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Gepubliceerd in: Quality of Life Research 1/2019

29-10-2018

Scoring the Child Health Utility 9D instrument: estimation of a Chinese child and adolescent-specific tariff

Auteurs: Gang Chen, Fei Xu, Elisabeth Huynh, Zhiyong Wang, Katherine Stevens, Julie Ratcliffe

Gepubliceerd in: Quality of Life Research | Uitgave 1/2019

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Abstract

Purpose

To derive children and adolescents’ preferences for health states defined by the Chinese version of Child Health Utility 9D (CHU9D-CHN) instrument in China that can be used to estimate quality-adjusted life years (QALYs) for economic evaluation.

Methods

A profile case best–worst scaling (BWS) and a time trade-off (TTO) method were combined to derive a Chinese-specific tariff for the CHU9D-CHN. The BWS survey recruited students from primary and high schools using a multi-stage random sampling method and was administered in a classroom setting, whilst the TTO survey adopted an interviewer-administrated conventional TTO task and was administered to a convenience sample of undergraduate students. A latent class modelling framework was adopted for analysing the BWS data.

Results

Two independent surveys were conducted in Nanjing, China, including a valid sample of 902 students (mean age 13 years) from the BWS survey and a valid sample of 38 students (mean age 18 years) from the TTO survey. The poolability of the best and the worst responses was rejected and the optimal result based on the best responses only. The optimal model suggests the existence of two latent classes. The BWS estimates were further re-anchored onto the QALY scale using the TTO generated health state values via a mapping approach.

Conclusion

This study provides further insights into the use of the BWS method to generate health state values with young people and highlights the potential different decision rules that young people may employ for determining best vs. worst choices in this context.
vorige artikel Psychometric performance assessment of Malay and Malaysian English version of EQ-5D-5L in the Malaysian population
volgende artikel Mapping the Positive and Negative Syndrome Scale scores to EQ-5D-5L and SF-6D utility scores in patients with schizophrenia
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Voetnoten
1
It should be noted that additional three students failed to complete the BWS choice experiment, and additional two students cannot complete the TTO tasks.
 
2
Considering the well-documented issue relating to the sensitivity of the log-likelihood ratio test, we have also tried to relax the constraint of some dimension level parameters (e.g. the 5th level of ‘activities’ dimension) in estimating the pooled BWS data, i.e. to allow estimated coefficients differ between the best and the worst. However, the conclusion to not pool the best and the worst data remains the same based on the Swait–Louviere test.
 
3
We have also estimated the scale-adjusted LC model and the log-scale factor estimated for the worst choices was − 0.3 (p < 0.001), indicating the worst choices exhibit less consistency (i.e. exp(− 0.3) = 0.74) across choice sets than the best choices.
 
4
Initially 2–4 classes were considered. The BIC values decreased along with the increased number of classes being specified (i.e. more class is favourable based on the information criteria); however, increasingly more inconsistencies among estimated dimension levels were observed in the 3- or 4-class case. Further considering the % reductions on the likelihood ratio chi-squared statistic L2 of a 7% reduction from 1-class to 2-classes, versus a 2%/1% from 2- to 3- or 3- to 4-class specifications. The 2-class case was selected as the optimal result, similar to the Australian adolescent study result reported by Ratcliffe et al. [23].
 
5
The inconsistencies have been widely reported in the valuation studies regardless of which valuation techniques been adopted [23, 42].
 
6
The adjustment was conducted within each class and with one inconsistence dimension level at a time. The adjustment slightly changed the proportions of the 2 class membership, from 57.32% versus 42.68–57.98% versus 42.02%. The BIC values were 29993.68 in the main model and 29834.74 in the final model.
 
7
In Australian study, respondents in Class 1 (63%) placed the most important weights on mental health dimensions and the least important weights on activities, and daily routine. In Class 2 (37%), respondents placed equal weights on all dimensions.
 
