Vaccine hesitancy and its association with demographics, mental health, and disability: Findings from the VH-3 study in the United States, India, and China
The novel coronavirus (SARS-CoV-2), which causes COVID-19, has claimed millions of lives since December 2019. The rapid development of vaccine candidates and treatments has led to increased confusion and mistrust regarding the development, emergency authorization, and approval processes. To better understand vaccine hesitancy, we analyzed two publicly available datasets: One from the Inter-University Consortium for Political and Social Research Covid-19 database and the other from the United States (US) Census Bureau’s Household Pulse Survey Phase 3.2. In India, 90.2% of 1,761 participants indicated acceptance of a COVID-19 vaccine. A binary logistic regression model, using vaccine hesitancy as a dichotomous variable, showed that rural populations had an odds ratio (OR) of 3.45 (p < 0.05) for vaccine hesitancy. In addition, income played a significant role, with individuals earning 7501 – 15,000 Indian Rupees (INR)/month, or US$ 91 – 183, having an OR of 1.41 compared to other income groups. In the US, 67.3% of 1,768 participants expressed willingness to accept the vaccine. White participants had an OR > 1 compared to other racial groups, while low-income groups earning US$ 2000 – 4999/month had an OR of 1.03. In China, 90.0% of 1,727 participants indicated they would accept a vaccine, with high-income groups showing the least resistance (OR = 0.96) compared to other groups. Among the three countries studied, the US exhibited the highest rate of vaccine hesitancy. This ongoing issue warrants attention from the World Health Organization.
Abraham, C., & Sheeran, P. (2015). The health belief model. In: Conner, M., Norman, P. (eds.). Predicting and Changing Health Behavior: Research and Practice with Social Cognition Models. 3rd ed. United States: McGraw Hill, p.30-69.
Ahmad, F.B., Cisewski, J.A., & Anderson, R. (2022). Provisional mortality data-United States, 2021. MMWR Morbidity and Mortality Weekly Report, 71(17):597-600. https://doi.org/10.15585/mmwr.mm7117e1
Albrecht, D. (2022). Vaccination, politics and COVID-19 impacts. BMC Public Health, 22(1):96. https://doi.org/10.1186/s12889-021-12432-x
Bertsimas, D., King, A., & Mazumder, R. (2016), Best subset selection via a modern optimization lens, The Annals of Statistics, 44(2), 813-852.
Casubhoy, I., Kretz, A., Tan, H.L., St Clair, L.A., Parish, M., Golding, H., et al. (2024). A scoping review of global COVID-19 vaccine hesitancy among pregnant persons. NPJ Vaccines, 9(1):131. https://doi.org/10.1038/s41541-024-00913-0
Deployment of COVID-19 Vaccines. (2024). Wikipedia. Available from: https://en.wikipedia.org/wiki/deployment_of_covid- 19_vaccines [Last accessed on 2025 Jan 08].
Dey, S., Kusuma, Y.S., Kant, S., Kumar, D., Gopalan, R.B., Sridevi, P., et al. (2024). COVID-19 vaccine acceptance and hesitancy in Indian context: A systematic review and meta-analysis. Pathogens and Global Health, 118(2):182-195. https://doi.org/10.1080/20477724.2023.2285184
Gao, J., Zheng, P., Jia, Y., Chen, H., Mao, Y., Chen, S., et al. (2020). Mental health problems and social media exposure during COVID-19 outbreak. PLoS One, 15(4):e0231924. https://doi.org/10.1371/journal.pone.0231924
Gatto, N.M., Lee, J.E., Massai, D., Zamarripa, S., Sasaninia, B., Khurana, D., et al. (2021). Correlates of COVID-19 vaccine acceptance, hesitancy and refusal among employees of a safety net California county health system with an early and aggressive vaccination program: Results from a Cross-sectional survey. Vaccines (Basel), 9(10):1152. https://doi.org/10.3390/vaccines9101152
Hastie, T., Tibshirani, R., & Wainwright, M. (2015). Statistical Learning with Sparsity the Lasso and Generalizations. New York: CRC Press.
Hastie, T., Tibshirani, R., & Friedman J. (2017). The Elements of Statistical Learning: Data Mining, Inference and Prediction, New York: Springer.
Hoerl, A. E., & Kennard, R. W. (1970). Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12(1), 55-67. https://doi.org/10.2307/1267351
Household Pulse Survey. (n.d.). Available from: https://www. census.gov/data/experimental-data-products/household-pulse-survey.html [Last accessed on 2022 Apr 12].
