Sources of COVID-19-Related Information in People with Various Levels of Risk Perception and Preventive Behaviors in Taiwan: A Latent Profile Analysis
Abstract
:1. Introduction
1.1. Risk Perception and Preventive Behaviors in Coronavirus Disease 2019 Pandemic
1.2. Roles of Information Sources
1.3. COVID-19 Pandemic and Its Impact in Taiwan
1.4. Study Aims and Hypotheses
2. Methods
2.1. Participants
2.2. Measures
2.2.1. Sociodemographics
2.2.2. Risk Perception about COVID-19
2.2.3. Adoption of Preventive Behaviors
2.2.4. COVID-19-Related Information Sources
2.3. Statistical Analysis
3. Results
3.1. Results of LPA
3.2. Information Sources Predicting the Latent Classes
4. Discussion
4.1. Classes of Risk Perception and Adoption of Preventive Behaviors
4.2. Information Sources
4.3. Further Studies
4.4. Limitations
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Taiwan Centers for Disease Control: COVID-19 Pandemic. Available online: https://sites.google.com/cdc.gov.tw/2019ncov/global (accessed on 3 January 2021).
- Wiersinga, W.J.; Rhodes, A.; Cheng, A.C.; Peacock, S.J.; Prescott, H.C. Pathophysiology, transmission, diagnosis, and treatment of coronavirus disease 2019 (COVID-19): A review. JAMA 2020, 324, 782–793. [Google Scholar] [CrossRef]
- Torales, J.; O’Higgins, M.; Castaldelli-Maia, J.M.; Ventriglio, A. The outbreak of COVID-19 coronavirus and its impact on global mental health. Int. J. Soc. Psychiatry 2020, 66, 317–320. [Google Scholar] [CrossRef] [Green Version]
- McIntyre, R.S.; Lee, Y. Preventing suicide in the context of the COVID-19 pandemic. World Psychiatry 2020, 19, 250–251. [Google Scholar] [CrossRef]
- Nicola, M.; Alsafi, Z.; Sohrabi, C.; Kerwan, A.; Al-Jabir, A.; Iosifidis, C.; Agha, M.; Agha, R. The socio-economic implications of the coronavirus and COVID-19 pandemic: A review. Int. J. Surg. 2020, 78, 185–193. [Google Scholar] [CrossRef]
- Rundle, A.G.; Park, Y.; Herbstman, J.B.; Kinsey, E.W.; Wang, Y.C. COVID-19-related school closings and risk of weight gain among children. Obesity 2020, 28, 1008–1009. [Google Scholar] [CrossRef] [Green Version]
- Zhang, Y.; Ma, Z.F. Impact of the COVID-19 pandemic on mental health and quality of life among local residents in Liaoning Province, China: A cross-sectional study. Int. J. Environ. Res. Public Health 2020, 17, 2381. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Crayne, M.P. The traumatic impact of job loss and job search in the aftermath of COVID-19. Psychol. Trauma. 2020, 12, S180–S182. [Google Scholar] [CrossRef]
- Oksanen, A.; Kaakinen, M.; Latikka, R.; Savolainen, I.; Savela, N.; Koivula, A. Regulation and trust: 3-month follow-up study on COVID-19 mortality in 25 European countries. JMIR Public Health Surveill. 2020, 6, e19218. [Google Scholar] [CrossRef] [PubMed]
- GAVI, the Vaccine Alliance. The COVID-19 Vaccine Race. Available online: https://www.gavi.org/ (accessed on 3 January 2021).
- The Centers for Disease Control and Prevention, the United States. How to Protect Yourself and Others. Available online: https://www.cdc.gov/coronavirus/2019-ncov/prevent-getting-sick/prevention.html (accessed on 3 January 2021).
