Assessing Values for Health: Numeracy Matters

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 Ps ≤ 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.

[1]  L A Lenert,et al.  Effects on Preferences of Violations of Procedural Invariance , 1999, Medical decision making : an international journal of the Society for Medical Decision Making.

[2]  C. Estrada,et al.  Health literacy and numeracy. , 1999, JAMA.

[3]  M. Mcgrath Cost Effectiveness in Health and Medicine. , 1998 .

[4]  E F Cook,et al.  Health values of hospitalized patients 80 years or older. HELP Investigators. Hospitalized Elderly Longitudinal Project. , 1998, JAMA.

[5]  Lisa M. Schwartz,et al.  The Role of Numeracy in Understanding the Benefit of Screening Mammography , 1997, Annals of Internal Medicine.

[6]  J. Bosch,et al.  The Relationship between Descriptive and Valuational Quality-of-life Measures in Patients with Intermittent Claudication , 1996, Medical decision making : an international journal of the Society for Medical Decision Making.

[7]  Harold C. Sox,et al.  Variation in Patient Utilities for Outcomes of the Management of Chronic Stable Angina: Implications for Clinical Practice Guidelines , 1995 .

[8]  Irwin S. Kirsch,et al.  Adult literacy in America , 1993 .

[9]  W Sumner,et al.  U-titer: a utility assessment tool. , 1991, Proceedings. Symposium on Computer Applications in Medical Care.

[10]  G W Torrance,et al.  Utility approach to measuring health-related quality of life. , 1987, Journal of chronic diseases.

[11]  R Tibshirani,et al.  Describing Health States: Methodologic Issues in Obtaining Values for Health States , 1984, Medical care.

[12]  M. Weinstein,et al.  Clinical Decision Analysis , 1980 .

[13]  G. Chapman,et al.  [Medical decision making]. , 1976, Lakartidningen.

[14]  J. Neumann,et al.  Theory of games and economic behavior , 1945, 100 Years of Math Milestones.