The Health Belief Model Predicts Intention to Receive the COVID-19 Vaccine in Saudi Arabia: Results from a Cross-Sectional Survey
Abstract
:1. Introduction
2. Methods
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Zhu, N.; Zhang, D.; Wang, W.; Li, X.; Yang, B.; Song, J.; Zhao, X.; Huang, B.; Shi, W.; Lu, R.; et al. A Novel Coronavirus from Patients with Pneumonia in China, 2019. N. Engl. J. Med. 2020, 382, 727–733. [Google Scholar] [CrossRef]
- Adhikari, S.P.; Meng, S.; Wu, Y.-J.; Mao, Y.-P.; Ye, R.-X.; Wang, Q.-Z.; Sun, C.; Sylvia, S.; Rozelle, S.; Raat, H.; et al. Epidemiology, causes, clinical manifestation and diagnosis, prevention and control of coronavirus disease (COVID-19) during the early outbreak period: A scoping review. Infect. Dis. Poverty 2020, 9, 29. [Google Scholar] [CrossRef] [Green Version]
- WHO. WHO Coronavirus (COVID-19) Dashboard. Available online: https://covid19.who.int/ (accessed on 5 May 2021).
- Mahmud, I.; Al-Mohaimeed, A. COVID-19: Utilizing local experience to suggest optimal global strategies to prevent and control the pandemic. Int. J. Health Sci. 2020, 14, 1–3. [Google Scholar]
- Obied, D.A.; Alhamlan, F.S.; Al-Qahtani, A.A.; Al-Ahdal, M.N. Containment of COVID-19: The unprecedented response of Saudi Arabia. J. Infect. Dev. Ctries 2020, 14, 699–706. [Google Scholar] [CrossRef]
- McArthur, L.; Sakthivel, D.; Ataide, R.; Chan, F.; Richards, J.S.; Narh, C.A. Review of Burden, Clinical Definitions, and Management of COVID-19 Cases. Am. J. Trop. Med. Hyg. 2020, 103, 625–638. [Google Scholar] [CrossRef]
- Thanh Le, T.; Andreadakis, Z.; Kumar, A.; Gómez Román, R.; Tollefsen, S.; Saville, M.; Mayhew, S. The COVID-19 vaccine development landscape. Nat. Rev. Drug Discov. 2020, 19, 305–306. [Google Scholar] [CrossRef] [PubMed]
- WHO. DRAFT Landscape of COVID-19 Candidate Vaccines-20 April 2020. [Internet]; WHO: Geneva, Switzerland, 2020. [Google Scholar]
- Mishra, S.K.; Tripathi, T. One year update on the COVID-19 pandemic: Where are we now? Acta Trop. 2021, 214, 105778. [Google Scholar] [CrossRef] [PubMed]
- WHO. WHO Lists Additional COVID-19 Vaccine for Emergency Use and Issues Interim Policy Recommendations; WHO: Geneva, Switzerland, 2021; Available online: https://www.who.int/news/item/07-05-2021-who-lists-additional-covid-19-vaccine-for-emergency-use-and-issues-interim-policy-recommendations (accessed on 15 May 2021).
- McGill University COVID19 Vaccine Tracker Team. COVID-19 Vaccine Tracker: Saudi Arabia. Available online: https://covid19.trackvaccines.org/country/saudi-arabia/ (accessed on 16 May 2021).
