlying the results available to ensure full reproducibility?

Yes

Is the study design appropriate and is the work technically sound?

Yes

Are the conclusions drawn adequately supported by the results?

Yes

Are sufficient details of methods and analysis provided to allow replication by others?

Partly

Reviewer Expertise:

Infectious Diseasis, Epidemiology, Public Health, Biostatistics

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

Harapan Harapan Syiah Kuala University, Indonesia

Competing interests: We do not have conflict of interest.

 31 8 2022

Dear reviewer,

Thank you for reviewing our manuscript. The comments and suggestions have improved the quality of our current manuscript significantly.

1. Study design and setting: What is the rationale of selecting the 10 countries included in the study? What was the selection strategy?

RESPONSE: Thank you for the question. Those countries were selected to cover of those in LMICs in three continents that had limited information of COVID-19. Also based on the availability of the collaborators. 

2. How was the selection for enrollment process? The questionnaire link was shared on Twitter, Facebook, and WhatsApp. More important information is needed about this process. How to ensure that the link has reached the targeted population? How WhatsApp numbers were selected, etc.?

RESPONSE: Thank you we have provided the information about the sampling technique. In the revised manuscript: “This study used nonprobability sampling technique, snowball sampling method, to recruit the respondents. The invited potential respondents were also requested to share the invitation to their friends or phone contacts.” 

Due to the this in the Discussion we discussed this as limitation of our study. “…this survey might have excluded individuals belonging to lower socioeconomic classes, those with lower educational qualifications, and those who are illiterate”. 

3.Study design and setting: Other study design elements also need to be clearly described in the manuscript for a better understanding of the studied population and what they represent. For example, what is being considered a healthcare-related job in this study?

RESPONSE: We have explained in some parts of the Study design and setting. The explanation of healthcare-related job has been provided under sub-heading “Explanatory variables”. We stated that “Healthcare-related job included doctor, dentist, nurse, physical therapist, nutritionist, pharmacist, paramedic, and laboratory staff.” 

4. What is the rationale of choosing Pakistan as a reference group for country variable?

RESPONSE: Pakistan was chosen after the initial analyzing showing Pakistan had the lowest perceived risk rate. 

5. Page 5: “able to read and understand English in the 10 studied countries.” Could this be source of bias, in respect to the aim of helping in the design of population-wide preventive measures?

RESPONSE: We appreciate this. We acknowledged for this potential bias and we have included this as one of limitations of our study. “In addition, since the survey was conducted in English, this could be source of bias because some of the individuals in those studied countries might unable to read and understand English.”

6. Page 13, discussion, 3rd paragraph: working in non-healthcare sectors was the only variable explained while the rest were repeated as results. Why did females have higher perceived risk than males?

RESPONSE: We have explained some explanatory variable. However, we focused to discuss the variables to determine the groups that could be targeted.

In our article, we provided the possible explanation why the females had higher perceived risk: “Men are less concerned about the potential health consequences associated with COVID-19 and, therefore, express a lower perceived risk of infection”.

7. Page 13, discussion: Some significant variables were omitted in discussion and not explained, e.g. religion, type of occupation. What about country as a predictor? Any explanation?

RESPONSE: Thank you for you question. We aim to provide general overview in Discussion not too detailed and discussed the factors to determine the specific groups that are able to targeted to increase the perceived risk in the future. We have discussed the factors associated with perceived risk such as gender, type of occupation, having the comorbid and soon in the Discussion. 

8. Page 5: “which 203 respondents were excluded due to incomplete information.” An advantage of online questionnaire is that it can be designed as a respondent cannot proceed with incomplete responses!

RESPONSE: Thank you. The survey has been set that all question should be answer to be able to go to the next questions. However, some respondents stopped the survey in the middle of the survey, of which this was classified as incomplete responses. 

9. Page 6: “65% were part of a family with a monthly household income of less than $2,000”. The tables show that 37.5% of the respondents had a monthly household income of less than $500. I think the later is better representative the studied population and it should be indicated.

RESPONSE: I think both of the statements are correct. We would like not to repeat what in the table and text. However, the we have revised the text based on the recommendation. In the revised manuscript: "The majority (81%) of the respondents lived in urban areas, and 37.5% were part of a family with a monthly household income of less than $500."  

10. Page 6, Results, Perceived risk of becoming infected with COVID-19 and the associated determinants, first paragraph: The variables found significant by the unadjusted model were also found significant in the adjusted model. The sentences can be rephrased without repeating the list of variables, especially with the variables and related statistics are mentioned in the next paragraph. Gender was found significant only in the adjusted model and this can be indicated.

RESPONSE: We have revised this section. We would like to thank you for the suggestion. The revised manuscript: “The unadjusted and adjusted 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). Gender was only associated significantly with perceived risk in adjusted logistic regression.”

11. Urbanicity or residency?

RESPONSE: Thank you for your suggestion. Now it has been changed to residency. 

12. Page 6, results: “…… 22.4% (370/1,649) had a perceived risk of dying from COVID-19, which was classified as high”. This can be rephrased to avoid confusion with a statement in page 3, conclusion “are relatively low”.

RESPONSE: We have revised the manuscript based on the suggestion. The revised manuscript: “Of the total respondents, only 22.4% (370/1,649) had an adequate (high) perceived risk of dying from COVID-19.”

13. Page 6, results: “….. knowing or not knowing someone …”, please rephrase.

RESPONSE: This has been corrected: “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 someone in the immediate social environment who were or had been infected with COVID-19 to varying degrees (Table 2).”

14. Page 6, results, perceived risk of becoming infected…: again, the sentences can be rephrased without repeating the list of variables, especially with the variables and related statistics are mentioned in the next paragraph in page 13.

RESPONSE: We would like to thank you for this. We have corrected this section by providing the adjusted logistic regression only. In the revised manuscript: “In the adjusted logistic regression, country, age group, gender, religion, type of job, having 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).”

15. Page 13, discussion: “This is lower compared to the other studies”. Before thiscomparison, it should be indicated that the overall reported rates were relatively low.

RESPONSE: Thank you. We have revised the text to provide the clear information. In the revised manuscript: “According to our findings, the percentages of participants who had adequate perceived risk of COVID-19 infection and perceived risk of dying from COVID-19 were relatively low, only 36.4% and 22.4%, respectively. These are lower compared to the other studies.  14 –  16”

Thank you