IDEAS home Printed from https://ideas.repec.org/a/sae/medema/v35y2015i8p937-947.html
   My bibliography  Save this article

Presenting Numeric Information with Percentages and Descriptive Risk Labels

Author

Listed:
  • Aleksandr Sinayev
  • Ellen Peters
  • Martin Tusler
  • Liana Fraenkel

Abstract

Background. Previous research demonstrated that providing (v. not providing) numeric information about the adverse effects (AEs) of medications increased comprehension and willingness to use medication but left open the question about which numeric format is best. The objective was to determine which of 4 tested formats (percentage, frequency, percentage + risk label, frequency + risk label) maximizes comprehension and willingness to use medication across age and numeracy levels. Methods. In a cross-sectional internet survey ( N = 368; American Life Panel, 15 May 2008 to 18 June 2008), respondents were presented with a hypothetical prescription medication for high cholesterol. AE likelihoods were described using 1 of 4 tested formats. Main outcome measures were risk comprehension (ability to identify AE likelihood from a table) and willingness to use the medication (7-point scale; not likely = 0, very likely = 6). Results. The percentage + risk label format resulted in the highest comprehension and willingness to use the medication compared with the other 3 formats (mean comprehension in percentage + risk label format = 95% v. mean across the other 3 formats = 81%; mean willingness = 3.3 v. 2.95, respectively). Comprehension differences between percentage and frequency formats were smaller among the less numerate. Willingness to use medication depended less on age and numeracy when labels were used. Generalizability is limited by the use of a sample that was older, more educated, and better off financially than national averages. Conclusions. Providing numeric AE-likelihood information in a percentage format with risk labels is likely to increase risk comprehension and willingness to use a medication compared with other numeric formats.

Suggested Citation

  • Aleksandr Sinayev & Ellen Peters & Martin Tusler & Liana Fraenkel, 2015. "Presenting Numeric Information with Percentages and Descriptive Risk Labels," Medical Decision Making, , vol. 35(8), pages 937-947, November.
    • Handle: RePEc:sae:medema:v:35:y:2015:i:8:p:937-947
    DOI: 10.1177/0272989X15584922
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0272989X15584922
    Download Restriction: no
    File URL: https://libkey.io/10.1177/0272989X15584922?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. Vivianne H. M. Visschers & Ree M. Meertens & Wim W. F. Passchier & Nanne N. K. De Vries, 2009. "Probability Information in Risk Communication: A Review of the Research Literature," Risk Analysis, John Wiley & Sons, vol. 29(2), pages 267-287, February.
    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. 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).