- Bartsch, S.M.; O’Shea, K.J.; Ferguson, M.C.; Bottazzi, M.E.; Wedlock, P.T.; Strych, U.; McKinnell, J.A.; Siegmund, S.S.; Cox, S.N.; Hotez, P.J.; et al. Vaccine Efficacy Needed for a COVID-19 Coronavirus Vaccine to Prevent or Stop an Epidemic as the Sole Intervention. Am. J. Prev. Med. 2020, 59, 493–503. [Google Scholar] [CrossRef]
- Iboi, E.; Ngonghala, C.N.; Gumel, A.B. Will an imperfect vaccine curtail the COVID-19 pandemic in the U.S.? medRxiv 2020, 5, 510–524. [Google Scholar] [CrossRef] [PubMed]
- Lazarus, J.V.; Ratzan, S.C.; Palayew, A.; Gostin, L.O.; Larson, H.J.; Rabin, K.; Kimball, S.; El-Mohandes, A. A global survey of potential acceptance of a COVID-19 vaccine. Nat. Med. 2020, 27, 225–228. [Google Scholar] [CrossRef]
- Qattan, A.M.N.; Alshareef, N.; Alsharqi, O.; Al Rahahleh, N.; Chirwa, G.C.; Al-Hanawi, M.K. Acceptability of a COVID-19 Vaccine Among Healthcare Workers in the Kingdom of Saudi Arabia. Front. Med. 2021, 8, 644300. [Google Scholar] [CrossRef]
- Alfageeh, E.I.; Alshareef, N.; Angawi, K.; Alhazmi, F.; Chirwa, G.C. Acceptability of a COVID-19 Vaccine among the Saudi Population. Vaccines 2021, 9, 226. [Google Scholar] [CrossRef]
- Almaghaslah, D.; Alsayari, A.; Kandasamy, G.; Vasudevan, R. COVID-19 Vaccine Hesitancy among Young Adults in Saudi Arabia: A Cross-Sectional Web-Based Study. Vaccines 2021, 9, 330. [Google Scholar] [CrossRef] [PubMed]
- Daly, M.; Robinson, E. Willingness to vaccinate against COVID-19 in the US: Longitudinal evidence from a nationally representative sample of adults from April–October 2020. medRxiv 2020. [Google Scholar] [CrossRef]
- Kabamba Nzaji, M.; Kabamba Ngombe, L.; Ngoie Mwamba, G.; Banza Ndala, D.B.; Mbidi Miema, J.; Luhata Lungoyo, C.; Lora Mwimba, B.; Cikomola Mwana Bene, A.; Mukamba Musenga, E. Acceptability of Vaccination Against COVID-19 Among Healthcare Workers in the Democratic Republic of the Congo. Pragmatic Obs. Res. 2020, 11, 103–109. [Google Scholar] [CrossRef]
- Roozenbeek, J.; Schneider, C.R.; Dryhurst, S.; Kerr, J.; Freeman, A.L.J.; Recchia, G.; van der Bles, A.M.; van der Linden, S. Susceptibility to misinformation about COVID-19 around the world. R. Soc. Open Sci. 2020, 7, 201199. [Google Scholar] [CrossRef]
- WHO. Ten Threats to Global Health; WHO: Geneva, Switzerland, 2021; Available online: https://www.who.int/news-room/spotlight/ten-threats-to-global-health-in-2019 (accessed on 7 January 2021).
- Larson, H.J.; Jarrett, C.; Eckersberger, E.; Smith, D.M.D.; Paterson, P. Understanding vaccine hesitancy around vaccines and vaccination from a global perspective: A systematic review of published literature, 2007–2012. Vaccine 2014, 32, 2150–2159. [Google Scholar] [CrossRef] [PubMed]
- Al-Mohaithef, M.; Padhi, B.K. Determinants of COVID-19 Vaccine Acceptance in Saudi Arabia: A Web-Based National Survey. J. Multidiscip. Healthc. 2020, 13, 1657–1663. [Google Scholar] [CrossRef] [PubMed]
