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Developing Adolescent-Specific Health State Values for Economic Evaluation

An Application of Profile Case Best-Worst Scaling to the Child Health Utility 9D

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Abstract

Background

The way that health is measured and valued is fundamental to economic evaluation. To date, adult health state values have been routinely used in the calculation of QALYs for the economic evaluation of healthcare treatment and preventive programmes, including those targeted at adolescents.

Objectives

The main objective of this study was to apply profile case best-worst scaling (BWS) discrete-choice experiment (DCE) methods to obtain adolescent-specific values for the Child Health Utility 9D (CHU9D), a new generic preference-based measure of health-related quality of life developed specifically for application in cost-effectiveness analyses of treatments and interventions targeted at young people. A secondary aim was to assess the feasibility of a web-based method of data collection for the valuation of health states defined by the CHU9D.

Methods

A web-based survey was developed including the CHU9D instrument and a series of BWS DCE questions. Specifically, respondents were asked to indicate the best and worst attribute levels from a series of ten health states defined by the CHU9D, presented one at a time. The survey was administered to a community-based sample of consenting adolescents (n=590) aged 11–17 years. A conditional logistic regression model was applied to estimate values (part-worth utilities) for each level of the nine attributes relating to the CHU9D. A marginal utility matrix was then estimated to generate an adolescent-specific scoring algorithm on the full health=1 and dead =0 scale required for the calculation of QALYs.

Results

The results indicate that participants were able to readily choose ‘best’ and ‘worst’ attribute levels for the CHU9D health states. Large differences in value were found between the first and fifth levels (indicating ‘no problems’ and ‘severe problems’, respectively) for all nine attributes relating to the CHU9D. In general, there was little differentiation between the middle levels of all attributes indicating only limited additional value for adolescents of moving between these levels. Comparison of the adolescent-specific algorithm and the existing adult scoring algorithm for the CHU9D revealed some significant differences in values for identical health states, which may have important implications for the application of the CHU9D to value adolescent treatment and service programmes particularly for mental health. In general, adolescents appeared to place more weight upon the CHU9D attributes relating to mental health (worried, sad and annoyed) than would be implied by application of the existing algorithm based upon adult values.

Conclusion

This study provides preliminary indications that there may be potentially important and systematic differences in the valuations attached to identical health states by adolescents in comparison with adult population groups. The study findings lend support to the potential future application of profile case BWS DCE methods to undertake large-scale health state valuation studies directly with young adolescent population samples and provide support for the feasibility and acceptability of a web-based mode of administration for this purpose.

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References

  1. Williams P, Holmbeck G, Greenley R. Adolescent health psychology. J Consult Clin Psychol 2002; 70: 828–42

    Article  PubMed  Google Scholar 

  2. United Nations. Convention on the rights of the child. London: HMSO, 1989

    Google Scholar 

  3. Kleinet S. Adolescent health: an opportunity not to be missed. Lancet 2007; 369: 1057–8

    Article  Google Scholar 

  4. Tylee A, Haller D, Graham T, et al. Youth friendly primary care services: how are we doing and what more needs to be done. Lancet 2007; 369: 1585–73

    Article  Google Scholar 

  5. National Health and Hospitals Reform Commission. A healthier future for all Australians: final report of the National Health and Hospitals Reform Commission. Barton (ACT): NHHRC, 2009 Jun

  6. Griebsch I, Coast J, Brown J. Quality adjusted life years lack quality in paediatric care: a critical review of published studies. Paediatrics 2005; 115: 600–14

    Article  Google Scholar 

  7. Ungar WJ. Challenges in health state valuation in paediatric economic evaluation: are QALYs contraindicated? Pharmacoeconomics 2011; 29(8): 641–52

    Article  PubMed  Google Scholar 

  8. Stevens K. Developing a descriptive system for a new preference-based measure of health related quality of life for children. Qual Life Res 2009; 18: 1105–13

    Article  PubMed  Google Scholar 

  9. Stevens K. Working with children to develop dimensions for a preference-based generic paediatric health related quality of life measure. Qual Health Res 2010; 20: 340–51

    Article  PubMed  Google Scholar 

  10. Stevens K. Assessing the performance of a new generic measure of health-related quality of life for children and refining it for use in health state valuation. Appl Health Econ Health Policy 2011; 9(3): 157–69

    Article  PubMed  Google Scholar 

  11. Ratcliffe J, Couzner L, Flynn T, et al. Valuing Child Health Utility 9D health states with a young adolescent sample: a feasibility study to compare best-worst scaling discretechoice experiment, standard gamble and time trade-off methods. Appl Health Econ Health Policy 2011; 9(1): 15–27

    Article  PubMed  Google Scholar 

  12. Ratcliffe J, Stevens K, Flynn T, et al. An assessment of the construct validity of the CHU9D in the Australian adolescent general population. Qual Life Res 2012; 21(4): 717–25

