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The EORTC QLU-C10D: the Hong Kong valuation study

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Abstract

Objective

The EORTC QLU-C10D is a new preference-based measure derived from the EORTC QLQ-C30. Country-specific value sets are required to support the cost-utility analysis of cancer-related interventions. This study aimed to generate an EORTC QLU-C10 value set for Hong Kong (HK).

Methods

A HK online panel was quota-sampled to achieve an adult general population sample representative by sex and age. Participants were invited to complete an online discrete choice experiment survey. Each participant was asked to complete 16 choice-pairs, randomly assigned from a total of 960 choice-pairs, each comprising two QLU-C10D health states and a duration attribute. Conditional and mixed logistic regression analyses were used to analyse the data.

Results

The analysis included data from 1041 respondents who had successfully completed the online survey. The distribution of sex did not differ from that of the general population, but a significant difference was found among age groups. A weighting analysis for non-representative variable (age) was used. Utility decrements were generally monotonic, with the largest decrements for physical functioning (− 0.308), role functioning (− 0.165), and pain (− 0.161). The mean QLU-C10D utility score of the participants was 0.804 (median = 0.838, worst to best = − 0.169 to 1). The value of the worst health state was − 0.223, which was sufficiently lower than 0 (being dead).

Conclusions

This study established HK utility weights for the QLU-C10D, which can facilitate cost-utility analyses across cancer-related health programmes and technologies.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Brazier, J.E., Rowen, D., Lloyd, A., et al.: Future directions in valuing benefits for estimating QALYs: is time up for the EQ-5D? Value Health 22, 62–68 (2019)

    Article  PubMed  Google Scholar 

  2. Norman, R., Mercieca-Bebber, R., Rowen, D., et al.: UK utility weights for the EORTC QLU-C10D. Health Econ. 28, 1385–1401 (2019). https://doi.org/10.1002/hec.3950

    Article  PubMed  Google Scholar 

  3. Herdman, M., Gudex, C., Lloyd, A., et al.: Development and preliminary testing of the new five-level version of EQ-5D (EQ-5D-5L). Qual. Life Res. 20, 1727–1736 (2011). https://doi.org/10.1007/s11136-011-9903-x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Brazier, J., Roberts, J., Deverill, M.: The estimation of a preference-based measure of health from the SF-36. J. Health Econ. 21, 271–292 (2002). https://doi.org/10.1016/S0167-6296(01)00130-8

    Article  PubMed  Google Scholar 

  5. Versteegh, M.M., Leunis, A., Uyl-de Groot, C.A., et al.: Condition-specific preference-based measures: benefit or burden? Value Health 15, 504–513 (2012). https://doi.org/10.1016/j.jval.2011.12.003

    Article  PubMed  Google Scholar 

  6. Rowen, D., Brazier, J., Ara, R., et al.: The role of condition-specific preference-based measures in health technology assessment. Pharmacoeconomics 35, 33–41 (2017). https://doi.org/10.1007/s40273-017-0546-9

    Article  PubMed  Google Scholar 

  7. Giesinger, J.M., Efficace, F., Aaronson, N., et al.: Past and current practice of patient-reported outcome measurement in randomized cancer clinical trials: a systematic review. Value Health 24, 585–591 (2021). https://doi.org/10.1016/j.jval.2020.11.004

    Article  PubMed  Google Scholar 

  8. McTaggart-Cowan, H., King, M.T., Norman, R., et al.: The EORTC QLU-C10D: the Canadian Valuation Study and algorithm to derive cancer-specific utilities from the EORTC QLQ-C30. MDM Policy Pract 4, 238146831984253 (2019). https://doi.org/10.1177/2381468319842532

    Article  Google Scholar 

  9. Revicki, D.A., King, M.T., Viney, R., et al.: United states utility algorithm for the EORTC QLU-C10D, a multiattribute utility instrument based on a cancer-specific quality-of-life instrument. Med. Decis. Mak. 41, 485–501 (2021). https://doi.org/10.1177/0272989X211003569

