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Are People Optimistically Biased about the Risk of COVID-19 Infection? Lessons from the First Wave of the Pandemic in Europe

Author

Listed:
  • Kathleen McColl

    (Unite des Virus Emergents, Institut de Recherche pour le Développement 190, Institut National de la Santé Et de la Recherche Médicale (INSERM) 1207, Health, Aix-Marseille University, 13009 Marseille, France
    École des Hautes Études en Santé Publique (EHESP) French School of Public Health, 35043 Rennes, France)

  • Marion Debin

    (Institut National de la Santé Et de la Recherche Médicale (INSERM), Institut Pierre Louis d’Épidémiologie et de Santé Publique (IPLESP), Sorbonne Université, F-75012 Paris, France)

  • Cecile Souty

    (Institut National de la Santé Et de la Recherche Médicale (INSERM), Institut Pierre Louis d’Épidémiologie et de Santé Publique (IPLESP), Sorbonne Université, F-75012 Paris, France)

  • Caroline Guerrisi

    (Institut National de la Santé Et de la Recherche Médicale (INSERM), Institut Pierre Louis d’Épidémiologie et de Santé Publique (IPLESP), Sorbonne Université, F-75012 Paris, France)

  • Clement Turbelin

    (Institut National de la Santé Et de la Recherche Médicale (INSERM), Institut Pierre Louis d’Épidémiologie et de Santé Publique (IPLESP), Sorbonne Université, F-75012 Paris, France)

  • Alessandra Falchi

    (Laboratoire de Virologie, Unité de Recherche 7310, Université de Corse, 20250 Corte, France)

  • Isabelle Bonmarin

    (Santé Publique France, 94410 Saint Maurice, France)

  • Daniela Paolotti

    (Istituto per l’Interscambio Scientifico, ISI Foundation, 10126 Turin, Italy)

  • Chinelo Obi

    (Public Health England, London SE1 8UG, UK)

  • Jim Duggan

    (School of Computer Science, National University of Ireland, H91 TK33 Galway, Ireland)

  • Yamir Moreno

    (Institute for Biocomputation and Physics and Complex Systems, University of Zaragoza, 50001 Zaragoza, Spain)

  • Ania Wisniak

    (Faculty of Medicine, Institute of Global Health, University of Geneva, 1202 Geneva, Switzerland)

  • Antoine Flahault

    (Faculty of Medicine, Institute of Global Health, University of Geneva, 1202 Geneva, Switzerland)

  • Thierry Blanchon

    (Institut National de la Santé Et de la Recherche Médicale (INSERM), Institut Pierre Louis d’Épidémiologie et de Santé Publique (IPLESP), Sorbonne Université, F-75012 Paris, France)

  • Vittoria Colizza

    (Institut National de la Santé Et de la Recherche Médicale (INSERM), Institut Pierre Louis d’Épidémiologie et de Santé Publique (IPLESP), Sorbonne Université, F-75012 Paris, France)

  • Jocelyn Raude

    (Unite des Virus Emergents, Institut de Recherche pour le Développement 190, Institut National de la Santé Et de la Recherche Médicale (INSERM) 1207, Health, Aix-Marseille University, 13009 Marseille, France
    École des Hautes Études en Santé Publique (EHESP) French School of Public Health, 35043 Rennes, France)

Abstract

Unrealistic optimism, the underestimation of one’s risk of experiencing harm, has been investigated extensively to understand better and predict behavioural responses to health threats. Prior to the COVID-19 pandemic, a relative dearth of research existed in this domain regarding epidemics, which is surprising considering that this optimistic bias has been associated with a lack of engagement in protective behaviours critical in fighting twenty-first-century, emergent, infectious diseases. The current study addresses this gap in the literature by investigating whether people demonstrated optimism bias during the first wave of the COVID-19 pandemic in Europe, how this changed over time, and whether unrealistic optimism was negatively associated with protective measures. Taking advantage of a pre-existing international participative influenza surveillance network ( n = 12,378), absolute and comparative unrealistic optimism were measured at three epidemic stages (pre-, early, peak), and across four countries—France, Italy, Switzerland and the United Kingdom. Despite differences in culture and health response, similar patterns were observed across all four countries. The prevalence of unrealistic optimism appears to be influenced by the particular epidemic context. Paradoxically, whereas absolute unrealistic optimism decreased over time, comparative unrealistic optimism increased, suggesting that whilst people became increasingly accurate in assessing their personal risk, they nonetheless overestimated that for others. Comparative unrealistic optimism was negatively associated with the adoption of protective behaviours, which is worrying, given that these preventive measures are critical in tackling the spread and health burden of COVID-19. It is hoped these findings will inspire further research into sociocognitive mechanisms involved in risk appraisal.

Suggested Citation

  • Kathleen McColl & Marion Debin & Cecile Souty & Caroline Guerrisi & Clement Turbelin & Alessandra Falchi & Isabelle Bonmarin & Daniela Paolotti & Chinelo Obi & Jim Duggan & Yamir Moreno & Ania Wisniak, 2021. "Are People Optimistically Biased about the Risk of COVID-19 Infection? Lessons from the First Wave of the Pandemic in Europe," IJERPH, MDPI, vol. 19(1), pages 1-23, December.
  • Handle: RePEc:gam:jijerp:v:19:y:2021:i:1:p:436-:d:715797
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    References listed on IDEAS

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    1. Caroline Rudisill, 2013. "How do we handle new health risks? Risk perception, optimism, and behaviors regarding the H1N1 virus," Journal of Risk Research, Taylor & Francis Journals, vol. 16(8), pages 959-980, September.
    2. N. Higgins & Michelle St AMAND & Gary Poole, 1997. "The Controllability of Negative Life Experiences Mediates Unrealistic Optimism," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 42(3), pages 299-323, November.
    3. Weinstein, N.D. & Klotz, M.L. & Sandman, P.M., 1988. "Optimistic biases in public perceptions of the risk from radon," American Journal of Public Health, American Public Health Association, vol. 78(7), pages 796-800.
    4. Martin Huber & Henrika Langen, 2020. "Timing matters: the impact of response measures on COVID-19-related hospitalization and death rates in Germany and Switzerland," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 156(1), pages 1-19, December.
    5. Raude, Jocelyn & MCColl, Kathleen & Flamand, Claude & Apostolidis, Themis, 2019. "Understanding health behaviour changes in response to outbreaks: Findings from a longitudinal study of a large epidemic of mosquito-borne disease," Social Science & Medicine, Elsevier, vol. 230(C), pages 184-193.
    6. Slovic, Paul & Finucane, Melissa L. & Peters, Ellen & MacGregor, Donald G., 2007. "The affect heuristic," European Journal of Operational Research, Elsevier, vol. 177(3), pages 1333-1352, March.
    7. Schoenbaum, M., 1997. "Do smokers understand the mortality effects of smoking? Evidence from the health and retirement survey," American Journal of Public Health, American Public Health Association, vol. 87(5), pages 755-759.
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    2. Dong-Suk Lee & Hyun-Ju Koo & Seung-Ok Choi & Ji-In Kim & Yeon Sook Kim, 2022. "Relationship between Preventive Health Behavior, Optimistic Bias, Hypochondria, and Mass Psychology in Relation to the Coronavirus Pandemic among Young Adults in Korea," IJERPH, MDPI, vol. 19(15), pages 1-9, August.

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