Skip to main content

Advertisement

Log in

Comparative performance of the EQ-5D-5L and SF-6D index scores in adults with type 2 diabetes

  • Published:
Quality of Life Research Aims and scope Submit manuscript

Abstract

Purpose

To explore the comparative performance including discriminative and longitudinal validity of EQ-5D-5L and SF-6D index scores in adults with type 2 diabetes.

Methods

Data from an on-going cohort study of adults with type 2 diabetes in Alberta, Canada, were used. Known-groups approach was used to examine discriminative validity. Correlation and agreement indices and scatter and Bland–Altman plots were used to examine the relationship between the two measures. Longitudinal validity was explored using Wilcoxon signed-rank test, effect size, and standardized response mean.

Results

In 1832 participants at baseline (age 64.3, standard deviation 10.6 years; 45% female), median EQ-5D-5L score was 0.85 [interquartile range (IQR) 0.17], and floor and ceiling effects of 0.1 and 16.1%, respectively; median SF-6D score was 0.72 (IQR 0.24), and floor and ceiling effects of 0.1 and 3.2%, respectively. EQ-5D-5L and SF-6D index scores were significantly correlated with an overall Spearman correlation coefficient of 0.73, and an ICC of 0.62 (95% CI 0.42–0.74). Both EQ-5D-5L and SF-6D scores demonstrated statistically significant differences in self-reported chronic conditions, depressive symptoms, and diabetes-related distress, and were able to detect changes in depressive symptoms and diabetes distress across all change groups.

Conclusions

Both EQ-5D-5L and SF-6D index scores provide valid measurement in this patient population. Considerable overlap between the measures means it is not necessary to include both in surveys, however, the advantages and disadvantages of each should be considered.

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
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Shaw, J. E., Sicree, R. A., & Zimmet, P. Z. (2010). Global estimates of the prevalence of diabetes for 2010 and 2030. Diabetes Research and Clinical Practice, 87(1), 4–14.

    Article  CAS  PubMed  Google Scholar 

  2. Egede, L. E. (2004). Diabetes, major depression, and functional disability among U.S. adults. Diabetes Care, 27(2), 421–428.

    Article  PubMed  Google Scholar 

  3. Gregg, E. W., Mangione, C. M., Cauley, J. A., Thompson, T. J., Schwartz, A. V., Ensrud, K. E., et al. (2002). Diabetes and incidence of functional disability in older women. Diabetes Care, 25(1), 61–67.

    Article  PubMed  Google Scholar 

  4. Maddigan, S., Feeny, D., & Johnson, J. A. (2005). Health-related quality of life deficits associated with diabetes and comorbidities in a Canadian National Population Health Survey. Quality of Life Research, 14(5), 1311–1320.

    Article  PubMed  Google Scholar 

  5. Marrero, D., Pan, Q., Barrett-Connor, E., de Groot, M., Zhang, P., Percy, C., et al. (2014). Impact of diagnosis of diabetes on health-related quality of life among high risk individuals: The Diabetes Prevention Program outcomes study. Quality of Life Research, 23(1), 75–88.

    Article  CAS  PubMed  Google Scholar 

  6. Maddigan, S. L., Feeny, D. H., Majumdar, S. R., Farris, K. B., & Johnson, J. A. (2006). Understanding the determinants of health for people with type 2 diabetes. American Journal of Public Health, 96(9), 1649–1655.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Drummond, M. (2001). Introducing economic and quality of life measurements into clinical trials. Annals of Medicine, 33, 344–349.

    Article  CAS  PubMed  Google Scholar 

  8. Drummond, M. F., O’Brien, B. J., Stoddart, G. L., & Torrance, G. W. (1997). Methods for the economic evaluation of health care programmes (2nd ed.). Oxford: Oxford University Press.

    Google Scholar 

  9. Garratt, A., Schmidt, L., Mackintosh, A., & Fitzpatrick, R. (2002). Quality of life measurement: Bibliographic study of patient assessed health outcome measures. BMJ, 324(7351), 1471.

    Article  Google Scholar 

  10. Xie, F., Pullenayegum, E., Gaebel, K., Bansback, N., Bryan, B., Ohinmaa, A., et al. (2016). A time trade-off-derived value set of the EQ-5D-5L for Canada. Medical Care, 54(1), 98–105.

    Article  PubMed  Google Scholar 

  11. Brazier, J. E., & Roberts, J. (2004). The estimation of a preference-based measure of health from the SF-12. Medical Care, 42(9), 851–859.

    Article  PubMed  Google Scholar 

  12. Whitehurst, D. G., Bryan, S., & Lewis, M. (2011). Systematic review and empirical comparison of contemporaneous EQ-5D and SF-6D group mean scores. Medical Decision Making, 31(6), E34–E44.

