Confidence, Acceptance and Willingness to Pay for the COVID-19 Vaccine among Migrants in Shanghai, China: A Cross-Sectional Study
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Design
2.2. Data Collection
2.3. Instruments
2.4. Statistical Analysis
3. Results
3.1. Sample Characteristics
3.2. Acceptance, Willingness to Pay and Confidence of COVID-19 Vaccine
3.3. Factors Associated with Acceptance and Willingness to Pay for COVID-19 Vaccine
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- World Health Organization. Coronavirus Disease (COVID-19) Pandemic. 2020. Available online: https://www.who.int/emergencies/diseases/novel-coronavirus-2019 (accessed on 21 April 2021).
- Helmy, Y.A.; Fawzy, M.; Elaswad, A.; Sobieh, A.; Kenney, S.P.; Shehata, A.A. The COVID-19 Pandemic: A Comprehensive Review of Taxonomy, Genetics, Epidemiology, Diagnosis, Treatment, and Control. J. Clin. Med. 2020, 9, 1225. [Google Scholar] [CrossRef] [PubMed]
- Criteria for COVID-19 Vaccine Prioritization. Available online: https://www.who.int/publications/m/item/criteria-for-covid-19-vaccine-prioritization (accessed on 21 April 2021).
- Understanding Vaccination Progress. Available online: https://origin-coronavirus.jhu.edu/vaccines/international (accessed on 21 April 2021).
- China Approves Its Homegrown COVID-19 Vaccine for Widespread Use. Available online: https://www.npr.org/sections/coronavirus-live-updates/2020/12/31/952269135/china-approves-its-homegrown-covid-19-vaccine-for-widespread-use (accessed on 31 December 2020).
- Nuño, M.; Chowell, G.; Gumel, A. Assessing the role of basic control measures, antivirals and vaccine in curtailing pandemic influenza: Scenarios for the US, UK and the Netherlands. J. R. Soc. Interface 2006, 4, 505–521. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Du Châtelet, I.P.; Antona, D.; Freymuth, F.; Muscat, M.; Halftermeyer-Zhou, F.; Maine, C.; Floret, D.; Lévy-Bruhl, D. Spotlight on measles 2010: Update on the ongoing measles outbreak in France, 2008–2010. Eurosurveillance 2010, 15, 19656. [Google Scholar] [CrossRef]
- De Serres, G.; Markowski, F.; Toth, E.; Landry, M.; Auger, D.; Mercier, M.; Bélanger, P.; Turmel, B.; Arruda, H.; Boulianne, N.; et al. Largest measles epidemic in North America in a decade—Quebec, Canada, 2011: Contribution of suscep-tibility, serendipity, and superspreading events. J. Infect. Dis. 2013, 207, 990–998. [Google Scholar] [CrossRef] [Green Version]
- Oostvogel, P.; Van Der Avoort, H.; Mulders, M.; Van Loon, A.; Spaendonck, M.C.-V.; Rümke, H.; Van Steenis, G.; Van Wijngaarden, J. Poliomyelitis outbreak in an unvaccinated community in the Netherlands, 1992–1993. Lancet 1994, 344, 665–670. [Google Scholar] [CrossRef]
- Falagas, M.E.; Zarkadoulia, E. Factors associated with suboptimal compliance to vaccinations in children in developed countries: A systematic review. Curr. Med. Res. Opin. 2008, 24, 1719–1741. [Google Scholar] [CrossRef]
- Leask, J. Target the fence-sitters. Nat. Cell Biol. 2011, 473, 443–445. [Google Scholar] [CrossRef] [Green Version]
- Leask, J.; Kinnersley, P.; Jackson, C.; Cheater, F.; Bedford, H.; Rowles, G. Communicating with parents about vaccination: A framework for health professionals. BMC Pediatr. 2012, 12, 154. [Google Scholar] [CrossRef]
- Ten Threats to Global Health in 2019. 2019. Available online: https://www.who.int/news-room/spotlight/ten-threats-to-global-health-in-2019 (accessed on 10 January 2019).
