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Exploring the Issues of Valuing Child and Adolescent Health States Using a Mixed Sample of Adolescents and Adults

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Abstract

Preferences for child and adolescent health states used to generate health state utility values can be elicited from adults, young adults, adolescents, or combinations of these. This commentary paper provides a critical overview of issues and implications arising from valuing child and adolescent health states using a novel approach of a mixed sample of adolescents and adults. The commentary is informed by critical analysis of normative, ethical, practical and theoretical arguments in the health state valuation literature. Discussion focusses upon adolescent empowerment, understanding and psychosocial maturity; ethical concerns; elicitation tasks; perspective; and selection of sample proportions across adolescents and adults. It is argued that valuation of child and adolescent health states by both adolescents and adults could involve all participants completing the same preference elicitation task using the same perspective (e.g. time trade-off imagining they are living in the health state), and all preferences being modelled to generate a combined value set that reflects both adolescent and adult preferences. It is concluded that the valuation of child and adolescent health states by a mixed adolescent and adult sample appears feasible and has the advantage that it includes some of the population who can potentially experience the health states, thus enabling adolescents to express their views around matters that may affect them, and the population that are taxpayers and voters. However, both the relative proportion of adults and adolescents to include in a valuation sample and the elicitation technique require careful consideration.

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References

  1. Chen G, Ratcliffe J. A review of the development and application of generic multi-attribute utility instruments for paediatric populations. Pharmacoeconomics. 2015;33(10):1013–28.

    Article  PubMed  Google Scholar 

  2. Beusterien KM, et al. Development of the multi-attribute Adolescent Health Utility Measure (AHUM). Health Qual Life Outcomes. 2012;10:102.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Richardson J, et al. Measurement of the quality of life for economic evaluation and the Assessment of Quality of Life (AQoL) Mark 2 instrument. Aust Econ Hist Rev. 2004;37:62–88.

    Article  Google Scholar 

  4. Stevens KJ. Working with children to develop dimensions for a preference-based, generic, pediatric health-related quality-of-life measure. Qual Health Res. 2010;20(3):340–51.

    Article  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  6. Stevens KJ. 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 

  7. Ravens-Sieberer U, et al. Feasibilty, reliability and validity of the EQ-5D-Y: results from a multinational study. Qual Life Res. 2010;19:887–97.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Wille N, et al. Development of the EQ-5D-Y: a child-friendly version of the EQ-5D. Qual Life Res. 2010;19:875–86.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Devlin N, Brooks R. EQ-5D and the EuroQol Group: past, present and future. Appl Health Econ Health Policy. 2017;15:127–37.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Kreimeier S, et al. EQ-5D-Y-5L: developing a revised EQ-5D-Y with increased response categories. Qual Life Res. 2019;28:1951–61.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Torrance G, et al. Multiattribute utility function for a comprehensive health status classification system: Health Utilities Index mark 2. Med Care. 1996;34:702–22.

    Article  CAS  PubMed  Google Scholar 

  12. Feeny D, et al. Multiattribute and single-attribute utility functions for the Health Utilities Index Mark3 System. Med Care. 2002;40(2):113–28.

    Article  PubMed  Google Scholar 

  13. Seiber WJ, et al. Quality of Well Being Self-Administered (QWB-SA) Scale: users manual. San Diego: Health Services Research Center, University of California; 2008.

    Google Scholar 

  14. Apajasalo M, et al. Quality of life in early adolescence: a sixteen-dimensional health-related measure (16D). Qual Life Res. 1996;5:205–11.

    Article  CAS  PubMed  Google Scholar 

  15. Apajasalo M, et al. Quality of life in pre-adolescence: a 17-dimensional health-related measure (17D). Qual Life Res. 1996;5:532–8.

    Article  CAS  PubMed  Google Scholar 

  16. Jabrayilov R, et al. Valuing health status in the first year of life: the infant health-related quality of life instrument. Value Health. 2019;22(6):721–7.

    Article  PubMed  Google Scholar 

  17. Rowen D, et al. Review of valuation methods of preference-based measures of health for economic evaluation in child and adolescent populations: where are we now and where are we going? Pharmacoeconomics. 2020;38(4):325–40.