- Rosenstock, I.M.; Strecher, V.J.; Becker, M.H. Social learning theory and the Health Belief Model. Health Educ. Q. 1988, 15, 175–183. [Google Scholar] [CrossRef] [PubMed]
- Chen, M.-F.; Wang, R.-H.; Schneider, J.K.; Tsai, C.-T.; Jiang, D.D.-S.; Hung, M.-N.; Lin, L.-J. Using the Health Belief Model to Understand Caregiver Factors Influencing Childhood Influenza Vaccinations. J. Commu. Health Nurs. 2011, 28, 29–40. [Google Scholar] [CrossRef]
- Nexøe, J.; Kragstrup, J.; Søgaard, J. Decision on influenza vaccination among the elderly. A questionnaire study based on the Health Belief Model and the Multidimensional Locus of Control Theory. Scand. J. Prim. Health Care 1999, 17, 105–110. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Coe, A.B.; Gatewood, S.B.S.; Moczygemba, L.R.; Goode, J.-V.K.R.; Beckner, J.O. The use of the health belief model to assess predictors of intent to receive the novel (2009) H1N1 influenza vaccine. Innov. Phar. 2012, 3, 1–11. [Google Scholar] [CrossRef] [PubMed]
- Mo, P.K.H.; Lau, J.T.F. Influenza vaccination uptake and associated factors among elderly population in Hong Kong: The application of the Health Belief Model. Health Educ. Res. 2015, 30, 706–718. [Google Scholar] [CrossRef] [Green Version]
- Fall, E.; Izaute, M.; Chakroun-Baggioni, N. How can the health belief model and self-determination theory predict both influenza vaccination and vaccination intention ? A longitudinal study among university students. Psychol. Health 2018, 33, 746–764. [Google Scholar] [CrossRef] [PubMed]
- Smith, P.J.; Humiston, S.G.; Marcuse, E.K.; Zhao, Z.; Dorell, C.G.; Howes, C.; Hibbs, B. Parental Delay or Refusal of Vaccine Doses, Childhood Vaccination Coverage at 24 Months of Age, and the Health Belief Model. Public Health Rep. 2011, 126, 135–146. [Google Scholar] [CrossRef] [Green Version]
- Donadiki, E.M.; Jiménez-García, R.; Hernández-Barrera, V.; Sourtzi, P.; Carrasco-Garrido, P.; López de Andrés, A.; Jimenez-Trujillo, I.; Velonakis, E.G. Health Belief Model applied to non-compliance with HPV vaccine among female university students. Public Health 2014, 128, 268–273. [Google Scholar] [CrossRef]
- Alabbad, A.A.; Alsaad, A.K.; Al Shaalan, M.A.; Alola, S.; Albanyan, E.A. Prevalence of influenza vaccine hesitancy at a tertiary care hospital in Riyadh, Saudi Arabia. J. Infect. Public Health 2018, 11, 491–499. [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. Hum. Vaccines Immunother. 2020, 16, 2204–2214. [Google Scholar] [CrossRef] [PubMed]
- Zampetakis, L.A.; Melas, C. The health belief model predicts vaccination intentions against COVID-19: A survey experiment approach. Appl. Psychol. Health Well-Being 2021, 13, 469–484. [Google Scholar] [CrossRef]
- Mercadante, A.R.; Law, A.V. Will they, or Won’t they? Examining patients’ vaccine intention for flu and COVID-19 using the Health Belief Model. Res. Soc. Adm. Pharm. 2020, 17, 1596–1605. [Google Scholar] [CrossRef]