- Majid, U.; Wasim, A.; Bakshi, S.; Truong, J. Knowledge, (mis-)conceptions, risk perception, and behavior change during pandemics: A scoping review of 149 studies. Public Underst. Sci. 2020, 29, 777–799. [Google Scholar] [CrossRef] [PubMed]
- Champion, V.L.; Skinner, C.S. The health belief model. In Health Behavior and Health Education: Theory, Research, and Practice; Glanz, K., Rimer, B.K., Viswanath, K., Eds.; Jossey-Bass: San Francisco, CA, USA, 2008; pp. 45–65. [Google Scholar]
- Rosenstock, I.M. Historical origins of the health belief model. Health Educ. Monogr. 1974, 2, 328–335. [Google Scholar] [CrossRef]
- Janz, N.K.; Becker, M.H. The health belief model: A decade later. Health Educ. Behav. 1984, 11, 1–47. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Wise, T.; Zbozinek, T.D.; Michelini, G.; Hagan, C.C.; Mobbs, D. Changes in risk perception and self-reported protective behaviour during the first week of the COVID-19 pandemic in the United States. R. Soc. Open Sci. 2020, 7, 200742. [Google Scholar] [CrossRef] [PubMed]
- Sadique, M.Z.; Edmunds, W.J.; Smith, R.D.; Meerding, W.J.; Zwart, O.D.; Brug, J.; Beutels, P. Precautionary behavior in response to perceived threat of pandemic influenza. Emerg. Infect. Dis. 2007, 13, 1307–1313. [Google Scholar] [CrossRef]
- Dryhurst, S.; Schneider, C.R.; Kerr, J.; Freeman, A.L.J.; Recchia, G.; van der Bles, A.M.; Spiegelhalter, D.; van der Linden, S. Risk perceptions of COVID-19 around the world. J. Risk Res. 2020, 23, 994–1006. [Google Scholar] [CrossRef]
- Rimal, R.N.; Real, K. Perceived risk and efficacy beliefs as motivators of change: Use of the risk perception attitude (RPA) framework to understand health behaviors. Hum. Commun. Res. 2003, 29, 370–399. [Google Scholar] [CrossRef]
- Kouzy, R.; Jaoude, J.A.; Kraitem, A.; El Alam, M.B.; Karam, B.; Adib, E.; Zarka, J.; Traboulsi, C.; Akl, E.W.; Baddour, K. Coronavirus goes viral: Quantifying the COVID-19 misinformation epidemic on Twitter. Cureus 2020, 12, e7255. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Abramowitz, S.; McKune, S.L.; Fallah, M.; Monger, J.; Tehoungue, K.; Omidian, P.A. The opposite of denial: Social learning at the onset of the Ebola emergency in Liberia. J. Health Commun. 2017, 22, 59–65. [Google Scholar] [CrossRef] [PubMed]
- Balkhy, H.H.; Abolfotouh, M.A.; Al-Hathlool, R.H.; Al-Jumah, M.A. Awareness, attitudes, and practices related to the swine influenza pandemic among the Saudi public. BMC Infect. Dis. 2010, 10, 42. [Google Scholar] [CrossRef] [Green Version]
- Jardine, C.G.; Boerner, F.U.; Boyd, A.D.; Driedger, S.M. The more the better? A comparison of the information sources used by the public during two infectious disease outbreaks. PLoS ONE 2015, 10, e0140028. [Google Scholar] [CrossRef] [Green Version]
- Lin, Y.H.; Liu, C.H.; Chiu, Y.C. Google searches for the keywords of “wash hands” predict the speed of national spread of COVID-19 outbreak among 21 countries. Brain Behav. Immun. 2020, 87, 30–32. [Google Scholar] [CrossRef]
- D’Souza, R.S.; D’Souza, S.; Strand, N.; Anderson, A.; Vogt, M.N.P.; Olatoye, O. YouTube as a source of medical information on the novel coronavirus 2019 disease (COVID-19) pandemic. Glob. Public Health 2020, 15, 935–942. [Google Scholar] [CrossRef]
- Fan, K.S.; Ghani, S.A.; Machairas, N.; Lenti, L.; Fan, K.H.; Richardson, D.; Scott, A.; Raptis, D.A. COVID-19 prevention and treatment information on the internet: A systematic analysis and quality assessment. BMJ Open 2020, 10, e040487. [Google Scholar] [CrossRef]
- Hernández-García, I.; Giménez-Júlvez, T. Assessment of Health Information about COVID-19 Prevention on the Internet: Infodemiological Study. JMIR Public Health Surveill. 2020, 6, e18717. [Google Scholar] [CrossRef] [Green Version]
- Li, H.O.; Bailey, A.; Huynh, D.; Chan, J. YouTube as a source of information on COVID-19: A pandemic of misinformation? BMJ Glob. Health 2020, 5, e002604. [Google Scholar] [CrossRef]
- Dutta, A.; Beriwal, N.; Van Breugel, L.M.; Sachdeva, S.; Barman, B.; Saikia, H.; Nelson, U.A.; Mahdy, A.; Paul, S. YouTube as a source of medical and epidemiological information during COVID-19 pandemic: A cross-sectional study of content across six languages around the globe. Cureus 2020, 12, e8622. [Google Scholar] [CrossRef]
- Yang, Z.; Xin, Z. Heterogeneous risk perception amid the outbreak of COVID-19 in China: Implications for economic confidence. Appl. Psychol. Health Well-Being 2020, 12, 1000–1018. [Google Scholar] [CrossRef]
- Wang, C.J.; Ng, C.Y.; Brook, R.H. Response to COVID-19 in Taiwan: Big data analytics, new technology, and proactive testing. JAMA 2020, 323, 1341–1342. [Google Scholar] [CrossRef] [PubMed]
- Cheng, H.Y.; Li, S.Y.; Yang, C.H. Initial rapid and proactive response for the COVID-19 outbreak—Taiwan’s experience. J. Formos. Med. Assoc. 2020, 119, 771–773. [Google Scholar] [CrossRef] [PubMed]
- Chang, H.H.; Lee, B.; Yang, F.A.; Liou, Y.Y. Does COVID-19 affect metro use in Taipei? J. Transp. Geogr. 2021, 91, 102954. [Google Scholar] [CrossRef] [PubMed]
- Directorate-General of Budget, Accounting and Statistics, Executive Yuan, Taiwan. Available online: https://www.dgbas.gov.tw/point.asp?index=3 (accessed on 4 February 2021).
- Bowen, A.M.; Daniel, C.M.; Williams, M.L.; Baird, G.L. Identifying multiple submissions in Internet research: Preserving data integrity. AIDS Behav. 2008, 12, 964–973. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Liao, Q.; Cowling, B.J.; Lam, W.W.; Ng, D.M.; Fielding, R. Anxiety, worry and cognitive risk estimate in relation to protective behaviors during the 2009 influenza A/H1N1 in Hong Kong: Ten cross-sectional surveys. BMC Infect. Dis. 2014, 14, 169. [Google Scholar] [CrossRef] [Green Version]
- Rosenberg, J.M.; Beymer, P.N.; Anderson, D.J.; van Lissa, C.J.; Schmidt, J.A. tidyLPA: An R package to easily carry out Latent Profile Analysis (LPA) using open-source or commercial software. J. Open Source Softw. 2019, 4, 978. [Google Scholar] [CrossRef] [Green Version]
