Skip to main content
SpringerLink
Log in

Mapping CHU9D Utility Scores from the PedsQLTM 4.0 SF-15

  • Original Research Article
  • Published:
PharmacoEconomics Aims and scope Submit manuscript

Abstract

Background

The Pediatric Quality of Life Inventory™ 4.0 Short Form 15 Generic Core Scales (hereafter the PedsQL) and the Child Health Utility-9 Dimensions (CHU9D) are two generic instruments designed to measure health-related quality of life in children and adolescents in the general population and paediatric patient groups living with specific health conditions. Although the PedsQL is widely used among paediatric patient populations, presently it is not possible to directly use the scores from the instrument to calculate quality-adjusted life-years (QALYs) for application in economic evaluation because it produces summary scores which are not preference-based.

Objective

This paper examines different econometric mapping techniques for estimating CHU9D utility scores from the PedsQL for the purpose of calculating QALYs for cost-utility analysis.

Methods

The PedsQL and the CHU9D were completed by a community sample of 755 Australian adolescents aged 15–17 years. Seven regression models were estimated: ordinary least squares estimator, generalised linear model, robust MM estimator, multivariate factorial polynomial estimator, beta-binomial estimator, finite mixture model and multinomial logistic model. The mean absolute error (MAE) and the mean squared error (MSE) were used to assess predictive ability of the models.

Results

The MM estimator with stepwise-selected PedsQL dimension scores as explanatory variables had the best predictive accuracy using MAE and the equivalent beta-binomial model had the best predictive accuracy using MSE.

Conclusions

Our mapping algorithm facilitates the estimation of health-state utilities for use within economic evaluations where only PedsQL data is available and is suitable for use in community-based adolescents aged 15–17 years. Applicability of the algorithm in younger populations should be assessed in further 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
Fig. 3

Similar content being viewed by others

References

  1. Brazier J, Ratcliffe J, Tsuchiya A, Salomon J. Measuring and valuing health benefits for economic evaluation. Oxford: Oxford University Press; 2007.

    Google Scholar 

  2. Fontaine KR, Barofsky I. Obesity and health-related quality of life. Obes Rev. 2001;2(3):173–82.

    Article  CAS  PubMed  Google Scholar 

  3. Drummond MF, Sculpher MJ, Torrance GW, O’Brien BJ, Stoddart GL. Methods for the economic evaluation of health care programmes. Oxford: Oxford University Press; 2005.

    Google Scholar 

  4. Harris A, Bulfone L. Getting value for money: “The Australian experience”. In: International M-H, Jost T, editors. Health care coverage determinations: an international comparative study. Maidenhead: Open University Press; 2004.

  5. National Institute for Health and Care Excellence. Guide to the methods of technology appraisal. National Health Service. 2010.

  6. Varni JW, Burwinkle TM, Seid M, Skarr D. The PedsQL 4.0 as a pediatric population health measure: feasibility, reliability, and validity. Ambul Pediatr. 2003;3(6):329–41.

    Article  PubMed  Google Scholar 

  7. Brazier JE, Yang Y, Tsuchiya A, Rowen DL. A review of studies mapping (or cross walking) non-preference based measures of health to generic preference-based measures. Eur J Health Econ. 2010;11(2):215–25. doi:10.1007/s10198-009-0168-z.

    Article  PubMed  Google Scholar 

  8. Chen G, Stevens K, Rowen D, Ratcliffe J. From KIDSCREEN-10 to CHU9D: creating a unique mapping algorithm for application in economic evaluation. Health Qual Life Outcomes. 2014;12:134. doi:10.1186/s12955-014-0134-z.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Furber G, Segal L, Leach M, Cocks J. Mapping scores from the Strengths and Difficulties Questionnaire (SDQ) to preference-based utility values. Qual Life Res. 2014;23(2):403–11. doi:10.1007/s11136-013-0494-6.

    Article  PubMed  Google Scholar 

  10. Khan KA, Petrou S, Rivero-Arias O, Walters SJ, Boyle SE. Mapping EQ-5D utility scores from the PedsQL generic core scales. Pharmacoeconomics. 2014;32(7):693–706. doi:10.1007/s40273-014-0153-y.

