IDEAS home Printed from https://ideas.repec.org/a/sae/medema/v43y2023i4p430-444.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0272989X231162824
    Download Restriction: no
    File URL: https://libkey.io/10.1177/0272989X231162824?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lyndal J. Trevena & Carissa Bonner & Yasmina Okan & Ellen Peters & Wolfgang Gaissmaier & Paul K. J. Han & Elissa Ozanne & Danielle Timmermans & Brian J. Zikmund-Fisher, 2021. "Current Challenges When Using Numbers in Patient Decision Aids: Advanced Concepts," Medical Decision Making, , vol. 41(7), pages 834-847, October.
    2. Volker Thoma & Elliott White & Asha Panigrahi & Vanessa Strowger & Irina Anderson, 2015. "Good Thinking or Gut Feeling? Cognitive Reflection and Intuition in Traders, Bankers and Financial Non-Experts," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-17, April.
    3. Gabriella Passerini & Laura Macchi & Maria Bagassi, 2012. "A methodological approach to ratio bias," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 7(5), pages 602-617, September.
    4. Yasmina Okan & Eric R. Stone & Jonathan Parillo & Wändi Bruine de Bruin & Andrew M. Parker, 2020. "Probability Size Matters: The Effect of Foreground‐Only versus Foreground+Background Graphs on Risk Aversion Diminishes with Larger Probabilities," Risk Analysis, John Wiley & Sons, vol. 40(4), pages 771-788, April.
    5. Mehdi Mourali & Zhiyong Yang, 2023. "Misperception of Multiple Risks in Medical Decision-Making," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 50(1), pages 25-47.
    6. Swait, J. & de Bekker-Grob, E.W., 2022. "A discrete choice model implementing gist-based categorization of alternatives, with applications to patient preferences for cancer screening and treatment," Journal of Health Economics, Elsevier, vol. 85(C).
    7. Timmons, Shane & Lunn, Pete, 2022. "Public understanding of climate change and support for mitigation," Research Series, Economic and Social Research Institute (ESRI), number RS135, June.
    8. Rasa Kanapickiene & Deimante Teresiene & Daiva Budriene & Greta Keliuotytė-Staniulėnienė & Jekaterina Kartasova, 2020. "The Impact Of Covid-19 On European Financial Markets And Economic Sentiment," Economy & Business Journal, International Scientific Publications, Bulgaria, vol. 14(1), pages 144-163.
    9. Eric R. Stone & Wändi Bruine de Bruin & Abigail M. Wilkins & Emily M. Boker & Jacqueline MacDonald Gibson, 2017. "Designing Graphs to Communicate Risks: Understanding How the Choice of Graphical Format Influences Decision Making," Risk Analysis, John Wiley & Sons, vol. 37(4), pages 612-628, April.
    10. Heard, Claire Louise & Rakow, Tim, 2022. "Examining insensitivity to probability in evidence‐based communication of relative risks: the role of affect and communication format," LSE Research Online Documents on Economics 113810, London School of Economics and Political Science, LSE Library.
    11. Bonnie Spring, 2008. "Health Decision Making: Lynchpin of Evidence-Based Practice," Medical Decision Making, , vol. 28(6), pages 866-874, November.
    12. Kevin E. Tiede & Wolfgang Gaissmaier, 2023. "How Do People Process Different Representations of Statistical Information? Insights into Cognitive Effort, Representational Inconsistencies, and Individual Differences," Medical Decision Making, , vol. 43(7-8), pages 803-820, October.
    13. Pieterse, Arwen H. & de Vries, Marieke & Kunneman, Marleen & Stiggelbout, Anne M. & Feldman-Stewart, Deb, 2013. "Theory-informed design of values clarification methods: A cognitive psychological perspective on patient health-related decision making," Social Science & Medicine, Elsevier, vol. 77(C), pages 156-163.
    14. Tammy C. Hoffmann & Mina Bakhit & Marie-Anne Durand & Lilisbeth Perestelo-Pérez & Catherine Saunders & Juan P. Brito, 2021. "Basing Information on Comprehensive, Critically Appraised, and Up-to-Date Syntheses of the Scientific Evidence: An Update from the International Patient Decision Aid Standards," Medical Decision Making, , vol. 41(7), pages 755-767, October.
    15. repec:cup:judgdm:v:14:y:2019:i:4:p:513-533 is not listed on IDEAS
    16. Cornelia Betsch & Niels Haase & Frank Renkewitz & Philipp Schmid, 2015. "The narrative bias revisited: What drives the biasing influence of narrative information on risk perceptions?," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 10(3), pages 241-264, May.
    17. repec:cup:judgdm:v:10:y:2015:i:3:p:241-264 is not listed on IDEAS
    18. Sobkow, Agata & Olszewska, Angelika & Traczyk, Jakub, 2020. "Multiple numeric competencies predict decision outcomes beyond fluid intelligence and cognitive reflection," Intelligence, Elsevier, vol. 80(C).
    19. Andreas D. Meid & Lucas Wirbka, 2022. "Can Machine Learning from Real-World Data Support Drug Treatment Decisions? A Prediction Modeling Case for Direct Oral Anticoagulants," Medical Decision Making, , vol. 42(5), pages 587-598, July.
    20. repec:cup:judgdm:v:7:y:2012:i:5:p:602-617 is not listed on IDEAS
    21. Max Boholm, 2019. "Risk and Quantification: A Linguistic Study," Risk Analysis, John Wiley & Sons, vol. 39(6), pages 1243-1261, June.
    22. Valerie F. Reyna, 2011. "The Paradoxes of Maurice Allais in Economics and Psychology," Medical Decision Making, , vol. 31(2), pages 221-222, March.
    23. Peder A. Halvorsen, 2010. "What Information Do Patients Need to Make a Medical Decision?," Medical Decision Making, , vol. 30(5_suppl), pages 11-13, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:medema:v:43:y:2023:i:4:p:430-444. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.