- Larson, H.J.; Jarrett, C.; Schulz, W.S.; Chaudhuri, M.; Zhou, Y.; Dube, E.; Schuster, M.; MacDonald, N.E.; Wilson, R. Measuring vaccine hesitancy: The development of a survey tool. Vaccine 2015, 33, 4165–4175. [Google Scholar] [CrossRef] [Green Version]
- Lazarus, J.V.; Ratzan, S.; 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, 1–4. [Google Scholar] [CrossRef]
- Fisher, K.A.; Bloomstone, S.J.; Walder, J.; Crawford, S.; Fouayzi, H.; Mazor, K.M. Attitudes toward a Potential SARS-CoV-2 Vaccine: A Survey of U.S. Adults. Ann. Intern. Med. 2020. [Google Scholar] [CrossRef]
- Malik, A.A.; McFadden, S.M.; Elharake, J.; Omer, S.B. Determinants of COVID-19 vaccine acceptance in the US. EClinicalMedicine 2020, 26, 100495. [Google Scholar] [CrossRef]
- Pogue, K.; Jensen, J.L.; Stancil, C.K.; Ferguson, D.G.; Hughes, S.J.; Mello, E.J.; Burgess, R.; Berges, B.K.; Quaye, A.; Poole, B.D. Influences on Attitudes Regarding Potential COVID-19 Vaccination in the United States. Vaccines 2020, 8, 582. [Google Scholar] [CrossRef]
- Reiter, P.L.; Pennell, M.L.; Katz, M.L. Acceptability of a COVID-19 vaccine among adults in the United States: How many people would get vaccinated? Vaccine 2020, 38, 6500–6507. [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. Eur. J. Health Econ. 2020, 21, 977–982. [Google Scholar] [CrossRef]
- Wang, J.; Jing, R.; Lai, X.; Zhang, H.; Lyu, Y.; Knoll, M.D.; Fang, H. Acceptance of COVID-19 Vaccination during the COVID-19 Pandemic in China. Vaccines 2020, 8, 482. [Google Scholar] [CrossRef]
- Yang, M.; Dijst, M.; Helbich, M. Mental Health among Migrants in Shenzhen, China: Does it Matter Whether the Migrant Population is Identified by Hukou or Birthplace? Int. J. Environ. Res. Public Health 2018, 15, 2671. [Google Scholar] [CrossRef] [Green Version]
- Yu, C.; Lou, C.; Cheng, Y.; Cui, Y.; Lian, Q.; Wang, Z.; Gao, E.; Wang, L. Young internal migrants’ major health issues and health seeking barriers in Shanghai, China: A qualitative study. BMC Public Health 2019, 19, 336. [Google Scholar] [CrossRef]
- Wang, B.; Li, X.; Stanton, B.; Fang, X. The influence of social stigma and discriminatory experience on psychological distress and quality of life among rural-to-urban migrants in China. Soc. Sci. Med. 2010, 71, 84–92. [Google Scholar] [CrossRef]
- Wen, M.; Zheng, Z.; Niu, J. Psychological distress of rural-to-urban migrants in two Chinese cities: Shenzhen and Shanghai. Asian Popul. Stud. 2016, 13, 1–20. [Google Scholar] [CrossRef]
- Glaeser, E.L.; Gorback, C.; Redding, S.J. JUE Insight: How much does COVID-19 increase with mobility? Evidence from New York and four other U.S. cities. J. Urban Econ. 2020, 103292. [Google Scholar] [CrossRef] [PubMed]
- Shanghai Statistical Bulletin on National Economic and Social Development 2019. Available online: http://tjj.sh.gov.cn/tjgb/20200329/05f0f4abb2d448a69e4517f6a6448819.html (accessed on 9 March 2020).
