Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Trust in science, social consensus and vaccine confidence

Nature Human Behaviour volume 5, pages 1528–1534 (2021)Cite this article

Subjects

Abstract

While scholarly attention to date has focused almost entirely on individual-level drivers of vaccine confidence, we show that macro-level factors play an important role in understanding individual propensity to be confident about vaccination. We analyse data from the 2018 Wellcome Global Monitor survey covering over 120,000 respondents in 126 countries to assess how societal-level trust in science is related to vaccine confidence. In countries with a high aggregate level of trust in science, people are more likely to be confident about vaccination, over and above their individual-level scientific trust. Additionally, we show that societal consensus around trust in science moderates these individual-level and country-level relationships. In countries with a high level of consensus regarding the trustworthiness of science and scientists, the positive correlation between trust in science and vaccine confidence is stronger than it is in comparable countries where the level of social consensus is weaker.

Access through your institution
Buy or subscribe

This is a preview of subscription content, access via your institution

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Strength of consensus around trust in science across countries.
Fig. 2: The strength of consensus around trust moderates the relationship between trust in science and support for vaccines.

Similar content being viewed by others

Data availability

The Wellcome Global Monitor dataset (https://doi.org/10.5255/UKDA-SN-8466-2) used in this paper can be downloaded from the UK Data Service website at https://beta.ukdataservice.ac.uk/datacatalogue/studies/study?id=8466.

Code availability

The R code used to fit the models in this paper is available via GitHub at https://github.com/PatrickSturgis/Trust-in-science-social-consensus-and-vaccine-confidence.

References

  1. Ritchie, H. et al. Coronavirus (COVID-19) vaccinations. Our World in Data https://ourworldindata.org/covid-vaccinations (2021).

  2. Heesterbeek, H. COVID-19 will probably become endemic—here’s what that means. The Conversation https://theconversation.com/covid-19-will-probably-become-endemic-heres-what-that-means-146435 (2020).

  3. Randolph, H. E. & Barreiro, L. B. Herd immunity: understanding COVID-19. Immunity 52, 737–741 (2020).

    Article  CAS  Google Scholar 

  4. Lazarus, J. V. et al. A global survey of potential acceptance of a COVID-19 vaccine. Nat. Med. https://doi.org/10.1038/s41591-020-1124-9 (2020).

  5. G7 countries perception of COVID-19, Wave 3. Kantar https://www.kantar.com/inspiration/politics/citizen-impact-covid19-infographic (2020).

  6. COVID-19: Global Attitudes Towards a COVID-19 Vaccine (Institute of Global Health Innovation, 2020); https://www.imperial.ac.uk/media/imperial-college/institute-of-global-health-innovation/GlobalVaccineInsights_ICL-Covid-19-Behaviour-Tracker-EMBARGOED-00.01-04.02.2021.pdf

  7. Markel, H. Taking shots: the modern miracle of vaccines. Medscape www.medscape.com/viewarticle/481059 (2004).

  8. Poland, G. A. & Jacobson, T. M. The age-old struggle against the antivaccinationists. N. Engl. J. Med. 364, 97–99 (2011).

    Article  CAS  Google Scholar 

  9. Larson, H. J. et al. Measuring trust in vaccination: a systematic review. Hum. Vaccin. Immunother. 14, 1599–1609 (2018).

    Article  Google Scholar 

  10. Goldstein, H. Multilevel Statistical Models (Wiley, 2011).

  11. Gelman, A. Red State, Blue State, Rich State, Poor State: Why Americans Vote the Way They Do (Princeton Univ. Press, 2008).

  12. Fairbrother, M. Trust and public support for environmental protection in diverse national contexts. Sociol. Sci. 3, 359–382 (2016).

    Article  CAS  Google Scholar 

  13. Browning, C. R., Dirlam, J. & Boettner, B. From heterogeneity to concentration: Latino immigrant neighborhoods and collective efficacy perceptions in Los Angeles and Chicago. Soc. Forces 95, 779–807 (2016).

    Article  Google Scholar 

  14. Downs, G. W. & Rocke, D. M. Interpreting heteroscedasticity. Am. J. Polit. Sci. 23, 816–828 (1979).

    Article  Google Scholar 

  15. Brunton‐Smith, I., Sturgis, P. & Leckie, G. How collective is collective efficacy? The importance of consensus in judgments about community cohesion and willingness to intervene. Criminology 56, 608–637 (2018).

    Article  Google Scholar 

  16. Reynolds, K. J. Social norms and how they impact behaviour. Nat. Hum. Behav. 3, 14–15 (2019).

    Article  Google Scholar 

  17. Larson, H. J. et al. Understanding vaccine hesitancy around vaccines and vaccination from a global perspective: a systematic review of published literature, 2007–2012. Vaccine 32, 2150–2159 (2014).

    Article  Google Scholar 

  18. Strategic Advisory Group of Experts on Immunization (SAGE) Report of the SAGE Working Group on Vaccine Hesitancy (WHO, 2014); www.who.int/immunization/sage/meetings/2014/october/SAGE_working_group_revised_report_vaccine_hesitancy.pdf?ua=1

  19. Macdonald, N. E. & SAGE Working Group on Vaccine Hesitancy. Vaccine hesitancy: Definition, scope and determinants. Vaccine 33, 4161–4164 (2015).

  20. Larson, H. J. The state of vaccine confidence 2016: global insights through a 67-country survey. EBioMedicine 12, 295–301 (2016).