    Article  PubMed  Google Scholar 

  13. Brazier, J., Roberts, J., Tsuchiya, A., & Busschbach, J. (2004). A comparison of the EQ-5D and SF-6D across seven patient groups. Health Economics, 13(9), 873–884.

    Article  PubMed  Google Scholar 

  14. De Smedt, D., Clays, E., Annemans, L., & De Bacquer, D. (2014). EQ-5D versus SF-12 in coronary patients: Are they interchangeable? Value in Health, 17(1), 84–89.

    Article  PubMed  Google Scholar 

  15. Kontodimopoulos, N., Pappa, E., Chadjiapostolou, Z., Arvanitaki, E., Papadopoulos, A. A., & Niakas, D. (2012). Comparing the sensitivity of EQ-5D, SF-6D and 15D utilities to the specific effect of diabetic complications. The European Journal of Health Economics, 13(1), 111–120.

    Article  PubMed  Google Scholar 

  16. Kontodimopoulos, N., Pappa, E., Papadopoulos, A. A., Tountas, Y., & Niakas, D. (2009). Comparing SF-6D and EQ-5D utilities across groups differing in health status. Quality of Life Research, 18(1), 87–97.

    Article  PubMed  Google Scholar 

  17. Mulhern, B., & Meadows, K. (2014). The construct validity and responsiveness of the EQ-5D, SF-6D and Diabetes Health Profile-18 in type 2 diabetes. Health and Quality of Life Outcomes, 12, 42.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Obradovic, M., Lal, A., & Liedgens, H. (2013). Validity and responsiveness of EuroQol-5 dimension (EQ-5D) versus Short Form-6 dimension (SF-6D) questionnaire in chronic pain. Health and Quality of Life Outcomes, 11, 110.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Petrou, S., & Hockley, C. (2005). An investigation into the empirical validity of the EQ-5D and SF-6D based on hypothetical preferences in a general population. Health Economics, 14(11), 1169–1189.

    Article  PubMed  Google Scholar 

  20. Pickard, A. S., Johnson, J. A., & Feeny, D. H. (2005). Responsiveness of generic health-related quality of life measures in stroke. Quality of Life Research, 14(1), 207–219.

    Article  PubMed  Google Scholar 

  21. van Stel, H. F., & Buskens, E. (2006). Comparison of the SF-6D and the EQ-5D in patients with coronary heart disease. Health and Quality of Life Outcomes, 4, 20.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Wu, J., Han, Y., Zhao, F. L., Zhou, J., Chen, Z., & Sun, H. (2014). Validation and comparison of EuroQoL-5 dimension (EQ-5D) and Short Form-6 dimension (SF-6D) among stable angina patients. Health and Quality of Life Outcomes, 12, 156.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Xie, F., Li, S. C., Luo, N., Lo, N. N., Yeo, S. J., Yang, K. Y., et al. (2007). Comparison of the EuroQol and short form 6D in Singapore multiethnic Asian knee osteoarthritis patients scheduled for total knee replacement. Arthritis and Rheumatism, 57(6), 1043–1049.

    Article  PubMed  Google Scholar 

  24. Agborsangaya, C. B., Lahtinen, M., Cooke, T., Johnson, J. A. (2014). Comparing the EQ-5D 3L and 5L: Measurement properties and association with chronic conditions and multimorbidity in the general population. Health and Quality of Life Outcomes, 12, 74.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Janssen, M. F., Pickard, A. S., Golicki, D., Gudex, C., Niewada, M., Scalone, L., et al. (2013). Measurement properties of the EQ-5D-5L compared to the EQ-5D-3L across eight patient groups: A multi-country study. Quality of Life Research, 22(7), 1717–1727.

    Article  CAS  PubMed  Google Scholar 

  26. Al Sayah, F., Majumdar, S. R., Soprovich, A., Wozniak, L., Johnson, S. T., Qiu, W., et al. (2015). The Alberta’s Caring for Diabetes (ABCD) Study: Rationale, design and baseline characteristics of a prospective cohort of adults with type 2 diabetes. Canadian Journal of Diabetes, 39(Suppl 3), S113–S119.

    Article  PubMed  Google Scholar 

  27. Herdman, M., Gudex, C., Lloyd, A., Janssen, M., Kind, P., Parkin, D., et al. (2011). Development and preliminary testing of the new five-level version of EQ-5D (EQ-5D-5L). Quality of Life Research, 20(10), 1727–1736.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Kroenke, K., Strine, T. W., Spitzer, R. L., Williams, J. B., Berry, J. T., & Mokdad, A. H. (2009). The PHQ-8 as a measure of current depression in the general population. Journal of Affective Disorders, 114(1–3), 163–173.