- Questionaire Star. Available online: www.wjx.cn (accessed on 21 April 2021).
- Wechat (Tencent’s Messaging Service App). Available online: https://baike.baidu.com/item/%E5%BE%AE%E4%BF%A1/3905974?fr=aladdin (accessed on 21 April 2021).
- 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, 1–11. [Google Scholar] [CrossRef] [PubMed]
- Dubé, E.; Laberge, C.; Guay, M.; Bramadat, P.; Roy, R.; Bettinger, J.A. Vaccine hesitancy: An overview. Hum. Vaccines Immunother. 2013, 9, 1763–1773. [Google Scholar] [CrossRef] [PubMed]
- Larson, H.J.; De Figueiredo, A.; Xiahong, Z.; Schulz, W.S.; Verger, P.; Johnston, I.G.; Cook, A.R.; Jones, N.S. The State of Vaccine Confidence 2016: Global Insights Through a 67-Country Survey. EBioMedicine 2016, 12, 295–301. [Google Scholar] [CrossRef] [Green Version]
- Dodd, R.H.; Cvejic, E.; Bonner, C.; Pickles, K.; McCaffery, K.J.; Ayre, J.; Batcup, C.; Copp, T.; Cornell, S.; Dakin, T.; et al. Willingness to vaccinate against COVID-19 in Australia. Lancet Infect. Dis. 2021, 21, 318–319. [Google Scholar] [CrossRef]
- Zhong, B.-L.; Luo, W.; Li, H.-M.; Zhang, Q.-Q.; Liu, X.-G.; Li, W.-T.; Li, Y. Knowledge, attitudes, and practices towards COVID-19 among Chinese residents during the rapid rise period of the COVID-19 outbreak: A quick online cross-sectional survey. Int. J. Biol. Sci. 2020, 16, 1745–1752. [Google Scholar] [CrossRef]
- Mheidly, N.; Fares, J. Leveraging media and health communication strategies to overcome the COVID-19 infodemic. J. Public Health Policy 2020, 41, 410–420. [Google Scholar] [CrossRef]
- Considerations for Wearing Masks. Available online: https://www.cdc.gov/coronavirus/2019-ncov/prevent-getting-sick/cloth-face-cover-guidance.html (accessed on 19 April 2021).
- Tu, S.; Sun, F.Y.; Chantler, T.; Zhang, X.; Jit, M.; Han, K.; Rodewald, L.; Du, F.; Yu, H.; Hou, Z.; et al. Caregiver and service provider vaccine confidence following the Changchun Changsheng vaccine incident in China: A cross-sectional mixed methods study. Vaccine 2020, 38, 6882–6888. [Google Scholar] [CrossRef]
- Haque, A.; Pant, A.B. Efforts at COVID-19 Vaccine Development: Challenges and Successes. Vaccines 2020, 8, 739. [Google Scholar] [CrossRef]
- Mahase, E. Covid-19: Oxford researchers halt vaccine trial while adverse reaction is investigated. BMJ 2020, 370, 3525. [Google Scholar] [CrossRef]
- García, L.Y.; Cerda, A.A. Contingent assessment of the COVID-19 vaccine. Vaccine 2020, 38, 5424–5429. [Google Scholar] [CrossRef]
- Acter, T.; Uddin, N.; Das, J.; Akhter, A.; Rabi, T.; Choudhury, T.R.; Kim, S. Evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as coronavirus disease 2019 (COVID-19) pandemic: A global health emergency. Sci. Total Environ. 2020, 730, 138996. [Google Scholar] [CrossRef]
