Skip to main content
Log in

Estimation of a Valuation Function for a Diabetes Mellitus-Specific Preference-Based Measure of Health

The Diabetes Utility Index®

  • Original Research Article
  • Published:
PharmacoEconomics Aims and scope Submit manuscript

Abstract

Background: Preference-based measures of health (PBMH) provide ‘preference’ or ‘utility’ weights that enable the calculation of QALYs for the economic evaluations of interventions. The Diabetes Utility Indexê (DUI) was developed as a brief, self-administered, diabetes mellitusspecific PBMH that can efficiently estimate patient-derived health state utilities.

Objective: To describe the development of the valuation function for the DUI, and to report the validation results of the valuation function.

Methods: Multi-Attribute Utility Theory (MAUT) was used as the framework to develop a valuation function for the DUI. Twenty of 768 possible health states of the DUI classified as anchor states, single-attribute level states including corner states, and marker states were selected and described for preference elicitation interviews. Visual analogue scale and standard gamble (SG) exercises were used to measure preferences from individuals with diabetes recruited from primary care and community settings in and around Morgantown, WV, USA for the 20 health states defined by combinations of DUI attributes and severity levels. Data collected in the interviews were used to develop a valuation function that calculates utilities for the DUI health states and calculates attribute-level utilities. A validation survey of the valuation function was conducted in collaboration with the West Virginia University (WVU) Diabetes Institute.

Results: A total of 100 individuals with diabetes were interviewed and their preferences for various DUI health states measured. From data generated in the interviews, a DUI valuation function was developed on a scale where 1.00 = perfect health (PH) and 0.00 = the all worse pits state, and adjusted to yield utilities on the conventional scale 1.00 =PH and 0.00 = dead.

A total of 396 patients with diabetes who received care at WVU clinics completed a DUI mail validation survey (response rate = 33%). Clinical data consisting of International Classification of Diseases, 9th edition, diagnosis codes and glycosylated haemoglobin (HbA1c) values for the respondents were merged with their responses to the DUI. The utilities calculated by the scoring function of the DUI compared favourably to cardinal SG utilities for three DUI health states for which both assessments were available. The DUI utility function slightly underestimated actual SG utilities for mild and moderate health states (mean absolute difference = 0.05).

There was a small but significant correlation between DUI utility scores and average past year HbA1c values (r=-0.30; p < 0.001). Respondents with two or more complications had significantly lower DUI utilities than those with no complications (p < 0.001) or one complication (p = 0.015). Insulin users had significantly lower DUI utilities than non-users (p < 0.001), and those with HbA1c values <7% had significantly higher DUI utilities than those with HbA1c values of 7% (p < 0.001). No significant association was found between DUI scores and age or sex.

Conclusion: These results show evidence of the feasibility and validity of the DUI. Further research is suggested to demonstrate the generalizability of these findings, to study the responsiveness of the DUI, and to examine the clinical meaningfulness of DUI change scores.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Table I
Table II
Fig. 2
Table III
Table IV

Similar content being viewed by others

References

  1. Gold MR, Patrick DL, Torrance GW, et al. Identifying and valuing outcomes. In: Gold MR, Siegel JE, Russell LB, et al., editors. Cost-effectiveness in health and medicine. New York: Oxford University Press, 1996: 82–134

    Google Scholar 

  2. Drummond MF, O’Brien BJ, Stoddart GL, et al. Costutility analysis: methods for the economic evaluation of healthcare programs. New York: Oxford University Press, 1997: 139–204

    Google Scholar 

  3. Feeny D, Furlong W, Boyle M, et al. Multi-attribute health status classification systems: Health Utilities Index. Pharmacoeconomics 1995; 7 (6): 490–502