8
Comparison of these established scoring algorithms, with the development of the new Chinese adolescent-specific scoring algorithm documented in this paper indicates some key methodological differences including (i) elicitation methods been used (SG vs. BWS & TTO), (ii) sample size, (iii) whose values were elicited from (adults vs. adolescents), (iv) source of respondents (random street/student sample vs. online panel), (v) the mode of survey delivery (face-to-face interview vs. online survey vs. hard copy questionnaire), (vi) the utility function to be estimated (whether potential interaction terms were considered and tested), and (vii) econometric techniques used to estimate the utility function (OLS vs. latent class modelling).
 
9
Two re-anchoring approaches were adopted in this study. Re-anchoring onto the PITS approach arguably uses less information from the second source of preference (i.e. only elicited utility score from the worst health state was used), as compared to the mapping approach (in which a series of elicited utility scores were all used); however, that does not necessary imply the preferential choice of the re-anchoring approach. Better goodness-of-fit (or less prediction errors) from the mapping approach in this study indicates that empirically it is a preferred approach. Whether this conclusion holds in future studies is unclear, but it is evident that re-scaling based on the PITS state only may not be an optimal approach.
 
Literatuur
1.
2.
Frisén, A. (2007). Measuring health-related quality of life in adolescence. Acta Paediatrica, 96(7), 963–968. CrossRefPubMed
3.
Brazier, J., Ratcliffe, J., Salomon, J. A., & Tsuchiya, A. (2017). Measuring and valuing health benefits for economic evaluation (2nd ed.). Oxford: Oxford University Press.
4.
Schwartzmann, L. (2009). Research and action: Toward good quality of life and equity in health. Expert Review of Pharmacoeconomics & Outcomes Research, 9(2), 143–147. CrossRef
5.
Huang, Y., Zhong, X.-N., Li, Q.-Y., Xu, D., Zhang, X.-L., Feng, C., et al. (2015). Health-related quality of life of the rural-China left-behind children or adolescents and influential factors: A cross-sectional study. Health and Quality of Life Outcomes, 13(1), 29. CrossRefPubMedPubMedCentral
6.
Ji, Y., Chen, S., Li, K., Xiao, N., Yang, X., Zheng, S., & Xiao, X. (2011). Measuring health-related quality of life in children with cancer living in mainland China: Feasibility, reliability and validity of the Chinese Mandarin version of PedsQL 4.0 Generic Core Scales and 3.0 Cancer Module. Health and Quality of Life Outcomes, 9(1), 103. CrossRefPubMedPubMedCentral
7.
Yang, X., Xiao, N., & Yan, J. (2011). The PedsQL in pediatric cerebral palsy: Reliability and validity of the Chinese version pediatric quality of life inventory 4.0 generic core scales and 3.0 cerebral palsy module. Quality of Life Research, 20(2), 243–252. CrossRefPubMed
8.
Zhou, Z., Fang, Y., Zhou, Z., Li, D., Wang, D., Li, Y., et al. (2017). Assessing income-related health inequality and horizontal inequity in China. Social Indicators Research, 132(1), 241–256. CrossRef
9.
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. CrossRefPubMed
10.
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(8), 1105–1113. CrossRefPubMed
11.
Stevens, K., & Ratcliffe, J. (2012). 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 in Health, 15(8), 1092–1099. CrossRefPubMed
12.