- Fishman, J.; Lushin, V.; Mandell, D.S. Predicting implementation: Comparing validated measures of intention and as-sessing the role of motivation when designing behavioral interventions. Implement. Sci. Commun. 2020, 1, 81. [Google Scholar] [CrossRef]
Characteristics | Total Sample, N (%) | COVID-19 Vaccine Acceptance | p-Value 1 | |
---|---|---|---|---|
Accept, N (Row%) | Hesitant, N (Row%) | |||
Total | 2126 | 1894 (89.09) | 232 (10.91) | - |
Gender | ||||
Male | 1070 (50.33) | 957 (89.44) | 113 (10.56) | 0.601 |
Female | 1056 (49.67) | 937 (88.73) | 119 (11.27) | |
Age (years) | ||||
≤25 | 491 (23.10) | 433 (88.19) | 58 (11.81) | <0.001 |
26–35 | 987 (46.43) | 905 (91.69) | 82 (8.31) | |
36–45 | 375 (17.64) | 337 (89.87) | 38 (10.13) | |
>45 | 273 (12.84) | 219 (80.22) | 54 (19.78) | |
Marital status | ||||
Single | 620 (29.16) | 549 (88.55) | 71 (11.45) | 0.406 |
Married | 1441 (67.78) | 1290 (89.52) | 151 (10.48) | |
Divorced or widow | 65 (3.06) | 55 (84.62) | 10 (15.38) | |
Number of family members | ||||
1 | 292 (13.73) | 254 (86.99) | 38 (13.01) | 0.022 |
2 | 455 (21.40) | 404 (88.79) | 51 (11.21) | |
3 | 654 (30.76) | 599 (91.59) | 55 (8.41) | |
4 | 355 (16.70) | 321 (90.42) | 34 (9.58) | |
≥5 | 370 (17.40) | 316 (85.41) | 54 (14.59) | |
Education | ||||
Primary school or below | 94 (4.42) | 70 (74.47) | 24 (25.53) | <0.001 |
Middle school | 459 (21.54) | 381 (83.19) | 77 (16.81) | |
High school | 584 (27.47) | 531 (90.92) | 53 (9.08) | |
Junior college | 592 (27.85) | 543 (91.72) | 49 (8.28) | |
Bachelor degree or above | 398 (18.72) | 369 (92.71) | 29 (7.29) | |
Monthly personal income (Chinese Yuan) | ||||
≤2500 | 204 (9.60) | 167 (81.86) | 37 (18.14) | 0.001 |
2501–5000 | 585 (27.52) | 513 (87.69) | 72 (12.31) | |
5001–7500 | 726 (34.15) | 654 (90.08) | 72 (9.92) | |
7501–10,000 | 347 (16.32) | 314 (90.49) | 33 (9.51) | |
>10,000 | 264 (12.42) | 246 (93.18) | 18 (6.82) | |
Years of local residence | ||||
≤0.5 | 257 (12.09) | 233 (90.66) | 24 (9.34) | <0.001 |
0.5–1 | 283 (13.31) | 259 (91.52) | 24 (8.48) | |
1–2 | 440 (20.70) | 409 (92.95) | 31 (7.05) | |
2–5 | 481 (22.62) | 440 (91.48) | 41 (8.52) | |
>5 | 665 (31.28) | 553 (83.16) | 112 (16.84) | |
Workplace | ||||
Food market or supermarket | 334 (15.71) | 256 (76.65) | 78 (23.35) | <0.001 |
Small service industry such as catering or express delivery | 514 (24.18) | 464 (90.27) | 50 (9.73) | |
Manufacturing industry such as factory | 266 (12.51) | 238 (89.47) | 28 (10.53) | |
Company or government agency | 768 (36.12) | 724 (94.27) | 44 (5.73) | |