    Article  PubMed  Google Scholar 

  11. Payakachat N, Tilford JM, Kuhlthau KA, van Exel NJ, Kovacs E, Bellando J, et al. Predicting health utilities for children with autism spectrum disorders. Autism Res. 2014;7(6):649–63. doi:10.1002/aur.1409.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Varni JW, Seid M, Kurtin PS. PedsQL 4.0: reliability and validity of the Pediatric Quality of Life Inventory version 4.0 generic core scales in healthy and patient populations. Med Care. 2001;39(8):800–12.

    Article  CAS  PubMed  Google Scholar 

  13. 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. doi:10.2165/11587350-000000000-00000.

    Article  PubMed  Google Scholar 

  14. Stevens K. Valuation of the Child Health Utility 9D Index. Pharmacoeconomics. 2012;30(8):729–47. doi:10.2165/11599120-000000000-00000.

    Article  PubMed  Google Scholar 

  15. Ratcliffe J, Flynn T, Terlich F, Stevens K, Brazier J, Sawyer M. Developing adolescent-specific health state values for economic evaluation: an application of profile case best-worst scaling to the Child Health Utility 9D. Pharmacoeconomics. 2012;30(8):713–27. doi:10.2165/11597900-000000000-00000.

    Article  PubMed  Google Scholar 

  16. Ratcliffe J, Huynh E, Chen G, Stevens K, Swait J, Brazier J, et al. Valuing the child health utility 9D: using profile case best worst scaling methods to develop a new adolescent specific scoring algorithm. Soc Sci Med. 2016;157:48–59. doi:10.1016/j.socscimed.2016.03.042.

    Article  PubMed  Google Scholar 

  17. Boyce W, Torsheim T, Currie C, Zambon A. The Family Affluence Scale as a measure of national wealth: validation of an adolescent self-report measure. Soc Indic Res. 2006;78(3):473–87.

    Article  Google Scholar 

  18. 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. Value Health. 2012;15(8):1092–9. doi:10.1016/j.jval.2012.07.011.

    Article  PubMed  Google Scholar 

  19. Ratcliffe J, Stevens K, Flynn T, Brazier J, Sawyer M. An assessment of the construct validity of the CHU9D in the Australian adolescent general population. Qual Life Res. 2012;21(4):717–25. doi:10.1007/s11136-011-9971-y.

    Article  PubMed  Google Scholar 

  20. Chen G, Flynn T, Stevens K, Brazier J, Huynh E, Sawyer M, et al. Assessing the health-related quality of life of Australian adolescents: an empirical comparison of the child health utility 9D and EQ-5D-Y instruments. Value Health. 2015;18(4):432–8. doi:10.1016/j.jval.2015.02.014.

    Article  PubMed  Google Scholar 

  21. Petrou S, Rivero-Arias O, Dakin H, Longworth L, Oppe M, Froud R, et al. The MAPS reporting statement for studies mapping onto generic preference-based outcome measures: explanation and elaboration. PharmacoEconomics. 2015;33(10):993–1011. doi:10.1007/s40273-015-0312-9.

    Article  PubMed  Google Scholar 

  22. Boers M, Verhoeven AC, Markusse HM, van de Laar MA, Westhovens R, van Denderen JC, et al. Randomised comparison of combined step-down prednisolone, methotrexate and sulphasalazine with sulphasalazine alone in early rheumatoid arthritis. Lancet. 1997;350(9074):309–18. doi:10.1016/s0140-6736(97)01300-7.

    Article  CAS  PubMed  Google Scholar 

  23. International CLL-IPI Working Group. An international prognostic index for patients with chronic lymphocytic leukaemia (CLL-IPI): a meta-analysis of individual patient data. Lancet Oncol. 2016;17(6):779–90. doi:10.1016/s1470-2045(16)30029-8.

