Quality of Life Research

, Volume 28, Issue 1, pp 1–12 | Cite as

A systematic review of utility values in children with cerebral palsy

  • Utsana TonmukayakulEmail author
  • Long Khanh-Dao Le
  • Shalika Bohingamu Mudiyanselage
  • Lidia Engel
  • Jessica Bucholc
  • Brendan Mulhern
  • Rob Carter
  • Cathrine Mihalopoulos



Project aims include the following: (i) to identify reported utility values associated with CP in children aged ≤ 18 years; (ii) to explore utility value elicitation techniques in published studies; and (iii) to examine performance of the measures and/or elicitation approaches.


Peer-reviewed studies published prior to March 2017 were identified from six electronic databases. Construct validity, convergent validity, responsiveness, and reliability of instruments were assessed.


Five studies met the inclusion criteria. Utility values of hypothetical general CP states obtained from a general population of parents ranged from 0.55 to 0.88 using time trade off (TTO) and 0.60–0.87 using standard gamble (SG) techniques. Utility values reported by clinicians of three hypothetical spastic quadriplegic CP states, using the Health Utility Index Mark 2 (HUI-2), ranged from 0.40 to 0.13. Other sources of utilities identified were based on both proxy and child ratings using Health Utility Index Mark 3 (HUI-3) (values ranged from − 0.013 to 0.84 depending on the valuation source) and the Assessment of Quality of Life 4 Dimension instrument, with values ranging from 0.01 to 0.58. Construct validity of the HUI-3 varied from moderate to strong, whereas mixed results were found for convergent validity. Responsiveness and reliability were not reported.


There was substantial variation in reported utilities. Indirect techniques (i.e. via multi-attribute utility instruments) were more frequently used than direct techniques (e.g. TTO, SG). Further research is required to improve the robustness of utility valuation of health-related quality of life in children with CP for use in economic evaluation.


Utility value Children Adolescent Cerebral palsy Quality of life Quality-adjusted life years Utility weight 



This study was funded by the Centre of Research Excellence in Cerebral Palsy (NHMRC APP 1057997) for supporting the conduct of this systematic review. The author UT has received PhD scholarship from the Centre of Research Excellence in Cerebral Palsy.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Human and animal participants

This article does not contain any studies with human participants performed by any of the authors.

Supplementary material

11136_2018_1955_MOESM1_ESM.docx (14 kb)
Supplementary material 1 (DOCX 14 KB)