    Article  PubMed  CAS  Google Scholar 

  4. Dolan P. Modeling valuations for EuroQol health states. Med Care 1997; 35 (11): 1095–108

    Article  PubMed  CAS  Google Scholar 

  5. Brazier J, Usherwood T, Harper R, et al. Deriving a preference-based single index from the UK SF-36 Health Survey. J Clin Epidemiol 1998; 51 (11): 1115–28

    Article  PubMed  CAS  Google Scholar 

  6. Froberg DG, Kane RL. Methodology for measuring healthstate preferences: II. Scaling methods. J Clin Epidemiol 1989; 42 (5): 459–71

    Article  PubMed  CAS  Google Scholar 

  7. Dolan P. The measurement of health-related quality of life. In: Culyer AJ, Newhouse J, editors. Handbook of health economics. Vol. 1B. Amsterdam: Elsevier, 2000: 1723–60

    Article  Google Scholar 

  8. Dolan P. Modelling the relationship between the description and valuation of health states. In: Murray CJL, Salomon JA, Mathers CD, et al., editors. Summary measures of population health: concepts, ethics, measurement and applications. Geneva: World Health Organization, 2002: 510–3

    Google Scholar 

  9. Feeny D. The utility approach to assessing population health. In: Murray CJL, Salomon JA, Mathers CD, et al., editors. Summary measures of population health: concepts, ethics, measurement and applications. Geneva: World Health Organization, 2002: 515–28

    Google Scholar 

  10. Brazier J, Roberts J, Deverill M. The estimation of a preference-based measure of health from the SF-36. J Health Econ 2002; 21 (2): 271–92

    Article  PubMed  Google Scholar 

  11. Brooks R. EuroQol: the current state of play. Health Policy 1996; 37 (1): 53–72

    Article  PubMed  CAS  Google Scholar 

  12. Kind P. The EuroQoL instrument: an index of health-related quality of life. In: Spilker B, editor. Quality of life and pharmacoeconomics in clinical trials. Philadelphia (PA): Lippincott-Raven, 1996: 191–201

    Google Scholar 

  13. Torrance GW, Feeny DH, Furlong WJ, et al. Multiattribute utility function for a comprehensive health status classification system: Health Utilities Index Mark 2. Med Care 1996; 34 (7): 702–22

    Article  PubMed  CAS  Google Scholar 

  14. Feeny D, Furlong W, Torrance GW, et al. Multiattribute and single-attribute utility functions for theHealth Utilities Index Mark 3 system. Med Care 2002; 40 (2): 113–28

    Article  PubMed  Google Scholar 

  15. Kaplan RM, Anderson JP. A general health policy model: update and applications. Health Serv Res 1988; 23 (2): 203–35

    PubMed  CAS  Google Scholar 

  16. Kaplan RM, Anderson JP. The general health policy model: an integrated approach. In: Spilker B, editor. Quality of life and pharmacoeconomics in clinical trials. Philadelphia (PA): Lippincott-Raven, 1996: 302–22

    Google Scholar 

  17. Brazier JE, Roberts J. The estimation of a preference-based measure of health from the SF-12. Med Care 2004; 42 (9): 851–9

    Article  PubMed  Google Scholar 

  18. Kantz ME, Harris WJ, Levitsky K, et al. Methods for assessing condition-specific and generic functional status outcomes after total knee replacement. Med Care 1992; 30 Suppl. 5: 240–52

    Google Scholar 

  19. Bombardier C, Melfi CA, Paul J, et al. Comparison of a generic and a disease-specific measure of pain and physical function after knee replacement surgery. Med Care 1995; 33 Suppl. 4: 131–44

    Google Scholar 

  20. Revicki DA, Leidy NK, Brennan-Diemer F, et al. Integrating patient preferences into health outcomes assessment: the multiattribute Asthma Symptom Utility Index. Chest 1998; 114 (4): 998–1007

    Article  PubMed  CAS  Google Scholar 

  21. Revicki DA, Leidy NK, Brennan-Diemer F, et al. Development and preliminary validation of themultiattributeRhinitis Symptom Utility Index. Qual Life Res 1998; 7 (8): 693–702