    Article  PubMed  Google Scholar 

  13. Stevens K, Ratcliffe J. Measuring and valuing health benefits for economic evaluation in adolescence: an assessment of the practicality and validity of the Child Health Utility 9D in the Australian adolescent population [working paper]. Adelaide (SA): Flinders Centre for Clinical Change and Health Care Research, 2011

    Google Scholar 

  14. Stevens KJ. Valuation of the Child Health Utility Index 9D (CHU9D) [Sheffield Health Economics and Decision Science discussion paper no. 10/07]. Sheffield: ScHARR, University of Sheffield, 2010

    Google Scholar 

  15. Brazier J, Ratcliffe J, Salomon J, et al. Measuring and valuing health benefits for economic evaluation. Oxford: Oxford University Press, 2007

    Google Scholar 

  16. Torrance GW. Social preferences for health states: an empirical evaluation of three measurement techniques. Socioecon Plann Sci 1976; 10: 129–36

    Article  Google Scholar 

  17. Arnesen T, Trommald M. Are QALYs based on time trade off comparable? A systematic review of TTO methodologies. Health Econ 2005; 14: 39–53

    Article  PubMed  Google Scholar 

  18. Thurstone LL. A law of comparative judgement. Psychol Rev 1927; 34: 273–86

    Article  Google Scholar 

  19. Lancaster K. A new approach to consumer theory. J Polit Econ 1966; 74: 134–57

    Article  Google Scholar 

  20. McFadden D. Conditional logit analysis of qualitative choice behaviour. Berkley (CA): University of California, 1973

    Google Scholar 

  21. Ratcliffe J, Buxton M. Patient’s preferences regarding the process and outcomes of life saving technology: an application of conjoint analysis to liver transplantation. Int J Technol Assess Health Care 1999; 15: 340–51

    PubMed  CAS  Google Scholar 

  22. Taylor S, Armour C. Consumer preference for dinoprostone vaginal gel using stated preference discrete choice modelling. Pharmacoeconomics 2003; 21(10): 721–35

    Article  PubMed  Google Scholar 

  23. Bishai D, Brice R, Girod I, et al. Conjoint analysis of French and German parents’ willingness to pay for meningococcal vaccine. Pharmacoeconomics 2007; 25(2): 143–54

    Article  PubMed  Google Scholar 

  24. McIntosh E. Using discrete choice experiments within a cost-benefit analysis framework: some considerations. Pharmacoeconomics 2006; 24(9): 855–68

    Article  PubMed  Google Scholar 

  25. Coast J, Flynn T, Natarajan L, et al. Valuing the ICECAP capability index for older people. Soc Sci Med 2008; 67(5): 874–992

    Article  PubMed  Google Scholar 

  26. Hakim Z, Pathak D. Modelling the EuroQol data: a comparison of discrete choice conjoint and conditional preference modelling. Health Econ 1999; 82: 103–16

    Article  Google Scholar 

  27. Ryan M, Netten A, Skatun D, et al. Using discrete choice experiments to estimate a preference based measure of outcome: an application to social care for older people. J Health Econ 2006; 25: 927–44

    Article  PubMed  Google Scholar 

  28. Ratcliffe J, Brazier J, Tsuchiya A, et al. A comparison of time trade off, discrete choice experiment and ranking data in estimating a preference based single index from the sexual quality of life questionnaire. Health Econ 2009; 18: 1261–76

    Article  PubMed  Google Scholar 

  29. Flynn T. Valuing citizen and patient preference in health: recent developments in three types of best-worst scaling. Expert Rev Pharmacoecon Outcomes Res 2010; 10(3): 259–67

    Article  PubMed  Google Scholar 

  30. Flynn TN, Louviere JJ, Peters TJ, et al. Best-worst scaling: what it can do for health care research and how to do it. J Health Econ 2007; 26(1): 171–37

    Article  PubMed  Google Scholar 

  31. Flynn T, Louviere J, Peters T, et al. Estimating preferences for a dermatology consultation using best-worst scaling: comparison of various methods of analysis. BMC Med Res Methodol 2008; 8: 76

    Article  PubMed  Google Scholar 

  32. Marley AAJ, Flynn TN, Louviere JJ. Probabilistic models of set-dependent and attribute-level best-worst choice. J Math Psychol 2008; 52: 281–96

    Article  Google Scholar 

  33. Szeinbach S, Barnes J, McGhan W. Using conjoint analysis to evaluate health state preferences. Drug Inf J 1999; 33: 849–58

    Google Scholar 

  34. Al Janabi H, Flynn T, Coast J. Estimation of a preference-based Carer Experience Scale. Med Decis Making 2011; 31(3): 458–68

    Article  Google Scholar 

  35. Gwaltney C, Sheilds A, Shiffman S. Equivalence of electronic and paper and pencil administration of patient-reported outcome measures: a meta-analytic review. Value Health 2008; 11(2): 322–33