  1. 1.
    Rosenbaum, P., Paneth, N., Leviton, A., Goldstein, M., & Bax, M. (2007). A report: The definition and classification of cerebral Palsy. Developmental Medicine & Child Neurology, 49(Suppl s2), 9.Google Scholar
  2. 2.
    Novak, I., Hines, M., Goldsmith, S., & Barclay, R. (2012). Clinical prognostic messages from a systematic review on cerebral palsy. Pediatrics, 130(5), e1285–e1312.CrossRefGoogle Scholar
  3. 3.
    Reid, S. M., McCutcheon, J., Reddihough, D. S., & Johnson, H. (2012). Prevalence and predictors of drooling in 7- to 14-year-old children with cerebral palsy: A population study. Developmental Medicine & Child Neurology, 54(11), 1032–1036.CrossRefGoogle Scholar
  4. 4.
    Australian Cerebral Palsy Register Group. (2017). Australian Cerebral Palsy Register Report 2016, Australian Cerebral Palsy Register.Google Scholar
  5. 5.
    Cerebral Palsy Alliance. (2013) Key facts and statistics. Retrieved from
  6. 6.
    Bourke-Taylor, H., Howie, L., & Law, M. (2011). Barriers to maternal workforce participation and relationship between paid work and health. Journal of Intellectual Disability Research, 55, 511–520.CrossRefGoogle Scholar
  7. 7.
    Access Economics Pty. (2008). The economic impact of cerebral Palsy in Australia in 2007. Cerebral Palsy Australia.Google Scholar
  8. 8.
    Drummond, M. F., Sculpher, M. J., Claxton, K., Stoddart, G. L., & Torrance, G. W. (2015). Methods for the economic evaluation of health care programmes (4th ed.). Oxford: Oxford University Press.Google Scholar
  9. 9.
    Brazier, J., Ratcliffe, J., Salomon, J. A., & Tsuchiya, A. (2017). Measuring and valuing health benefits for economic evaluation (2nd ed.). Oxford: Oxford University Press.Google Scholar
  10. 10.
    Brazier, J., Ratcliffe, J., Tsuchiya, A., & Solomon, J. A. (2017). Measuring and valuing health benefits for economic evaluation. New York: Oxford University Press.Google Scholar
  11. 11.
    Arnold, D., Girling, A., Stevens, A., & Lilford, R. (2009) Comparison of direct and indirect methods of estimating health state utilities for resource allocation: Review and empirical analysis. BMJ. Scholar
  12. 12.
    Chen, G., & Ratcliffe, J. (2015). A review of the development and application of generic multi-attribute utility instruments for paediatric populations. Pharmacoeconomics, 33(10), 1013–1028.CrossRefGoogle Scholar
  13. 13.
    Seiber, W. J., Groessl, E. J., David, K. M., & Ganiats, T. G., Kaplan, R. M. (2008). Quality of Well Being Self-Administered (QWB-SA) Scale user’s manual. Sandiego: Health services Research Center, University of California.Google Scholar
  14. 14.
    Torrance, G., Feeny, D., Furlong, W., Barr, R., Zhang, Y., & Wang, Q. (1996). Multiattribute utility function for a comprehensive health status classification system: Health Utilities Index Mark 2. Medical Care, 34, 702–722.CrossRefGoogle Scholar
  15. 15.
    Feeny, D., Furlong, W., & Torrance, G. (2002). Multi-attribute and single-attribute utility functions for Health utilities Index Mark 3 system. Medical Care, 40(2), 113–128.CrossRefGoogle Scholar
  16. 16.
    Apajasalo, M., Sintonen, H., holmberg, C., Sinkkonen, J., Aalberg, V., Pihko, H., et al. (1996). Quality of life in early adolescence; a sixteen-dimensional health-related measure (16D). Quality of Life Research, 5(2), 205–211.CrossRefGoogle Scholar
  17. 17.
    Apajassalo, M., Rautonen, J., holmberg, C., Sinkkonen, J., Aalberg, V., Pihko, H., et al. (1996). Quality of life in pre-adolescence: A 17-dimensional ehalth-related measure (17D). Quality of Life Research, 5(6), 532–538.CrossRefGoogle Scholar
  18. 18.
    Moodie, M., Richardson, J., Rankin, B., & Iezzi, A., Sinha, K. (2010). Prdicting time trade-off health state valuations of adolescents in four Pacific countries using the Assessment of Quality-of-Life (AQoL-6D) instrument. Value Health, 13(8), 1014–1027.CrossRefGoogle Scholar
  19. 19.