Ratcliffe, J., Stevens, K., Flynn, T., Brazier, J., & Sawyer, M. (2012). An assessment of the construct validity of the CHU9D in the Australian adolescent general population. Quality of Life Research, 21(4), 717–725. CrossRefPubMed
13.
Chen, G., Flynn, T., Stevens, K., Brazier, J., Huynh, E., Sawyer, M., et al. (2015). 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 in Health, 18(4), 432–438. CrossRefPubMed
14.
Petersen, K. D., Chen, G., Mpundu-Kaambwa, C., Stevens, K., Brazier, J., & Ratcliffe, J. (2018). Measuring health related quality of life in adolescent populations: An empirical comparison of the CHU9D and the PedsQL™ 4.0 Short Form 15. The Patient, 11(1), 29–37. CrossRefPubMed
15.
Xu, F., Chen, G., Stevens, K., Zhou, H., Qi, S., Wang, Z., et al. (2014). Measuring and valuing health-related quality of life among children and adolescents in Mainland China—A pilot study. PLoS ONE, 9(2), e89222. CrossRefPubMedPubMedCentral
16.
Wild, D., Grove, A., Martin, M., Eremenco, S., McElroy, S., Verjee-Lorenz, A., et al. (2005). Principles of good practice for the translation and cultural adaptation process for patient-reported outcomes (PRO) measures: Report of the ISPOR Task Force for Translation and Cultural Adaptation. Value in Health, 8(2), 94–104. CrossRef
17.
Louviere, J. J., Flynn, T. N., & Carson, R. T. (2010). Discrete choice experiments are not conjoint analysis. Journal of Choice Modelling, 3(3), 57–72. CrossRef
18.
McCabe, C., Brazier, J., Gilks, P., Tsuchiya, A., Roberts, J., O’Hagan, A., & Stevens, K. (2006). Using rank data to estimate health state utility models. Journal of Health Economics, 25(3), 418–431. CrossRefPubMed
19.
Flynn, T. N., Huynh, E., Peters, T. J., Al-Janabi, H., Clemens, S., Moody, A., & Coast, J. (2015). Scoring the ICECAP-A capability instrument. Estimation of a UK general population tariff. Health Economics, 24(3), 258–269. CrossRefPubMed
20.
Coast, J., Huynh, E., Kinghorn, P., & Flynn, T. (2016). Complex valuation: Applying ideas from the complex intervention framework to valuation of a new measure for end-of-life care. Pharmacoeconomics, 34(5), 499–508. CrossRefPubMed
21.
Louviere, J. J., Flynn, T. N., & Marley, A. A. J. (2015). Best-worst scaling: Theory, methods and applications. Cambridge: Cambridge University Press. CrossRef
22.
Ratcliffe, J., Couzner, L., Flynn, T., Sawyer, M., Stevens, K., Brazier, J., & Burgess, L. (2011). 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. Applied Health Economics and Health Policy, 9(1), 15–27. CrossRefPubMed
23.
Ratcliffe, J., Huynh, E., Chen, G., Stevens, K., Swait, J., Brazier, J., et al. (2016). Valuing the Child Health Utility 9D: Using profile case best worst scaling methods to develop a new adolescent specific scoring algorithm. Social Science & Medicine, 157, 48–59. CrossRef
24.
Ratcliffe, J., Chen, G., Stevens, K., Bradley, S., Couzner, L., Brazier, J., et al. (2015). Valuing Child Health Utility 9D health states with young adults: Insights from a time trade off study. Applied Health Economics and Health Policy, 13(5), 485–492. CrossRefPubMed
25.
Dolan, P., Gudex, C., Kind, P., & Williams, A. (1996). The time trade-off method: Results from a general population study. Health Economics, 5(2), 141–152. CrossRefPubMed
26.
Swait, J., & Louviere, J. (1993). The role of the scale parameter in the estimation and comparison of multinomial logit models. Journal of Marketing Research, 30(3), 305–314. CrossRef
27.
Vermunt, J. K., & Magidson, J. (2016). Upgrade manual for latent GOLD 5.1. Belmont Massachusetts: Statistical Innovations Inc.
28.
