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SF-6D health state utilities for lifestyle, sociodemographic and clinical characteristics of a large international cohort of people with multiple sclerosis

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Abstract

Background

While many studies have examined the impacts of multiple sclerosis (MS) on health-related quality of life (HRQoL), none have used the SF-6D multi-attribute utility instrument in a large international cohort (> 2000 subjects) of people with MS.

Objectives

To derive SF-6D health state utilities (HSUs) for participants of the HOLISM (Health Outcomes and Lifestyle In a Sample of people with Multiple Sclerosis) international cohort and to describe the distribution and determinants thereof.

Methods

HSUs were generated using the SF-6D for participants with sufficient SF-36 data [n = 2185/2466 (88.6%)]. Mean HSUs for sociodemographic, clinical and modifiable lifestyle factors (including diet, physical activity, supplement use) were evaluated. Determinants of HSU were then evaluated by linear regression, adjusted for age, sex, MS type, disability, fatigue, and prescription antidepressant use.

Results

Mean HSU for the sample was 0.67 (SD = 0.13) and diminished with increasing MS-related disability, robust to adjustment, supporting the SF-6D’s discriminatory power in people with MS. Severe disability and clinically significant fatigue were each associated with 11% lower HSU (95% CI = − 0.13, − 0.10 and − 0.12, − 0.10), and depression risk with 10%-lower HSU (95% CI = − 0.11, − 0.08). Employment, higher socioeconomic and married/partnered statuses, larger social-network size, greater physical activity, and vitamin D and omega-3 supplement use were associated with significantly higher HSU, and overweight/obese BMI and tobacco smoking with lower HSU. Age, sex, and education were not associated.

Conclusion

Modifiable lifestyle factors including healthy diet, increased physical activity and supplement use were associated with higher HRQOL among people with MS. The SF-6D instrument revealed significant discriminatory power in this international cohort of people with MS.

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Data availability

Data may not be shared due to the conditions approved by our institutional ethics committee, in that all data are stored as re-identifiable information at the University of Melbourne in the form of password-protected computer databases, and only the listed investigators have access to the data. All data have been reported on a group basis, summarising the group findings rather than individual findings so personal information cannot be identified. Therefore, we can supply aggregate group data on request. Readers may contact Steve Simpson-Yap or Tracey Weiland.

References

  1. Compston, A., McDonald, I., Noseworthy, J. H., et al. (2005). McAlpine's multiple sclerosis (4th ed.). Oxford: Churchill Livingston.

    Google Scholar 

  2. Hadgkiss, E. J., Jelinek, G. A., Weiland, T. J., et al. (2015). The association of diet with quality of life, disability, and relapse rate in an international sample of people with multiple sclerosis. Nutritional Neuroscience., 18, 125–136.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Wingerchuk, D. M. (2011). Environmental factors in multiple sclerosis: Epstein-Barr virus, vitamin D, and cigarette smoking. Mount Sinai Journal of Medicine, 78, 221–230.

    Article  PubMed  Google Scholar 

  4. Canto, E., & Oksenberg, J. R. (2018). Multiple sclerosis genetics. Multiple Sclerosis, 24, 75–79.

    Article  CAS  PubMed  Google Scholar 

  5. Tao, C., Simpson, S., Jr., van der Mei, I., et al. (2016). Higher latitude is significantly associated with an earlier age of disease onset in multiple sclerosis. Journal of Neurology, Neurosurgery and Psychiatry, 87, 1343–1349.

    Article  PubMed  Google Scholar 

  6. Palmer, A. J., Colman, S., O'Leary, B., et al. (2013). The economic impact of multiple sclerosis in Australia in 2010. Multiple Sclerosis, 19, 1640–1646.

    Article  PubMed  Google Scholar 

  7. Ahmad, H. C., Campbell, J. A., Taylor, B. V., van der Mei, I., & Palmer, A. J. (2018). Health Economic Impact of Multiple Sclerosis in Australia in 2017 (An analysis of MS Research Australia's platform—The Australian MS Longitudinal Study (AMSLS). https://msra.org.au/wp-content/uploads/2018/08/health-economic-impact-of-ms-in-australia-in-2017_ms-research-australia_web.pdf.

  8. Browne, P., Chandraratna, D., Angood, C., et al. (2014). Atlas of multiple sclerosis 2013: A growing global problem with widespread inequity. Neurology., 83, 1022–1024.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Campbell, J. A., Simpson, S., Jr., Ahmad, H., et al. (2019). Change in multiple sclerosis prevalence over time in Australia 2010–2017 utilising disease-modifying therapy prescription data. Multiple Sclerosis. https://doi.org/10.1177/1352458519861270.

    Article  PubMed  Google Scholar 

  10. Austin, E., LeRouge, C., Hartzler, A. L., et al. (2020). Capturing the patient voice: Implementing patient-reported outcomes across the health system. Quality of Life Research, 29(2), 347–355. https://doi.org/10.1007/s11136-019-02320-8.

    Article  PubMed  Google Scholar 

  11. International Society for Quality of Life Research (ISOQOL) https://www.isoqol.org/about-isoqol/what-is-health-related-quality-of-life-research.

