Keywords
COVID-19, perceived risk, online cross-sectional study, preventive measure, determinants
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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 2; peer review: 2 approved]. F1000Research 2022, 11:345 (https://doi.org/10.12688/f1000research.109575.2)
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Research Article
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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]
PUBLISHED 07 Sep 2022
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Abstract
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 2; peer review: 2 approved]. F1000Research 2022, 11:345 (https://doi.org/10.12688/f1000research.109575.2)
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)
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Revised 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.
See the authors' detailed response to the review by Erwin Astha Triyono
See the authors' detailed response to the review by Hesham M Al-Mekhlafi
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. 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 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 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 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 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.
Table 1. 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 | ||||||
| Noa(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 | ||||||
| Noa(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 | ||||||
| Noa(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 |
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).
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 | ||||||
| Noa(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 | ||||||
| Noa(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 | ||||||
| Noa(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 | ||||||
| Noa(R) | 1044 (63.3) | 215 (20.6) | 1 | 1 | ||
| Yesb | 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 |
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:
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).
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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 2; peer review: 2 approved]. F1000Research 2022, 11:345 (https://doi.org/10.12688/f1000research.109575.2)
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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 2; peer review: 2 approved]. F1000Research 2022, 11:345 (https://doi.org/10.5256/f1000research.138139.r149780)
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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 2; peer review: 2 approved]. F1000Research 2022, 11:345 (https://doi.org/10.5256/f1000research.138139.r149780)
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Triyono EA. 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 2; peer review: 2 approved]. F1000Research 2022, 11:345 (https://doi.org/10.5256/f1000research.138139.r149781)
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Triyono EA. 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 2; peer review: 2 approved]. F1000Research 2022, 11:345 (https://doi.org/10.5256/f1000research.138139.r149781)
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Triyono EA. 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 2; peer review: 2 approved]. F1000Research 2022, 11:345 (https://doi.org/10.5256/f1000research.121089.r146559)
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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
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Triyono EA. 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 2; peer review: 2 approved]. F1000Research 2022, 11:345 (https://doi.org/10.5256/f1000research.121089.r146559)
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Author Response 07 Sep 2022Harapan Harapan, Tropical Disease Centre, School of Medicine, Universitas Syiah Kuala, Banda Aceh, 23111, Indonesia07 Sep 2022Author ResponseDear 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, ... Continue reading
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Author Response 07 Sep 2022Harapan Harapan, Tropical Disease Centre, School of Medicine, Universitas Syiah Kuala, Banda Aceh, 23111, Indonesia07 Sep 2022Author ResponseDear 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, ... Continue reading
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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 2; peer review: 2 approved]. F1000Research 2022, 11:345 (https://doi.org/10.5256/f1000research.121089.r130137)
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Reviewer Report 24 May 2022
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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 ... Continue reading
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 ... Continue reading
CITE
HOW TO CITE THIS REPORT
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 2; peer review: 2 approved]. F1000Research 2022, 11:345 (https://doi.org/10.5256/f1000research.121089.r130137)
The direct URL for this report is:
https://f1000research.com/articles/11-345/v1#referee-response-130137
https://f1000research.com/articles/11-345/v1#referee-response-130137
NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article.
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Author Response 07 Sep 2022Harapan Harapan, Tropical Disease Centre, School of Medicine, Universitas Syiah Kuala, Banda Aceh, 23111, Indonesia07 Sep 2022Author ResponseDear 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 ... Continue reading
COMMENTS ON THIS REPORT
-
Author Response 07 Sep 2022Harapan Harapan, Tropical Disease Centre, School of Medicine, Universitas Syiah Kuala, Banda Aceh, 23111, Indonesia07 Sep 2022Author ResponseDear 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 ... Continue reading
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