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Quality of Life Research

, Volume 26, Issue 5, pp 1177–1186 | Cite as

Does the EQ-5D capture the effects of physical and mental health status on life satisfaction among older people? A path analysis approach

  • Eithne SextonEmail author
  • Kathleen Bennett
  • Tom Fahey
  • Caitriona Cahir
Article

Abstract

Purpose

To examine the extent to which EQ-5D utility scores capture the effect of mental and physical health status on life satisfaction (LS) in older adults.

Methods

Retrospective cohort study of 884 patients aged ≥70 years from 15 general practices in Ireland, including medical records, pharmacy claims, and self-completion questionnaire. Path analysis was used to evaluate the direct and indirect effects of: (1) chronic disease burden (based on medications data); (2) activity limitation (basic and instrumental activities of daily living); (3) anxiety symptoms and; (4) depressive symptoms (Hospital Anxiety and Depression Scale) on LS (Life Satisfaction Index Z), via a utility score based on responses to the EQ-5D scale. Utility scores were calculated using UK time trade-off utility weights. Covariates included age and socioeconomic status.

Results

The final path model fitted the data well (goodness of fit χ2 = 7.5, df (7), p = 0.37). The direct effects of chronic disease burden and disability on LS were not statistically significant and were excluded from the final model, indicating that EQ-5D score mediated 100% of the total effect on LS. The direct and indirect effects of anxiety and depression on LS were statistically significant, but the size of the indirect effect was small (4% of the total effect for anxiety and 6% of the total effect for depression).

Conclusion

The EQ-5D does not adequately capture the effects of anxiety and depression on LS among older adults, suggesting that it may lead to inaccurate assessments of the effectiveness of interventions in this cohort.

Keywords

Health-related quality of life Utility Anxiety Depression Life satisfaction Older adults 

Notes

Acknowledgements

This work was supported by the Health Research Board PhD Scholar’s Programme in Health Services Research in Ireland under Grant No. PHD/2007/16 and the Health Research Board Centre for Primary Care Research Grant HRC/2007/1.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflicts of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

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

Supplementary material

11136_2016_1459_MOESM1_ESM.docx (71 kb)
Supplementary material 1 (DOCX 70 kb)