    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. Reynolds, J.P. & Archer, S. & Pilling, M. & Kenny, M. & Hollands, G.J. & Marteau, T.M., 2019. "Public acceptability of nudging and taxing to reduce consumption of alcohol, tobacco, and food: A population-based survey experiment," Social Science & Medicine, Elsevier, vol. 236(C), pages 1-1.
    2. Jantsje M. Mol & W. J. Wouter Botzen & Julia E. Blasch & Hans de Moel, 2020. "Insights into Flood Risk Misperceptions of Homeowners in the Dutch River Delta," Risk Analysis, John Wiley & Sons, vol. 40(7), pages 1450-1468, July.
    3. Jurriaan P. Oudhoff & Daniëlle R. M. Timmermans, 2015. "The Effect of Different Graphical and Numerical Likelihood Formats on Perception of Likelihood and Choice," Medical Decision Making, , vol. 35(4), pages 487-500, May.
    4. Weina Liu & Chaonan Xu & Yajie Peng & Xinlong Xu, 2023. "Evolution of Tourism Risk Communication: A Bibliometric Analysis and Meta-Analysis of the Antecedents of Communicating Risk to Tourists," Sustainability, MDPI, vol. 15(12), pages 1-31, June.
    5. 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.
    6. Warut Khern-am-nuai & Matthew J. Hashim & Alain Pinsonneault & Weining Yang & Ninghui Li, 2023. "Augmenting Password Strength Meter Design Using the Elaboration Likelihood Model: Evidence from Randomized Experiments," Information Systems Research, INFORMS, vol. 34(1), pages 157-177, March.
    7. Kelly Klima & Wändi Bruine de Bruin & M. Granger Morgan & Iris Grossmann, 2012. "Public Perceptions of Hurricane Modification," Risk Analysis, John Wiley & Sons, vol. 32(7), pages 1194-1206, July.
    8. Timmons, Shane & Robertson, Deirdre & Lunn, Pete, 2022. "Combining nudges and boosts to increase precautionary saving: A large-scale field experiment," Papers WP722, Economic and Social Research Institute (ESRI).
    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. Tanja Perko & Baldwin van Gorp & Catrinel Turcanu & Peter Thijssen & Benny Carle, 2013. "Communication in Nuclear Emergency Preparedness: A Closer Look at Information Reception," Risk Analysis, John Wiley & Sons, vol. 33(11), pages 1987-2001, November.
    11. Aven, Terje, 2018. "Perspectives on the nexus between good risk communication and high scientific risk analysis quality," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 290-296.
    12. Yasmina Okan & Dafina Petrova & Samuel G. Smith & Vedran Lesic & Wändi Bruine de Bruin, 2019. "How Do Women Interpret the NHS Information Leaflet about Cervical Cancer Screening?," Medical Decision Making, , vol. 39(7), pages 738-754, October.
    13. Joseph Daron & Susanne Lorenz & Andrea Taylor & Suraje Dessai, 2021. "Communicating future climate projections of precipitation change," Climatic Change, Springer, vol. 166(1), pages 1-20, May.
    14. Cristóbal De La Maza & Alex Davis & Cleotilde Gonzalez & Inês Azevedo, 2019. "Understanding Cumulative Risk Perception from Judgments and Choices: An Application to Flood Risks," Risk Analysis, John Wiley & Sons, vol. 39(2), pages 488-504, February.
    15. Floris Goerlandt & Jie Li & Genserik Reniers, 2020. "The Landscape of Risk Communication Research: A Scientometric Analysis," IJERPH, MDPI, vol. 17(9), pages 1-31, May.
    16. Chris M. R. Smerecnik & Ilse Mesters & Loes T. E. Kessels & Robert A. C. Ruiter & Nanne K. De Vries & Hein De Vries, 2010. "Understanding the Positive Effects of Graphical Risk Information on Comprehension: Measuring Attention Directed to Written, Tabular, and Graphical Risk Information," Risk Analysis, John Wiley & Sons, vol. 30(9), pages 1387-1398, September.
    17. Hangjian Wu & Emmanouil Mentzakis & Marije Schaafsma, 2022. "Exploring Different Assumptions about Outcome-Related Risk Perceptions in Discrete Choice Experiments," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 81(3), pages 531-572, March.
    18. Theresa A. K. Knoblauch & Michael Stauffacher & Evelina Trutnevyte, 2018. "Communicating Low‐Probability High‐Consequence Risk, Uncertainty and Expert Confidence: Induced Seismicity of Deep Geothermal Energy and Shale Gas," Risk Analysis, John Wiley & Sons, vol. 38(4), pages 694-709, April.
    19. Hao‐Che Wu & Michael K. Lindell & Carla S. Prater & Charles D. Samuelson, 2014. "Effects of Track and Threat Information on Judgments of Hurricane Strike Probability," Risk Analysis, John Wiley & Sons, vol. 34(6), pages 1025-1039, June.
    20. Shereen J. Chaudhry & Michael Hand & Howard Kunreuther, 2020. "Broad bracketing for low probability events," Journal of Risk and Uncertainty, Springer, vol. 61(3), pages 211-244, December.

    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:35:y:2015:i:8:p:937-947. 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.