Skip to main content
SpringerLink
Log in

Involving Members of the Public in Health Economics Research: Insights from Selecting Health States for Valuation to Estimate Quality-Adjusted Life-Year (QALY) Weights

  • Practical Application
  • Published:
Applied Health Economics and Health Policy Aims and scope Submit manuscript

Abstract

Over recent years, public involvement in health research has expanded considerably. However, public involvement in designing and conducting health economics research is seldom reported. Here we describe the development, delivery and assessment of an approach for involving people in a clearly defined piece of health economics research: selecting health states for valuation in estimating quality-adjusted life-years (QALYs). This involvement formed part of a study to develop a condition-specific preference-based measure of health-related quality of life, the Multiple Sclerosis Impact Scale (MSIS-8D), and the work reported here relates to the identification of plausible, or realistic, health states for valuation. An Expert Panel of three people with multiple sclerosis (MS) was recruited from a local involvement network, and two health economists designed an interactive task that enabled the Panel to identify health states that were implausible, or unlikely to be experienced. Following some initial confusion over terminology, which was resolved by discussion with the Panel, the task worked well and can be adapted to select health states for valuation in the development of any preference-based measure. As part of the involvement process, five themes were identified by the Panel members and the researchers which summarised our experiences of public involvement in this health economics research example: proportionality, task design, prior involvement, protectiveness and partnerships. These are described in the paper, along with their practical implications for involving members of the public in health economics research. Our experience demonstrates how members of the public and health economists can work together to improve the validity of health economics research.

Plain Language Summary It has become commonplace to involve members of the public in health service research. However, published reports of involving people in designing health economics research are rare. We describe how we designed a way of involving people in a particular piece of health economics research.

The aim of the work was to produce descriptions of different states of health experienced by people with multiple sclerosis (MS). These descriptions have since been rated in terms of how good or bad they are in a way that can be used by the National Institute for Health and Care Excellence (NICE) to make decisions about what services to fund on the NHS.

We formed a panel of three people with MS, and designed a task to help the group produce health descriptions likely to be experienced by people with MS. After discussion about jargon, and working together to find more layman’s terms, the task worked well, and can be adapted to produce health descriptions for any condition.

We identified some key themes about working together that give insights into how members of the public can be involved in health economics research, and show the importance of their involvement in improving the relevance of this research.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. INVOLVE. What is public involvement in research? 2016 [cited 2016 28-07-2016]. http://www.invo.org.uk/find-out-more/what-is-public-involvement-in-research-2/.

  2. van Voorn GAK, et al. The Missing Stakeholder Group: why patients should be involved in health economic modelling. Appl Health Econ Health Policy. 2016;14:129–33.

    Article  PubMed  Google Scholar 

  3. NICE. Guide to the methods of technology appraisal 2013. London: National Institute for Health and Care Excellence; 2013.

  4. Brazier J, et al. Measuring and valuing health benefits for economic evaluation. Oxford: Oxford University Press; 2007.

    Google Scholar 

  5. Fisk JD, et al. A comparison of health utility measures for the evaluation of multiple sclerosis treatments. J Neurol Neurosurg Psychiatry. 2005;76(1):58–63.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Kuspinar A, Mayo NE. Do generic utility measures capture what is important to the quality of life of people with multiple sclerosis? Health Qual Life Outcomes. 2013;11:71.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Orme M, et al. The effect of disease, functional status, and relapses on the utility of people with multiple sclerosis in the UK. Value Health. 2007;10(1):54–60.

    Article  PubMed  Google Scholar 

  8. Bandari DS, et al. Assessing quality of life in patients with multiple sclerosis. Int J MS Care. 2010;12:34–41.

    Article  Google Scholar 

  9. Benito-Leon J, et al. A review about the impact of multiple sclerosis on health-related quality of life. Disabil Rehabil. 2003;25(23):1291–303.

    Article  PubMed  Google Scholar 

  10. Gruenewald DA, et al. Quality of life measures for the palliative care of people severely affected by multiple sclerosis: a systematic review. Mult Scler. 2004;10(6):690–704.

    Article  CAS  PubMed  Google Scholar 

  11. Opara JA, Jaracz K, Brola W. Quality of life in multiple sclerosis. J Med Life. 2010;3(4):352–8.

    PubMed  PubMed Central  Google Scholar 

  12. Kuspinar A, Mayo NE. A review of the psychometric properties of generic utility measures in multiple sclerosis. Pharmacoeconomics. 2014;32(8):759–73.

    Article  PubMed  Google Scholar 

  13. Brazier J, Tsuchiya A. Preference-based condition-specific measures of health: what happens to cross programme comparability? Health Econ. 2010;19:125–9.

    Article  PubMed  Google Scholar 

  14. Hobart J, Cano S. Improving the evaluation of therapeutic interventions in multiple sclerosis: the role of new psychometric methods. Health Technol Assess. 2009;13(12):iii, ix–x, 1–177.

  15. Goodwin E, Green C. A quality-adjusted life-year measure for multiple sclerosis: developing a patient-reported health state classification system for a multiple sclerosis-specific preference-based measure. Value Health. 2015;18:1016–10124.

    Article  PubMed  Google Scholar 

  16. Brazier JE, et al. Developing and testing methods for deriving preference-based measures of health from condition-specific measures (and other patient-based measures of outcome). Health Technol Assess. 2012;16(32):1–114.

