https://doi.org/10.4081/monaldi.2022.2117
Identifying psychological antecedents and predictors of vaccine hesitancy through machine learning: A cross sectional study among chronic disease patients of deprived urban neighbourhood, India
Submitted: October 9, 2021
Accepted: March 8, 2022
Published: March 16, 2022
Accepted: March 8, 2022
Abstract Views: 1750
PDF: 537
Supplementary: 234
Supplementary: 234
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
COVID-19 vaccine hesitancy among chronic disease patients can severely impact individual health with the potential to impede mass vaccination essential for containing the pandemic. The present study was done to assess the COVID-19 vaccine antecedents and its predictors among chronic disease patients. This cross-sectional study was conducted among chronic disease patients availing care from a primary health facility in urban Jodhpur, Rajasthan. Factor and reliability analysis was done for the vaccine hesitancy scale to validate the 5 C scale. Predictors assessed for vaccine hesitancy were modelled with help of machine learning (ML). Out of 520 patients, the majority of participants were female (54.81%). Exploratory factor analysis revealed four psychological antecedents’ “calculation”; “confidence”; “constraint” and “collective responsibility” determining 72.9% of the cumulative variance of vaccine hesitancy scale. The trained ML algorithm yielded an R2 of 0.33. Higher scores for COVID-19 health literacy and preventive behaviour, along with family support, monthly income, past COVID-19 screening, adherence to medications and age were associated with lower vaccine hesitancy. Behaviour changes communication strategies targeting COVID-19 health literacy and preventive behaviour especially among population sub-groups with poor family support, low income, higher age groups and low adherence to medicines may prove instrumental in this regard.
World Health Organization. Middle East respiratory syndrome coronavirus (MERS-CoV). Accessed August 5, 2021. Available from: http://www.who.int/mediacentre/factsheets/mers-cov/en/
World Health Organization. Severe Acute Respiratory Syndrome (SARS). Accessed August 5, 2021. Available from:https://www.who.int/westernpacific/health-topics/severe-acute-respiratory-syndrome
World Health Organization. Kazakhstan. Statistics. Accessed July 8, 2021. Available from: https://www.who.int/countries/kaz
Abedin M, Islam MA, Rahman FN, et al. Willingness to vaccinate against COVID-19 among Bangladeshi adults: Understanding the strategies to optimize vaccination coverage. PLoS One 2021;16:e0250495.
DOI: https://doi.org/10.1371/journal.pone.0250495
Guaraldi F, Montalti M, Di Valerio Z, et al. Rate and predictors of hesitancy toward SARS-CoV-2 vaccine among type 2 diabetic patients: Results from an Italian survey. Vaccines (Basel) 2021;9:460.
DOI: https://doi.org/10.3390/vaccines9050460
Betsch C, Bach Habersaat K, Deshevoi S, et al. Sample study protocol for adapting and translating the 5C scale to assess the psychological antecedents of vaccination. BMJ Open 2020;10:e034869.
DOI: https://doi.org/10.1136/bmjopen-2019-034869
Mallapaty S. India’s massive COVID surge puzzles scientists. Nature 2021;592:667-8.
DOI: https://doi.org/10.1038/d41586-021-01059-y
Government of Rajasthan District Jodhpur [Internet]. Population. Accessed August 7, 2021. Available from: https://jodhpur.rajasthan.gov.in/content/raj/jodhpur/en/about-jodhpur/population.html
National Centre for Disease Control Delhi. Training module for medical officers for prevention, control and population level screening of hypertension, diabetes and common cancer (oral, breast & cervical). 2017. Accessed February 7, 2022. Available from: https://main.mohfw.gov.in/sites/default/files/Training%20Module%20for%20Medical%20Officers%20for%20Prevention%2C%20Control%20and%20Population%20Level%20Screening%20of%20NCDs_1.pdf
Bloch T, Sacks R. Comparing machine learning and rule-based inferencing for semantic enrichment of BIM models. Autom Constr 2018;91:256-72.