    Article  Google Scholar 

  24. Chappell LC, Seed PT, Myers J, Taylor RS, Kenny LC, Dekker GA, et al. Exploration and confirmation of factors associated with uncomplicated pregnancy in nulliparous women: prospective cohort study. Bmj. 2013;347:f6398. doi:10.1136/bmj.f6398.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Kuk D, Varadhan R. Model selection in competing risks regression. Stat Med. 2013;32(18):3077–88. doi:10.1002/sim.5762.

    Article  PubMed  Google Scholar 

  26. Allen LA, Yager JE, Funk MJ, Levy WC, Tulsky JA, Bowers MT, et al. Discordance between patient-predicted and model-predicted life expectancy among ambulatory heart failure patients. JAMA J Am Med Assoc. 2008;299(21):2533–42. doi:10.1001/jama.299.21.2533.

    Article  CAS  Google Scholar 

  27. StataCorp. Stata Statistical Software: Release 14. 2015.

  28. Dakin H. Review of studies mapping from quality of life or clinical measures to EQ-5D: an online database. Health Qual Life Outcomes. 2013;11:151. doi:10.1186/1477-7525-11-151.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Longworth L, Rowen D. Mapping to obtain EQ-5D utility values for use in NICE health technology assessments. Value Health. 2013;16(1):202–10. doi:10.1016/j.jval.2012.10.010.

    Article  PubMed  Google Scholar 

  30. Hernandez Alava M, Wailoo A, Wolfe F, Michaud K. A comparison of direct and indirect methods for the estimation of health utilities from clinical outcomes. Med Decis Making. 2014;34(7):919–30. doi:10.1177/0272989x13500720.

    Article  PubMed  Google Scholar 

  31. Huang IC, Frangakis C, Atkinson MJ, Willke RJ, Leite WL, Vogel WB, et al. Addressing ceiling effects in health status measures: a comparison of techniques applied to measures for people with HIV disease. Health Serv Res. 2008;43(1 Pt 1):327–39. doi:10.1111/j.1475-6773.2007.00745.x.

    PubMed  PubMed Central  Google Scholar 

  32. Payakachat N, Summers KH, Pleil AM, Murawski MM, Thomas J 3rd, Jennings K, et al. Predicting EQ-5D utility scores from the 25-item National Eye Institute Vision Function Questionnaire (NEI-VFQ 25) in patients with age-related macular degeneration. Qual Life Res. 2009;18(7):801–13. doi:10.1007/s11136-009-9499-6.

    Article  PubMed  Google Scholar 

  33. Gujarati DN. Basic econometrics. 4th ed. Boston. Mass. London: McGraw-Hill; 2003.

  34. Chen G, Khan MA, Iezzi A, Ratcliffe J, Richardson J. Mapping between 6 multiattribute utility instruments. Med Decis Making. 2016;36(2):160–75. doi:10.1177/0272989x15578127.

    Article  PubMed  Google Scholar 

  35. McCullagh P, Nelder JA. Generalized linear models. 2nd ed. London: Chapman & Hall; 1989.

    Book  Google Scholar 

  36. Royston P, Sauerbrei W. Multivariable modeling with cubic regression splines: a principled approach. Stata J. 2007;7(1):45–70.

    Google Scholar 

  37. Briggs A, Sculpher M, Claxton K. Decision modelling for health economic evaluation. Oxford: Oxford University Press; 2006.

  38. Ospina R, Ferrari SL. A general class of zero-or-one inflated beta regression models. Comput Stat Data Anal. 2012;56(6):1609–23.

    Article  Google Scholar 

  39. Basu A, Manca A. Regression estimators for generic health-related quality of life and quality-adjusted life years. Med Decis Making. 2012;32(1):56–69. doi:10.1177/0272989x11416988.

    Article  PubMed  Google Scholar 

  40. Khan I, Morris S. A non-linear beta-binomial regression model for mapping EORTC QLQ-C30 to the EQ-5D-3L in lung cancer patients: a comparison with existing approaches. Health Qual Life Outcomes. 2014;12(1):1–16. doi:10.1186/s12955-014-0163-7.