    Article  PubMed  Google Scholar 

  18. Dolan P. Modeling valuations for EuroQol health states. Med Care. 1997;35(11):1095–108.

    Article  CAS  PubMed  Google Scholar 

  19. Brazier J, Roberts J, Deverill M. The estimation of a preference-based measure of health from the SF-36. J Health Econ. 2002;21(2):271–92.

    Article  PubMed  Google Scholar 

  20. Brazier J, Roberts J. The estimation of a preference-based measure of health from the SF-12. Med Care. 2004;42(9):851–9.

    Article  PubMed  Google Scholar 

  21. Brazier J, et al. Developing a new version of the SF-6D Health State Classification System from the SF-36v2: SF-6Dv2. Med Care. 2020;58(6):557–65.

    Article  PubMed  Google Scholar 

  22. Pickard S, et al. United states valuation of EQ-5D-5L Health States using an international protocol. Value Health. 2019;22(8):931–41.

    Article  PubMed  Google Scholar 

  23. Chen G, et al. Scoring the Child Health Utility 9D instrument: estimation of a Chinese child and adolescent-specific tariff. Qual Life Res. 2019;28(1):163–76.

    Article  PubMed  Google Scholar 

  24. Ratcliffe J, et al. Nothing about us without us? A comparison of adolescent and adult health-state values for the child health utility-9D using profile case best–worst scaling. Health Econ. 2016;25(4):486–96.

    Article  PubMed  Google Scholar 

  25. Dalziel K, et al. Feasibility, validity and differences in adolescent and adult EQ-5D-Y health state valuation in Australia and Spain: an application of best–worst scaling. Pharmacoeconomics. 2020;38(5):499–513.

    Article  PubMed  Google Scholar 

  26. Stevens KJ. Because that’s what matters to me. A pilot study to test the feasibility and reliability of ordinal valuation methods for health state valuation with children. In: HEDS Discussion Paper. Sheffield: University of Sheffield; 2015.

  27. Saigal S, et al. Self-perceived health status and health-related quality of life of extremely low-birth-weight infants at adolescence. JAMA. 1996;276(6):453–9.

    Article  CAS  PubMed  Google Scholar 

  28. Tong A, et al. Quality of life of young adults and adolescents with chronic kidney disease. J Pediatr. 2013;163(4):1179-85.e5.

    Article  PubMed  Google Scholar 

  29. Prevolnik Rupel V, et al. Comparison of adult and adolescent preferences toward EQ-5D-Y-3L health states. Value Health. 2021;24(9):1350–9.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Mott DJ, et al. Valuing EQ-5D-Y-3L health states using a discrete choice experiment: do adult and adolescent preferences differ? Med Decis Mak. 2021;41(5):584–96.

    Article  Google Scholar 

  31. Saigal S, et al. Differences in preferences for neonatal outcomes among health care professionals, parents, and adolescents. JAMA. 1999;281(21):1991–7.

    Article  CAS  PubMed  Google Scholar 

  32. Nations, U. Convention on the rights of the child. London: United Nation; 1989.

    Google Scholar 

  33. Health, D.o. Chief Medical Officer’s annual report our children deserve better: prevention pays. London: Department of Health; 2012.

    Google Scholar 

  34. Commission, N.H.a.H.R. A healthier future for all Australians: final report of the National Health and Hospital Reform Commission. 2009.

  35. Consumo, M.d.D.y. Ganar Salud con la Juventud. Madrid: Ministerio de Sanidad y Consumo; 2008.

    Google Scholar 

  36. Wong N, Zimmerman M, Parker E. A typology of youth participation and empowerment for child and adolescent health promotion. Am J Community Psychol. 2010;46:100–14.

    Article  PubMed  Google Scholar 

  37. Duell N, et al. Age patterns in risk taking across the world. J Youth Adolesc. 2018;47(5):1052–72.

    Article  PubMed  Google Scholar 

  38. Powell P, et al. Valuing child and adolescent health states: a qualitative study of adult general public perspectives and priorities. Health Qual Life Outcomes. 2021;19:222.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Icenogle G, et al. Adolescents’ cognitive capacity reaches adult levels prior to their psychosocial maturity: evidence for a “maturity gap” in a multinational, cross-sectional sample. Law Hum Behav. 2019;43(1):69.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Figner B, et al. Affective and deliberative processes in risky choice: age differences in risk taking in the Columbia Card Task. J Exp Psychol Learn Mem Cogn. 2009;35:709.