DOI: https://doi.org/10.1016/j.autcon.2018.03.018
Solís Arce JS, Warren SS, Meriggi NF, et al. COVID-19 vaccine acceptance and hesitancy in low- and middle-income countries. Nat Med 2021;27:1385-94.
DOI: https://doi.org/10.1038/s41591-021-01454-y
Okan O, Bollweg TM, Berens EM, et al. Coronavirus-related health literacy: A cross-sectional study in adults during the COVID-19 infodemic in Germany. Int J Environ Res Public Health 2020;17:5503.
DOI: https://doi.org/10.3390/ijerph17155503
Sørensen K, Pelikan JM, Röthlin F, et al. Health literacy in Europe: comparative results of the European health literacy survey (HLS-EU). Eur J Public Health 2015;25:1053-8.
DOI: https://doi.org/10.1093/eurpub/ckv043
Sørensen K, Van den Broucke S, Pelikan JM, et al. Measuring health literacy in populations: illuminating the design and development process of the European Health Literacy Survey Questionnaire (HLS-EU-Q). BMC Public Health 2013;13:948.
DOI: https://doi.org/10.1186/1471-2458-13-948
Government of India - Ministry of Health and Family Welfare. An illustrated guide on COVID appropriate behaviour. Accessed July 15, 2021. Available from: https://www.mohfw.gov.in/pdf/Illustrativeguidelineupdate.pdf
Eremenco SL, Cella D, Arnold BJ. A comprehensive method for the translation and cross-cultural validation of health status questionnaires. Eval Health Prof 2005;28:212-32.
DOI: https://doi.org/10.1177/0163278705275342
Ho TK. Random decision forests. IEEE Proc 3rd Int Conf on Document Analysis and Recognition 1995;1:278-82.
DOI: https://doi.org/10.1109/ICDAR.1995.598994
Lundberg S, Lee SI. A unified approach to interpreting model predictions. 2017 ArXiv170507874v2.
Field A. Discovering statistics using IBM SPSS Statistics. Online Resources. SAGE Publications. Accessed August 5, 2021. Available from: https://study.sagepub.in/field_dsiss4e
The Indu [Internet]. Coronavirus | List of comorbidities for priority in COVID-19 vaccination. Accessed February 6, 2022. Available from: https://www.thehindu.com/sci-tech/health/coronavirus-list-of-comorbidities-for-priority-in-covid-19-vaccination/article33950281.ece
Seale H, Heywood AE, Leask J, et al. Examining Australian public perceptions and behaviors towards a future COVID-19 vaccine. BMC Infect Dis 2021;21:120.
DOI: https://doi.org/10.1186/s12879-021-05833-1
Nutbeam D. Health promotion glossary. Health Promot Int (Oxf) 1998;13:349-64.
DOI: https://doi.org/10.1093/heapro/13.4.349
Zhang F, Or PL, Chung WYJ. How different health literacy dimensions influences health and well‐being among men and women: The mediating role of health behaviours. Health Expect 2021;24:617-27.
DOI: https://doi.org/10.1111/hex.13208
Zhang F, Or PPL, Chung JWY. The effects of health literacy in influenza vaccination competencies among community-dwelling older adults in Hong Kong. BMC Geriatr 2020;20:103.
DOI: https://doi.org/10.1186/s12877-020-1504-5
Montagni I, Ouazzani-Touhami K, Mebarki A, et al. Acceptance of a Covid-19 vaccine is associated with ability to detect fake news and health literacy. J Public Health (Oxf) 2021;43:695-702.
DOI: https://doi.org/10.1093/pubmed/fdab028
Government of India Ministry of Health and Family Welfare. COVID 19 vaccine communication strategy. Accessed July 24, 2021. Available from https://www.mohfw.gov.in/pdf/Covid19CommunicationStrategy2020.pdf
Government of India Ministry of Health and Family Welfare. How to be a COVID-19 youth champion. Accessed August 3, 2021. Available from: https://www.mohfw.gov.in/pdf/EncouragingYouthtoadvocateagainstS&DduringCOVID19EnglishToolkit_final.pdf
Barry V, Dasgupta S, Weller DL, et al. Patterns in COVID-19 vaccination coverage, by social vulnerability and urbanicity - United States, December 14, 2020-May 1, 2021. MMWR Morb Mortal Wkly Rep 2021;70:818-24.
