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Testing Explanations for Skepticism of Personalized Risk Information

Author

Listed:
  • Erika A. Waters

    (Washington University in St. Louis, Saint Louis, Missouri, USA)

  • Jennifer M. Taber

    (Kent State University, Kent, Ohio, USA)

  • Nicole Ackermann

    (Washington University in St. Louis, Saint Louis, Missouri, USA)

  • Julia Maki

    (Washington University in St. Louis, Saint Louis, Missouri, USA)

  • Amy M. McQueen

    (Washington University in St. Louis, Saint Louis, Missouri, USA)

  • Laura D. Scherer

    (University of Colorado, Aurora, Colorado, USA)

Abstract

Background The promise of precision medicine could be stymied if people do not accept the legitimacy of personalized risk information. We tested 4 explanations for skepticism of personalized diabetes risk information. Method We recruited participants ( N = 356; M age = 48.6 [ s = 9.8], 85.1% women, 59.0% non-Hispanic white) from community locations (e.g., barbershops, churches) for a risk communication intervention. Participants received personalized information about their risk of developing diabetes and heart disease, stroke, colon cancer, and/or breast cancer (women). Then they completed survey items. We combined 2 items (recalled risk, perceived risk) to create a trichotomous risk skepticism variable (acceptance, overestimation, underestimation). Additional items assessed possible explanations for risk skepticism: 1) information evaluation skills (education, graph literacy, numeracy), 2 ) motivated reasoning (negative affect toward the information, spontaneous self-affirmation, information avoidance); 3) Bayesian updating (surprise), and 4) personal relevance (racial/ethnic identity). We used multinomial logistic regression for data analysis. Results Of the participants, 18% believed that their diabetes risk was lower than the information provided, 40% believed their risk was higher, and 42% accepted the information. Information evaluation skills were not supported as a risk skepticism explanation. Motivated reasoning received some support; higher diabetes risk and more negative affect toward the information were associated with risk underestimation, but spontaneous self-affirmation and information avoidance were not moderators. For Bayesian updating, more surprise was associated with overestimation. For personal relevance, belonging to a marginalized racial/ethnic group was associated with underestimation. Conclusion There are likely multiple cognitive, affective, and motivational explanations for risk skepticism. Understanding these explanations and developing interventions that address them will increase the effectiveness of precision medicine and facilitate its widespread implementation.

Suggested Citation

  • Erika A. Waters & Jennifer M. Taber & Nicole Ackermann & Julia Maki & Amy M. McQueen & Laura D. Scherer, 2023. "Testing Explanations for Skepticism of Personalized Risk Information," Medical Decision Making, , vol. 43(4), pages 430-444, May.
    • Handle: RePEc:sae:medema:v:43:y:2023:i:4:p:430-444
    DOI: 10.1177/0272989X231162824
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    References listed on IDEAS

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    1. Valerie F. Reyna, 2008. "A Theory of Medical Decision Making and Health: Fuzzy Trace Theory," Medical Decision Making, , vol. 28(6), pages 850-865, November.
    2. Erika A. Waters & Julia Maki & Ying Liu & Nicole Ackermann & Chelsey R. Carter & Hank Dart & Deborah J. Bowen & Linda D. Cameron & Graham A. Colditz, 2021. "Risk Ladder, Table, or Bulleted List? Identifying Formats That Effectively Communicate Personalized Risk and Risk Reduction Information for Multiple Diseases," Medical Decision Making, , vol. 41(1), pages 74-88, January.
    3. Stephanie L. Fowler & William M. P. Klein & Linda Ball & Jaclyn McGuire & Graham A. Colditz & Erika A. Waters, 2017. "Using an Internet-Based Breast Cancer Risk Assessment Tool to Improve Social-Cognitive Precursors of Physical Activity," Medical Decision Making, , vol. 37(6), pages 657-669, August.
    4. Paul K. J. Han & Christine Lary & Adam Black & Caitlin Gutheil & Hayley Mandeville & Jason Yahwak & Mayuko Fukunaga, 2019. "Effects of Personalized Risk Information on Patients Referred for Lung Cancer Screening with Low-Dose CT," Medical Decision Making, , vol. 39(8), pages 950-961, November.
    5. Yasmina Okan & Eva Janssen & Mirta Galesic & Erika A. Waters, 2019. "Using the Short Graph Literacy Scale to Predict Precursors of Health Behavior Change," Medical Decision Making, , vol. 39(3), pages 183-195, April.
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