    Article  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  42. Crump RT, et al. Reliability, validity, and feasibility of direct elicitation of children’s preferences for health states: a systematic review. Med Decis Mak. 2017;37(3):314–26.

    Article  Google Scholar 

  43. Crump R, Lau R, Cox E, et al. Testing the feasibility of eliciting preferences for health states from adolescents using direct methods. BMC Pediatr. 2018;18:199. https://doi.org/10.1186/s12887-018-1179-7.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Bharij A, et al. Health utility and quality of life in pediatric liver transplant recipients. Pediatr Transplant. 2020;24(4):e13720.

    Article  PubMed  Google Scholar 

  45. Borges PA, et al. The influence of body image on surgical decisions in adolescent idiopathic scoliosis patients. Clinics (Sao Paulo, Brazil). 2017;72(3):130–3.

    Article  Google Scholar 

  46. Dillman JR, et al. Relationship of Bowel MR Imaging to health-related quality of life measures in newly diagnosed pediatric small Bowel Crohn disease. Radiology. 2016;280(2):568–75.

    Article  PubMed  Google Scholar 

  47. Brazier J, et al. Measuring and valuing health benefits for economic evaluation. Oxford: Oxford University Press; 2016.

    Book  Google Scholar 

  48. Rowen D, Brazier J, Van Hout B. A comparison of methods for converting DCE values onto the full health-dead QALY scale. Med Decis Mak. 2015;35:328–40.

    Article  Google Scholar 

  49. Yi MS, et al. Health values of adolescents with cystic fibrosis. J Pediatr. 2003;142(2):133–40.

    Article  PubMed  Google Scholar 

  50. McCabe C, et al. Health state values for the HUI 2 descriptive system: results from a UK survey. Health Econ. 2005;14:231–44.

    Article  PubMed  Google Scholar 

  51. Le Gales C, et al. Development of a preference-weighted health status classification system in France: the health utilities index 3. Health Care Manag Sci. 2002;5:41–51.

    Article  Google Scholar 

  52. Stevens K. Valuation of the child health utility 9D index. Pharmacoeconomics. 2012;30(8):729–47.

    Article  PubMed  Google Scholar 

  53. Fyffe HE, et al. The reliability of two methods of utility assessment in dentistry. Community Dent Health. 1999;16(2):72–9.

    CAS  PubMed  Google Scholar 

  54. Cheng S, et al. Health-related quality of life anticipated with different management strategies for paediatric febrile neutropaenia. Br J Cancer. 2011;105(5):606–11.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Fox D, Kay EJ, O’Brien K. A new method of measuring how much anterior tooth alignment means to adolescents. Eur J Orthod. 2000;22(3):299–305.

    Article  CAS  PubMed  Google Scholar 

  56. Moodie M, et al. Predicting time trade-off health state valuations of adolescents in four Pacific countries using the Assessment of Quality-of-Life (AQoL-6D) instrument. Value Health. 2010;13(8):1014–27.

    Article  PubMed  Google Scholar 

  57. Saw SM, et al. Utility values and myopia in teenage school students. Br J Ophthalmol. 2003;87(3):341–5.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Trent M, et al. Adolescent and parental utilities for the health states associated with pelvic inflammatory disease. Sex Trans Infect. 2011;87(7):583–7.

    Article  Google Scholar 

  59. Sung L, et al. Health-related quality of life (HRQL) scores reported from parents and their children with chronic illness differed depending on utility elicitation method. J Clin Epidemiol. 2004;57(11):1161–6.

    Article  CAS  PubMed  Google Scholar 

  60. Tong A, et al. Quality of life of adolescent kidney transplant recipients. J Pediatr. 2011;159(4):670-5.e2.

    Article  PubMed  Google Scholar 

  61. Yi MS, et al. Health values in adolescents with or without inflammatory bowel disease. J Pediatr. 2009;154(4):527–34.