    Article  Google Scholar 

  10. King, M.T., Viney, R., Simon Pickard, A., et al.: Australian utility weights for the EORTC QLU-C10D, a multi-attribute utility instrument derived from the cancer-specific quality of life questionnaire, EORTC QLQ-C30. Pharmacoeconomics 36, 225–238 (2018). https://doi.org/10.1007/s40273-017-0582-5

    Article  PubMed  Google Scholar 

  11. Kemmler, G., Gamper, E., Nerich, V., et al.: German value sets for the EORTC QLU-C10D, a cancer-specific utility instrument based on the EORTC QLQ-C30. Qual. Life Res. 28, 3197–3211 (2019). https://doi.org/10.1007/s11136-019-02283-w

    Article  PubMed  PubMed Central  Google Scholar 

  12. Nerich, V., Gamper, E.M., Norman, R., et al.: French value-set of the QLU-C10D, a cancer-specific utility measure derived from the QLQ-C30. Appl. Health Econ. Health Policy (2020). https://doi.org/10.1007/s40258-020-00598-1

    Article  Google Scholar 

  13. Gamper, E.M., King, M.T., Norman, R., et al.: EORTC QLU-C10D value sets for Austria, Italy, and Poland. Qual. Life Res. 29, 2485–2495 (2020). https://doi.org/10.1007/s11136-020-02536-z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Atlas TC. Southern, Eastern, & South-Eastern Asia. https://canceratlas.cancer.org/the-burden/south-east-se-asia/. Accessed 28 May 2021

  15. Hong Kong Cancer Registry. Overview of Hong Kong Cancer Statistics. Hong Kong Hospital Authority (2020). https://www3.ha.org.hk/cancereg. Accessed 28 May 2021

  16. Campolina, A.G., Yuba, T.Y., Decimoni, T.C., et al.: Health economic evaluations of cancer in Brazil: a systematic review. Front. Public Health 6, 205 (2018). https://doi.org/10.3389/fpubh.2018.00205

    Article  PubMed  PubMed Central  Google Scholar 

  17. Johnson, J.A., Luo, N., Shaw, J.W., et al.: Valuations of EQ-5D health states: are the United States and United Kingdom different? Med. Care 43, 221–228 (2005). https://doi.org/10.1097/00005650-200503000-00004

    Article  PubMed  Google Scholar 

  18. Zhao, H., Kanda, K.: Translation and validation of the standard Chinese version of the EORTC QLQ-C30. Qual. Life Res. 9, 129–137 (2000). https://doi.org/10.1023/A:1008981520920

    Article  CAS  PubMed  Google Scholar 

  19. Finch, A.P., Gamper, E., Norman, R., et al.: Estimation of an EORTC QLU-C10 value set for Spain using a discrete choice experiment. Pharmacoeconomics 39, 1085–1098 (2021). https://doi.org/10.1007/s40273-021-01058-x

    Article  PubMed  PubMed Central  Google Scholar 

  20. HKSAR Government. Hong Kong Population By-census (2016). http://www.bycensus2016.gov.hk/en/bc-mt.html. Accessed 13 Oct 2021

  21. Lancsar, E., Louviere, J.: Conducting discrete choice experiments to inform healthcare decision making: a users guide. Pharmacoeconomics 26, 661–677 (2008). https://doi.org/10.2165/00019053-200826080-00004

    Article  PubMed  Google Scholar 

  22. Norman, R., Viney, R., Aaronson, N.K., et al.: Using a discrete choice experiment to value the QLU-C10D: feasibility and sensitivity to presentation format. Qual. Life Res. 25, 637–649 (2016). https://doi.org/10.1007/s11136-015-1115-3