References

  1. 1.
    EuroQoL Group. (1990). EuroQol—a new facility for the measurement of health-related quality of life. Health Policy, 16, 199–208. doi: 10.1016/0168-8510(90)90421-9.CrossRefGoogle Scholar
  2. 2.
    Wilson, I. B., & Cleary, P. D. (1995). Linking clinical variables with health-related quality of life. A conceptual model of patient outcomes. JAMA: The Journal of the American Medical Association, 273(1), 59–65. http://www.ncbi.nlm.nih.gov/pubmed/7996652.
  3. 3.
    Ferrans, C. E., Zerwic, J. J., Wilbur, J. E., & Larson, J. L. (2005). Conceptual model of health-related quality of life. Journal of Nursing Scholarship, 37(4), 336–42. http://www.ncbi.nlm.nih.gov/pubmed/16396406.
  4. 4.
    Camfield, L., & Skevington, S. M. (2008). On subjective well-being and quality of life. Journal of Health Psychology, 13(6), 764–775. doi: 10.1177/1359105308093860.CrossRefPubMedGoogle Scholar
  5. 5.
    Ryff, C. D., & Singer, B. H. (2008). Know thyself and become what you are: A eudaimonic approach to psychological well-being. Journal of Happiness Studies, 9(1), 13–39. doi: 10.1007/s10902-006-9019-0.CrossRefGoogle Scholar
  6. 6.
    Mulhern, B., Mukuria, C., Barkham, M., Knapp, M., Byford, S., Soeteman, D., & Brazier, J. (2012). Using preference based measures in mental health conditions: The psychometric validity of the EQ-5D and SF-6D (No. 13/04). HEDS Discussion Paper.Google Scholar
  7. 7.
    Peasgood, T., Brazier, J., & Papaionnou, D. (2011). A sysyematic review of the validity and responsiveness of EQ-5D and SF-6D for depression and anxiety (No. 12/15). HEDS Discussion Paper. http://eprints.whiterose.ac.uk/74892.
  8. 8.
    Burström, K., Johannesson, M., & Diderichsen, F. (2001). Swedish population health-related quality of life results using the EQ-5D. Quality of Life Research, 10, 621–635. http://link.springer.com/article/10.1023/A:1013171831202.
  9. 9.
    Brazier, J. (2010). Is the EQ-5D fit for purpose in mental health? British Journal of Psychiatry, 197(5), 348–349. doi: 10.1192/bjp.bp.110.082453.CrossRefPubMedGoogle Scholar
  10. 10.
    Dolan, P. (2013). Addressing misconceptions in valuing health. Expert Review of Pharmacoeconomics and Outcomes Research, 13(1), 1–3. doi: 10.1586/erp.12.90.CrossRefPubMedGoogle Scholar
  11. 11.
    Dolan, P., & Metcalfe, R. (2012). Valuing health: A brief report on subjective well-being versus preferences. Medical Decision Making, 32,578–582. doi: 10.1177/0272989X11435173
  12. 12.
    Peeters, Y., & Stiggelbout, A. M. (2010). Health state valuations of patients and the general public analytically compared: A meta-analytical comparison of patient and population health state utilities. Value in Health, 13(2), 306–309. doi: 10.1111/j.1524-4733.2009.00610.x.CrossRefPubMedGoogle Scholar
  13. 13.
    Gerhards, S. A., Evers, S. M., Sabel, P. W., & Huibers, M. J. (2011). Discrepancy in rating health-related quality of life of depression between patient and general population. Quality of Life Research: An International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation, 20(2), 273–279. doi: 10.1007/s11136-010-9746-x.CrossRefGoogle Scholar
  14. 14.
    Pyne, J. M., Fortney, J. C., Tripathi, S., Feeny, D., Ubel, P., & Brazier, J. (2009). How bad is depression? Preference score estimates from depressed patients and the general population. Health Services Research, 44(4), 1406–1423. doi: 10.1111/j.1475-6773.2009.00974.x.CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Papageorgiou, K., Vermeulen, K. M., Schroevers, M. J., Stiggelbout, A. M., Buskens, E., Krabbe, P. F. M., et al. (2015). Do individuals with and without depression value depression differently? And if so, why? Quality of Life Research, 24(11), 2565–2575. doi: 10.1007/s11136-015-1018-3.CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Ubel, P. A., Loewenstein, G., Schwarz, N., & Smith, D. (2005). Misimagining the unimaginable: The disability paradox and health care decision making. Health Psychology, 24(4), S57–S62. doi: 10.1037/0278-6133.24.4.S57.CrossRefPubMedGoogle Scholar
  17. 17.
    Ubel, P. A., Loewenstein, G., & Jepson, C. (2003). Whose quality of life? A commentary exploring discrepancies between health state evaluations of patients and the general public. Quality of Life Research, 12(6), 599–607.CrossRefPubMedGoogle Scholar
  18. 18.
    Graham, C., Higuera, L., & Lora, E. (2011). Which health conditions cause the most unhappiness. Health Economics, 20, 1431–1447. doi: 10.1002/hec.CrossRefPubMedGoogle Scholar
  19. 19.
    Böckerman, P., Johansson, E., & Saarni, S. I. (2011). Do established health-related quality-of-life measures adequately capture the impact of chronic conditions on subjective well-being? Health policy (Amsterdam, Netherlands), 100(1), 91–95. doi: 10.1016/j.healthpol.2010.10.008.CrossRefGoogle Scholar