- Celeux, G.; Soromenho, G. An Etropy criterion for assessing the number of clusters in a mixture model. J. Classif. 1996, 13, 195–212. [Google Scholar] [CrossRef] [Green Version]
- Tein, J.Y.; Coxe, S.; Cham, H. Statistical power to detect the correct number of classes in latent profile analysis. Structl. Equ. Modeling 2013, 20, 640–657. [Google Scholar] [CrossRef]
- Ding, Y.; Xu, J.; Huang, S.; Li, P.; Lu, C.; Xie, S. Risk perception and depression in public health crises: Evidence from the COVID-19 crisis in China. Int. J. Environ. Res. Public Health 2020, 17, 5728. [Google Scholar] [CrossRef]
- Barasheed, O.; Alfelali, M.; Mushta, S.; Bokhary, H.; Alshehri, J.; Attar, A.A.; Booy, R.; Rashid, H. Uptake and effectiveness of facemask against respiratory infections at mass gatherings: A systematic review. Int. J. Infect. Dis. 2016, 47, 105–111. [Google Scholar] [CrossRef] [Green Version]
- Feng, S.; Shen, C.; Xia, N.; Song, W.; Fan, M.; Cowling, B.J. Rational use of face masks in the COVID-19 pandemic. Lancet Respir. Med. 2020, 8, 434–436. [Google Scholar] [CrossRef]
- Tai, Y.L.; Chi, H.; Chiu, N.C.; Tseng, C.Y.; Huang, Y.N.; Lin, C.Y. A name-based mask rationing plan in Taiwan may contribute to reduced public anxiety during the COVID-19 pandemic: An observational study. JMIR Form. Res. 2021, 5, e21409. [Google Scholar] [CrossRef] [PubMed]
- Romer, D.; Jamieson, K.H. Conspiracy theories as barriers to controlling the spread of COVID-19 in the U.S. Soc. Sci. Med. 2020, 263, 113356. [Google Scholar] [CrossRef]
- Scerri, M.; Grech, V. To wear or not to wear? Adherence to face mask use during the COVID-19 and Spanish influenza pandemics. Early Hum. Dev. 2020, 105253. [Google Scholar] [CrossRef]
- Park, T.; Ju, I.; Ohs, J.E.; Hinsley, A. Optimistic bias and preventive behavioral engagement in the context of COVID-19. Res. Soc. Adm. Pharm. 2021, 17, 1859–1866. [Google Scholar] [CrossRef] [PubMed]
- Baldassarre, A.; Giorgi, G.; Alessio, F.; Lulli, L.G.; Arcangeli, G.; Mucci, N. Stigma and discrimination (SAD) at the time of the SARS-CoV-2 pandemic. Int. J. Environ. Res. Public Health. 2020, 17, 6341. [Google Scholar] [CrossRef]
- Irigoyen-Camacho, M.E.; Velazquez-Alva, M.C.; Zepeda-Zepeda, M.A.; Cabrer-Rosales, M.F.; Lazarevich, I.; Castaño-Seiquer, A. Effect of income level and perception of susceptibility and severity of COVID-19 on stay-at-home preventive behavior in a group of older adults in Mexico City. Int. J. Environ. Res. Public Health 2020, 17, 7418. [Google Scholar] [CrossRef] [PubMed]
- World Health Organization. Available online: https://www.who.int/risk-communication/background/en/ (accessed on 3 January 2021).
- Heydari, S.T.; Zarei, L.; Sadati, A.K.; Moradi, N.; Akbari, M.; Mehralian, G.; Lankarani, K.B. The effect of risk communication on preventive and protective behaviours during the COVID-19 outbreak: Mediating role of risk perception. BMC Public Health 2021, 21, 54. [Google Scholar] [CrossRef] [PubMed]
