Document Type : Original Article
Authors
- . Amin Mirzaei
- . Farogh Kazembeigi 1
- . Hojatollah Kakaei 2
- . Mohsen Jalilian
- . Sajad Mazloomi 1
- . Heshmatollah Nourmoradi 1
1 Environmental Health Engineering, Ilam University of Medical Sciences, Ilam, Iran,
2 Occupational Health Engineering, Ilam University of Medical Sciences, Ilam, Iran
Abstract
BACKGROUND: The novel coronavirus (COVID‑19) has infected nearly 9.5 million people in 216
countries, areas, or territories in the world. The fight against the COVID‑19 has become a very serious
international challenge. The aim of this study was to determine the predictors of COVID‑19‑preventive
behaviors using the health belief model (HBM).
MATERIALS AND METHODS: This cross‑sectional study was conducted with the participation of
558 samples from the adult population of Iran. The online convenience sampling was conducted
in this research. The online 68‑item questionnaire link was published all over Iran through social
networks including Telegram and WhatsApp, which are common in Iran. The data were analyzed
using SPSS software version 19. Descriptive statistics, bivariate Pearson’s correlation test, and
multiple linear regression were used to analyze the data.
RESULTS: The mean age of the subjects was 33.3 ± 10.01 years. The participants were often
female (61.3%), married (57.9%), and resident of the city (81.0%) with university educational
level (78.8%). The results showed that the HBM structures predicted 29.3% of the preventive
behaviors of COVID‑19 in the subjects. The perceived benefits, perceived barriers, and self‑efficacy
significantly predicted the preventive behaviors, but the perceived susceptibility and perceived
severity were not significant in the regression model. The internet and virtual social networks (49.8%),
broadcast (33.5%), and healthcare providers (15.8%) were the most important sources of information
related with COVID‑19. In response to COVID‑19‑related internal cues to action, 36.6% did not
pay attention and 34.7% tried to self‑medicate. Only 28.5% of the subjects referred to the hospital,
healthcare center, or physician.
CONCLUSION: Self‑efficacy, perceived barriers, and perceived benefits were the key determinants
of COVID‑19‑preventive behaviors in the subjects. It can be concluded that the HBM is a good tool
to predict COVID‑19‑preventive behaviors in Iranian population.
Keywords
Community responses during the early phase of the COVID-19
epidemic in Hong Kong: Risk perception, information exposure
and preventive measures. medRxiv. 2020. DOI: 10.3201/
eid2607.200500.
2. Zhu N, Zhang D, Wang W, Li X, Yang B, Song J, et al. A novel
coronavirus from patients with pneumonia in China, 2019. N Engl
J Med. 2020;382:727-33.
3. Biondi Zoccai G, Landoni G, Carnevale R, Cavarretta E,
Sciarretta S, Frati G. SARS-CoV-2 and COVID-19: Facing the
pandemic together as citizens and cardiovascular practitioners.
Minerva Cardioangiol. 2020;68:61-4.
4. World Health Organization. Coronavirus Disease (COVID-19)
Outbreak Situation. Available from: https://www.who.int/
emergencies/diseases/novel-coronavirus-2019. [Last accessed
on 2020 Apr 15].
5. Iranian Ministry of Health and Medical Education. COVID-19
Daily Epidemiology Journal; 14 April, 2020. Available from:
http://corona.behdasht.gov.ir/files/site1/files/Factsheet_240126-Fa.pdf. [Last accessed on 2020 Apr 14].
6. Wu Z, McGoogan JM. Characteristics of and important lessons
from the coronavirus disease 2019 (COVID-19) outbreak in China:
Summary of a report of 72 314 cases from the Chinese Center for
Disease Control and Prevention. JAMA. 2020;323 (13):1239-1242.7. Adzerikho RD, Aksentsev SL, Okun’ IM, Konev SV. Letter:
Change in trypsin sensitivity during structural rearrangements
in biological membranes. Biofizika. 1975;20:942-4.
8. Qian M, Wu Q, Wu P, Hou Z, Liang Y, Cowling BJ, Yu H.
Psychological responses, behavioral changes and public
perceptions during the early phase of the COVID-19 outbreak
in China: A population based cross-sectional survey. medRxiv.
2020. DOI: 10.1101/2020.02.18.20024448.
9. Rosenstock IM, Strecher VJ, Becker MH. Social learning theory
and the health belief model. Health Educ Q. 1988;15:175-83.
10. Ajzen I, Fishbein M. Understanding Attitudes and Predicting
Social Behavior. Englewood Cliffs (NJ): Prentice-Hall; 1980.
11. Jalilian M, Mostafavi F, Mahaki B, Delpisheh A, Rad GS. An
application of a theory of planned behaviour to determine the
association between behavioural intentions and safe road-crossing
in college students: Perspective from Isfahan, Iran. J Pak Med
Assoc. 2015;65:742-6.
12. Prochaska JO, DiClemente CC, Norcross JC. In search of how
people change: Application to addictive behavior. Am Psychol.
1992;47:1102-14.
13. Saeidi A, Mirzaei A, Mahaki B, Jalali A, Jalilian M. Physical activity
stage of change and its related factors in secondary school students
of Sarableh City: A perspective from Iran. Open Access Maced J
Med Sci. 2018;6:1517-21.
14. Rogers RW. A cognitive and physiological processes in fear
appeals and attitude change: A revised theory of protection
motivation. In: Cacioppo JR, Petty RE, editors. Social Sychology:
A Sourcebook. New York: Guilford; 1983. p. 153-76.