    Stevens, K. (2009). Developing a descriptive system for a new preference-based measure of health-related quality of life for children. Quality of Life Research, 18, 1105–1113.CrossRefGoogle Scholar
  20. 20.
    Wille, N., Badia, X., Bonsel, G., et al. (2010). Development of the EQ-5D-Y: A child friendly version of the EQ-5D. Quality of Life Research, 19(6), 875–886.CrossRefGoogle Scholar
  21. 21.
    Beusterien, K., Yeung, J.-E., Pang, F., & Brazier, J. (2012). Development of the multi-attribute Adolescent Health Utility Measure (AHUM). Health and Quality of Life Outcomes, 10, 102.CrossRefGoogle Scholar
  22. 22.
    Tosh, J., Brazier, J., Evans, P., & Longworth, L. (2012). A review of generic preference-based measures of health-related quality of life in visual disorders. Value In Health: The Journal of the International Society for Pharmacoeconomics and Outcomes Research, 15, 118–127.CrossRefGoogle Scholar
  23. 23.
    Longworth, L., Yang, Y., Young, T., Mulhern, B., Hernandez Alava, M., Mukuria, C., et al. (2014). Use of generic and condition-specific measures of health-related quality of life in NICE decision-making: A systematic reveiw, statistical modelling and survey. Health Technology Assessment (Winchester, England), 18(9), 1–224Google Scholar
  24. 24.
    Papaioannou, D., Brazier, J., & Parry, G. (2011). How valid and responsive are generic health status measures, such as the EQ-5D and SF-36, in schizophrenia? A systematic review. Value Health: The Journal of the International Society for Pharmacoeconomics and Outcomes Research, 14, 907–920.CrossRefGoogle Scholar
  25. 25.
    Selai, C. E., Trimble, M. R., Price, M. J., & Remak, E. (2005). Evaluation of health status in epilepsy using the EQ-5D questionnaire: A prospective, observational, 6-month study of adjunctive therapy with anti-epileptic drugs. Current Medical Research and Opinion, 21(5), 733–739.CrossRefGoogle Scholar
  26. 26.
    Neumann, P. J. (2005). Health utilities in Alzheimer’s disease and implications for cost-effectiveness analysis. Pharmacoeconomics, 23(6), 537–541.CrossRefGoogle Scholar
  27. 27.
    Rowen, D., Brazier, J., Ara, R., & Azzabi, Z. I. (2017). The role of condition-specific preference-based measures in health technology assessment. Pharmacoeconomics, 35(Suppl 1), 33–41.CrossRefGoogle Scholar
  28. 28.
    Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2009) Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. BMJ. Scholar
  29. 29.
    Tonmukayakul, U., Le, L., Bohingamu, S., Engel, L., Bucholc, J., & Mihalopoulos, C. A systematic review of utility values in children with cerebral palsy. PROSPERO 2017 CRD42017058643.
  30. 30.
    Engel, L., Bansback, N., Bryan, S., Doyle-Waters, M. M., & Whitehurst, D. G. (2016). Exclusion Criteria in National Health State valuation studies: A systematic review. Medical Decision Making, 36(7), 798–810.CrossRefGoogle Scholar
  31. 31.
    Kennedy-Martin, T., Paczkowski, R., & Rayner, S. (2015). Utility values in diabetic kidney disease: A literature review. Current Medical Research and Opinion, 31(7), 1271–1282.CrossRefGoogle Scholar
  32. 32.
    Liem, Y. S., Bosch, J. L., & Hunink, M. G. (2008). Preference-based quality of life of patients on renal replacement therapy: A systematic review and meta-analysis. Value Health, 11(4), 733–741.CrossRefGoogle Scholar
  33. 33.
    Wyld, M., Morton, R. L., Hayen, A., Howard, K., & Webster, A. C. (2012). A systematic review and meta-analysis of utility-based quality of life in chronic kidney disease treatments. PLoS Medicine, 9(9), e1001307.CrossRefGoogle Scholar
  34. 34.
    Novak, I., McIntyre, S., Morgan, C., Campbell, L., Dark, L., Morton, N., Stumbles, E., Wilson, S. A., & Goldsmith, S. (2013). A systematic review of interventions for children with cerebral palsy: State of the evidence. Developmental Medicine & Child Neurology, 55(10), 855–910.CrossRefGoogle Scholar
  35. 35.