    Article  PubMed  Google Scholar 

  36. Louviere JJ, Hensher DA, Swait JD. Stated choice methods: analysis and application. Cambridge: Cambridge University Press, 2000

    Book  Google Scholar 

  37. Burgess L, Street D. The optimal size of choice sets in choice experiments. Statistics 2006; 40(60): 507–15

    Article  Google Scholar 

  38. Hensher D, Rose J, Greene W. Applied choice analysis: a primer. Cambridge: Cambridge University Press, 2005

    Book  Google Scholar 

  39. Anderson A, Krolner R, Currie C, et al. High agreement on family affluence between children and parents’ self-reports: international study of 11 year old children. J Epidemiol Commun Health 2008; 62: 1092–4

    Article  Google Scholar 

  40. Ravens-Sieber U, Erhart M, Rajmil L, et al. Reliability, construct and criterion validity of the KIDSCREEN-10 score: a short measure for children and adolescents’ well-being and health-related quality of life. Qual Life Res 2010; 19: 1487–500

    Article  Google Scholar 

  41. Flynn TN, Louviere J, Marley AA, et al. Rescaling quality of life tariffs from discrete choice experiments for use as QALYs: a cautionary tale. Popul Health Metr 2008; 6: 6

    Article  PubMed  Google Scholar 

  42. Flynn T. Using conjoint analysis and choice experiments to estimate QALY values: issues to consider. Pharmacoeconomics 2010; 28(9): 711–22

    Article  PubMed  Google Scholar 

  43. Craig B, Busschbach J, Salomon J. Keep it simple: ranking health states yields values similar to cardinal measurement approaches. J Clin Epidemiol 2009; 62: 296–305

    Article  PubMed  Google Scholar 

  44. Moodie M, Richardson J, Rankin B, et al. Predicting time trade-off health state valuations of adolescents in four Pacific countries using the AQoL-6D instrument. Value Health 2010; 13: 1014–27

    Article  PubMed  Google Scholar 

  45. Viney R, Norman R, King M, et al. Using a discrete choice experiment to value EQ-5D health states. 8th World Congress of the International Health Economics Association; 2011 Jul 10–13; Toronto (ON)

  46. Bansback N, Brazier J, Tsuchiya A, et al. A comparison of using discrete choice experiments and the time trade off to value health states for quality adjusted life years. Health Economists’ Study Group Meeting; 2009 Jul 22–24; Sheffield

  47. Flynn T, Louviere J, Peters T, et al. Using discrete choice experiments to understand preferences for quality of life: variance scale heterogeneity matters. Soc Sci Med 2010; 70: 1957–65

    Article  PubMed  Google Scholar 

  48. Norman R, King M, Clarke D, et al. Street D. Does mode of administration matter? Comparison of online and face to face administration of a time trade off task. Qual Life Res 2010; 19: 499–508

    Google Scholar 

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Acknowledgements

This study was approved by the Flinders University Social and Behavioural Research Ethics Committee (project no. 4701). Financial support from a Flinders University seeding grant is gratefully acknowledged by the authors. There are no conflicts of interests for any of the authors.

JR, TF, KS, JB and MS conceived the study and wrote the original grant proposal. JR led the development of the survey instrument, led data collection, assisted with data analysis and interpretation, and drafted the manuscript. TF contributed to the design of the survey instrument and data collection, led the data analysis, and contributed to production of the final manuscript. FT assisted TF and JR with data collection and analysis, and contributed to production of the final manuscript. KS, JB and MS contributed to the development of the survey instrument, the interpretation of data and production of the final manuscript. All authors read and approved the final manuscript. JR is the guarantor for the overall content of this article.

This paper is part of a theme issue co-edited by Lisa Prosser, University of Michigan, USA, and no external funding was used to support the publication of this theme issue.

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Authors and Affiliations

  1. Flinders Clinical Effectiveness, School of Medicine, Flinders University, Room 52, A Block, Repatriation General Hospital, Daws Road, Daw Park, Adelaide, South Australia, SA 5041, Australia

    Julie Ratcliffe

  2. Centre for the Study of Choice (CenSoC), University of Technology Sydney, Sydney, New South Wales, Australia

    Terry Flynn & Frances Terlich

  3. Health Economics and Decision Science (HEDS), School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK

    Katherine Stevens & John Brazier

  4. Discipline of Paediatrics, University of Adelaide, Adelaide, South Australia, Australia

    Michael Sawyer

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  1. Julie Ratcliffe
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  3. Frances Terlich
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  4. Katherine Stevens
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Correspondence to Julie Ratcliffe.

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Ratcliffe, J., Flynn, T., Terlich, F. et al. Developing Adolescent-Specific Health State Values for Economic Evaluation. PharmacoEconomics 30, 713–727 (2012). https://doi.org/10.2165/11597900-000000000-00000

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