    Article  PubMed  Google Scholar 

  62. Chen CL, et al. A community-based study of acne-related health preferences in adolescents. Arch Dermatol. 2008;144(8):988–94.

    Article  PubMed  Google Scholar 

  63. Lee JM, et al. Health utilities for children and adults with type 1 diabetes. Med Care. 2011;49(10):924–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Rogers HJ, Marshman Z, Rodd H, Rowen D. Discrete choice experiments or best-worst scaling? A qualitative study to determine the suitability of preference elicitation tasks in research with children and young people. J Patient Rep Outcomes. 2021;5(1):26. https://doi.org/10.1186/s41687-021-00302-4.

    Article  PubMed  PubMed Central  Google Scholar 

  65. Ramos-Goni JM, et al. International valuation protocol for the EQ-5D-Y-3L. Pharmacoeconomics. 2020;38(7):653–63.

    Article  PubMed  Google Scholar 

  66. van den Bos W, et al. Changing brains, changing perspectives: the neurocognitive development of reciprocity. Psychol Sci. 2011;22(1):60–70.

    Article  PubMed  Google Scholar 

  67. Kind P, et al. Can adult weights be used to value child health states? Testing the influence of perspective in valuing EQ-5D-Y. Qual Life Res. 2015;24:2519–39.

    Article  PubMed  PubMed Central  Google Scholar 

  68. Kreimeier S, et al. Valuation of EuroQol Five-Dimensional Questionnaire, Youth Version (EQ-5D-Y) and EuroQol Five-Dimensional Questionnaire, Three-Level Version (EQ-5D-3L) health states: the impact of wording and perspective. Value Health. 2018;21(11):1291–8.

    Article  PubMed  Google Scholar 

  69. Shah KK, et al. An exploration of methods for obtaining 0 = dead anchors for latent scale EQ-5D-Y values. Eur J Health Econ. 2020;06:06.

    Google Scholar 

  70. Lipman S, Reckers-Droog VT, Kreimeier S. Think of the Children: a discussion of the rationale for and implications of the perspective used for EQ-5D-Y health state valuation. Value Health. 2021;24(7):P976–82.

    Article  Google Scholar 

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Acknowledgements

We would like to thank Sophie Cooper, Sarah Dewilde, Alan Lamb, Rosie Lovett, Stavros Petrou and Oliver Rivero-Arias for comments on a previous draft. We would also like to thank Donna Davis and Liz Mclintock for project management and formatting of the report, and Ruth Wong for undertaking the literature searches.

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Authors and Affiliations

  1. School of Health and Related Research, University of Sheffield, Regent Court, 30 Regent St, Sheffield, S1 4DA, UK

    Donna Rowen, Clara Mukuria, Philip A. Powell & Allan Wailoo

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  1. Donna Rowen
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  2. Clara Mukuria
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  3. Philip A. Powell
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  4. Allan Wailoo
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Correspondence to Donna Rowen.

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Author contributions

Concept and design: DR, CM, PAP, AW. Acquisition of data: Not applicable. Analysis and interpretation of data: Not applicable. Drafting of the manuscript: DR, CM, PAP, AW. Critical revision of the paper for important intellectual content: DR, CM, PAP, AW. Statistical analysis: Not applicable.

Conflict of interest

Drs. Rowen, Mukuria, Powell and Professor Wailoo reported receiving Grants from the National Institute of Health and Care Excellence (NICE) Decision Support Unit (NICE DSU) during the conduct of the study. No other disclosures were reported.

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This work was funded by the National Institute for Health and Care Excellence (NICE) through its Decision Support Unit. The views, and any errors or omissions, expressed in this document are of the authors only. NICE may take account of part or all of this document if it considers it appropriate, but it is not bound to do so.

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The funder had no role in the design and conduct of the study; management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.

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Rowen, D., Mukuria, C., Powell, P.A. et al. Exploring the Issues of Valuing Child and Adolescent Health States Using a Mixed Sample of Adolescents and Adults. PharmacoEconomics 40, 479–488 (2022). https://doi.org/10.1007/s40273-022-01133-x

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