DOI: https://doi.org/10.15585/mmwr.mm7022e1
Caspi G, Dayan A, Eshal Y, et al. Socioeconomic disparities and COVID-19 vaccination acceptance: a nationwide ecologic study. Clin Microbiol Infect 2021;27:1502-6.
DOI: https://doi.org/10.1016/j.cmi.2021.05.030
DiMatteo MR. Social support and patient adherence to medical treatment: a meta-analysis. Health Psychol 2004;23:207-18.
DOI: https://doi.org/10.1037/0278-6133.23.2.207
Bosworth HB, Oddone EZ. A model of psychosocial and cultural antecedents of blood pressure control. J Natl Med Assoc 2002;94:236-48.
Takahashi O, Noguchi Y, Rahman M, et al. Influence of family on acceptance of influenza vaccination among Japanese patients. Fam Pract 2003;20:162-6.
DOI: https://doi.org/10.1093/fampra/20.2.162
Chadda RK, Deb KS. Indian family systems, collectivistic society and psychotherapy. Indian J Psychiatry 2013;55:S299-309.
DOI: https://doi.org/10.4103/0019-5545.105555
Al-Mohaithef M, Padhi BK. Determinants of COVID-19 vaccine acceptance in Saudi Arabia: A web-based national survey. J Multidiscip Healthc 2020;13:1657-63.
DOI: https://doi.org/10.2147/JMDH.S276771
Malik AA, McFadden SM, Elharake J, Omer SB. Determinants of COVID-19 vaccine acceptance in the US. EClinicalMedicine 2020;26:100495.
DOI: https://doi.org/10.1016/j.eclinm.2020.100495
World Health Organization. Improving vaccination demand and addressing hesitancy. Accessed April 13, 2020. Available from: http://www.who.int/immunization/programmes_systems/vaccine_hesitancy/en
Livemint [Internet]. Mumbai: Door-to-door vaccination for bed-ridden people to start 1 2021. Accessed August 15, 2021. Available from: https://www.livemint.com/news/india/mumbai-door-to-door-vaccination-for-bed-ridden-people-to-start-1-aug-says-maha-govt-11626770496914.html
Hindustan Times [Internet]. TMC converts bus into mobile vax unit for senior citizens, specially abled. 2021. Accessed August 15, 2021. Available from: https://www.hindustantimes.com/cities/mumbai-news/tmc-converts-bus-into-mobile-vax-unit-for-senior-citizens-specially-abled-101623092436911.html
Reuters [Internet]. 'A blessing': at-home vaccination program brings shots to U.S. homebound. Accessed August 15, 2021. Available from: https://www.reuters.com/news/picture/a-blessing-at-home-vaccination-program-b-idUSKBN2BL2TU
Centers for Disease and Control [Internet]. Interim considerations: Preparing for the potential management of anaphylaxis at COVID-19 vaccine sites. Available from: https://www.cdc.gov/vaccines/covid-19/downloads/IntermConsid-Anaphylaxis-covid19-vaccine-sites.pdf
Pati S, Mahapatra P, Kanungo S, et al. Managing multimorbidity (multiple chronic diseases) amid COVID-19 pandemic: A community based study from Odisha, India. Front Public Health 2020;8:584408.
DOI: https://doi.org/10.3389/fpubh.2020.584408
How to Cite
Rustagi, Neeti, Yachana Choudhary, Shahir Asfahan, Kunal Deokar, Abhishek Jaiswal, Prasanna Thirunavukkarasu, Nitesh Kumar, and Pankaja Raghav. 2022. “Identifying Psychological Antecedents and Predictors of Vaccine Hesitancy through Machine Learning: A Cross Sectional Study Among Chronic Disease Patients of Deprived Urban Neighbourhood, India”. Monaldi Archives for Chest Disease 92 (4). https://doi.org/10.4081/monaldi.2022.2117.
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