IDEAS home Printed from https://ideas.repec.org/a/sae/medema/v30y2010i4p464-473.html
   My bibliography  Save this article

Breast Cancer Patients’ Treatment Expectations after Exposure to the Decision Aid Program Adjuvant Online: The Influence of Numeracy

Author

Listed:
  • Isaac M. Lipkus

    (Duke University School of Nursing, Durham, NC, isaac.lipkus@duke.edu)

  • Ellen Peters

    (Decision Research, Eugene, OR)

  • Gretchen Kimmick

    (Duke University Medical Center, Durham, NC)

  • Vlayka Liotcheva

    (Duke University Medical Center, Durham, NC)

  • Paul Marcom

    (Duke University Medical Center, Durham, NC)

Abstract

The decision aid called ‘‘Adjuvant Online’’ (Adjuvant! for short) helps breast cancer patients make treatment decisions by providing numerical estimates of treatment efficacy (e.g., 10-y relapse or survival). Studies exploring how patients’ numeracy interacts with the estimates provided by Adjuvant! are lacking. Pooling across 2 studies totaling 105 women with estrogen receptor—positive, early-stage breast cancer, the authors explored patients’ treatment expectations, perceived benefit from treatments, and confidence of personal benefit from treatments. Patients who were more numerate were more likely to provide estimates of cancer-free survival that matched the estimates provided by Adjuvant! for each treatment option compared with patients with lower numeracy (odds ratios of 1.6 to 2.4). As estimates of treatment efficacy provided by Adjuvant! increased, so did patients’ estimates of cancer-free survival (0.37 > r s > 0.48) and their perceptions of treatment benefit from hormonal therapy (r s = 0.28) and combined therapy (r s = 0.27). These relationships were significantly more pronounced for those with higher numeracy, especially for perceived benefit of combined therapy. Results suggest that numeracy influences a patient’s ability to interpret numerical estimates of treatment efficacy from decision aids such as Adjuvant!.

Suggested Citation

  • Isaac M. Lipkus & Ellen Peters & Gretchen Kimmick & Vlayka Liotcheva & Paul Marcom, 2010. "Breast Cancer Patients’ Treatment Expectations after Exposure to the Decision Aid Program Adjuvant Online: The Influence of Numeracy," Medical Decision Making, , vol. 30(4), pages 464-473, July.
  • Handle: RePEc:sae:medema:v:30:y:2010:i:4:p:464-473
    DOI: 10.1177/0272989X09360371
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0272989X09360371
    Download Restriction: no

    File URL: https://libkey.io/10.1177/0272989X09360371?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. Nathan F. Dieckmann & Paul Slovic & Ellen M. Peters, 2009. "The Use of Narrative Evidence and Explicit Likelihood by Decisionmakers Varying in Numeracy," Risk Analysis, John Wiley & Sons, vol. 29(10), pages 1473-1488, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Talya Miron-Shatz & Yaniv Hanoch & Benjamin A. Katz & Glen M. Doniger & Elissa M. Ozanne, 2015. "Willingness to test for BRCA1/2 in high risk women: Influenced by risk perception and family experience, rather than by objective or subjective numeracy?," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 10(4), pages 386-399, July.
    2. E. Peters & H. Kunreuther & N. Sagara & P. Slovic & D. R. Schley, 2012. "Protective Measures, Personal Experience, and the Affective Psychology of Time," Risk Analysis, John Wiley & Sons, vol. 32(12), pages 2084-2097, December.
    3. Ye, Jun & Zhou, Kun & Chen, Rui, 2021. "Numerical or verbal Information: The effect of comparative information in social comparison on prosocial behavior," Journal of Business Research, Elsevier, vol. 124(C), pages 198-211.
    4. Michael R. Eber & Cass R. Sunstein & James K. Hammitt & Jennifer M. Yeh, 2021. "The modest effects of fact boxes on cancer screening," Journal of Risk and Uncertainty, Springer, vol. 62(1), pages 29-54, February.
    5. repec:cup:judgdm:v:10:y:2015:i:4:p:386-399 is not listed on IDEAS
    6. William J. Burns & Ellen Peters & Paul Slovic, 2012. "Risk Perception and the Economic Crisis: A Longitudinal Study of the Trajectory of Perceived Risk," Risk Analysis, John Wiley & Sons, vol. 32(4), pages 659-677, April.
    7. Yasmina Okan & Eric R. Stone & Wändi Bruine de Bruin, 2018. "Designing Graphs that Promote Both Risk Understanding and Behavior Change," Risk Analysis, John Wiley & Sons, vol. 38(5), pages 929-946, May.