Jennings, W., Valgarðsson, V., McKay, L., Stoker, G., Mello, E., & Baniamin, H.M. (2023). Trust and vaccine hesitancy during the COVID-19 pandemic: A cross-national analysis. Vaccine X, 14:100299. https://doi.org/10.1016/j.jvacx.2023.100299
Jia, X., Ahn, S., & Carcioppolo, N. (2022). Measuring information overload and message fatigue toward COVID-19 prevention messages in USA and China. Health Promotion International, 38(3):daac003. https://doi.org/10.1093/heapro/daac003
Kafadar, A.H., Tekeli, G.G., Jones, K.A., Stephan, B., & Dening, T. (2022). Determinants for COVID-19 vaccine hesitancy in the general population: A systematic review of reviews. Zeitschrift fur Gesundheitswissenschaften, 1-17. https://doi.org/10.1007/s10389-022-01753-9
Khubchandani, J., Sharma, S., Price, J.H., Wiblishauser, M.J., Sharma, M., & Webb, F.J. (2021). COVID-19 vaccination hesitancy in the United States: A rapid national assessment. Journal of Community Health, 46(2):270-277. https://doi.org/10.1007/S10900-020-00958-X
Lazarus, J.V., Wyka, K., White, T.M., Picchio, C.A., Gostin, L.O., Larson, H.J., et al. (2023). A survey of COVID-19 vaccine acceptance across 23 countries in 2022. Nature Medicine, 29(2):366-375. https://doi.org/10.1038/s41591-022-02185-4
Limbu, Y.B., Gautam, R.K., & Pham, L. (2022). The health belief model applied to COVID-19 vaccine hesitancy: A systematic review. Vaccines (Basel), 10(6):973. https://doi.org/10.3390/vaccines10060973
Lin, C., Tu, P., & Beitsch, L.M. (2020). Confidence and receptivity for COVID-19 vaccines: A rapid systematic review. Vaccines (Basel), 9(1):16. https://doi.org/10.3390/vaccines9010016
MacDonald, N.E., Eskola, J., Liang, X., Chaudhuri, M., Dube, E., Gellin, B., et al. (2015). Vaccine hesitancy: Definition, scope and determinants. Vaccine, 33(34):4161-4164. https://doi.org/10.1016/J.VACCINE.2015.04.036
Machine Learning Mastery. (2020). What is a Confusion Matrix in Machine Learning. Available from: https:// machinelearningmastery.com/confusion-matrix-machine-learning [Last accessed on 2022 Apr 12].
Marzo, R.R., Sami, W., Alam, M.Z., Acharya, S., Jermsittiparsert, K., Songwathana, K., et al. (2022). Hesitancy in COVID-19 vaccine uptake and its associated factors among the general adult population: A cross-sectional study in six Southeast Asian countries. Tropical Medicine and Health, 50(1):4. https://doi.org/10.1186/s41182-021-00393-1
Nair, A.T., Nayar, K.R., Koya, S.F., Abraham, M., Lordson, J., Grace, C., et al. (2021). Social media, vaccine hesitancy and trust deficit in immunization programs: A qualitative enquiry in Malappuram District of Kerala, India. Health Research Policy and Systems, 19(2):56. https://doi.org/10.1186/s12961-021-00698-x
Nwachukwu, G., Rihan, A., Nwachukwu, E., Uduma, N., Elliott, K.S., & Tiruneh, Y.M. (2024). Understanding COVID-19 vaccine hesitancy in the United States: A systematic review. Vaccines (Basel), 12(7):747. https://doi.org/10.3390/vaccines12070747
Pourrazavi, S., Fathifar, Z., Sharma, M., & Allahverdipour, H. (2023). COVID-19 vaccine hesitancy: A systematic review of cognitive determinants. Health Promotion Perspectives, 13(1):21-35. https://doi.org/10.34172/hpp.2023.03
Rahbeni, T.A., Satapathy, P., Itumalla, R., Marzo, R.R., Mugheed, K.A.L., Khatib, M.N., et al. (2024). COVID-19 vaccine hesitancy: Umbrella review of systematic reviews and meta-analysis. JMIR Public Health and Surveillance, 10:e54769. https://doi.org/10.2196/54769. Erratum in: JMIR Public Health and Surveillance, 10:e64080. https://doi.org/10.2196/64080
Robinson, E., Jones, A., Lesser, I., & Daly, M. (2021). International estimates of intended uptake and refusal of COVID-19 vaccines: A rapid systematic review and meta-analysis of large nationally representative samples. Vaccine, 39(15):2024-2034. https://doi.org/10.1016/j.vaccine.2021.02.005
Rossi, R., Socci, V., Talevi, D., Mensi, S., Niolu, C., Pacitti, F., et al. (2020). COVID-19 pandemic and lockdown measures impact on mental health among the general population in Italy. Frontiers in Psychiatry, 11:790. https://doi.org/10.3389/FPSYT.2020.00790
RStudio. (n.d.). Open Source and Professional Software for Data Science Teams - RStudio. Available from: https://www. rstudio.com [Last accessed on 2022 Apr 17].