    Article  PubMed  CAS  Google Scholar 

  22. Casey R, Tarride JE, Keresteci MA, et al. The Erectile Function Visual Analog Scale (EF-VAS): a disease-specific utility instrument for the assessment of erectile function. Can J Urol 2006; 13 (2): 3016–25

    PubMed  CAS  Google Scholar 

  23. Brazier JE, Roberts J, Platts M, et al. Estimating a preference-based index for a menopause specific health quality of life questionnaire. Health Qual Life Outcomes 2005; 3 (1) 13: 12–20

    Article  Google Scholar 

  24. Beusterien K, Leigh N, Jackson C, et al. Integrating preferences into health status assessment for amyotrophic lateral sclerosis: the ALS Utility Index. Amyotroph Lateral Scler Other Motor Neuron Disord 2005; 6 (3): 169–76

    Article  PubMed  Google Scholar 

  25. Sundaram M, Smith MJ, Nath C. Development of a classification system for a diabetes-specific preference-based measure of health. Value Health 2008; 11 (3): A233

    Article  Google Scholar 

  26. Sundaram M, Smith MJ, Revicki DA, et al. Rasch analysis informed the development of a classification system for a diabetes-specific preference-based measure of health. J Clin Epidemiol 2009 Aug; 62 (8): 845–56

    Article  PubMed  Google Scholar 

  27. Furlong W, Feeny DH, Torrance GW, et al. Multiplicative multi-attribute utility function for the Health Utilities Index Mark 3 (HUI 3): a technical report, 98-11. Hamilton (ON): Center for Health Economics and Policy Analysis, McMaster University, 1998

    Google Scholar 

  28. Keeny RL, Raiffa H. Decisions with multiple objectives: preferences and value trade-offs. 1st ed. New York: John Wiley & Sons, 1976

    Google Scholar 

  29. Keeny RL, Raiffa H. Decisions with multiple objectives: preferences and value trade-offs. 2nd ed. New York: Cambridge University Press, 1993

    Google Scholar 

  30. Torrance GW, Boyle MH, Horwood SP. Application of multi-attribute utility theory to measure social preferences for health states. Oper Res 1982; 30 (6): 1043–69

    Article  PubMed  CAS  Google Scholar 

  31. Torrance GW, Furlong W, Feeny D, et al. Multi-attribute preference functions: Health Utilities Index. Pharmacoeconomics 1995; 7 (6): 503–20

    Article  PubMed  CAS  Google Scholar 

  32. Torrance GW, Feeny D, Furlong W. Visual analog scales: do they have a role in the measurement of preferences for health states? Med Decis Making 2001; 21 (4): 329–34

    PubMed  CAS  Google Scholar 

  33. von Neumann J, Morgenstern O, Rubinstein A. Theory of games and economic behavior. Princeton (NJ): Princeton University Press, 2004

    Google Scholar 

  34. Furlong W, Feeny DH, Torrance GW, et al. Guide to design and development of health state utility instrumentation. Hamilton (ON): Center for Health Economics and Policy Analysis, McMaster University, 1990

    Google Scholar 

  35. Lau CY, Qureshi AK, Scott SG. Association between glycaemic control and quality of life in diabetes mellitus. J Postgrad Med 2004; 50 (3): 189–93

    PubMed  CAS  Google Scholar 

  36. Sundaram M, Kavookjian J, Patrick JH, et al. Quality of life, health status and clinical outcomes in type 2 diabetes patients. Qual Life Res 2007; 16 (2): 165–77

    Article  PubMed  Google Scholar 

  37. Polonsky WH, Anderson BJ, Lohrer PA, et al. Assessment of diabetes-related distress. Diabetes Care 1995; 18 (6): 754–60