15. Tang CS, Wong CY. Factors influencing the wearing of facemasks
to prevent the severe acute respiratory syndrome among adult
Chinese in Hong Kong. Prev Med. 2004;39:1187-93.
16. Carico RR Jr., Sheppard J, Thomas CB. Community pharmacists
and communication in the time of COVID-19: Applying the
health belief model. Res Social Adm Pharm. 2020. DOI: 10.1016/j.
sapharm. 2020.03.017.
17. Carpenter CJ. A meta-analysis of the effectiveness of health
belief model variables in predicting behavior. Health Commun.
2010;25:661-9.
18. Janz NK, Champion VL, Strecher VJ. The health belief model.
In: Glanz K, Rimer BK, Lewis FM, editors. Health Behavior and
Health Education: Theory, Research, and Practice. 3. Jossey-Bass:
San Francisco; 2002. p. 45-66.
19. Norman P, Brain K. An application of an extended health belief
model to the prediction of breast self-examination among women
with a family history of breast cancer. Br J Health Psychol.
2005;10:1-6.
20. Mirzaei A, Esmaeili F, Jalilian M. Predictors of complementary
feeding in infants aged 6 to 18 months: An application of health
belief model. Sri Lanka J Child Health. 2020;49:48-53.
21. Wong CY, Tang CS. Practice of habitual and volitional health
behaviors to prevent severe acute respiratory syndrome
among Chinese adolescents in Hong Kong. J Adolesc Health.
2005;36:193-200.
22. Alsulaiman SA, Rentner TL. The health belief model and
preventive measures: A study of the Ministry of Health Campaign
on Coronavirus in Saudi Arabia. J Int Crisis Risk Communicat
Res. 2018;1:3.
23. Coe AB, Gatewood SB, Moczygemba LR, Goode JV, Beckner JO.
The use of the health belief model to assess predictors of intent to
receive the novel (2009) H1N1 influenza vaccine. Innov Pharm.
2012;3:1-11.
24. Sim SW, Moey KS, Tan NC. The use of facemasks to prevent
respiratory infection: A literature review in the context of the
health belief model. Singapore Med J. 2014;55:160-7.
25. Li JB, Yang A, Dou K, Wang LX, Zhang MC, Lin X. Chinese
public’s knowledge, perceived severity, and perceived
controllability of the COVID-19 and their associations with
emotional and behavioural reactions, social participation, and
precautionary behaviour: A national survey. 2020. Available
from: https://assets.researchsquare.com/files/rs-16572/v1/
manuscript.pdf. [Last accessed on 2020 Apr 16].
26. Tuite AR, Bogoch II, Sherbo R, Watts A, Fisman D, Khan K.
Estimation of coronavirus disease 2019 (COVID-19) burden and
potential for international dissemination of infection from Iran.
Ann Intern Med. 2020;172:699-701.
27. Takian A, Raoofi A, Kazempour-Ardebili S. COVID-19
battle during the toughest sanctions against Iran. Lancet.
2020;395:1035-6.
28. Herrera-Diestra JL, Meyers LA. Local risk perception enhances
epidemic control. PLoS One. 2019;14:e0225576.
29. Mirzaei A, Ghofranipour F, Ghazanfari Z. Social cognitive
predictors of breakfast consumption in primary school’s male
students. Glob J Health Sci. 2015;8:124-32.
30. Yzer MC, FisherJD, Bakker AB, Siero FW, Misovich SJ. The effects
of information about AIDS risk and self-efficacy on women’s
intention to engage in AIDS preventive behavior. J Appl Soc
Psychol. 1998;28:1837-52.
31. Geldsetzer P. Knowledge and perceptions of coronavirus disease
2019 among the general public in the United States and the United
Kingdom: A cross-sectional online survey. medRxiv. 2020. DOI:
10.1101/2020.03.13.20035568.
32. Husnayain A, Fuad A, Su EC. Applications of Google search
trends for risk communication in infectious disease management:
A case study of the COVID-19 outbreak in Taiwan. Int J Infect
Dis. 2020;95:221-3.
33. Bayham J, Kuminoff NV, Gunn Q, Fenichel EP. Measured
voluntary avoidance behavior during the 2009 A/H1N1 epidemic.
Proceed Royal Soc B Biol Sci. 2015;282: 1-6.
34. Rubin GJ, Amlôt R, Page L, Wessely S. Public perceptions, anxiety,
and behaviour change in relation to the swine flu outbreak: Cross
sectional telephone survey. BMJ. 2009;339:b2651.
35. Pennycook G, McPhetres J, Zhang Y, Lu JG, Rand DG. Fighting
COVID-19 misinformation on social media: Experimental
evidence for a scalable accuracy-nudge intervention. Psychol Sci.
2020;31:770-80.
36. Statistical Center of Iran; The detailed results of Iran. National
Population and Housing Census; 2016. Available from: https://
www.amar.org.ir. [Last accessed on 2020 Apr 15].
37. Salmani B, Mohammadzadeh P, Zoolghadr H. Investigating the
effect of economic factors on Internet diffusion in developing
countries. Q J Appl Theor Econ 2015;2: 81-102.
38. Zarabi V, Mohammadian Khorasani I, Maddah M. Predicting
the Internet diffusion rate in Iran by providing a Fuzzy-diffusion
model. J Technol Develop Manag 2013;3:123-51.
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