Unemployed | 105 (4.94) | 92 (87.62) | 13 (12.38) | |
Others | 139 (6.54) | 120 (86.33) | 19 (13.67) | |
Frequency of contact with local residents | ||||
Frequent | 1157 (54.42) | 1053 (91.01) | 104 (8.99) | 0.002 |
Not frequent | 969 (45.58) | 841 (86.79) | 128 (13.21) | |
Self-rated health status | ||||
Good | 1705 (80.20) | 1545 (90.62) | 160 (9.38) | <0.001 |
Fair or poor | 421 (19.80) | 349 (82.90) | 72 (17.10) |
Variables (Reference) | Logistic Regression for Vaccine Acceptance (Accept vs. Hesitant) | Ordered Logistic Regression for Willingness to Pay | ||
---|---|---|---|---|
Basic Model | Additional Model | BASIC Model | Additional Model | |
Female | 1.07 (0.79–1.45) | 1.05 (0.75–1.47) | 1.28 (1.09–1.51) ** | 1.29 (1.10–1.51) ** |
Age (≤25, years) | ||||
26–35 | 1.42 (0.91–2.23) | 1.47 (0.90–2.40) | 0.77 (0.61–0.97) * | 0.73 (0.58–0.92) ** |
36–45 | 1.64 (0.92–2.92) | 1.59 (0.85–3.00) | 0.83 (0.62–1.12) | 0.79 (0.59–1.06) |
>45 | 1.30 (0.71–2.36) | 1.56 (0.80–3.03) | 0.60 (0.43–0.85) ** | 0.61 (0.43–0.86) ** |
Marital status (single) | ||||
Married | 1.45 (0.93–2.26) | 1.18 (0.72–1.92) | 0.99 (0.79–1.24) | 0.98 (0.78–1.23) |
Divorced or widow | 0.85 (0.38–1.90) | 1.08 (0.44–2.64) | 1.40 (0.86–2.27) | 1.44 (0.88–2.35) |
Number of family members (1) | ||||
2 | 1.37 (0.83–2.25) | 1.28 (0.74–2.23) | 1.16 (0.88–1.5) | 1.03 (0.78–1.36) |
3 | 1.41 (0.87–2.28) | 1.12 (0.65–1.92) | 1.30 (1.00–1.68) * | 1.14 (0.88–1.48) |
4 | 1.42 (0.83–2.43) | 1.28 (0.71–2.33) | 1.33 (1.00–1.77) | 1.17 (0.87–1.56) |
≥5 | 0.97 (0.59–1.59) | 0.95 (0.55–1.66) | 1.55 (1.16–2.06) ** | 1.42 (1.06–1.89)* |
Education (primary school or below) | ||||
Middle school | 1.11 (0.62–1.97) | 1.04 (0.54–2.02) | 0.92 (0.59–1.44) | 0.93 (0.60–1.46) |
High school | 1.69 (0.90–3.18) | 1.57 (0.77–3.20) | 0.97 (0.61–1.52) | 0.94 (0.60–1.48) |
Junior college | 1.33 (0.67–2.62) | 1.26 (0.58–2.71) | 0.91 (0.57–1.46) | 0.88 (0.55–1.41) |
Bachelor degree or above | 1.40 (0.66–2.94) | 1.64 (0.71–3.80) | 0.70 (0.43–1.14) | 0.70 (0.43–1.15) |
Monthly personal income (≤2500 Chinese Yuan) | ||||
2501–5000 | 1.38 (0.86–2.20) | 1.37 (0.80–2.32) | 1.29 (0.96–1.74) | 1.25 (0.93–1.68) |
5001–7500 | 1.32 (0.81–2.15) | 1.23 (0.71–2.14) | 2.12 (1.56–2.88) ** | 2.03 (1.49–2.76) ** |
7501–10,000 | 1.39 (0.79–2.46) | 1.01 (0.54–1.90) | 3.68 (2.62–5.17) ** | 3.34 (2.37–4.70) ** |
>10,000 | 1.87 (0.96–3.64) | 1.64 (0.78–3.44) | 4.07 (2.82–5.89) ** | 3.96 (2.73–5.74) ** |
Years of local residence (≤0.5 years) | ||||
0.5–1 | 0.84 (0.45–1.56) | 0.79 (0.40–1.57) | 1.31 (0.97–1.77) | 1.27 (0.94–1.72) |
1–2 | 0.94 (0.53–1.69) | 0.91 (0.48–1.73) | 1.33 (1.01–1.76) * | 1.31 (1.00–1.74) |