- Kabir, R.; Mahmud, I.; Chowdhury, M.T.H.; Vinnakota, D.; Jahan, S.S.; Siddika, N.; Isha, S.N.; Nath, S.K.; Hoque Apu, E. COVID-19 Vaccination Intent and Willingness to Pay in Bangladesh: A Cross-Sectional Study. Vaccines 2021, 9, 416. [Google Scholar] [CrossRef]
- Loomba, S.; de Figueiredo, A.; Piatek, S.J.; de Graaf, K.; Larson, H.J. Measuring the impact of COVID-19 vaccine misinformation on vaccination intent in the UK and USA. Nat. Hum. Behav. 2021, 5, 337–348. [Google Scholar] [CrossRef] [PubMed]
- Murphy, J.; Vallières, F.; Bentall, R.P.; Shevlin, M.; McBride, O.; Hartman, T.K.; McKay, R.; Bennett, K.; Mason, L.; Gibson-Miller, J.; et al. Psychological characteristics associated with COVID-19 vaccine hesitancy and resistance in Ireland and the United Kingdom. Nat. Commun. 2021, 12, 29. [Google Scholar] [CrossRef]
- 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, 42. [Google Scholar] [CrossRef] [PubMed]
- Ward, J.K.; Alleaume, C.; Peretti-Watel, P.; Group, C. The French public’s attitudes to a future COVID-19 vaccine: The politicization of a public health issue. Soc. Sci. Med. 2020, 265, 113414. [Google Scholar] [CrossRef] [PubMed]
- Tran, V.D.; Pak, T.V.; Gribkova, E.I.; Galkina, G.A.; Loskutova, E.E.; Dorofeeva, V.V.; Dewey, R.S.; Nguyen, K.T.; Pham, D.T. Determinants of COVID-19 vaccine acceptance in a high infection-rate country: A cross-sectional study in Russia. Pharm. Pract. 2021, 19, 2276. [Google Scholar] [CrossRef]
- Reno, C.; Maietti, E.; Fantini, M.P.; Savoia, E.; Manzoli, L.; Montalti, M.; Gori, D. Enhancing COVID-19 Vaccines Acceptance: Results from a Survey on Vaccine Hesitancy in Northern Italy. Vaccines 2021, 9, 378. [Google Scholar] [CrossRef] [PubMed]
- Wong, M.C.S.; Wong, E.L.Y.; Huang, J.; Cheung, A.W.L.; Law, K.; Chong, M.K.C.; Ng, R.W.Y.; Lai, C.K.C.; Boon, S.S.; Lau, J.T.F.; et al. Acceptance of the COVID-19 vaccine based on the health belief model: A population-based survey in Hong Kong. Vaccine 2021, 39, 1148–1156. [Google Scholar] [CrossRef] [PubMed]
- van der Linden, S.; Dixon, G.; Clarke, C.; Cook, J. Inoculating against COVID-19 vaccine misinformation. EClinicalMedicine 2021, 33, 100772. [Google Scholar] [CrossRef]
- Bhartiya, S.; Kumar, N.; Singh, T.; Murugan, S.; Rajavel, S.; Wadhwani, M. Knowledge, attitude and practice towards COVID-19 vaccination acceptance in West India. Int. J. Commun. Med. Public Health 2021, 8, 7. [Google Scholar] [CrossRef]
Characteristics | n (%) |
---|---|
Total study participants | 1387 |
Gender | |
Male | 848 (61.1) |
Female | 539 (38.9) |
Age | |
18–29 years | 454 (32.7) |
30–39 years | 456 (32.9) |
40–49 years | 277 (20.0) |
50 years or more | 200 (14.4) |
Ethnicity | |
Saudi | 1193 (86.0) |
Non-Saudi (African) | 16 (1.2) |
Non-Saudi (Asian) | 90 (6.5) |
Non-Saudi (European) | 3 (0.2) |
Non-Saudi (Middle East) | 69 (5.0) |
Non-Saudi (Others) | 16 (1.2) |
Regions | |
Al baha | 5 (0.4) |
Al jouf | 58 (4.2) |
Asir | 39 (2.8) |
Eastern | 128 (9.2) |
Hail | 14 (1.0) |
Jazan | 10 (0.7) |