  20. 20.
    Richardson, J., Chen, G., Khan, M. A., & Iezzi, A. (2015). Can multi-attribute utility instruments adequately account for subjective well-being? Medical Decision Making, 35(3), 292–304. doi: 10.1177/0272989X14567354.CrossRefPubMedGoogle Scholar
  21. 21.
    Cahir, C., Bennett, K., Teljeur, C., & Fahey, T. (2014). Potentially inappropriate prescribing and adverse health outcomes in community dwelling older patients. British Journal of Clinical Pharmacology, 77(1), 201–210. doi: 10.1111/bcp.12161.CrossRefPubMedGoogle Scholar
  22. 22.
    Rabin, R., & de Charro, F. (2001). EQ-5D: A measure of health status from the EuroQol Group. Annals of Medicine, 33(5), 337–343. doi: 10.3109/07853890109002087.CrossRefPubMedGoogle Scholar
  23. 23.
    Dolan, P. (1997). Modeling valuations for EuroQol health states. Medical Care, 35(11), 1095–1108. http://www.jstor.org/stable/10.2307/3767472.
  24. 24.
    Neugarten, B. L., Havinghurst, R. J., & Tobin, S. S. (1961). The measurement of life satisfaction. Journal of Gerontology, 16, 134–143. doi: 10.1093/geronj/16.2.134.CrossRefPubMedGoogle Scholar
  25. 25.
    Adams, D. (1969). Analysis of a life satisfaction index. Journal of Gerontology, 24, 470–474.CrossRefPubMedGoogle Scholar
  26. 26.
    McDowell, I. (2010). Measures of self-perceived well-being. Journal of Psychosomatic Research, 69(1), 69–79. doi: 10.1016/j.jpsychores.2009.07.002.CrossRefPubMedGoogle Scholar
  27. 27.
    Wallace, K. A., & Wheeler, A. J. (2002). Reliability generalization of the life satisfaction index. Educational and Psychological Measurement, 62(4), 674–684. doi: 10.1177/0013164402062004009.CrossRefGoogle Scholar
  28. 28.
    Sloan, K. L., Sales, A. E., Liu, C.-F., Fishman, P., Nichol, P., Suzuki, N. T., et al. (2003). Construction and characteristics of the RxRisk-V: A VA-adapted pharmacy-based case-mix instrument. Medical Care, 41(6), 761–774. doi: 10.1097/01.MLR.0000064641.84967.B7.PubMedGoogle Scholar
  29. 29.
    WHO Collaborating Centre for Drug Statistics Methodology. (2010). Anatomical therapeutic classification (ATC) classification index. Oslo: WHO Collaborating Centre for Drug Statistics Methodology.Google Scholar
  30. 30.
    Fishman, P. A., Goodman, M. J., Hornbrook, M. C., Meenan, R. T., Bachman, D. J., & Rosetti, M. C. O. (2003). Risk adjustment using automated ambulatory pharmacy data. Medical Care, 41(1), 84–99. doi: 10.1097/00005650-200301000-00011.CrossRefPubMedGoogle Scholar
  31. 31.
    Saliba, D., Elliott, M., Rubenstein, L. Z., Solomon, D. H., Young, R. T., Kamberg, C. J., et al. (2001). The vulnerable elders survey: A tool for identifying vulnerable older people in the community. Journal of the American Geriatrics Society, 49(12), 1691–1699. doi: 10.1046/j.1532-5415.2001.49281.x.CrossRefPubMedGoogle Scholar
  32. 32.
    Zigmond, A. S., & Snaith, R. P. (1983). The hospital anxiety and depression scale. Acta Psychiatrica Scandinavica, 67(6), 361–370. doi: 10.1111/j.1600-0447.1983.tb09716.x.CrossRefPubMedGoogle Scholar
  33. 33.
    Snaith, R. P. (2003). The hospital anxiety and depression scale. Health and quality of life outcomes, 1, 29. doi: 10.1186/1477-7525-1-29.CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Kelly, A., & Teljeur, C. (2007). The national deprivation index for health and health services research. Dublin: Small Area Health Research Unit.Google Scholar
  35. 35.
    Central Statistics Office. (2006). Census 2006. Dublin: Central Statistics Office, Information Section.Google Scholar
  36. 36.
    Sousa, K. H., & Kwok, O.-M. (2006). Putting Wilson and Cleary to the test: Analysis of a HRQOL conceptual model using structural equation modeling. Quality of Life Research, 15(4), 725–737. doi: 10.1007/s11136-005-3975-4.CrossRefPubMedGoogle Scholar
  37. 37.
    Sullivan, M. D., Kempen, G. I., Van Sonderen, E., & Ormel, J. (2000). Models of health-related quality of life in a population of community-dwelling Dutch elderly. Quality of Life Research, 9(7), 801–810. http://www.ncbi.nlm.nih.gov/pubmed/11297022.
  38. 38.
    Byrne, B. M. (2012). Structural equation modeling with Mplus: Basic concepts, applications, and programming. Multivariate applications series. New York: Routledge.Google Scholar
  39. 39.
    Preacher, K. J., & Kelley, K. (2011). Effect size measures for mediation models: Quantitative strategies for communicating indirect effects. Psychological Methods, 16(2), 93–115. doi: 10.1037/a0022658.CrossRefPubMedGoogle Scholar
  40. 40.
    Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155–159.CrossRefPubMedGoogle Scholar