Flynn, T. N., Louviere, J. J., Peters, T. J., & Coast, J. (2010). Using discrete choice experiments to understand preferences for quality of life. Variance-scale heterogeneity matters. Social Science & Medicine, 70(12), 1957–1965. CrossRef
29.
Rowen, D., Brazier, J., & Van Hout, B. (2015). A comparison of methods for converting DCE values onto the full health-dead QALY scale. Medical Decision Making, 35(3), 328–340. CrossRefPubMed
30.
Liu, G. G., Wu, H., Li, M., Gao, C., & Luo, N. (2014). Chinese time trade-off values for EQ-5D health states. Value in Health, 17(5), 597–604. CrossRefPubMed
31.
Luo, N., Liu, G., Li, M., Guan, H., Jin, X., & Rand-Hendriksen, K. (2017). Estimating an EQ-5D-5L value set for China. Value in Health, 20(4), 662–669. CrossRefPubMed
32.
Krucien, N., Watson, V., & Ryan, M. (2017). Is best-worst scaling suitable for health state valuation? A comparison with discrete choice experiments. Health Economics, 26(12), e1–e16. CrossRefPubMed
33.
Canaway, A. G., & Frew, E. J. (2013). Measuring preference-based quality of life in children aged 6–7 years: A comparison of the performance of the CHU-9D and EQ-5D-Y—The WAVES Pilot Study. Quality of Life Research, 22(1), 173–183. CrossRefPubMed
34.
Chen, G., Ratcliffe, J., Olds, T., Magarey, A., Jones, M., & Leslie, E. (2014). BMI, health behaviors, and quality of life in children and adolescents: A school-based study. Pediatrics, 133(4), e868–e874. CrossRefPubMed
35.
Boyer, N. R. S., Miller, S., Connolly, P., & McIntosh, E. (2016). Paving the way for the use of the SDQ in economic evaluations of school-based population health interventions: An empirical analysis of the external validity of SDQ mapping algorithms to the CHU9D in an educational setting. Quality of Life Research, 25(4), 913–923. CrossRefPubMedPubMedCentral
36.
Ungar, W. J., Hadioonzadeh, A., Najafzadeh, M., Tsao, N. W., Dell, S., & Lynd, L. D. (2015). Parents and adolescents preferences for asthma control: A best-worst scaling choice experiment using an orthogonal main effects design. BMC Pulmonary Medicine, 15, 146. CrossRefPubMedPubMedCentral
37.
Flynn, T. N. (2010). Using conjoint analysis and choice experiments to estimate QALY values. Pharmacoeconomics, 28(9), 711–722. CrossRefPubMed
38.
Dolan, P. (1997). Modeling valuations for EuroQol health states. Medical Care, 35(11), 1095–1108. CrossRefPubMed
39.
Viney, R., Norman, R., King, M. T., Cronin, P., Street, D. J., Knox, S., & Ratcliffe, J. (2011). Time trade-off derived EQ-5D weights for Australia. Value in Health, 14(6), 928–936. CrossRefPubMed
40.
Stevens, K. (2012). Valuation of the Child Health Utility 9D index. Pharmacoeconomics, 30(8), 729–747. CrossRefPubMed
41.
Yang, P., Chen, G., Wang, P., Zhang, K., Deng, F., Yang, H., & Zhuang, G. (2018). Psychometric evaluation of the Chinese version of the Child Health Utility 9D (CHU9D-CHN): A school-based study in China. Quality of Life Research, 27(7), 1921–1931. CrossRefPubMed
42.
Brazier, J., & Roberts, J. (2004). The estimation of a preference-based measure of health from the SF-12. Medical Care, 42(9), 851–859. CrossRefPubMed
Metagegevens
Titel
Scoring the Child Health Utility 9D instrument: estimation of a Chinese child and adolescent-specific tariff
Auteurs
Gang Chen
Fei Xu
Elisabeth Huynh
Zhiyong Wang
Katherine Stevens
Julie Ratcliffe
Publicatiedatum
29-10-2018
Uitgeverij
Springer International Publishing
Gepubliceerd in
Quality of Life Research / Uitgave 1/2019
Print ISSN: 0962-9343
Elektronisch ISSN: 1573-2649
DOI
https://doi.org/10.1007/s11136-018-2032-z

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