Madinah | 77 (5.6) |
Narjan | 3 (0.2) |
Qassim | 390 (28.1) |
Riyadh | 637 (45.9) |
Tabuk | 17 (1.2) |
The Northern Border | 9 (0.6) |
Education | |
Primary or below | 10 (0.7) |
Secondary | 199 (14.3) |
Tertiary (college/university) | 1178 (84.9) |
Occupation | |
General worker | 32 (2.3) |
Healthcare workers/professionals | 284 (20.5) |
Housewife | 154 (11.1) |
Student | 203 (14.6) |
Unemployed | 121 (8.7) |
Other professional/managerial | 387 (27.9) |
Other | 206 (14.9) |
Participant has chronic disease | |
No | 1116 (80.5) |
Yes | 229 (16.5) |
Participant diagnosed with COVID-19 | |
No | 1179 (85.0) |
Yes | 208 (15.0) |
Family member diagnosed with COVID-19 | |
No | 837 (60.3) |
Yes | 550 (39.7) |
Relative/friend/neighbor/colleague diagnosed with COVID-19 | |
No | 89 (6.4) |
Yes | 1298 (93.6) |
Receive flu vaccine every year | |
No | 1044 (75.3) |
Yes | 343 (24.7) |
Intent to receive COVID-19 vaccine | |
Definitely not | 217 (15.6) |
Probably not | 372 (26.8) |
Definitely yes | 379 (27.3) |
Probably yes | 419 (30.2) |
Perceived COVID-19 Related Health Beliefs | Agree, n (%) | Disagree, n (%) |
---|---|---|
Perceived susceptibility | ||
Chance of getting COVID-19 in the future is very high | 458 (33.0) | 929 (67.0) |
Currently, getting COVID-19 is a strong possibility | 610 (44.0) | 777 (56.0) |
Perceived severity | ||
Complications of COVID-19 is very serious | 804 (58.0) | 583 (42.0) |
Will be very sick if I get COVID-19 | 372 (26.8) | 1015 (73.2) |
Perceived benefits | ||
Vaccination will decrease my chances of getting COVID-19 | 915 (66.0) | 472 (34.0) |
Perceived barriers | ||
Concerned about the efficacy of the vaccination available | 785 (56.6) | 602 (43.4) |
Concerned about the safety/side effects of the vaccination available | 900 (64.9) | 487 (35.1) |
Concerned about the halal nature of the vaccination available | 230 (16.6) | 1157 (83.4) |
Cues to action | ||
Will get vaccine after receiving complete information | 1025 (73.9) | 362 (26.1) |
Will get vaccine if it is received by many in the public | 447 (32.2) | 940 (67.8) |
Will get vaccine if it does not cause undue problems to vaccinated people | 870 (62.7) | 517 (37.3) |
Intention to Receive COVID-19 Vaccine | Definitely or Probably YES, n (%) | Definitely or Probably NO, n (%) | Unadjusted Analyses | Adjusted Analyses | ||||
---|---|---|---|---|---|---|---|---|
p | OR | 95% CIs | p | AOR | 95% CIs | |||
Total study participants | 798 | 589 | ||||||
Gender | ||||||||
Male | 495 (62.0) | 353 (59.9) | 1 | 1 | ||||
Female | 303 (38.0) | 236 (40.1) | 0.428 | 0.92 | 0.74–1.14 | 0.147 | 0.81 | 0.61–1.08 |
Age | ||||||||
18–29 years | 229 (28.7) | 225 (38.2) | 1 | 1 | ||||
30–39 years | 269 (33.7) | 187 (31.7) | 0.010 | 1.41 | 1.09–1.84 | 0.226 | 1.22 | 0.88–1.68 |
40–49 years | 160 (20.1) | 117 (19.9) | 0.055 | 1.34 | 0.99–1.82 | 0.448 | 1.15 | 0.80–1.67 |
50 years or more | 140 (17.5) | 60 (10.2) | <0.001 | 2.29 | 1.61–3.27 | <0.001 | 2.11 | 1.38–3.23 |