    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. Yaniv Hanoch & Jonathan Rolison & Alexandra M. Freund, 2019. "Reaping the Benefits and Avoiding the Risks: Unrealistic Optimism in the Health Domain," Risk Analysis, John Wiley & Sons, vol. 39(4), pages 792-804, April.
    2. Victoria A. Shaffer & Brian J. Zikmund-Fisher, 2013. "All Stories Are Not Alike," Medical Decision Making, , vol. 33(1), pages 4-13, January.
    3. Wändi Bruine de Bruin & Annika Wallin & Andrew M. Parker & JoNell Strough & Janel Hanmer, 2017. "Effects of Anti- Versus Pro-Vaccine Narratives on Responses by Recipients Varying in Numeracy: A Cross-sectional Survey-Based Experiment," Medical Decision Making, , vol. 37(8), pages 860-870, November.
    4. Ian G. J. Dawson, 2018. "Assessing the Effects of Information About Global Population Growth on Risk Perceptions and Support for Mitigation and Prevention Strategies," Risk Analysis, John Wiley & Sons, vol. 38(10), pages 2222-2241, October.
    5. Yasmina Okan & Eric R. Stone & Wändi Bruine de Bruin, 2018. "Designing Graphs that Promote Both Risk Understanding and Behavior Change," Risk Analysis, John Wiley & Sons, vol. 38(5), pages 929-946, May.
    6. Nathan F. Dieckmann & Ellen Peters & Robin Gregory, 2015. "At Home on the Range? Lay Interpretations of Numerical Uncertainty Ranges," Risk Analysis, John Wiley & Sons, vol. 35(7), pages 1281-1295, July.
    7. William J. Burns & Ellen Peters & Paul Slovic, 2012. "Risk Perception and the Economic Crisis: A Longitudinal Study of the Trajectory of Perceived Risk," Risk Analysis, John Wiley & Sons, vol. 32(4), pages 659-677, April.
    8. Jun Yao & Harmen Oppewal & Di Wang, 2020. "Cheaper and smaller or more expensive and larger: how consumers respond to unit price increase tactics that simultaneously change product price and package size," Journal of the Academy of Marketing Science, Springer, vol. 48(6), pages 1075-1094, November.
    9. Ian G. J. Dawson & Johnnie E. V. Johnson & Michelle A. Luke, 2013. "Helping Individuals to Understand Synergistic Risks: An Assessment of Message Contents Depicting Mechanistic and Probabilistic Concepts," Risk Analysis, John Wiley & Sons, vol. 33(5), pages 851-865, May.
    10. Nathan F. Dieckmann & Robin Gregory & Ellen Peters & Robert Hartman, 2017. "Seeing What You Want to See: How Imprecise Uncertainty Ranges Enhance Motivated Reasoning," Risk Analysis, John Wiley & Sons, vol. 37(3), pages 471-486, March.
    11. Ellen Peters & P. Sol Hart & Martin Tusler & Liana Fraenkel, 2014. "Numbers Matter to Informed Patient Choices," Medical Decision Making, , vol. 34(4), pages 430-442, May.
    12. Shoots-Reinhard, Brittany & Goodwin, Raleigh & Bjälkebring, Pär & Markowitz, David M. & Silverstein, Michael C. & Peters, Ellen, 2021. "Ability-related political polarization in the COVID-19 pandemic," Intelligence, Elsevier, vol. 88(C).
    13. Nathan F. Dieckmann & Ellen Peters & Robin Gregory & Martin Tusler, 2012. "Making sense of uncertainty: advantages and disadvantages of providing an evaluative structure," Journal of Risk Research, Taylor & Francis Journals, vol. 15(7), pages 717-735, August.
    14. 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.
    15. Nathan F. Dieckmann & Robert Mauro & Paul Slovic, 2010. "The Effects of Presenting Imprecise Probabilities in Intelligence Forecasts," Risk Analysis, John Wiley & Sons, vol. 30(6), pages 987-1001, June.
    16. Robin Gregory & Nathan Dieckmann & Ellen Peters & Lee Failing & Graham Long & Martin Tusler, 2012. "Deliberative Disjunction: Expert and Public Understanding of Outcome Uncertainty," Risk Analysis, John Wiley & Sons, vol. 32(12), pages 2071-2083, December.
    17. Ellen Peters & P. Sol Hart & Liana Fraenkel, 2011. "Informing Patients," Medical Decision Making, , vol. 31(3), pages 432-436, May.

    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:30:y:2010:i:4:p:464-473. 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.