    Article  PubMed  Google Scholar 

  29. Manea, L., Gilbody, S., & McMillan, D. (2012). Optimal cut-off score for diagnosing depression with the Patient Health Questionnaire (PHQ-9): A meta-analysis. Canadian Medical Association Journal, 184(3), E191–E196.

    Article  PubMed  PubMed Central  Google Scholar 

  30. McGuire, B. E., Morrison, T. G., Hermanns, N., Skovlund, S., Eldrup, E., Gagliardino, J., et al. (2010). Short-form measures of diabetes-related emotional distress: The Problem Areas in Diabetes Scale (PAID)-5 and PAID-1. Diabetologia, 53(1), 66–69.

    Article  CAS  PubMed  Google Scholar 

  31. Norman, G. R., Sloan, J. A., & Wyrwich, K. W. (2003). Interpretation of changes in health-related quality of life: The remarkable universality of half a standard deviation. Medical Care, 41(5), 582–592.

    PubMed  Google Scholar 

  32. Fritz, C. O., Morris, P. E., & Richler, J. J. (2012). Effect size estimates: Current use, calculations, and interpretation. Journal of Experimental Psychology: General, 141(1), 2–18.

    Article  Google Scholar 

  33. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates Inc.

    Google Scholar 

  34. Mukaka, M. (2012). A guide to appropriate use of correlation coefficient in medical research. Malawi Medical Journal, 24(3), 69–71.

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Cicchetti, D. V. (1994). Guidelines, criteria, and rules of thumb for evaluating normed and standardized assessment instruments in psychology. Psychological Assessment, 6(4), 284–290.

    Article  Google Scholar 

  36. Giavarina, D. (2015). Understanding Bland Altman analysis. Biochemia Medica, 25(2), 141–151.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Biering, K., Hjollund, N. H., & Frydenberg, M. (2015). Using multiple imputation to deal with missing data and attrition in longitudinal studies with repeated measures of patient-reported outcomes. Clinical Epidemiology, 16(7), 91–106.

    Article  Google Scholar 

  38. Revicki DA. (2002). Analyzing longitudinal health-related quality of life data: Missing data and imputation methods. In: M. Mesbah, B. F. Cole, M. L. Ting Lee (Eds.). Statistical methods for quality of life studies: Design, measurements and analysis (1st ed., pp 103–112). New York: Springer.

    Chapter  Google Scholar 

  39. Royston, P., & White, I. R. (2011) Multiple Imputation by Chained Equations (MICE): Implementation in Stata. Journal of Statistical Software, 45(4), 1–20.

    Article  Google Scholar 

  40. Wee, H. L., Machin, D., Loke, W. C., Li, S. C., Cheung, Y. B., Luo, N., et al. (2007). Assessing differences in utility scores: A comparison of four widely used preference-based instruments. Value in Health, 10(4), 256–265.

    Article  PubMed  Google Scholar 

  41. Yordanova, S., Petkova, V., Petrova, G., Dimitrov, M., Naseva, E., Dimitrova, M., et al. (2014). Comparison of health-related quality-of-life measurement instruments in diabetic patients. Biotechnology and Biotechnological Equipment, 28(4), 769–774.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Pattanaphesaj, J., & Thavorncharoensap, M. (2015). Measurement properties of the EQ-5D-5L compared to EQ-5D-3L in the Thai diabetes patients. Health and Quality of Life Outcomes, 13, 14.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Brazier, J., Deverill, M., Green, C., Harper, R., & Booth, A. (1999). A review of the use of health status measures in economic evaluation. Health Technology Assessment, 3(9), 1–164.

    Google Scholar 

  44. Bryan, S., & Longworth, L. (2005). Measuring health-related utility: Why the disparity between EQ-5D and SF-6D? The European Journal of Health Economics, 6(3), 253–260.

    Article  PubMed  Google Scholar 

Download references

Funding

This work was supported by grant from Alberta Health, and a CIHR Team Grant to the Alliance for Canadian Health Outcomes Research in Diabetes (reference #: OTG- 88588), sponsored by the CIHR Institute of Nutrition, Metabolism and Diabetes (INMD).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jeffrey A. Johnson.

Ethics declarations

Conflict of interest

JAJ is a member of the Board of Directors for the EuroQol Research Foundation, which holds the copyright for EQ-5D instruments. The other co-authors have no conflicts of interest to declare.

Ethical approval

Ethical approval for the study was granted by the Health Research Ethics Board at the University of Alberta.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sayah, F.A., Qiu, W., Xie, F. et al. Comparative performance of the EQ-5D-5L and SF-6D index scores in adults with type 2 diabetes. Qual Life Res 26, 2057–2066 (2017). https://doi.org/10.1007/s11136-017-1559-8

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11136-017-1559-8

Keywords

Navigation