Ethnicity | ||||||||
Non-Saudi | 136 (17.0) | 58 (9.8) | 1 | 1 | ||||
Saudi | 662 (83.0) | 531 (90.2) | <0.001 | 0.53 | 0.38–0.74 | 0.644 | 0.91 | 0.61–1.36 |
Regions | ||||||||
Riyadh | 327 (41.0) | 310 (52.6) | <0.001 | 0.63 | 0.50–0.77 | 0.025 | 0.72 | 0.55–0.96 |
Qassim | 249 (31.2) | 141 (23.9) | 0.003 | 1.44 | 1.13–1.83 | 0.709 | 0.94 | 0.68–1.30 |
Education | ||||||||
Primary or below | 5 (0.6) | 5 (0.8) | 1 | 1 | ||||
Secondary | 117 (14.7) | 82 (13.9) | 0.584 | 1.43 | 0.40–5.09 | 0.477 | 1.64 | 0.42–6.36 |
Tertiary (college/university) | 676 (84.7) | 502 (85.2) | 0.639 | 1.35 | 0.39–4.68 | 0.735 | 1.26 | 0.33–4.84 |
Occupation | ||||||||
General worker | 11 (1.4) | 21 (3.6) | 0.007 | 0.38 | 0.18–0.79 | 0.134 | 0.54 | 0.24–1.21 |
Healthcare workers/professionals | 222 (27.8) | 62 (10.5) | <0.001 | 3.28 | 2.41–4.45 | <0.001 | 2.50 | 1.58–3.94 |
Housewife | 81 (10.2) | 73 (12.4) | 0.189 | 0.80 | 0.57–1.12 | 0.261 | 1.33 | 0.81–2.20 |
Student | 98 (12.3) | 105 (17.8) | 0.004 | 0.65 | 0.48–0.87 | 0.582 | 1.14 | 0.71–1.82 |
Unemployed | 53 (6.6) | 68 (11.5) | 0.002 | 0.55 | 0.37–0.79 | 0.471 | 0.84 | 0.51–1.36 |
Other professional/managerial | 220 (27.6) | 167 (28.4) | 0.550 | 0.96 | 0.76–1.22 | 0.233 | 1.25 | 0.87–1.79 |
Other | 113 (14.2) | 93 (15.8) | 0.399 | 0.88 | 0.65–1.18 | NA | NA | NA |
Participant has chronic disease | ||||||||
No | 654 (82.0) | 504 (85.6) | 1 | 1 | ||||
Yes | 144 (18.0) | 85 (14.4) | 0.073 | 1.31 | 0.97–1.75 | 0.782 | 1.05 | 0.76–1.45 |
Participant diagnosed with COVID-19 | ||||||||
No | 667 (83.6) | 512 (86.9) | 1 | 1 | ||||
Yes | 131 (16.4) | 77 (13.1) | 0.085 | 1.31 | 0.96–1.77 | 0.237 | 1.23 | 0.87–1.74 |
Family member diagnosed with COVID-19 | ||||||||
No | 490 (61.4) | 347 (58.9) | 1 | 1 | ||||
Yes | 308 (38.6) | 242 (41.1) | 0.349 | 0.90 | 0.73–1.12 | 0.332 | 0.88 | 0.69–1.14 |
Relative/friend/neighbor/colleague diagnosed with COVID-19 | ||||||||
No | 52 (6.5) | 37 (6.3) | 1 | 1 | ||||
Yes | 746 (93.5) | 552 (93.7) | 0.860 | 0.96 | 0.62–1.49 | 0.300 | 1.30 | 0.79–2.13 |
Receive flu vaccine every year | ||||||||
No | 528 (66.2) | 516 (87.6) | 1 | 1 | ||||
Yes | 270 (33.8) | 73 (12.4) | <0.001 | 3.61 | 2.72–4.81 | <0.001 | 2.63 | 1.93–3.58 |
Perceived COVID-19 Related Health Beliefs | Definitely or Probably YES, n (%) | Definitely or Probably NO, n (%) | Unadjusted Analyses | Adjusted Analyses | ||||
---|---|---|---|---|---|---|---|---|
p | OR | 95% CIs | p | AOR | 95% CIs | |||
Total study participants | 798 | 589 | ||||||
Perceived susceptibility | ||||||||
Chance of getting COVID-19 in the future is very high | 466 (58.4) | 463 (78.6) | 1 | 1 | ||||
Disagree | 332 (41.6) | 126 (21.4) | <0.001 | 2.62 | 2.06–3.33 | <0.001 | 2.16 | 1.65–2.82 |
Agree | ||||||||
Currently, getting COVID-19 is a strong possibility | ||||||||
Disagree | 390 (48.9) | 387 (65.7) | 1 | 1 | ||||
Agree | 408 (51.1) | 202 (34.3) | <0.001 | 2.00 | 1.61–2.50 | <0.001 | 1.76 | 1.39–2.24 |
