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Are Perceived Prevalences of Infection also Biased and How? Lessons from Large Epidemics of Mosquito-Borne Diseases in Tropical Regions

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  • Jocelyn Raude

    (EHESP Rennes, Université Sorbonne Paris Cité, France
    Aix Marseille University, IRD French Institute of Research for Development, EHESP French School of Public Health, UMR_D 190 Emergence des Pathologies Virales, Marseille, France
    UMR PIMIT, INSERM 1187, CNRS 9192, IRD 249. Plateforme Technologique CYROI, Université de La Réunion, Réunion, France)

  • Patrick Peretti-Watel

    (Aix Marseille Univ, IRD, AP-HM, SSA, VITROME, IHU-Méditerranée Infection, Marseille, France
    ORS PACA, Southeastern Health Regional Observatory, Marseille, France)

  • Jeremy Ward

    (Aix Marseille Univ, IRD, AP-HM, SSA, VITROME, IHU-Méditerranée Infection, Marseille, France
    ORS PACA, Southeastern Health Regional Observatory, Marseille, France
    Université Paris-Diderot, CNRS, LIED, Interdisciplinary Laboratory of Tomorrow's Energies, Paris, France)

  • Claude Flamand

    (Institut Pasteur de Guyane, Unité d’Epidémiologie, Cayenne, France)

  • Pierre Verger

    (Aix Marseille Univ, IRD, AP-HM, SSA, VITROME, IHU-Méditerranée Infection, Marseille, France
    ORS PACA, Southeastern Health Regional Observatory, Marseille, France)

Abstract

Background. Although people are likely to underestimate the frequencies of risks to health from common diseases and overestimate those from rare diseases, we still do not know much about reasons for this systematic bias, which is also referred to as “primary bias†in the literature. In this study, we take advantage of a series of large epidemics of mosquito-borne diseases to examine the accuracy of judgments of risk frequencies. In this aim, we assessed the perceived v. observed prevalence of infection by Zika, chikungunya or dengue fever during these outbreaks, as well as their variations among different subpopulations and epidemiological settings. Methods. We used data drawn from 4 telephone surveys, conducted between 2006 and 2016, among representative samples of the adult population in tropical regions (Reunion, Martinique, and French Guiana). The participants were asked to estimate the prevalence of these infections by using a natural frequency scale. Results. The surveys showed that 1) most people greatly overestimated the prevalence of infection by arbovirus, 2) these risk overestimations fell considerably as the actual prevalence of these diseases increased, 3) the better-educated and male participants consistently yielded less inaccurate risk estimates across epidemics, and 4) these biases in the perception of prevalence of these infectious diseases are relatively well predicted by the probability weighting function developed in the field of behavioral decision making. Conclusions. These findings suggest that the primary bias, which has been found in laboratory experiments to characterize a variety of probabilistic judgments, equally affects perception of prevalence of acute infectious diseases in epidemic settings. They also indicate that numeracy may play a considerable role in people’s ability to transform epidemiological observations from their social environment to more accurate risk estimates.

Suggested Citation

  • Jocelyn Raude & Patrick Peretti-Watel & Jeremy Ward & Claude Flamand & Pierre Verger, 2018. "Are Perceived Prevalences of Infection also Biased and How? Lessons from Large Epidemics of Mosquito-Borne Diseases in Tropical Regions," Medical Decision Making, , vol. 38(3), pages 377-389, April.
    • Handle: RePEc:sae:medema:v:38:y:2018:i:3:p:377-389
    DOI: 10.1177/0272989X17750845
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Costa-Font, Joan & Vilaplana-Prieto, Cristina, 2022. "Risky restrictions? Mobility restriction effects on risk awareness and anxiety," Health Policy, Elsevier, vol. 126(11), pages 1090-1102.

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