Submitted:
15 August 2024
Posted:
16 August 2024
You are already at the latest version
Abstract
Keywords:
1. Introduction
2. Methods
2.1. Study Design and Data Collection
2.2. Measures
2.2.1. Outcome Variables
2.2.2. Independent Variables
- Socio-economic and demographic variables
- Health-related variables
- Knowledge of COVID-19 vaccine
- Knowledge about the Vaccination Process
- COVID-19 Vaccine Conspiracy
- Preventive behavioral practices related to COVID-19
- Theory of Planned Behavior
- Health Belief Model:
2.3. Statistical Analysis
2.4. Ethical Approval
3. Results
3.1. Background Characteristics of the Participants
3.2. The Prevalence of Willingness to Pay (WTP) for the COVID-19 Vaccine
3.3. Differentials of WTP for COVID-19 Vaccine
3.4. Correlates of WTP for COVID-19 Vaccine
3.5. Willingness to Pay the Highest Amount of Money
4. Discussion
5. Conclusion
Author Contributions
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Ahmed, S. Hoque, M. E., Sarker, A. R., Sultana, M., Islam, Z., Gazi, R., & Khan, J. A. M. Willingness-to-pay for community-based health insurance among informal workers in urban bangladesh. PLoS ONE 2016, 11, 1–16. [Google Scholar] [CrossRef]
- Ajzen, I. The theory of planned behavior. Organizational Behavior and Human Decision Processes 1991, 50, 179–211. [Google Scholar] [CrossRef]
- Ali, I. The COVID-19 Pandemic: Making Sense of Rumor and Fear. Medical Anthropology 2020, 39, 376–379. [Google Scholar] [CrossRef] [PubMed]
- Ali, M. & Hossain, A. What is the extent of COVID-19 vaccine hesitancy in Bangladesh? : A cross-sectional rapid national survey. MedRxiv 2121. medRxiv:2021.02.17.21251917. [Google Scholar] [CrossRef]
- Banik, R. Islam, M. S., Pranta, M. U. R., Rahman, Q. M., Rahman, M., Pardhan, S., Driscoll, R., Hossain, S., & Sikder, M. T. Understanding the determinants of COVID-19 vaccination intention and willingness to pay: findings from a population-based survey in Bangladesh. BMC Infectious Diseases 2021, 21, 892. [Google Scholar] [CrossRef]
- BBS. Report on Bangladesh Sample Vital Statistics. 2018. [Google Scholar]
- BBS. Final Report On Household Income and Expenditure Survey 2016.
- Berghea, F., Berghea, C. E., Abobului, M., & Vlad, V. M. Willingness to Pay for a for a Potential Vaccine Against SARS-CoV-2 / COVID-19 Among Adult Persons. Research Square 2020, 1–11. [CrossRef]
- Bhuiyan, T. Mahmud, I., & Alam, B. Usability Analysis of Sms Alert System for Immunization in the Context of Bangladesh. International Journal of Research in Engineering and Technology 2013, 02, 300–305. [Google Scholar] [CrossRef]
- Catma, S. & Varol, S. Willingness to Pay for a Hypothetical COVID-19 Vaccine in the United States: A Contingent Valuation Approach. Vaccines 2021, 9. [Google Scholar] [CrossRef]
- Cerda, A.A. García, L.Y. Willingness to Pay for a COVID-19 Vaccine. Appl Health Econ Health Policy 2021, 19, 343–351. [Google Scholar] [CrossRef]
- de Figueiredo, A. Simas, C., Karafillakis, E., Paterson, P., & Larson, H. J. Mapping global trends in vaccine confidence and investigating barriers to vaccine uptake: a large-scale retrospective temporal modelling study. The Lancet 2020, 396, 898–908. [Google Scholar] [CrossRef]
- Dias-Godói, I. P. Tadeu Rocha Sarmento, T., Afonso Reis, E., Peres Gargano, L., Godman, B., de Assis Acurcio, F., … Mariano Ruas, C. Acceptability and willingness to pay for a hypothetical vaccine against SARS CoV-2 by the Brazilian consumer: a cross-sectional study and the implications. Expert Review of Pharmacoeconomics & Outcomes Research 2021, 22, 119–129. [Google Scholar] [CrossRef]
- Elbarazi, I. Devlin, N. J., Katsaiti, M. S., Papadimitropoulos, E. A., Shah, K. K., & Blair, I. The effect of religion on the perception of health states among adults in the United Arab Emirates: A qualitative study. BMJ Open 2017, 7, 1–8. [Google Scholar] [CrossRef]
- García, L. Y. & Cerda, A. A. Contingent assessment of the COVID-19 vaccine. Vaccine 2020, 38, 5424–5429. [Google Scholar]
- Hajj Hussein, I. Chams, N., Chams, S., El Sayegh, S., Badran, R., Raad, M., Gerges-Geagea, A., Leone, A., & Jurjus, A. Vaccines Through Centuries: Major Cornerstones of Global Health. Frontiers in Public Health 2015, 3, 1–16. [Google Scholar] [CrossRef]
- Harapan, H. Fajar, J. K., Sasmono, R. T., & Kuch, U. Dengue vaccine acceptance and willingness to pay. Human Vaccines and Immunotherapeutics 2017, 13, 786–790. [Google Scholar] [CrossRef] [PubMed]
- Harapan, H. Wagner, A. L., Yufika, A., Winardi, W., Anwar, S., Gan, A. K., Setiawan, A. M., Rajamoorthy, Y., Sofyan, H., Vo, T. Q., Hadisoemarto, P. F., Müller, R., Groneberg, D. A., & Mudatsir, M. Willingness-to-pay for a COVID-19 vaccine and its associated determinants in Indonesia. Human Vaccines and Immunotherapeutics 2020, 16, 3074–3080. [Google Scholar] [CrossRef]
- Hossain, M. B. Alam, M. Z., Islam, M. S., Sultan, S., Faysal, M. M., Rima, S., Hossain, M. A., Mahmood, M. M., Kashfi, S. S., Mamun, A. Al, Monia, H. T., & Shoma, S. S. Population-Level Preparedness About Preventive Practices Against Coronavirus Disease 2019: A Cross-Sectional Study Among Adults in Bangladesh. Frontiers in Public Health 2021, 8. [Google Scholar] [CrossRef]
- Hou, Z. Chang, J., Yue, D., Fang, H., Meng, Q., & Zhang, Y. Determinants of willingness to pay for self-paid vaccines in China. Vaccine 2014, 32, 4471–4477. [Google Scholar] [CrossRef] [PubMed]
- Institute, N. C. Theory at a Glance: A Guide For Health Promotion Practice 2005, 5. Available online: http://www.ncbi.nlm.nih.gov/pubmed/24443779.
- Islam, S. M. S., Lechner, A., Ferrari, U., Seissler, J., Holle, R., & Niessen, L. W. Mobile phone use and willingness to pay for SMS for diabetes in Bangladesh. Journal of Public Health (United Kingdom) 2015. [CrossRef]
- Kabir, R. Mahmud, I., Chowdhury, M. T. H., Vinnakota, D., Jahan, S. S., Siddika, N., Isha, S. N., Nath, S. K., & Apu, E. H. COVID-19 vaccination intent and willingness to pay in Bangladesh : a cross-sectional study. Vaccines 2021, 9. [Google Scholar]
- Kim, S.-Y. Sagiraju, H. K. R., Russell, L. B., & Sinha, A. Willingness-To-Pay for Vaccines in Low- and Middle-Income Countries: A Systematic Review. Annals of Vaccines and Immunization 2014, 1, 1001. [Google Scholar]