Perceived severity | ||||||||
Complications of COVID-19 are very serious | ||||||||
Disagree | 247 (31.0) | 336 (57.0) | 1 | 1 | ||||
Agree | 551 (69.0) | 253 (43.0) | <0.001 | 2.96 | 2.37–3.70 | <0.001 | 2.68 | 2.11–3.41 |
Will be very sick if I get COVID-19 | ||||||||
Disagree | 533 (66.8) | 482 (81.9) | 1 | 1 | ||||
Agree | 265 (33.2) | 107 (18.2) | <0.001 | 2.24 | 1.73–2.89 | <0.001 | 2.02 | 1.53–2.68 |
Perceived benefits | ||||||||
Vaccination will decrease my chances of getting COVID-19 | ||||||||
Disagree | 92 (11.5) | 380 (64.5) | 1 | 1 | ||||
Agree | 706 (88.5) | 209 (35.5) | <0.001 | 13.1 | 10.6–18.4 | <0.001 | 16.3 | 11.2–22.2 |
Perceived barriers | ||||||||
Concerned about the efficacy of the vaccination available | ||||||||
Disagree | 483 (60.5) | 119 (20.2) | 1 | 1 | ||||
Agree | 315 (39.5) | 470 (79.8) | <0.001 | 0.17 | 0.13–0.21 | <0.001 | 0.12 | 0.09–0.16 |
Concerned about the safety/side effects of the vaccination available | ||||||||
Disagree | 407 (51.0) | 80 (13.6) | 1 | 1 | ||||
Agree | 391 (49.0) | 509 (96.4) | <0.001 | 0.15 | 0.11–0.20 | <0.001 | 0.13 | 0.09–0.17 |
Concerned about the halal nature of the vaccination available | ||||||||
Disagree | 698 (87.5) | 459 (77.9) | 1 | 1 | ||||
Agree | 100 (12.5) | 130 (22.1) | <0.001 | 0.51 | 0.38–0.67 | <0.001 | 0.29 | 0.20–0.42 |
Cues to action | ||||||||
Will get vaccine after receiving complete information | ||||||||
Disagree | 133 (16.7) | 229 (38.9) | 1 | 1 | ||||
Agree | 665 (83.3) | 360 (61.1) | <0.001 | 3.18 | 2.48–4.08 | <0.001 | 2.77 | 2.12–3.60 |
Will get vaccine if it is received by many in the public | ||||||||
Disagree | 493 (61.8) | 447 (75.9) | 1 | 1 | ||||
Agree | 305 (38.2) | 142 (24.1) | <0.001 | 1.95 | 1.54–2.47 | <0.001 | 1.82 | 1.41–2.34 |
Will get vaccine if that does not cause undue problems to vaccinated people | ||||||||
Disagree | 358 (44.9) | 159 (27.0) | 1 | 1 | ||||
Agree | 440 (55.1) | 430 (73.0) | <0.001 | 0.45 | 0.36–0.57 | <0.001 | 0.43 | 0.34–0.55 |
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Mahmud, I.; Kabir, R.; Rahman, M.A.; Alradie-Mohamed, A.; Vinnakota, D.; Al-Mohaimeed, A. The Health Belief Model Predicts Intention to Receive the COVID-19 Vaccine in Saudi Arabia: Results from a Cross-Sectional Survey. Vaccines 2021, 9, 864. https://doi.org/10.3390/vaccines9080864
Mahmud I, Kabir R, Rahman MA, Alradie-Mohamed A, Vinnakota D, Al-Mohaimeed A. The Health Belief Model Predicts Intention to Receive the COVID-19 Vaccine in Saudi Arabia: Results from a Cross-Sectional Survey. Vaccines. 2021; 9(8):864. https://doi.org/10.3390/vaccines9080864
Chicago/Turabian StyleMahmud, Ilias, Russell Kabir, Muhammad Aziz Rahman, Angi Alradie-Mohamed, Divya Vinnakota, and Abdulrahman Al-Mohaimeed. 2021. "The Health Belief Model Predicts Intention to Receive the COVID-19 Vaccine in Saudi Arabia: Results from a Cross-Sectional Survey" Vaccines 9, no. 8: 864. https://doi.org/10.3390/vaccines9080864