  41. 41.
    Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). New York: The Guilford Press.Google Scholar
  42. 42.
    Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55.CrossRefGoogle Scholar
  43. 43.
    Kind, P., Hardman, G., & Macran, S. (1999). UK population norms for EQ-5D (No. 172). Centre for Health Economics Discussion Paper Series. York: Centre for Health Economics. http://www.york.ac.uk/che/pdf/DP172.pdf.
  44. 44.
    McTaggart-Cowan, H., Tsuchiya, A., O’Cathain, A., & Brazier, J. (2011). Understanding the effect of disease adaptation information on general population values for hypothetical health states. Social Science and Medicine, 72(11), 1904–1912. doi: 10.1016/j.socscimed.2011.03.036.CrossRefPubMedGoogle Scholar
  45. 45.
    Richardson, J., Iezzi, A., Khan, M. A., Chen, G., & Maxwell, A. (2015). Measuring the sensitivity and construct validity of 6 utility instruments in 7 disease areas. Medical Decision Making,. doi: 10.1177/0272989X15613522.Google Scholar
  46. 46.
    National Institute for Health and Care Excellence. (2013). Guide to the methods of technology appraisal 2013. National Institute for Health and Care Excellence. http://www.nice.org.uk/media/D45/1E/GuideToMethodsTechnologyAppraisal2013.pdf.
  47. 47.
    Health Information and Quality Authority. (2010). Guidance on developing key performance indicators and minimum data sets to monitor healthcare Quality. Health (San Francisco). http://www.hiqa.ie/media/pdfs/HI_KPI_Guidelines.pdf.
  48. 48.
    American Geriatrics Society Expert Panel on the Care of Older Adults with Multimorbidity. (2012). Patient-centered care for older adults with multiple chronic conditions: A stepwise approach from the American Geriatrics. Journal of the American Geriatrics Society, 60, 1957–1968. doi: 10.1111/j.1532-5415.2012.04187.x.CrossRefPubMedCentralGoogle Scholar
  49. 49.
    Patten, S. B., Williams, J. V., Lavorato, D. H., Bulloch, A. G. M., Currie, C., & Emery, H. (2014). Depression and painful conditions: Patterns of association with health status and health utility ratings in the general population. Quality of Life Research: An International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation, 23(1), 363–371. doi: 10.1007/s11136-013-0449-y.CrossRefGoogle Scholar
  50. 50.
    Makai, P., Brouwer, W. B. F., Koopmanschap, M. A., Stolk, E. A., & Nieboer, A. P. (2014). Quality of life instruments for economic evaluations in health and social care for older people: A systematic review. Social Science and Medicine (1982), 102, 83–93. doi: 10.1016/j.socscimed.2013.11.050.CrossRefGoogle Scholar
  51. 51.
    Al-Janabi, H., Flynn, T. N., & Coast, J. (2012). Development of a self-report measure of capability wellbeing for adults: The ICECAP-A. Quality of Life Research, 21(1), 167–176. doi: 10.1007/s11136-011-9927-2.CrossRefPubMedGoogle Scholar
  52. 52.
    Al-Janabi, H., Peters, T. J., Brazier, J., Bryan, S., Flynn, T. N., Clemens, S., et al. (2013). An investigation of the construct validity of the ICECAP-A capability measure. Quality of Life Research, 22(7), 1831–1840. doi: 10.1007/s11136-012-0293-5.CrossRefPubMedGoogle Scholar
  53. 53.
    Flynn, T. N., Huynh, E., Peters, T. J., Al-Janabi, H., Clemens, S., Moody, A., et al. (2015). Scoring the ICECAP-A capability instrument. Estimation of a UK general population tariff. Health Seconomics, 24, 258–269.Google Scholar
  54. 54.
    Makai, P., Brouwer, W. B. F., Koopmanschap, M. A., Stolk, E. A., & Nieboer, A. P. (2014). Quality of life instruments for economic evaluations in health and social care for older people: A systematic review. Social Science and Medicine (1982), 102, 83–93. doi: 10.1016/j.socscimed.2013.11.050.CrossRefGoogle Scholar
  55. 55.
    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. doi: 10.1007/s11136-011-9903-x.CrossRefPubMedPubMedCentralGoogle Scholar
  56. 56.
    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(1), 74. doi: 10.1186/1477-7525-12-74.CrossRefPubMedPubMedCentralGoogle Scholar
  57. 57.
    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. doi: 10.1007/s11136-012-0322-4.CrossRefPubMedGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Eithne Sexton
    • 1
    Email author
  • Kathleen Bennett
    • 2
  • Tom Fahey
    • 3
  • Caitriona Cahir
    • 2
  1. 1.Department of PsychologyRoyal College of Surgeons in IrelandDublinIreland
  2. 2.Population and Health SciencesRoyal College of Surgeons in IrelandDublinIreland
  3. 3.Health Research Board (HRB) Centre for Primary Care ResearchRoyal College of Surgeons in IrelandDublinIreland

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