- Larson, H. J. Schulz, W. S., Tucker, J. D., & Smith, D. M. D. Measuring Vaccine Confidence : Introducing a Global Vaccine Confidence Index. PLOS Currents Outbreaks 2015. [Google Scholar] [CrossRef]
- Lin, Y. Hu, Z., Zhao, Q., Alias, H., Danaee, M., & Wong, L. P. Understanding COVID-19 vaccine demand and hesitancy: A nationwide online survey in China. PLoS Neglected Tropical Diseases 2020, 14, e0008961. [Google Scholar] [CrossRef]
- Lu, P. O’Halloran, A., Kennedy, E. D., Williams, W. W., Kim, D., Fiebelkorn, A. P., Donahue, S., & Bridges, C. B. Awareness among adults of vaccine-preventable diseases and recommended vaccinations, United States, 2015. Vaccine 2017, 35, 3104–3115. [Google Scholar] [CrossRef] [PubMed]
- McQuestion, M. Gnawali, D., Kamara, C., Kizza, D., Mambu-Ma-Disu, H., Mbwangue, J., & Quadros, C. Creating Sustainable Financing And Support For Immunization Programs In Fifteen Developing Countries. Health Affairs 2011, 30. [Google Scholar] [CrossRef]
- MoHFW. Bangladesh National Health Accounts 1997-2012; 2015. [Google Scholar] [CrossRef]
- Mora, T. & Trapero-Bertran, M. The influence of education on the access to childhood immunization: The case of Spain. BMC Public Health 2018, 18, 1–9. [Google Scholar] [CrossRef]
- Mudatsir, M. Anwar, S., Fajar, J. K., Yufika, A., Ferdian, M. N., Salwiyadi, S., Imanda, A. S., Azhars, R., Ilham, D., Timur, A. U., Sahputri, J., Yordani, R., Pramana, S., Rajamoorthy, Y., Wagner, A. L., Jamil, K. F., & Harapan, H. Willingness-to-pay for a hypothetical Ebola vaccine in Indonesia: A cross-sectional study in Aceh. F1000Research 2020, 8, 1441. [Google Scholar] [CrossRef]
- Neumann-Böhme, S. Varghese, N. E., Sabat, I., Barros, P. P., Brouwer, W., van Exel, J., Schreyögg, J., & Stargardt, T. Once we have it, will we use it? A European survey on willingness to be vaccinated against COVID-19. European Journal of Health Economics 2020, 21, 977–982. [Google Scholar] [CrossRef]
- Rajamoorthy, Y. Radam, A., Taib, N. M., Rahim, K. A., Munusamy, S., Wagner, A. L., Mudatsir, M., Bazrbachi, A., & Harapan, H. Willingness to pay for hepatitis B vaccination in Selangor, Malaysia: A cross-sectional household survey. PLoS ONE 2019, 14, 1–17. [Google Scholar] [CrossRef]
- Rezaei, S. Woldemichael, A., Mirzaei, M., Mohammadi, S., & Karami Matin, B. Mothers’ willingness to accept and pay for vaccines to their children in western Iran: A contingent valuation study. BMC Pediatrics 2020, 20. [Google Scholar] [CrossRef]
- Sallam M, Anwar S, Yufika A, Fahriani M, Husnah M, Kusuma HI, Raad R, Khiri NM, Abdalla RY, Adam RY, Ismaeil MI, Ismail AY, Kacem W, Teyeb Z, Aloui K, Hafsi M, Dahman NBH, Ferjani M, Deeb D, Emad D, Sami FS, Abbas KS, Monib FA, R S, Panchawagh S, Sharun K, Anandu S, Gachabayov M, Haque MA, Emran TB, Wendt GW, Ferreto LE, Castillo-Briones MF, Inostroza-Morales RB, Lazcano-Díaz SA, Ordóñez-Aburto JT, Troncoso-Rojas JE, Balogun EO, Yomi AR, Durosinmi A, Adejumo EN, Ezigbo ED, Arab-Zozani M, Babadi E, Kakemam E, Ullah I, Malik NI, Dababseh D, Rosiello F, Enitan SS. Willingness-to-pay for COVID-19 vaccine in ten low-middle-income countries in Asia, Africa and South America: A cross-sectional study. Narra J. 2022, 2, e74. [CrossRef] [PubMed]