2–5 | 0.58 (0.33–1.04) | 0.59 (0.31–1.11) | 1.08 (0.81–1.44) | 1.03 (0.77–1.37) |
>5 | 0.37 (0.22–0.64) ** | 0.43 (0.24–0.80) ** | 0.94 (0.71–1.24) | 0.98 (0.74–1.29) |
Workplace (food market or supermarket) | ||||
Small service industry | 2.41 (1.53–3.78) ** | 2.18 (1.32–3.62) ** | 1.10 (0.84–1.45) | 0.96 (0.73–1.27) |
Manufacturing industry | 2.20 (1.32–3.67) ** | 1.99 (1.11–3.58) * | 0.75 (0.55–1.02) | 0.68 (0.50–0.93) * |
Company or government agency | 4.03 (2.49–6.52) ** | 3.13 (1.83–5.35) ** | 0.85 (0.65–1.11) | 0.78 (0.60–1.03) |
Unemployed | 2.18 (1.07–4.44) * | 1.64 (0.76–3.55) | 0.74 (0.48–1.13) | 0.67 (0.43–1.02) |
Others | 1.65 (0.90–3.03) | 1.37 (0.69–2.70) | 0.97 (0.67–1.41) | 0.94 (0.65–1.37) |
Frequent contact with local residents | 1.79 (1.32–2.43) ** | 1.48 (1.05–2.09) * | 1.61 (1.37–1.89) ** | 1.48 (1.26–1.75) ** |
Good self-rated health | 1.78 (1.29–2.45) ** | 1.40 (0.98–2.00) | 1.27 (1.05–1.55) * | 1.12 (0.92–1.36) |
High susceptibility of COVID-19 | 1.59 (0.91–2.80) | 1.56 (1.25–1.95) ** | ||
Confident in importance of COVID-19 vaccine | 8.71 (5.89–12.89) ** | 1.88 (1.40–2.51) ** | ||
Confident in safety of COVID-19 vaccine | 1.80 (1.24–2.61) ** | 1.06 (0.86–1.32) | ||
Confident in effectiveness of COVID-19 vaccine | 2.66 (1.83–3.87) ** | 1.91 (1.52–2.39) ** |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 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 (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Han, K.; Francis, M.R.; Zhang, R.; Wang, Q.; Xia, A.; Lu, L.; Yang, B.; Hou, Z. Confidence, Acceptance and Willingness to Pay for the COVID-19 Vaccine among Migrants in Shanghai, China: A Cross-Sectional Study. Vaccines 2021, 9, 443. https://doi.org/10.3390/vaccines9050443
Han K, Francis MR, Zhang R, Wang Q, Xia A, Lu L, Yang B, Hou Z. Confidence, Acceptance and Willingness to Pay for the COVID-19 Vaccine among Migrants in Shanghai, China: A Cross-Sectional Study. Vaccines. 2021; 9(5):443. https://doi.org/10.3390/vaccines9050443
Chicago/Turabian StyleHan, Kaiyi, Mark R. Francis, Ruiyun Zhang, Qian Wang, Aichen Xia, Linyao Lu, Bingyi Yang, and Zhiyuan Hou. 2021. "Confidence, Acceptance and Willingness to Pay for the COVID-19 Vaccine among Migrants in Shanghai, China: A Cross-Sectional Study" Vaccines 9, no. 5: 443. https://doi.org/10.3390/vaccines9050443
APA StyleHan, K., Francis, M. R., Zhang, R., Wang, Q., Xia, A., Lu, L., Yang, B., & Hou, Z. (2021). Confidence, Acceptance and Willingness to Pay for the COVID-19 Vaccine among Migrants in Shanghai, China: A Cross-Sectional Study. Vaccines, 9(5), 443. https://doi.org/10.3390/vaccines9050443