  12. Mott, D. J. (2018). Incorporating quantitative patient preference data into healthcare decision making processes: Is HTA falling behind? Patient, 11(3), 249–252. https://doi.org/10.1007/s40271-018-0305-9.

    Article  PubMed  Google Scholar 

  13. Richardson, J., Iezzi, A., Khan, M. A., et al. (2016). Measuring the sensitivity and construct validity of 6 utility instruments in 7 disease areas. Medical Decision Making, 36, 147–159.

    Article  PubMed  Google Scholar 

  14. Campbell, J. A., Palmer, A. J., Venn, A., et al. (2016). A head-to-head comparison of the EQ-5D-5L and AQoL-8D multi-attribute utility instruments in patients who have previously undergone bariatric surgery. Patient, 9, 311–322.

    Article  PubMed  Google Scholar 

  15. Brazier, J., Ratcliffe, J., Saloman, J., et al. (2017). Measuring and valuing health benefits for economic evaluation. Oxford: Oxford University Press.

    Google Scholar 

  16. Fayers, P. M., & Machin, D. (2013). Quality of life: the assessment, analysis and interpretation of patient-reported outcomes. New York: Wiley.

    Google Scholar 

  17. Drummond, M. F., Sculpher, M. J., Claxton, K., et al. (2015). Methods for the economic evaluation of health care programmes (4th ed.). Oxford: Oxford University Press.

    Google Scholar 

  18. Clarke, P. M., Hayes, A. J., Glasziou, P. G., et al. (2009). Using the EQ-5D index score as a predictor of outcomes in patients with type 2 diabetes. Medical Care, 47, 61–68.

    Article  PubMed  Google Scholar 

  19. Skinner, E. H., Denehy, L., Warrillow, S., et al. (2013). Comparison of the measurement properties of the AQoL and SF-6D in critical illness. Critical Care and Resuscitation, 15, 205.

    PubMed  Google Scholar 

  20. Claflin, S. B., van der Mei, I. A. F., & Taylor, B. V. (2018). Complementary and alternative treatments of multiple sclerosis: A review of the evidence from 2001 to 2016. The Journal of Neurology, Neurosurgery, and Psychiatry, 89, 34–41.

    Article  PubMed  Google Scholar 

  21. Hadgkiss, E. J., Jelinek, G. A., Weiland, T. J., et al. (2013). Methodology of an international study of people with multiple sclerosis recruited through Web 2.0 platforms: Demographics, lifestyle, and disease characteristics. Neurology Research International, 2013, 580596.

    PubMed  PubMed Central  Google Scholar 

  22. Jelinek, G. A., De Livera, A. M., Marck, C. H., et al. (2016). Lifestyle, medication and socio-demographic determinants of mental and physical health-related quality of life in people with multiple sclerosis. BMC Neurology, 16, 235.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Marck, C. H., Hadgkiss, E. J., Weiland, T. J., et al. (2014). Physical activity and associated levels of disability and quality of life in people with multiple sclerosis: A large international survey. BMC Neurology, 14, 143.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Jelinek, G. A., Hadgkiss, E. J., Weiland, T. J., et al. (2013). Association of fish consumption and Omega 3 supplementation with quality of life, disability and disease activity in an international cohort of people with multiple sclerosis. International Journal of Neuroscience, 123, 792–800.

    Article  CAS  PubMed  Google Scholar 

  25. Weiland, T. J., Hadgkiss, E. J., Jelinek, G. A., et al. (2014). The association of alcohol consumption and smoking with quality of life, disability and disease activity in an international sample of people with multiple sclerosis. Journal of the Neurological Sciences, 336, 211–219.

    Article  CAS  PubMed  Google Scholar 

  26. Fisk, J. D., Brown, M. G., Sketris, I. S., et al. (2005). A comparison of health utility measures for the evaluation of multiple sclerosis treatments. The Journal of Neurology, Neurosurgery, and Psychiatry, 76, 58–63.

    Article  CAS  PubMed  Google Scholar 

  27. Hohol, M. J., Orav, E. J., & Weiner, H. L. (1995). Disease steps in multiple sclerosis: A simple approach to evaluate disease progression. Neurology, 45, 251–255.

    Article  CAS  PubMed  Google Scholar 

  28. Kister, I., Chamot, E., Salter, A. R., et al. (2013). Disability in multiple sclerosis: A reference for patients and clinicians. Neurology, 80, 1018–1024.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Krupp, L. B., LaRocca, N. G., Muir-Nash, J., et al. (1989). The fatigue severity scale. Application to patients with multiple sclerosis and systemic lupus erythematosus. Archives of Neurology, 46, 1121–1122.

    Article  CAS  PubMed  Google Scholar 

  30. Kroenke, K., Spitzer, R. L., & Williams, J. B. (2003). The Patient Health Questionnaire-2: Validity of a two-item depression screener. Medical Care, 41(1), 1284–1292.

    Article  PubMed  Google Scholar 

  31. McKellar, S., Horsley, P., Chambers, R., Bauer, J. D., Vendersee, P., Clarke, C., et al. (2008). Development of the diet habits questionnaire for use in cardiac rehabilitation. Australian Journal of Primary Health, 14(3), 43–47.