    Article  PubMed  CAS  Google Scholar 

  38. Nerenz DR, Repasky DP, Whitehouse FW, et al. Ongoing assessment of health status in patients with diabetes mellitus. Med Care 1992; 30 (5 Suppl.): MS112–24

    Google Scholar 

  39. Guttmann-Bauman I, Flaherty BP, Strugger M, et al. Metabolic control and quality-of-life self-assessment in adolescents with IDDM. Diabetes Care 1998; 21 (6): 915–8

    Article  PubMed  CAS  Google Scholar 

  40. Sundaram M, Kavookjian J, Patrick JH. Health-related quality of life and quality of life in type 2 diabetes: relationships in a cross-sectional study. Patient 2009; 2 (2): 121–33

    Article  PubMed  Google Scholar 

  41. Lloyd A, Sawyer W, Hopkinson P. Impact of long-term complications on quality of life in patientswith type 2 diabetes not using insulin. Value Health 2001; 4 (5): 392–400

    Article  PubMed  CAS  Google Scholar 

  42. Weinberger M, Kirkman MS, Samsa GP, et al. The relationship between glycemic control and health-related quality of life in patients with non-insulin-dependent diabetes mellitus. Med Care 1994; 32 (12): 1173–81

    Article  PubMed  CAS  Google Scholar 

  43. Maddigan SL, Feeny DH, Johnson JA. Construct validity of the RAND-12 and Health Utilities Index Mark 2 and 3 in type 2 diabetes. Qual Life Res 2004; 13 (2): 435–48

    Article  PubMed  Google Scholar 

  44. Petrillo J, Cairns J. Converting condition-specific measures into preference-based outcomes for use in economic evaluation. Expert Rev Pharmacoecon Outcomes Res 2008; 8 (5): 453–61

    Article  PubMed  Google Scholar 

  45. Dowie J. Decision validity should determine whether a generic or condition-specific HRQOL measure is used in health care decisions. Health Econ 2002; 11 (1): 1–8

    Article  PubMed  Google Scholar 

  46. Siegel JE, Weinstein MC, Russell LB, et al. Recommendations for reporting cost-effectiveness analyses. Panel on Cost-Effectiveness in Health and Medicine. JAMA 1996; 276 (16): 1339–41

    Article  PubMed  CAS  Google Scholar 

  47. Weinstein MC, Siegel JE, Gold MR, et al. Recommendations of the Panel on Cost-effectiveness in Health and Medicine. JAMA 1996; 276 (15): 1253–8

    Article  PubMed  CAS  Google Scholar 

  48. Russell LB, Gold MR, Siegel JE, et al. The role of costeffectiveness analysis in health and medicine. Panel on Cost-Effectiveness in Health and Medicine. JAMA 1996; 276 (14): 1172–7

    Article  PubMed  CAS  Google Scholar 

  49. National Institute for Health and Clinical Excellence. Guide to the methods of technology appraisal. London: NICE, 2008

    Google Scholar 

Download references

Acknowledgements

The work detailed in this paper was supported by financial assistance from Eli Lilly and Company in the form of a dissertation research grant. The funding source had no influence on the design, analysis or reporting of research results. There are no known financial or other conflicts of interest between the authors of this paper and the funding source of the research.

The authors would like to express their gratitude to Bill Furlong of the HUI group for guidance in the application of the MAUT to our work. At the time of submission of this manuscript, Dr Murali Sundaram was employed as Scientist at QualityMetric Incorporated, Lincoln, Rhode Island, USA.

The DUI is protected by copyright. For further inquiries regarding the DUI, please contact Dr Murali Sundaram at doctormurali@gmail.com

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Murali Sundaram.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Sundaram, M., Smith, M.J., Revicki, D.A. et al. Estimation of a Valuation Function for a Diabetes Mellitus-Specific Preference-Based Measure of Health. Pharmacoeconomics 28, 201–216 (2010). https://doi.org/10.2165/11313990-000000000-00000

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.2165/11313990-000000000-00000

Keywords

Navigation