    Article  CAS  Google Scholar 

  17. Goodwin E, Green C, Spencer A. Estimating a preference-based index for an eight dimensional health state classification system derived from the Multiple Sclerosis Impact Scale (MSIS-29). Value Health. 2015;18:1025–36.

    Article  PubMed  Google Scholar 

  18. Johnson FR, et al. Are chemotherapy patients’ HRQoL importance weights consistent with linear scoring rules? A stated-choice approach. Qual Life Res. 2006;15(2):285–98.

    Article  PubMed  Google Scholar 

  19. Young TA, et al. Developing preference-based health measures: using Rasch analysis to generate health state values. Qual Life Res. 2010;19(6):907–17.

    Article  PubMed  Google Scholar 

  20. Mavranezouli I, et al. Estimating a preference-based index from the Clinical Outcomes in Routine Evaluation-Outcome Measure (CORE-OM): valuation of CORE-6D. Med Decis Making. 2013;33(3):381–95.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Chiou CF, et al. Development of the multi-attribute Pediatric Asthma Health Outcome Measure (PAHOM). Int J Qual Health Care. 2005;17(1):23–30.

    Article  PubMed  Google Scholar 

  22. Poissant L, et al. The development and preliminary validation of a Preference-Based Stroke Index (PBSI). Health Qual Life Outcomes. 2003;1:43.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Zajicek JP, et al. Patient-orientated longitudinal study of multiple sclerosis in south west England (The South West Impact of Multiple Sclerosis Project, SWIMS) 1: protocol and baseline characteristics of cohort. BMC Neurol. 2010;10:88.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Britten N, et al. Patient involvement in drug licensing: a case study. Soc Sci Med. 2015;131:289–96.

    Article  PubMed  Google Scholar 

  25. PenCLAHRC. Meet PenPIG. 2016 [cited 2015 09-08-2016]. http://clahrc-peninsula.nihr.ac.uk/meet-penpig.

  26. INVOLVE. Information for researchers. 2016 [cited 2016 28-07-2016]. http://www.invo.org.uk/find-out-more/information-for-researchers/.

  27. Goodwin E, Green C. A systematic review of the literature on the development of condition-specific preference-based measures of health. Appl Health Econ Health Policy. 2016;14:161–83.

    Article  PubMed  Google Scholar 

  28. Rentz AM, et al. Development of a preference-based index from the National Eye Institute Visual Function Questionnaire-25. JAMA Ophthalmol. 2014;132(3):310–8.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Cho S, et al. Utility estimation of hypothetical chronic obstructive pulmonary disease health states by the general population and health professionals. Health Qual Life Outcomes. 2015;13:34.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Pearson M, et al. Involving patients and the public in healthcare operational research—the challenges and opportunities. Oper Res Health Care 2013;2:86–9.

    Article  Google Scholar 

  31. Boote J, et al. Involving the public in systematic reviews: a narrative review of organizational approaches and eight case examples. J Comp Eff Res. 2012;1:409–20.

    Article  PubMed  Google Scholar 

  32. Hollin IL, et al. Developing a patient-centred benefit-risk survey: a community-engaged process. Value Health. 2016;19:751–7.

    Article  PubMed  Google Scholar 

  33. Janssen EM, et al. A framework for instrument development of a choice experiment: an application to type 2 diabetes. Patient. 2016;9:465–79.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

We would like to thank the Expert Panel of people with multiple sclerosis for their help and support with this research.

Author information

Authors and Affiliations

  1. Health Economics Group, University of Exeter Medical School, University of Exeter, South Cloisters, St Luke’s Campus, Magdalen Road, Exeter, EX1 2LU, UK

    Elizabeth Goodwin & Annie Hawton

  2. NIHR CLAHRC South West Peninsula (PenCLAHRC), University of Exeter Medical School, University of Exeter, Exeter, UK

    Kate Boddy & Annie Hawton

  3. Patient and Public Involvement Group (PenPIG), University of Exeter Medical School, University of Exeter, Exeter, UK

    Lynn Tatnell

Authors
  1. Elizabeth Goodwin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kate Boddy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lynn Tatnell
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Annie Hawton
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

EG co-conceived the initial idea for this work, actively contributed to each stage of the work, and co-wrote and revised the manuscript. KB actively contributed to each stage of the work, and co-wrote and revised the manuscript. LT actively contributed to each stage of the work, and co-wrote and revised the manuscript. AH co-conceived the initial idea for this work, actively contributed to each stage of the work, and co-wrote and revised the manuscript.

Corresponding author

Correspondence to Annie Hawton.

Ethics declarations

Ethical approval

Ethical approval was granted by the University of Exeter Medical School Research Ethics Committee.

Funding

This research was funded by the MS Society and supported by the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care South West Peninsula. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health.

Conflict of interest

Elizabeth Goodwin, Kate Boddy, Lynn Tatnell and Annie Hawton have no conflicts of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Goodwin, E., Boddy, K., Tatnell, L. et al. Involving Members of the Public in Health Economics Research: Insights from Selecting Health States for Valuation to Estimate Quality-Adjusted Life-Year (QALY) Weights. Appl Health Econ Health Policy 16, 187–194 (2018). https://doi.org/10.1007/s40258-017-0355-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40258-017-0355-5

Search

Navigation