    Article  Google Scholar 

  32. Craig, C. L., Marshall, A. L., Sjöström, M., Bauman, A. E., Booth, M. L., Ainsworth, B. E., et al. (2003). International physical activity questionnaire: 12-country reliability and validity. Medicine and Science in Sports and Exercise, 35(8), 1381–1395.

    Article  PubMed  Google Scholar 

  33. Vickrey, B. G., Hays, R. D., Harooni, R., et al. (1995). A health-related quality of life measure for multiple sclerosis. Quality of Life Research, 4, 187–206.

    Article  CAS  PubMed  Google Scholar 

  34. Brazier, J., Roberts, J., & Deverill, M. (2002). The estimation of a preference-based measure of health from the SF-36. Journal of Health Economics, 21, 271–292.

    Article  PubMed  Google Scholar 

  35. Ferreira, L. N., Ferreira, P. L., Pereira, L. N., et al. (2013). Exploring the consistency of the SF-6D. Value Health, 16, 1023–1031.

    Article  PubMed  Google Scholar 

  36. Richardson, J., Khan, M. A., Iezzi, A., et al. (2015). Comparing and explaining differences in the magnitude, content, and sensitivity of utilities predicted by the EQ-5D, SF-6D, HUI 3, 15D, QWB, and AQoL-8D multiattribute utility instruments. Medical Decision Making, 35, 276–291.

    Article  PubMed  Google Scholar 

  37. Richardson, J., Iezzi, A., & Khan, M. A. (2015). Why do multi-attribute utility instruments produce different utilities: The relative importance of the descriptive systems, scale and ‘micro-utility’ effects. Quality of Life Research, 24, 2045–2053.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Box, G., & Cox, D. (1964). An analysis of transformations. Journal of the Royal Statistical Society, Series B, 26, 211–252.

    Google Scholar 

  39. Kohn, C. G., Sidovar, M. F., Kaur, K., Zhu, Y., & Coleman, C. I.(2014) Estimating a minimal clinically important difference for the EuroQol 5-Dimension health status index in persons with multiple sclerosis. Health and Quality of Life Outcomes, 12(1):66.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Abel, H., Kephart, G., Packer, T., & Warner, G. (2017). Discordance in utility measurement in persons with neurological conditions: A comparison of the SF-6D and the HUI3. Value Health, 20, 1157–1165.

    Article  PubMed  Google Scholar 

  41. Diaz-Cruz, C., Chua, A. S., Malik, M. T., et al. (2017). The effect of alcohol and red wine consumption on clinical and MRI outcomes in multiple sclerosis. Multiple Sclerosis and Related Disorders, 17, 47–53.

    Article  PubMed  Google Scholar 

  42. Greb, E. (2012). Half of patients with MS do not take disease-modifying medication within four years of diagnosis. Neurology Reviews, 20, 5–5.

    Google Scholar 

  43. Norman, R., Church, J., van den Berg, B., et al. (2013). Australian health-related quality of life population norms derived from the SF-6D. The Australian and New Zealand Journal of Public Health, 37, 17–23.

    Article  PubMed  Google Scholar 

  44. 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 

  45. Learmonth, Y. C., Motl, R. W., Sandroff, B. M., et al. (2013). Validation of patient determined disease steps (PDDS) scale scores in persons with multiple sclerosis. BMC Neurology, 13, 37.

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank all the participants in the HOLISM study for taking the survey and the Bloom Foundation, Wal Pisciotta and the Horne Family Charitable Trust for supporting this study.

Funding

The study was funded by the Bloom Foundation, Wal Pisciotta, and the Horne Family Charitable Trust.

Author information

Authors and Affiliations

Authors

Contributions

GJ, TW, and SN conceived the HOLISM Study, alongside others not included in the authorship of the present manuscript. JC & SSY conceived the present analysis. SSY & ADL contributed to cohort management and cleaned and prepared the data for analysis. JC undertook HSU estimation using the algorithm developed by BM. JC and SSY conceived the data analyses. SSY undertook data analyses. JC and SSY drafted and edited the manuscript. All authors commented on and approved the final version of the manuscript.

Corresponding author

Correspondence to Steve Simpson-Yap.

Ethics declarations

Conflict of interest

GJ receives royalties for his books, Overcoming Multiple Sclerosis and Recovering from Multiple Sclerosis. GJ & SN have received remuneration for conducting lifestyle educational workshops for people with MS. All other authors declare no competing interests.

Ethics approval

The Health Sciences Human Ethics Sub-Committee at the University of Melbourne provided ethical approval for the study (Ethics ID: 1545102).

Informed consent

Participants were asked to read the participant information and to consent before entering the survey.

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Campbell, J.A., Jelinek, G.A., Weiland, T.J. et al. SF-6D health state utilities for lifestyle, sociodemographic and clinical characteristics of a large international cohort of people with multiple sclerosis. Qual Life Res 29, 2509–2527 (2020). https://doi.org/10.1007/s11136-020-02505-6

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