Sallam, M. (2021). COVID-19 vaccine hesitancy worldwide: A concise systematic review of vaccine acceptance rates. Vaccines, 9(2):160. https://doi.org/10.3390/VACCINES9020160
Sallam, M., Al-Sanafi, M., & Sallam, M.A. (2022). Global Map of COVID-19 vaccine acceptance rates per country: An updated concise narrative review. The Journal of Multidisciplinary Healthcare, 15:21-45. https://doi.org/10.2147/JMDH.S347669
Schmid, P., Rauber, D., Betsch, C., Lidolt, G., & Denker, M.L. (2017). Barriers of influenza vaccination intention and behavior - A systematic review of influenza vaccine hesitancy, 2005-2016. PLoS One, 12(1):e0170550. https://doi.org/10.1371/JOURNAL.PONE.0170550
Shen, S., & Dubey, V. (2019). Addressing vaccine hesitancy: Clinical guidance for primary care physicians working with parents. Canadian Family Physician, 65(3):175-181.
Smith, K., Lambe, S., Freeman, D., & Cipriani, A. (2021). COVID-19 vaccines, hesitancy and mental health. Evidence Based Mental Health, 24(2):47-48. https://doi.org/10.1136/EBMENTAL-2021-300266
Talevi, D., Socci, V., Carai, M., Carnaghi, G., Faleri, S., Trebbi, E., et al. (2020). Mental health outcomes of the covid-19 pandemic. Rivista Di Psichiatria, 55(3):137-144. https://doi.org/10.1708/3382.33569
Trogen, B., & Pirofski, L.A. (2021). Understanding vaccine hesitancy in COVID-19. Med, 2(5):498-501. https://doi.org/10.1016/j.medj.2021.04.002
Wilson, S.L., & Wiysonge, C. (2020). Social media and vaccine hesitancy. BMJ Global Health, 5(10):e004206. https://doi.org/10.1136/BMJGH-2020-004206
Wong, L.P., Alias, H., Danaee, M., Ahmed, J., Lachyan, A., Cai, C.Z., et al. (2021). COVID-19 vaccination intention and vaccine characteristics influencing vaccination acceptance: A global survey of 17 countries. Infectious Diseases of Poverty, 10(1):122. https://doi.org/10.1186/S40249-021-00900-W
Xiao, H., Zhang, Z., & Zhang, L. (2023). An investigation on information quality, media richness, and social media fatigue during the disruptions of COVID-19 pandemic. Current Psychology, 42(3):2488-2499. https://doi.org/10.1007/s12144-021-02253-x
Xiong, J., Lipsitz, O., Nasri, F., Lui, L.M.W., Gill, H., Phan, L., et al. (2020). Impact of COVID-19 pandemic on mental health in the general population: A systematic review. Journal of Affective Disorders, 277:55-64. https://doi.org/10.1016/J.JAD.2020.08.001
Yamanis, N. (2024). Current Global Access to the COVID-19 Vaccine. Q/A: What is the Status of COVID-19 Vaccine Access Globally. Available from: https://www.american.edu/ sis/news/20240424 [Last accessed on 2025 Jan 08].
Yanto, T.A., Lugito, N.P.H., Hwei, L.R.Y., Virliani, C., & Octavius, G.S. (2022). Prevalence and determinants of COVID-19 vaccine acceptance in South East Asia: A systematic review and meta-analysis of 1,166,275 respondents. Tropical Medicine and Infectious Disease, 7(11):361. https://doi.org/10.3390/tropicalmed7110361
Zhang, F., Shih, S.F., Harapan, H., Rajamoorthy, Y., Chang, H.Y., Singh, A., et al. (2021). Changes in COVID-19 risk perceptions: Methods of an internet survey conducted in six countries. BMC Research Notes, 14(1):428. https://doi.org/10.1186/S13104-021-05846-8
Zychlinsky Scharff, A., Paulsen, M., Schaefer, P., Tanisik, F., Sugianto, R.I., Stanislawski, N., et al. (2022). Students’ age and parental level of education influence COVID-19 vaccination hesitancy. European Journal of Pediatrics, 181(4):1757-1762. https://doi.org/10.1007/S00431-021-04343-1