    Article  CAS  Google Scholar 

  41. Everitt B, Hand D. Finite mixture distributions. London and New York: Chapman and Hall; 1981.

    Book  Google Scholar 

  42. McLachlan G, Peel D. Finite mixture models. New York: Wiley; 2000.

    Book  Google Scholar 

  43. Kent S, Gray A, Schlackow I, Jenkinson C, McIntosh E. Mapping from the Parkinson’s disease questionnaire PDQ-39 to the generic EuroQol EQ-5D-3L: the value of mixture models. Med Decis Making. 2015;35(7):902–11. doi:10.1177/0272989x15584921.

    Article  PubMed  Google Scholar 

  44. Deb P. Finite mixture models. 2008. http://repec.org/snasug08/deb_fmm_slides.pdf. Accessed 11 Sept 2016.

  45. Gray AM, Rivero-Arias O, Clarke PM. Estimating the association between SF-12 responses and EQ-5D utility values by response mapping. Med Decis Making. 2006;26(1):18–29. doi:10.1177/0272989x05284108.

    Article  PubMed  Google Scholar 

  46. Le QA, Doctor JN. Probabilistic mapping of descriptive health status responses onto health state utilities using Bayesian networks: an empirical analysis converting SF-12 into EQ-5D utility index in a national US sample. Med Care. 2011;49(5):451–60. doi:10.1097/MLR.0b013e318207e9a8.

    Article  PubMed  Google Scholar 

  47. Koch GG. Intraclass Correlation Coefficient.  Encyclopedia of Statistical Sciences. John Wiley & Sons, Inc.; 2004. doi:10.1002/0471667196.ess1275

  48. Hyndman RJ, Koehler AB. Another look at measures of forecast accuracy. Int J Forecast. 2006;22(4):679–88. doi:10.1016/j.ijforecast.2006.03.001.

    Article  Google Scholar 

  49. Shcherbakov MV, Brebels B, Shcherbakova NL, Tyukov AP, Janovsky TA, Kamae VA. A survey of forecast error measures. World Appl Sci J 24 (Information Technologies in Modern Industry, Education and Society). 2013;24(24):171–6.

  50. Wong CK, Lam CL, Rowen D, McGhee SM, Ma KP, Law WL, et al. Mapping the functional assessment of cancer therapy-general or -colorectal to SF-6D in Chinese patients with colorectal neoplasm. Value Health. 2012;15(3):495–503. doi:10.1016/j.jval.2011.12.009.

    Article  PubMed  Google Scholar 

  51. Wu EQ, Mulani P, Farrell MH, Sleep D. Mapping FACT-P and EORTC QLQ-C30 to patient health status measured by EQ-5D in metastatic hormone-refractory prostate cancer patients. Value Health. 2007;10(5):408–14. doi:10.1111/j.1524-4733.2007.00195.x.

    Article  PubMed  Google Scholar 

  52. Petrou S, Rivero-Arias O, Dakin H, Longworth L, Oppe M, Froud R, et al. Preferred reporting items for studies mapping onto preference-based outcome measures: the MAPS statement. Qual Life Res. 2016;25(2):275–81. doi:10.1007/s11136-015-1082-8.

    Article  PubMed  Google Scholar 

  53. Chuang LH, Whitehead SJ. Mapping for economic evaluation. Br Med Bull. 2012;101:1–15. doi:10.1093/bmb/ldr049.

    Article  PubMed  Google Scholar 

  54. Pinedo-Villanueva RA, Turner D, Judge A, Raftery JP, Arden NK. Mapping the Oxford hip score onto the EQ-5D utility index. Qual Life Res. 2013;22(3):665–75. doi:10.1007/s11136-012-0174-y.

    Article  PubMed  Google Scholar 

  55. Tsuchiya A, Brazier JE, McColl E, Parkin D. Deriving preference-based single indices from non-preference based condition-specific instruments: Converting AQLQ into EQ5D indices Sheffield Health Economics Group Discussion Paper Series. 2002; Ref 02/1.