- Sallam, M. Dababseh, D., Eid, H., Al-Mahzoum, K., Al-Haidar, A., Taim, D., Yaseen, A., Ababneh, N. A., Bakri, F. G., & Mahafzah, A. High Rates of COVID-19 Vaccine Hesitancy and Its Association with Conspiracy Beliefs: A Study in Jordan and Kuwait among Other Arab Countries. Vaccines 2021, 9. [Google Scholar]
- Sarker, A. R. Islam, Z., Sultana, M., Sheikh, N., Mahumud, R. A., Taufiqul Islam, M., Van Der Meer, R., Morton, A., Khan, A. I., Clemens, J. D., Qadri, F., & Khan, J. A. M. Willingness to pay for oral cholera vaccines in urban Bangladesh. PLoS ONE 2020, 15, 1–16. [Google Scholar] [CrossRef]
- Sulaiman, K.-D. O. An Assessment of Muslims Reactions to The Immunization of Children in Northern Nigeria. Medical Journal of Islamic World Academy of Sciences 2014, 22, 123–132. [Google Scholar] [CrossRef]
- Wang, J. Lyu, Y., Zhang, H., Jing, R., Lai, X., Feng, H., Knoll, M. D., & Fang, H. Willingness to pay and financing preferences for COVID-19 vaccination in China. Vaccine 2021, 39, 1968–1976. [Google Scholar] [CrossRef] [PubMed]
- Wong, L. P. Alias, H., Wong, P. F., Lee, H. Y., & AbuBakar, S. The use of the health belief model to assess predictors of intent to receive the COVID-19 vaccine and willingness to pay. Human Vaccines and Immunotherapeutics 2020, 16, 2204–2214. [Google Scholar] [CrossRef] [PubMed]
- Worasathit, R. Wattana, W., Okanurak, K., Songthap, A., Dhitavat, J., & Pitisuttithum, P. Health education and factors influencing acceptance of and willingness to pay for influenza vaccination among older adults. BMC Geriatrics 2015, 15, 1–15. [Google Scholar] [CrossRef]
- Zajacova, A. & Lawrence, E. M. The Relationship between Education and Health: Reducing Disparities Through a Contextual Approach. Annual Review of Public Health 2018, 39, 273–289. [Google Scholar] [CrossRef]


| Variable | Study sample | Willingness To Pay | ||
|---|---|---|---|---|
| N (%)/ mean (SD) | No (%) | Yes (%)/ R | P value | |
| Sex | 0.717 | |||
| Women | 692 (46.2) | 329 (47.5) | 363 (52.5) | |
| Men | 805 (53.8) | 391 (48.6) | 414 (51.4) | |
| Religion | <0.001 | |||
| Others | 196 (13.1) | 70 (35.7) | 126 (64.3) | |
| Muslim | 1301 (86.9) | 650 (50) | 651 (50) | |
| Marital status | 0.009 | |||
| Unmarried | 575 (38.4) | 252 (43.8) | 323 (56.2) | |
| Married | 922 (61.6) | 468 (50.8) | 454 (49.2) | |
| Education | <0.001 | |||
| No education | 129 (8.6) | 90 (69.8) | 39 (30.2) | |
| Primary | 179 (12.0) | 112 (62.6) | 67 (37.4) | |
| Secondary and higher secondary | 448 (29.9) | 236 (52.70) | 212 (47.30) | |
| Graduate | 400 (26.7) | 156 (39) | 244 (61) | |
| Masters and MPhil/PhD | 341 (22.8) | 126 (37) | 215 (63) | |
| Place of residence | <0.001 | |||
| Rural | 963 (64.3) | 510 (53) | 453 (47) | |
| Urban (other than city corporation) | 179 (12) | 66 (36.9) | 113 (63.1) | |
| City Corporation | 355 (23.7) | 144 (40.6%) | 211 (59.4) | |