    Article  CAS  PubMed  Google Scholar 

  23. Jansen, F., Verdonck-de Leeuw, I.M., Gamper, E., et al.: Dutch utility weights for the EORTC cancer-specific utility instrument: the Dutch EORTC QLU-C10D. Qual. Life Res. (2021). https://doi.org/10.1007/s11136-021-02767-8

    Article  PubMed  PubMed Central  Google Scholar 

  24. Deming, W.E., Stephan, F.F.: On a least squares adjustment of a sampled frequency table when the expected marginal totals are known. Ann. Math. Stat. 11, 427–444 (1940). https://doi.org/10.1214/aoms/1177731829

    Article  Google Scholar 

  25. Gu, Y., Norman, R., Viney, R.: Estimating health state utility values from discrete choice experiments—a QALY space model approach. Health Econ. 23, 1098–1114 (2014). https://doi.org/10.1002/hec.3066

    Article  PubMed  Google Scholar 

  26. Hong Kong Food and Health Bureau. Mental health review report (2018). https://www.healthbureau.gov.hk/download/press_and_publications/otherinfo/180500_mhr/e_mhr_full_report.pdf. Accessed 25 Dec 2021

  27. Kilkkinen, A., Kao-Philpot, A., O’Neil, A., et al.: Prevalence of psychological distress, anxiety and depression in rural communities in Australia. Aust. J. Rural Health 15, 114–119 (2007). https://doi.org/10.1111/j.1440-1584.2007.00863.x

    Article  PubMed  Google Scholar 

  28. Mental Health Foundation. Mental health statistics: depression. Mental Health Foundation. https://www.mentalhealth.org.uk/statistics/mental-health-statistics-depression. Accessed 30 Mar 2022

  29. Mental health in Spain. https://www.statista.com/topics/8060/mental-health-in-spain/#dossierKeyfigures. Accessed 29 Dec 2021

  30. Stratton, E., Lampit, A., Choi, I., et al.: Effectiveness of eHealth interventions for reducing mental health conditions in employees: a systematic review and meta-analysis. PLoS ONE 12, e0189904 (2017)

    Article  PubMed  PubMed Central  Google Scholar 

  31. Wong, D.K.-K., Cheung, M.-K.: Online health information seeking and eHealth literacy among patients attending a primary care clinic in Hong Kong: a cross-sectional survey. J. Med. Internet Res. 21, e10831 (2019). https://doi.org/10.2196/10831

    Article  PubMed  PubMed Central  Google Scholar 

  32. Rowen, D., Azzabi Zouraq, I., Chevrou-Severac, H., et al.: International regulations and recommendations for utility data for health technology assessment. Pharmacoeconomics 35, 11–19 (2017). https://doi.org/10.1007/s40273-017-0544-y

    Article  PubMed  Google Scholar 

  33. Kennedy-Martin, M., Slaap, B., Herdman, M., et al.: Which multi-attribute utility instruments are recommended for use in cost-utility analysis? A review of national health technology assessment (HTA) guidelines. Eur. J. Health Econ. 21, 1245–1257 (2020). https://doi.org/10.1007/s10198-020-01195-8

    Article  PubMed  PubMed Central  Google Scholar 

  34. Chiang, C.-L., Chan, S.-K., Lee, S.-F., et al.: First-line atezolizumab plus bevacizumab versus sorafenib in hepatocellular carcinoma: a cost-effectiveness analysis. Cancers (Basel) (2021). https://doi.org/10.3390/cancers13050931

    Article  PubMed  PubMed Central  Google Scholar 

  35. Lee, S.F., Choi, H.C.W., Chan, S.K., et al.: Cost-effectiveness of anti-epidermal growth factor receptor therapy versus bevacizumab in KRAS wild-type (WT), Pan-RAS WT, and Pan-RAS WT left-sided metastatic colorectal cancer. Front. Oncol. (2021). https://doi.org/10.3389/fonc.2021.651299