    Article  Google Scholar 

  21. Larson, H. J., Schulz, W. S., Tucker, J. D. & Smith, D. M. D. Measuring vaccine confidence: introducing a global vaccine confidence index. PLoS Curr. Outbreaks https://doi.org/10.1371/currents.outbreaks.ce0f6177bc97332602a8e3fe7d7f7cc4 (2015).

  22. Cummings, L. The ‘trust’ heuristic: arguments from authority in public health. Health Commun. 29, 1043–1056 (2014).

    Article  Google Scholar 

  23. Barber, B. Trust in science. Minerva 25, 123–134 (1987).

    Article  Google Scholar 

  24. Merk, C. & Pönitzsch, G. The role of affect in attitude formation toward new technologies: the case of stratospheric aerosol injection. Risk Anal. 37/12, 2289–2304 (2017).

    Article  Google Scholar 

  25. Midden, C. J. & Huijts, N. The role of trust in the affective evaluation of novel risks: the case of CO2 storage. Risk Anal. 29/5, 743–751 (2009).

    Article  Google Scholar 

  26. Buskens, V. & Weesie, J. An experiment on the effects of embeddedness in trust situations: buying a used car. Rationality Soc. 12, 227–253 (2000).

    Article  Google Scholar 

  27. Barrera, D. & Buskens, V. Imitation and learning under uncertainty: a vignette experiment. Int. Sociol. 22, 367–396 (2007).

    Article  Google Scholar 

  28. Rothstein, B. Trust, social dilemmas and collective memories. J. Theor. Polit. 12, 477–501 (2000).

    Article  Google Scholar 

  29. Ostrom, E. in Trust and Reciprocity: Interdisciplinary Lessons from Experimental Research (eds Ostrom, E. & Walker, J.) 19–79 (Russell Sage Foundation, 2003).

  30. Wellcome Global Monitor—First Wave Findings (Gallup, 2019); https://wellcome.org/reports/wellcome-global-monitor/2018

  31. Fine, P., Eames, K. & Heymann, D. ‘Herd immunity’: a rough guide. Clin. Infect. Dis. 52, 911–916 (2011).

    Article  Google Scholar 

  32. Gallup World Poll (Gallup, 2018).

  33. Harkness, J. in Cross-Cultural Survey Methods (eds Harkness, J. et al.) 35–56 (Wiley, 2002).

  34. Holmqvist, G. & Natali, L. Exploring the Late Impact of the Great Recession Using Gallup World Poll Data Innocenti Working Paper No. 2014-14 (UNICEF Office of Research, 2014).

  35. Worldwide Methodology and Codebook (Gallup, 2017); https://data-services.hosting.nyu.edu/wp-content/uploads/2017/10/World_Poll_Methodology_102717.pdf

  36. de Figueiredo, A. et al. Mapping global trends in vaccine confidence and investigating barriers to vaccine uptake: a large-scale retrospective temporal modelling study. Lancet 396, 898–908 (2020).

    Article  Google Scholar 

  37. Lord, F. M. Applications of Item Response Theory to Practical Testing Problems (Lawrence Erlbaum Associates, 1980).

  38. Hedeker, D., Mermelstein, R. J. & Demirtas, H. An application of a mixed‐effects location scale model for analysis of ecological momentary assessment (EMA) data. Biometrics 64, 627–634 (2008).

    Article  Google Scholar 

  39. Leckie, G., French, R., Charlton, C. & Browne, W. J. Modeling heterogeneous variance–covariance components in two‐level models. J. Educ. Behav. Stat. 39, 307–332 (2014).

    Article  Google Scholar 

  40. Jackson, J. et al. Police legitimacy and the norm to cooperate: using a mixed effects location-scale model to estimate social norms at a small spatial scale. J. Quant. Criminol. https://doi.org/10.1007/s10940-020-09467-5 (2020).

  41. Huber, B. et al. Fostering public trust in science: the role of social media. Public Underst. Sci. 28, 759–777 (2019).

    Article  Google Scholar 

  42. Angrist, H. A. & Angrist, N. Global Dataset on Education Quality: A Review and Update (2000–2017) Policy Research Working Paper No. WPS 8592 (World Bank Group, 2018).

  43. Buerkner, P.-C. brms: an R package for Bayesian multilevel models using Stan. J. Stat. Softw. 80, 1–28 (2018).

    Google Scholar 

Download references

Acknowledgements

The authors received no specific funding for this work.

Author information

Authors and Affiliations

  1. Department of Methodology, London School of Economics and Political Science, London, UK

    Patrick Sturgis & Jonathan Jackson

  2. Department of Sociology, University of Surrey, Guildford, UK

    Ian Brunton-Smith

  3. University of Sydney Law School, Camperdown, New South Wales, Australia

    Jonathan Jackson

Authors
  1. Patrick Sturgis
    View author publications

    Search author on:PubMed Google Scholar

  2. Ian Brunton-Smith
    View author publications

    Search author on:PubMed Google Scholar

  3. Jonathan Jackson
    View author publications

    Search author on:PubMed Google Scholar

Contributions

All authors made full and substantial contributions. P.S. contributed to conceptualization, writing, reviewing and editing; I.B.-S. contributed to data processing and analysis, writing and editing; and J.J. contributed to conceptualization and writing.

Corresponding author

Correspondence to Patrick Sturgis.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Human Behaviour thanks Gretchen Chapman, Heidi Larson, Eitan Tzelgov and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sturgis, P., Brunton-Smith, I. & Jackson, J. Trust in science, social consensus and vaccine confidence. Nat Hum Behav 5, 1528–1534 (2021). https://doi.org/10.1038/s41562-021-01115-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41562-021-01115-7

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing