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Communicating treatment risk reduction to people with low numeracy skills: A cross-cultural comparison

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  • Garcia-Retamero, R.
  • Galesic, M.

Abstract

Objectives. We sought to address denominator neglect (i.e. the focus on the number of treated and nontreated patients who died, without sufficiently considering the overall numbers of patients) in estimates of treatment risk reduction, and analyzed whether icon arrays aid comprehension. Methods. We performed a survey of probabilistic, national samples in the United States and Germany in July and August of 2008. Participants received scenarios involving equally effective treatments but differing in the overall number of treated and nontreated patients. In some conditions, the number who received a treatment equaled the number who did not; in others the number was smaller or larger. Some participants received icon arrays. Results. Participants - particularly those with low numeracy skills - showed denominator neglect in treatment risk reduction perceptions. Icon arrays were an effective method for eliminating denominator neglect. We found cross-cultural differences that are important in light of the countries' different medical systems. Conclusions. Problems understanding numerical information often reside not in the mind but in the problem's representation. These findings suggest suitable ways to communicate quantitative medical data.

Suggested Citation

  • Garcia-Retamero, R. & Galesic, M., 2009. "Communicating treatment risk reduction to people with low numeracy skills: A cross-cultural comparison," American Journal of Public Health, American Public Health Association, vol. 99(12), pages 2196-2202.
    • Handle: RePEc:aph:ajpbhl:10.2105/ajph.2009.160234_9
    DOI: 10.2105/AJPH.2009.160234
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    1. repec:cup:judgdm:v:9:y:2014:i:1:p:15-34 is not listed on IDEAS
    2. Wolfgang Gaissmaier & Britta L. Anderson & Jay Schulkin, 2014. "How Do Physicians Provide Statistical Information about Antidepressants to Hypothetical Patients?," Medical Decision Making, , vol. 34(2), pages 206-215, February.
    3. Schonlau Matthias & Peters Ellen, 2012. "Comprehension of Graphs and Tables Depend on the Task: Empirical Evidence from Two Web-Based Studies," Statistics, Politics and Policy, De Gruyter, vol. 3(2), pages 1-35, August.
    4. Saima Ghazal & Edward T. Cokely & Rocio Garcia-Retamero, 2014. "Predicting biases in very highly educated samples: Numeracy and metacognition," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 9(1), pages 15-34, January.
    5. Munehito Machida & Michio Murakami & Aya Goto, 2022. "Differences in Data Trustworthiness and Risk Perception between Bar Graphs and Pictograms," IJERPH, MDPI, vol. 19(8), pages 1-9, April.
    6. Garcia-Retamero, Rocio & Hoffrage, Ulrich, 2013. "Visual representation of statistical information improves diagnostic inferences in doctors and their patients," Social Science & Medicine, Elsevier, vol. 83(C), pages 27-33.
    7. Marie-Anne Durand & Renata W Yen & James O’Malley & Glyn Elwyn & Julien Mancini, 2020. "Graph literacy matters: Examining the association between graph literacy, health literacy, and numeracy in a Medicaid eligible population," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-14, November.

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