- Lim, S.; Nakazato, H. The emergence of risk communication networks and the development of citizen health-related behaviors during the COVID-19 pandemic: Social selection and contagion processes. Int. J. Environ. Res. Public Health 2020, 17, 4148. [Google Scholar] [CrossRef]
- Dong, E.; Du, H.; Gardner, L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect. Dis. 2020, 20, 533–534. [Google Scholar] [CrossRef]
- Lurie, N.; Saville, M.; Hatchett, R.; Halton, J. Developing COVID-19 Vaccines at Pandemic Speed. N. Engl. J. Med. 2020, 382, 1969–1973. [Google Scholar] [CrossRef]
- Dubé, E.; Laberge, C.; Guay, M.; Bramadat, P.; Roy, R.; Bettinger, J. Vaccine hesitancy: An overview. Hum. Vaccines Immunother. 2013, 9, 1763–1773. [Google Scholar] [CrossRef] [PubMed]
- Bobkowski, P.; Smith, J. Social media divide: Characteristics of emerging adults who do not use social network websites. Media Cult. Soc. 2013, 35, 771–781. [Google Scholar] [CrossRef]
- Thornton, L.; Batterham, P.J.; Fassnacht, D.B.; Kay-Lambkin, F.; Calear, A.L.; Hunt, S. Recruiting for health, medical or psychosocial research using Facebook: Systematic review. Internet Interv. 2016, 4, 72–81. [Google Scholar] [CrossRef] [Green Version]
- Whitaker, C.; Stevelink, S.; Fear, N. The use of Facebook in recruiting participants for health research purposes: A systematic review. J. Med Internet Res. 2017, 19, e290. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Variable | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
---|---|---|---|---|---|---|---|---|
1. Worry of COVID-19 if flu-like symptoms occurring | 1 | |||||||
2. Past worry for COVID-19 | 0.45 | 1 | ||||||
3. Current worry for COVID-19 | 0.48 | 0.55 | 1 | |||||
4. Anticipated worry for COVID-19 | 0.26 | 0.47 | 0.37 | 1 | ||||
5. Chances of contracting COVID-19 compared with other people | 0.18 | 0.33 | 0.23 | 0.57 | 1 | |||
6. Avoiding crowded places | 0.30 | 0.21 | 0.27 | 0.14 | 0.08 | 1 | ||
7. Washing hands | 0.25 | 0.21 | 0.21 | 0.10 | 0.10 | 0.32 | 1 | |
8. Wearing a mask | 0.27 | 0.22 | 0.25 | 0.14 | 0.13 | 0.33 | 0.47 | 1 |
Mean | 2.93 | 1.59 | 6.13 | 2.48 | 2.53 | 1.75 | 1.68 | 1.66 |
SD | 0.92 | 1.00 | 2.25 | 1.14 | 1.28 | 0.55 | 0.62 | 0.67 |
No. of Classes | AIC | BIC | Entropy | BLRT (p-Value) |
---|---|---|---|---|
1 | 45,569.5 | 45,659.2 | 1.00 | <0.01 |
2 | 42,451.8 | 42,591.9 | 0.98 | <0.01 |
3 | 41,457.2 | 41,647.7 | 0.80 | <0.01 |
4 | 41,009.1 | 41,250.1 | 0.83 | <0.01 |
5 | 41,127.0 | 41,418.4 | 0.78 | <0.01 |
6 | 40,238.0 | 40,579.9 | 0.85 | <0.01 |
Variable | Risk Neutrals with High PB (n = 976) n (%) | Risk Exaggerators with High PB (n = 205) n (%) | OR 1 | Risk Deniers with Moderate PB (n = 380) n (%) | OR 2 | Risk Deniers with Low PB (n = 423) n (%) | OR 3 |
---|---|---|---|---|---|---|---|
Age a | |||||||
<35 | 397 (40.8) | 93 (45.37) | 1.00 | 168 (44.09) | 1.00 | 163 (38.53) | 1.00 |
35–49 | 437 (44.91) | 77 (37.56) | 1.33 (0.95–1.85) | 146 (38.32) | 1.05 (0.72–1.53) | 199 (47.04) | 1.47 (1.02–2.13) |
≥50 | 139 (14.29) | 35 (17.07) | 0.93 (0.60–1.44) | 67 (17.59) | 0.99 (0.61–1.62) | 61 (14.42) | 1.06 (0.66–1.71) |
