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Assessing Values for Health: Numeracy Matters

Author

Listed:
  • Steven Woloshin

    (VA Outcomes Group, White River Junction, Vermont, Center for the Evaluative Clinical Sciences, Dartmouth Medical School, Hanover, New Hampshire, Norris Cotton Cancer Center)

  • Lisa M. Schwartz

    (VA Outcomes Group, White River Junction, Vermont, Center for the Evaluative Clinical Sciences, Dartmouth Medical School, Hanover, New Hampshire, Norris Cotton Cancer Center)

  • Megan Moncur

    (Clinical Research Section, Department of Medicine)

  • Sherine Gabriel

    (Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota)

  • Anna N. A. Tosteson

    (Clinical Research Section, Department of Medicine, Center for the Evaluative Clinical Sciences, Dartmouth Medical School, Hanover, New Hampshire, Norris Cotton Cancer Center)

Abstract

Background. Patients’ values are fundamental to decision models, cost-effectiveness analyses, and pharmacoeconomic analyses. The standard methods used to assess how patients value different health states are inherently quantitative. People without strong quantitative skills (i.e., low numeracy) may not be able to complete these tasks in a meaningful way. Methods. To determine whether the validity of utility assessments depends on the respondent’s level of numeracy, the authors conducted in-person interviews and written surveys and assessed utility for the current health for 96 women volunteers. Numeracy was measured using a previously validated 3-item scale. The authors examined the correlation between self-reported health and utility for current health (assessed using the standard gamble, time trade-off, and visual analog techniques) across levels of numeracy. For half of the women, the authors also assessed standard gamble utility for 3 imagined health states (breast cancer, heart disease, and osteoporosis) and asked how much the women feared each disease. Results. Respondent ages ranged from 50 to 79 years (mean = 63), all were high school graduates, and 52% had a college or postgraduate degree. Twenty-six percent answered 0 or only 1 of the numeracy questions correctly, 37% answered 2 correctly, and 37% answered all 3 correctly. Among women with the lowest level of numeracy, the correlation between utility for current health and self-reported health was in the wrong direction (i.e., worse health valued higher than better health): for standard gamble, Spearman r = -0.16 , P =0.44; for time trade-off, Spearman r = -0.13 , P = 0.54. Among the most numerate women, the authors observed a fair to moderate positive correlation with both standard gamble (Spearman r = 0.22 , P = 0.19) and time trade-off (Spearman r = 0.50 , P = 0.002). In contrast, using the visual analog scale, the authors observed a substantial correlation in the expected direction at all levels of numeracy (Spearman r =0.82, 0.50, and 0.60 for women answering 0-1, 2, and 3 numeracy questions, respectively; all P s ≤ 0.003). With regard to the imagined health states, the most feared disease had the lowest utility for 35% of the women with the lowest numeracy compared to 76% of the women with the highest numeracy (P = 0.03). Conclusions. The validity of standard utility assessments is related to the subject’s facility with numbers. Limited numeracy may be an important barrier to meaningfully assessing patients’ values using the standard gamble and time trade-off techniques.

Suggested Citation

  • Steven Woloshin & Lisa M. Schwartz & Megan Moncur & Sherine Gabriel & Anna N. A. Tosteson, 2001. "Assessing Values for Health: Numeracy Matters," Medical Decision Making, , vol. 21(5), pages 382-390, October.
  • Handle: RePEc:sae:medema:v:21:y:2001:i:5:p:382-390
    DOI: 10.1177/0272989X0102100505
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    Citations

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    Cited by:

    1. Kempter, Elisabeth & Upadhayay, Neha Bhardwaj, 2022. "Uncovering the role of education in the uptake of preventive measures against Malaria in the African population," University of Tübingen Working Papers in Business and Economics 155, University of Tuebingen, Faculty of Economics and Social Sciences, School of Business and Economics.
    2. Smeele, Nicholas V.R. & Chorus, Caspar G. & Schermer, Maartje H.N. & de Bekker-Grob, Esther W., 2023. "Towards machine learning for moral choice analysis in health economics: A literature review and research agenda," Social Science & Medicine, Elsevier, vol. 326(C).
    3. F. E. van Nooten & X. Koolman & W. B. F. Brouwer, 2009. "The influence of subjective life expectancy on health state valuations using a 10 year TTO," Health Economics, John Wiley & Sons, Ltd., vol. 18(5), pages 549-558, May.
    4. van Praag, Bernard M. S. & Ferrer-i-Carbonell, Ada, 2001. "Age-Differentiated QALY Losses," IZA Discussion Papers 314, Institute of Labor Economics (IZA).
    5. P. Stalmeier & A. Verheijen, 2013. "Maximal endurable time states and the standard gamble: more preference reversals," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 14(6), pages 971-977, December.
    6. Suzanne Robinson, 2011. "Test–retest reliability of health state valuation techniques: the time trade off and person trade off," Health Economics, John Wiley & Sons, Ltd., vol. 20(11), pages 1379-1391, November.
    7. Fasolo, Barbara & Bana e Costa, Carlos A., 2014. "Tailoring value elicitation to decision makers' numeracy and fluency: Expressing value judgments in numbers or words," Omega, Elsevier, vol. 44(C), pages 83-90.
    8. Michele Graffeo & Nicolao Bonini, 2018. "The interaction between frames and numeracy in the evaluation of price reductions," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 35(1), pages 239-250, April.
    9. Lisa Prosser & James Hammitt & Ron Keren, 2007. "Measuring Health Preferences for Use in Cost-Utility and Cost-Benefit Analyses of Interventions in Children," PharmacoEconomics, Springer, vol. 25(9), pages 713-726, September.

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