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Choice-based preference elicitation methods such as the discrete choice experiment (DCE) present hypothetical choices to respondents, with an expectation that these hypothetical choices accurately reflect a ‘real world’ health-related decision context and that consequently the choice data can be held to be a true representation of the respondent’s health or treatment preferences. For this to be the case, careful consideration needs to be given to the format of the choice task in a choice experiment. The overarching aim of this paper is to highlight important aspects to consider when designing and ‘setting up’ the choice tasks to be presented to respondents in a DCE. This includes the importance of considering the potential impact of format (e.g. choice context, choice set presentation and size) as well as choice set content (e.g. labelled and unlabelled choice sets and inclusion of reference alternatives) and choice questions (stated choice versus additional questions designed to explore complete preference orders) on the preference estimates that are elicited from studies. We endeavoure to instil a holistic approach to choice task design that considers format alongside content, experimental design and analysis.

Value sets for the EQ-5D-Y-3L published to date appear to have distinctive characteristics compared with value sets for corresponding adult instruments: in many cases, the value for the worst health state is higher and there are fewer values < 0. The aim of this paper is to consider how and why values for child and adult health differ; and what the implications of that are for the use of EQ-5D-Y-3L values in economic evaluations to inform healthcare resource allocation decisions. We posit four potential explanations for the differences in values: (a) The wording of severity labels may mean the worst problems on the EQ-5D-Y-3L are descriptively less severe than those on the EQ-5D-5L; (b) Adults may genuinely consider that children are less badly affected than adults by descriptively similar health issues. That is, for any given health problem, adult respondents in valuation studies consider children’s overall health-related quality of life (HRQoL) on average to be higher than that for adults; (c) Values are being sought by eliciting adults’ stated preferences for HRQoL in another person, rather than in themselves (regardless of whether the ‘other person’ concerned is a child); and (d) The need to elicit preferences for child HRQoL that are anchored at dead = 0 invokes special considerations regarding children’s survival. Existing evidence does not rule out the possibility that (c) and (d) exert an upward bias in values. We consider the implications of that for the interpretation and use of values for pediatric HRQoL. Alternative methods for valuing children’s HRQoL in a manner that is not ‘age specific’ are possible and may help to avoid issues of non-comparability. Use of these methods would place the onus on health technology assessment bodies to reflect any special considerations regarding child quality-adjusted life-year gains.

Background: The availability of population norms from generic health-related quality of life (HRQoL) instruments can support the interpretation of health outcomes. This study aimed to provide Indonesian youth population norms for the generic HRQoL measures: EQ-5D-Y-3 L, EQ-5D-Y-5 L, and the PedsQL Generic Core Scales. In addition the opportunity arising from the generation of a large representative sample was taken to explore the relationships between HRQoL, health, and socio-economic factors. Methods: A representative sample of 1103 Indonesian children (aged 8–16 years) completed EQ-5D-Y-3 L, EQ-5D-Y-5 L, the PedsQL Generic Core Scales, and questions related to demographic data and self-reported health status. A stratified quota sampling design was used to represent Indonesian children in terms of residence, age, gender, and geographical area. Family expenses per capita per month were retrieved from parents to determine a child’s economic status. Results: The total sample was representative of the Indonesian youth general population. The proportions of participants who reported problems were 43.35% (EQ-5D-Y-3 L), 44.10% (EQ-5D-Y-5 L), and 94.93% (PedsQL Generic), with 31.7% of children reporting health complaints. Older children (13–16 years) reported more problems than younger children (8–12 years). Children living in urban areas reported more problems than children living in rural areas. The lowest value health state reported was ‘12332’ (valued at 0.54), and the minimum EQ VAS score was 60.00. Moderate correlations were found between EQ-5D-Y-3 L values to EQ VAS scores and to PedsQL Total Score. Hierarchical regression analysis showed that females, older age, and having health complaints contributed to a lower level of HRQoL as measured by EQ-5D-Y-3 L values, EQ VAS, and PedsQL Total Score. Remarkably, children with high economic status had lower EQ VAS and PedsQL Total Scores. Among symptoms, ‘having stress’ had the largest influence with respect to lower EQ

Objectives: In cost-effectiveness analysis of health technologies, health state utilities are needed. They are often elicited with a composite time trade-off (cTTO) method, particularly for the widely used EQ-5D-5L. Unfortunately, cTTO discriminatory power is hindered by (1) respondents’ nontrading (NT) of time for quality, (2) censoring of utilities at 21, and (3) poor correlation of negative utilities with state severity. We investigated whether modifying cTTO can mitigate these effects. Methods: We interviewed online 478 students (February to April, 2021) who each valued the same 10 EQ-5D-5L health states in 1 of 3 arms. Arm A used a standard cTTO, expanded with 2 questions to explore reasons for NT and censoring. Arms B and C used a time trade-off with modified alternatives offered to overcome loss aversion, to unify the tasks for positive and negative utilities, and to enable eliciting utilities , 21. Results: In arms B and C, we observed less NT than in A (respectively, 4% and 4% vs 10%), more strictly negative utilities (38% and 40% vs 25%), and more utilities # 21 (18% and 30% vs 10%). The average utility of state 55555 dropped to 22.15 and 22.52 from 20.53. Enabling finer trades in arm A reduced NT by 70%. Arms B and C yielded an intuitive association between negative utilities and state severity. These arms were considered more difficult and resulted in more inconsistencies. Conclusions: The discriminatory power of cTTO can be improved, but it may require increasing the difficulty of the task. The standard cTTO may overestimate the utilities, especially of severe states.