| Administrative division of Bangladesh | 0.004 | |||
| Barisal | 114 (7.6) | 67 (58.8) | 47 (41.2) | |
| Chattogram | 253 (16.9) | 114 (45.1) | 139 (54.9) | |
| Dhaka | 478 (31.9) | 209 (43.7) | 269 (56.3) | |
| Khulna | 137 (9.2) | 75 (54.7) | 62 (45.3) | |
| Mymensingh | 108 (7.2) | 64 (59.3) | 44 (40.7) | |
| Rajshahi | 180 (12.0) | 88 (48.9) | 92 (51.1) | |
| Rangpur | 114 (7.6) | 58 (50.9) | 56 (49.1) | |
| Sylhet | 113 (7.5) | 45 (39.8) | 68 (60.2) | |
| Occupation | <0.001 | |||
| Government, private, & NGO sector job | 202 (13.5) | 81 (40.1) | 121 (59.9) | |
| Professional | 277 (18.5) | 115 (41.5) | 162 (58.5) | |
| Homemakers | 348 (23.2) | 195 (56) | 153 (44) | |
| Students and unemployed | 473 (31.6) | 210 (44.4) | 263 (55.6) | |
| Agriculture, Day Laborer | 102 (6.81) | 65 (63.7) | 37 (36.3) | |
| Others | 95 (6.34) | 54 (56.8) | 41 (43.2) | |
| Health status | 0.073 | |||
| Bad/ very bad | 51 (3.4) | 27 (52.9) | 24 (47.1) | |
| Moderate | 333 (22.2) | 177 (53.2) | 156 (46.8) | |
| Good/ very good | 1113 (74.3) | 516 (46.4) | 597 (53.6) | |
| The Respondent got infected with the Coronavirus | 0.026 | |||
| No | 1411 (94.3) | 689 (48.8) | 722 (51.2) | |
| Yes | 86 (5.7) | 31 (36.0) | 55 (64.0) | |
| Respondent’s family members got infected with Coronavirus. | 0.016 | |||
| No | 1345 (89.8) | 661 (49.1) | 684 (50.9) | |
| Yes | 152 (10.2) | 59 (38.8) | 93 (61.2) | |
| Respondent’s friends got infected with Coronavirus. | <0.001 | |||
| No | 897 (59.9) | 480 (53.5) | 417 (46.5) | |
| Yes | 600 (40.1) | 240 (40) | 360 (60) | |
| Age | 33.67 (12.94) | -.060 | 0.020 | |
| Household members | 4.95 (1.98) | -0.030 | 0.242 | |
| Household income | 37627 (81296) | .123 | <0.001 | |
| Number of morbidities | 0.92 (1.10) | -0.007 | 0.772 | |
| Knowledge about the COVID-19 vaccine | 11.40 (2.19) | .093 | <0.001 | |
| Knowledge about the vaccination process | 2.84 (1.97) | .195 | <0.001 | |
| Covid-19 vaccine conspiracy | 12.65 (3.69) | -.148 | <0.001 | |
| Behavioral practice to prevent COVID-19 | 8.80 (2.71) | .230 | <0.001 | |
| Total | 1497 (100) | 720 (48.1) | 777 (50.9) | |
| Predictors | Model 1 | Model 2 | Model 3 | |||
|---|---|---|---|---|---|---|
| aOR | P value | aOR | P value | aOR | P value | |
| Age | 1.0 (1.0, 1.0) | 0.972 | 1.0 (1.0, 1.0) | 0.820 | 1.0 (1.0, 1.0) | 0.824 |
| Religion other (Muslim as RC) | 1.8 (1.3, 2.6) | 0.001 | 1.5 (1.1, 2.2) | 0.020 | 1.5 (1.0, 2.1) | 0.037 |
| Marital status: unmarried (married as RC) | 1.0 (0.7, 1.5) | 0.836 | 1.1 (0.8, 1.7) | 0.476 | 1.1 (0.8, 1.7) | 0.551 |
| Education (no education as RC) | 0.011 | 0.008 | 0.008 | |||
| Primary | 1.2 (0.7, 1.9) | 0.593 | 1.1 (0.6, 1.8) | 0.807 | 1.0 (0.6, 1.7) | 0.958 |
| Secondary and higher secondary | 1.5 (0.9, 2.5) | 0.112 | 1.3 (0.8, 2.3) | 0.252 | 1.3 (0.8, 2.2) | 0.325 |
| Graduate | 2.4 (1.4, 4.1) | 0.002 | 2.3 (1.3, 4.1) | 0.005 | 2.2 (1.2, 4.0) | 0.007 |
| Masters & MPhil/ PhD | 2.0 (1.1, 3.6) | 0.021 | 2.0 (1.1, 3.7) | 0.021 | 2.0 (1.1, 3.6) | 0.030 |
| Place of residence (Rural as RC) | 0.623 | 0.689 | 0.776 | |||