    Peasgood, T., & Brazier, J. (2015). Is meta-analysis for utility values appropriate given the potential impact different elicitation methods have on values? Pharmacoeconomics, 33(11), 1101–1105.CrossRefGoogle Scholar
  36. 36.
    Brazier, J., & Deverill, M. (1999). A checklist for judging preference-based measures of health related quality of life: Learning from psychometrics. Health economics, 8(1), 41–51.CrossRefGoogle Scholar
  37. 37.
    Hinkle, D. E., Wiersma, W., & Jurs, S. G. (2003). Applied statistics for the behavioural sciences (5th ed.). Boston: Houghton Mifflin.Google Scholar
  38. 38.
    Lissovoy, G. M., L. S.; Green, H.; Werner, M.; Edgar, T (2007). Cost-effectiveness of intrathecal baclofen therapy for the treatment of severe spasticity associated with cerebral palsy. Journal of Child Neurology, 22(1), 49–59.CrossRefGoogle Scholar
  39. 39.
    Carroll, A. E., & Downs, S. M. (2009). Improving decision analyses: Parent preferences (utility values) for pediatric health outcomes. The Journal of Pediatrics, 155(1), 21.e1–25.e5.CrossRefGoogle Scholar
  40. 40.
    Petrou, S., & Kupek, E. (2009). Estimating preference-based health utilities index mark 3 utility scores for childhood conditions in England and Scotland. Medical Decision Making, 29(3), 291–303.CrossRefGoogle Scholar
  41. 41.
    Young, N. L., Rochon, T. G., McCormick, A., Law, M., Wedge, J. H., & Fehlings, D. (2010). The health and quality of life outcomes among youth and young adults with cerebral palsy. Archives of Physical Medicine and Rehabilitation, 91(1), 143–148.CrossRefGoogle Scholar
  42. 42.
    Rosenbaum, P. L., Livingston, M. H., Palisano, R. J., Galuppi, B. E., & Russell, D. J. (2007). Quality of life and health-related quality of life of adolescents with cerebral palsy. Developmental Medicine & Child Neurology, 49(7), 516–521.CrossRefGoogle Scholar
  43. 43.
    Sport England. (2000). Disability Survey: Survey of young people with a disability and sport. London, UK: Sport England.Google Scholar
  44. 44.
    Palisano, R. J., Rosenbaum, P., Bartlett, D., & Livingston, M. H. (2008). Content validity of the expanded and revised gross motor function classification system. Developmental Medicine & Child Neurology, 50(10), 744–750.CrossRefGoogle Scholar
  45. 45.
    Palisano, R., Rosenbaum, P., Walter, S., Russel, D., Wood, E., & Galuppi, B. (1997). Development and reliability of a system to classify gross motor function in children with cerebral palsy. Developmental Medicine & Child Neurology, 39, 214–223.CrossRefGoogle Scholar
  46. 46.
    Hawthorne, G., Richardson, J., & Osborne, R. (1999). The Assessment of Quality of Life (AQoL) instrument: A psychometric measure of health-related quality of life. Quality of Life Research, 8(3), 209–224.CrossRefGoogle Scholar
  47. 47.
    Horsman, J., Furlong, W., Feeny, D., & Torrance, G. (2003) The Health Utilities Index (HUI(®)): Concepts, measurement properties and applications. Health and Quality of Life Outcomes, 1(1), 54.CrossRefGoogle Scholar
  48. 48.
    Feeny, D., Furlong, W., Torrance, G. W., Goldsmith, C. H., Zhu, Z., DePauw, S., et al. (2002). Multiattribute and single-attribute utility functions for the health utilities index mark 3 system. Medical Care, 40(2), 113–128.CrossRefGoogle Scholar
  49. 49.
    Hawthorne, G., & Richardson, J. (1997) The Assessment of Quality of Life (AQoL) instrument construction, initial validation and utility scaling. Melbourne: Centre for Health Program Evaluation.Google Scholar
  50. 50.
    Mihalopoulos, C., Chen, G., Iezzi, A., Khan, M. A., & Richardson, J. (2014). Assessing outcomes for cost-utility analysis in depression: Comparison of five multi-attribute utility instruments with two depression-specific outcome measures. British Journal of Psychiatry, 205(5), 390–397.CrossRefGoogle Scholar
  51. 51.
    Renwick, R., Fudge Schormans, A., & Zekovic, B. (2003). Quality of life: A new conceptual framework for children with disabilities. Journal on Developmental Disabilities, 10, 107–114.Google Scholar