  56. Brennan DS, Spencer AJ. Mapping oral health related quality of life to generic health state values. BMC Health Serv Res. 2006;6:96. doi:10.1186/1472-6963-6-96.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Sauerland S, Weiner S, Dolezalova K, Angrisani L, Noguera CM, Garcia-Caballero M, et al. Mapping utility scores from a disease-specific quality-of-life measure in bariatric surgery patients. Value Health. 2009;12(2):364–70. doi:10.1111/j.1524-4733.2008.00442.x.

    Article  PubMed  Google Scholar 

  58. Bansback N, Marra C, Tsuchiya A, Anis A, Guh D, Hammond T, et al. Using the health assessment questionnaire to estimate preference-based single indices in patients with rheumatoid arthritis. Arthritis Rheum. 2007;57(6):963–71. doi:10.1002/art.22885.

    Article  PubMed  Google Scholar 

  59. Australian Demographic Statistics, 2015, ‘Table 8: Estimated resident population, by age and sex—at 30 June 2015’, data cube: Excel spreadsheet, cat. no. 31010do002_201512 [database on the Internet]. Australian Bureau of Statistics 2015. Available from: http://www.abs.gov.au/AUSSTATS/abs@.nsf/DetailsPage/3101.0Dec%202015?OpenDocument. Accessed: 6 Sept 2016.

  60. Australian Institute of Health and Welfare. Young Australians: their health and wellbeing 2011 (Cat. no. PHE 140). Canberra, Australia: Australian Institute of Health and Welfare; 2011.

  61. Manning WG, Mullahy J. Estimating log models: to transform or not to transform? J Health Econ. 2001;20(4):461–94

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We would like to thank all the study participants who generously gave up their time to participate in this study.

Author contributions

CMK analysed the data, interpreted the results, wrote the first draft and will act as a guarantor for the work. GC formulated the idea for the study, oversaw the design and collection of data, analysed the data, interpreted the results and made critical revisions to the manuscript. RR interpreted the results and made critical revisions to the manuscript. KS formulated the idea for the study, oversaw the design and collection of data, interpreted the results and made critical revisions to the manuscript. KDP interpreted the results and made critical revisions to the manuscript. JR formulated the idea for the study, oversaw the design and collection of data, interpreted the results and made critical revisions to the manuscript. All authors approved the final draft.

Author information

Authors and Affiliations

  1. Institute for Choice, University of South Australia, Business School, Adelaide, South Australia, Australia

    Christine Mpundu-Kaambwa

  2. Centre for Health Economics, Monash Business School, Monash University, Melbourne, Victoria, Australia

    Gang Chen

  3. Faculty of Health Sciences, School of Medicine, Flinders University, Adelaide, South Australia, Australia

    Remo Russo

  4. Department of Paediatric Rehabilitation, Women’s and Children’s Hospital, Adelaide, South Australia, Australia

    Remo Russo

  5. Health Economics and Decision Science, University of Sheffield, Sheffield, United Kingdom

    Katherine Stevens

  6. Department of Business and Management, Faculty of Social Sciences, Aalborg University, Aalborg East, Denmark

    Karin Dam Petersen

  7. Flinders Health Economics Group, Flinders University, Adelaide, South Australia, Australia

    Julie Ratcliffe

Authors
  1. Christine Mpundu-Kaambwa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gang Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Remo Russo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Katherine Stevens
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Karin Dam Petersen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Julie Ratcliffe
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Christine Mpundu-Kaambwa.

Ethics declarations

Ethical approval for this study was obtained from the Social and Behavioural Research Ethics Committee, Flinders University (Project Number 5508).

Competing interests

CMK, GC, RR, KS, KDP and JR declare that they have no conflict of interest.

Funding

This study was funded by an Australian NHMRC Project Grant (Grant Number 1021899) entitled ‘Adolescent values for the economic evaluation of adolescent health care treatment and preventive programs’.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 20 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mpundu-Kaambwa, C., Chen, G., Russo, R. et al. Mapping CHU9D Utility Scores from the PedsQLTM 4.0 SF-15. PharmacoEconomics 35, 453–467 (2017). https://doi.org/10.1007/s40273-016-0476-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40273-016-0476-y

Keywords

Search

Navigation