| Urban (other than CC) | 1.2 (0.8, 1.7) | 0.356 | 1.2 (0.8, 1.7) | 0.455 | 1.1 (0.8, 1.7) | 0.532 |
| City Corporation | 1.0 (0.7, 1.4) | 0.983 | 1.1 (0.8, 1.6) | 0.523 | 1.1 (0.8, 1.6) | 0.597 |
| Administrative division of Bangladesh (Barisal as RC) | <0.001 | <0.001 | <0.001 | |||
| Chattogram | 2.3 (1.4, 3.8) | 0.001 | 2.5 (1.5, 4.1) | 0.001 | 2.5 (1.5, 4.3) | 0.001 |
| Dhaka | 2.1 (1.3, 3.3) | 0.002 | 2.3 (1.4, 3.7) | 0.001 | 2.3 (1.4, 3.8) | 0.001 |
| Khulna | 1.8 (1.1, 3.2) | 0.032 | 2.4 (1.4, 4.4) | 0.003 | 2.5 (1.4, 4.5) | 0.003 |
| Mymensingh | 1.0 (0.6, 1.9) | 0.871 | 1.1 (0.6, 2.0) | 0.816 | 1.1 (0.6, 2.1) | 0.734 |
| Rajshahi | 2.3 (1.3, 3.8) | 0.002 | 2.6 (1.5, 4.6) | 0.001 | 2.7 (1.5, 4.7) | 0.001 |
| Rangpur | 1.9 (1.0, 3.3) | 0.040 | 2.4 (1.3, 4.4) | 0.006 | 2.4 (1.3, 4.6) | 0.005 |
| Sylhet | 3.3 (1.9, 6.0) | <0.001 | 4.0 (2.2, 7.4) | <0.001 | 4.1 (2.2, 7.7) | <0.001 |
| Occupation ( government, private, NGO sector job as RC) | 0.571 | 0.478 | 0.434 | |||
| Professional | 1.1 (0.8, 1.7) | 0.548 | 1.1 (0.7, 1.7) | 0.578 | 1.1 (0.7, 1.7) | 0.632 |
| Homemakers | 1.4 (0.9, 2.3) | 0.141 | 1.4 (0.9, 2.3) | 0.140 | 1.5 (0.9, 2.4) | 0.132 |
| Students & unemployed | 1.1 (0.7, 1.7) | 0.740 | 1.0 (0.7, 1.7) | 0.848 | 1.1 (0.7, 1.8) | 0.719 |
| Agriculture and day labor | 1.2 (0.7, 2.2) | 0.533 | 1.2 (0.6, 2.2) | 0.650 | 1.1 (0.6, 2.1) | 0.689 |
| Others | 0.9 (0.5, 1.6) | 0.736 | 0.9 (0.5, 1.5) | 0.610 | 0.8 (0.5, 1.5) | 0.585 |
| Income | 1.0 (1.0, 1.0) | 0.039 | 1.0 (1.0, 1.0) | 0.036 | 1.0 (1.0, 1.0) | 0.039 |
| Knowledge about the COVID-19 vaccine | 1.1 (1.1, 1.2) | <0.001 | 1.1 (1.0, 1.2) | 0.002 | 1.1 (1.0, 1.2) | 0.003 |
| Knowledge about the vaccination process | 1.1 (1.1, 1.2) | <0.001 | 1.1 (1.0, 1.2) | 0.029 | 1.1 (1.0, 1.2) | 0.058 |
| Behavioral practice to prevent COVID-19 | 1.1 (1.1, 1.2) | <0.001 | 1.1 (1.0, 1.2) | <0.001 | 1.1 (1.0, 1.2) | <0.001 |
| Covid-19 vaccine conspiracy | 0.9 (0.9, 1.0) | <0.001 | 1.0 (1.0, 1.0) | 0.840 | 1.0 (1.0, 1.0) | 0.495 |
| Attitude toward vaccine | 0.9 (0.9, 0.9) | <0.001 | 0.9 (0.9, 0.9) | <0.001 | ||
| Subjective norms | 1.3 (1.1, 1.4) | 0.002 | 1.2 (1.1, 1.4) | 0.009 | ||
| Perceived behavioral control | 0.8 (0.8, 0.9) | 0.002 | 0.9 (0.8, 1.0) | 0.006 | ||
| Anticipated regret | 1.2 (1.1, 1.3) | 0.002 | 1.2 (1.1, 1.3) | 0.005 | ||
| Susceptibility | 0.9 (0.9, 1.0) | 0.124 | ||||
| Severity | 1.0 (0.9, 1.1) | 0.988 | ||||
| Benefits | 1.1 (1.0, 1.1) | 0.029 | ||||
| Barriers | 1.0 (0.9, 1.0) | 0.533 | ||||
| Respondent got infected with Coronavirus ( no as RC) | 0.9 (0.5, 1.5) | 0.615 | ||||
| Respondent’s family member got infected with Coronavirus (no as RC) | 0.9 (0.6, 1.4) | 0.801 | ||||
| Respondents friends or peers got infected with Coronavirus (no as RC) | 0.8 (0.6, 1.1) | 0.214 | ||||
| Constant | 0.034 | <0.001 | 0.042 | <0.001 | 0.044 | <0.001 |
| Model Summary | ||||||
| N | 1497 | 1497 | 1497 | |||
| -2 Log likelihood | 1851 | 1753 | 1744 | |||
| Cox & Snell R Square | 0.138 | 0.192 | 0.197 | |||
| Nagelkerke R Square | 0.184 | 0.257 | 0.263 | |||
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).