  52. 52.
    Varni, J. W., Limbers, C. A., & Burwinkle, T. M. (2007). How young can children reliably and validly self-report their health-related quality of life? An analysis of 8,591 children across age subgroups with the PedsQL™ 4.0 Generic Core Scales. Health and Quality of Life Outcomes, 5(1), 1.CrossRefGoogle Scholar
  53. 53.
    Eiser, C. (1995). Choices in measuring quality of life in children with cancer: A comment. Psycho-Oncology, 4(2), 121–131.CrossRefGoogle Scholar
  54. 54.
    Thorrington, D., & Eames, K. (2015). Measuring health utilities in children and adolescents: A systematic reveiw of the literature. PLoS ONE, 10(8), e0135672.CrossRefGoogle Scholar
  55. 55.
    Bray, N., Noyes, J., Harris, N., & Edwards, R. T. (2017). Measuring the health-related quality of life of children with impaired mobility: Examining correlation and agreement between children and parent proxies. BMC Research Notes, 10(1), 377.CrossRefGoogle Scholar
  56. 56.
    Ungar, W. J. (2011). Challenges in health state valuation in paediatric economic evaluation: Are QALYs contraindicated? Pharmacoeconomics, 29(8), 641–652.CrossRefGoogle Scholar
  57. 57.
    Petrou, S. (2003). Methodological issues raised by preference-based approaches to measuring the health status of children. Health Economics, 12(8), 697–702.CrossRefGoogle Scholar
  58. 58.
    Ratcliffe, J., Huynh, E., Stevens, K., Brazier, J., Sawyer, M., & Flynn, T. (2015). Nothing about us without us? A compariosn of adolescent and adult health-state values for the child health utility-9D using profile case bast-worst scaling. Health Economics, 25(4), 486–496.CrossRefGoogle Scholar
  59. 59.
    NICE. (2013). Guide to the methods of technology appraisal. London: NICE.Google Scholar
  60. 60.
    Canadian Agency for Drugs and Technologies in Health. (2006). Guidelines for the economic evaluation of health technologies (3rd ed.). Ottawa: Canadian Agency for Drugs and Technologies in Health.Google Scholar
  61. 61.
    Livingston, M. H., Rosenbaum, P. L., Russell, D. J., & Palisano, R. J. (2007). Quality of life among adolescents with cerebral palsy: What does the literature tell us? Developmental Medicine & Child Neurology, 49(3), 225–231.CrossRefGoogle Scholar
  62. 62.
    Whitehurst, D. G. T., Mittmann, N., Noonan, V. K., Dvorak, M. F., & Bryan, S. (2016). Health state descriptions, valuations and individuals’ capacity to walk: A comparative evaluation of preference-based instruments in the context of spinal cord injury. Quality of Life Research, 25(10), 2481–2496.CrossRefGoogle Scholar
  63. 63.
    Griebsch, I., Coast, J., & Brown, J. (2005). Quality-adjusted life-years lack quality in pediatric care: A critical review of published cost-utility studies in child health. Pediatrics, 115(5), e600–e614.CrossRefGoogle Scholar
  64. 64.
    Brazier, J. E., Rowen, D., Mavranezouli, I., Tsuchiya, A., Young, T., Yang, Y., et al. (2012). Developing and testing methods for deriving preference-based measures of health from condition-specific measures (and other patient-based measures of outcome). Health Technology Assessment ((Winchester, England)), 16(32), 1–114. Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Utsana Tonmukayakul
    • 1
    • 3
    Email author
  • Long Khanh-Dao Le
    • 1
  • Shalika Bohingamu Mudiyanselage
    • 1
  • Lidia Engel
    • 1
  • Jessica Bucholc
    • 1
  • Brendan Mulhern
    • 2
  • Rob Carter
    • 1
  • Cathrine Mihalopoulos
    • 1
  1. 1.Deakin Health Economics, Centre for Population Health ResearchDeakin UniversityGeelongAustralia
  2. 2.Centre for Health Economics Research and EvaluationUniversity of Technology SydneySydneyAustralia
  3. 3.Deakin Health Economics, Centre for Population Health ResearchDeakin UniversityMelbourneAustralia

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