Gender a | |||||||
Female | 669 (68.8) | 145 (70.7) | 1.00 | 233 (61.2) | 1.00 | 276 (65.2) | 1.00 |
Male | 304 (31.2) | 60 (29.3) | 0.92 (0.66–1.27) | 148 (38.8) | 1.41 (1.10–1.82) | 147 (34.8) | 1.18 (0.93–1.49) |
Education levels a | |||||||
High school or below | 100 (10.3) | 23 (11.2) | 1.00 | 37 (9.7) | 1.00 | 59 (13.9) | 1.00 |
Bachelor’s degree | 582 (59.8) | 117 (57.1) | 1.03 (0.61–1.75) | 219 (57.5) | 0.87 (0.57–1.34) | 225 (53.2) | 1.24 (0.85–1.81) |
Master’s degree and above | 291 (29.9) | 65 (31.7) | 0.90 (0.65–1.26) | 125 (32.8) | 0.88 0.68–1.14) | 139 (32.9) | 0.81 (0.63–1.05) |
COVID-19 information sources (high-frequency) b | |||||||
Internet media | 815 (83.8) | 172 (83.9) | 1.02 (0.67–1.54) | 302 (79.3) | 0.76 (0.56–1.03) | 306 (72.3) | 0.51 (0.39–0.68) |
Traditional media | 531 (54.6) | 122 (59.5) | 1.24 (0.91–1.68) | 193 (50.7) | 0.87 (0.69–1.11) | 200 (47.3) | 0.74 (0.59–0.93) |
Family members | 246 (25.3) | 67 (32.7) | 1.40 (1.01–1.94) | 80 (21.0) | 0.79 (0.59–1.05) | 93 (22.0) | 0.83 (0.63–1.09) |
Coworkers | 252 (25.9) | 67 (32.7) | 1.41 (1.02–1.96) | 74 (19.4) | 0.72 (0.54–0.97) | 77 (18.2) | 0.64 (0.48–0.86) |
Friends | 227 (23.3) | 55 (26.8) | 1.21 (0.86–1.71) | 63 (16.5) | 0.66 (0.48–0.90) | 72 (17.0) | 0.68 (0.51–0.91) |
Academic courses | 200 (20.6) | 50 (24.4) | 1.27 (0.89–1.82) | 74 (19.4) | 0.95 (0.70–1.28) | 75 (17.7) | 0.84 (0.62–1.13) |
Medical staff | 198 (20.3) | 55 (26.8) | 1.48 (1.04–2.10) | 62 (16.3) | 0.79 (0.58–1.09) | 61 (14.4) | 0.67 (0.49–0.91) |
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Wang, P.-W.; Chen, Y.-L.; Chang, Y.-P.; Wu, C.-F.; Lu, W.-H.; Yen, C.-F. Sources of COVID-19-Related Information in People with Various Levels of Risk Perception and Preventive Behaviors in Taiwan: A Latent Profile Analysis. Int. J. Environ. Res. Public Health 2021, 18, 2091. https://doi.org/10.3390/ijerph18042091
Wang P-W, Chen Y-L, Chang Y-P, Wu C-F, Lu W-H, Yen C-F. Sources of COVID-19-Related Information in People with Various Levels of Risk Perception and Preventive Behaviors in Taiwan: A Latent Profile Analysis. International Journal of Environmental Research and Public Health. 2021; 18(4):2091. https://doi.org/10.3390/ijerph18042091
Chicago/Turabian StyleWang, Peng-Wei, Yi-Lung Chen, Yu-Ping Chang, Chia-Fen Wu, Wei-Hsin Lu, and Cheng-Fang Yen. 2021. "Sources of COVID-19-Related Information in People with Various Levels of Risk Perception and Preventive Behaviors in Taiwan: A Latent Profile Analysis" International Journal of Environmental Research and Public Health 18, no. 4: 2091. https://doi.org/10.3390/ijerph18042091
APA StyleWang, P. -W., Chen, Y. -L., Chang, Y. -P., Wu, C. -F., Lu, W. -H., & Yen, C. -F. (2021). Sources of COVID-19-Related Information in People with Various Levels of Risk Perception and Preventive Behaviors in Taiwan: A Latent Profile Analysis. International Journal of Environmental Research and Public Health, 18(4), 2091. https://doi.org/10.3390/ijerph18042091