    Article  PubMed  PubMed Central  Google Scholar 

  36. You, J.H.S., Cho, W.C.S., Ming, W., et al.: EGFR mutation-guided use of afatinib, erlotinib and gefitinib for advanced non-small-cell lung cancer in Hong Kong—a cost-effectiveness analysis. PLoS ONE 16, 1–14 (2021). https://doi.org/10.1371/journal.pone.0247860

    Article  CAS  Google Scholar 

  37. Lai, X.B., Ching, S.S.Y., Wong, F.K.Y., et al.: The cost-effectiveness of a nurse-led care program for breast cancer patients undergoing outpatient-based chemotherapy—a feasibility trial. Eur. J. Oncol. Nurs. 36, 16–25 (2018). https://doi.org/10.1016/j.ejon.2018.07.001

    Article  PubMed  Google Scholar 

  38. Wong, E.L.Y., Ramos-Goñi, J.M., Cheung, A.W.L., et al.: Assessing the use of a feedback module to model EQ-5D-5L health states values in Hong Kong. Patient 11, 235–247 (2018). https://doi.org/10.1007/s40271-017-0278-0

    Article  PubMed  Google Scholar 

  39. Xu, R.H., Keetharuth, A.D., Wang, L., et al.: Psychometric evaluation of the Chinese Recovering Quality of Life (ReQoL) outcome measure and assessment of health-related quality of life during the COVID-19 pandemic. Front. Psychol. (2021). https://doi.org/10.3389/fpsyg.2021.663035

    Article  PubMed  PubMed Central  Google Scholar 

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Funding

This study was supported by the European Organisation for Research and Treatment of Cancer Quality of Life Group, Grant No. 001/2018.

Author information

Authors and Affiliations

  1. Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hung Hom, Hong Kong SAR, China

    Richard Huan Xu

  2. JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Sha Tin, Hong Kong SAR, China

    Richard Huan Xu & Eliza Lai-yi Wong

  3. Saw Swee Hock School of Public Health, The National University of Singapore, Singapore, Singapore

    Nan Luo

  4. School of Public Health, Curtin University, Perth, WA, Australia

    Richard Norman

  5. Division of Psychiatry II, Department of Psychiatry, Psychotherapy and Psychosomatics, Medical University of Innsbruck, Innsbruck, Austria

    Jens Lehmann & Bernhard Holzner

  6. School of Psychology, University of Sydney, Sydney, NSW, Australia

    Madeleine T. King

  7. Division of Psychiatry I, Department of Psychiatry, Psychotherapy and Psychosomatics, Medical University of Innsbruck, Innsbruck, Austria

    Georg Kemmler

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  1. Richard Huan Xu
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Consortia

the EORTC QLG

Contributions

Concept and design: EW, RN, JL, BH, GK, and MK. Acquisition of data: GK. Analysis and interpretation of data: RX, RN, JL, BN, and GK. Drafting of the manuscript: RX and LN. Critical revision of paper for important intellectual content: RX, EW, LN, RN, BH, JL, GK, and MK. Statistical analysis: GK. Provision of study materials or patients: MK. Obtaining funding: BH and GK. Administrative, technical, or logistic support: JL. Supervision: EW.

Corresponding author

Correspondence to Richard Huan Xu.

Ethics declarations

Conflict of interest

RX, EW, NL, RN, BH, and HT has nothing to disclose. JL and GK reports grants from EORTC Quality of Life Group during the conduct of the study. MK reports grants from the Australian National Health and Medical Research Council (NHMRC Project Grant 632662, to develop the QLU-C10D descriptive system and valuation methods used in the current study) and from the EORTC QOL Group for the conduct of the current study.

Ethics approval

The study was approved by the Ethics Committee, Hong Kong Polytechnic University, with approval number HSEARS20220523001.

Informed consent

Informed consent was collected from all participants.

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Xu, R.H., Wong, E.Ly., Luo, N. et al. The EORTC QLU-C10D: the Hong Kong valuation study. Eur J Health Econ (2023). https://doi.org/10.1007/s10198-023-01632-4

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