Bibliographic record
Abstract
What Was the Question? There are many generic preference-based instruments intended for use in children and adolescents (those aged < 18 years, hereafter referred to as children); however, the optimal methods for measuring and valuing health-related quality of life (HRQoL) in children are unclear, at least in part because of conceptual and methodological challenges related to determining what dimensions of HRQoL should be considered for children across developmental stages; who should be asked to complete the instruments (e.g., self-report versus proxy report by parents, caregivers, or clinicians); how health states should be valued and by whom (e.g., adults, children); and whether or how the impact of interventions on the HRQoL of caregivers and family members of pediatric patients (i.e., spillover effects) should be incorporated. There is a need for clearer guidance about how to address these methods in economic evaluations, as well as research into the impact of methodological and normative choices on estimates of the cost-effectiveness of interventions. What Did We Do? We conducted 3 literature reviews to provide an overview of the current state of evidence worldwide related to the measurement and valuation of generic preference-based instruments and value sets for children; comparison of health-state preferences between adults and children; and the impact of including spillover effects (i.e., the impact on HRQoL of family members and caregivers) in economic evaluations. What Did We Find? We identified 15 generic preference-based instruments intended for use in children, with 29 country-specific utility value sets available for 16 countries. Canada-specific value sets were identified only for the Health Utility Index Mark 2 (HUI2) and Health Utility Index Mark 3 (HUI3), with preferences obtained from the adult general population. Children may have lower preferences compared to adults for the same health states, yet the implication of this on estimates of cost-effectiveness analyses that span a lifetime horizon are unknown. Few health technology assessment (HTA) agencies provide guidance on measuring and valuing child health or whether and how to incorporate spillover effects. There is no consensus in the literature on how spillover effects should be measured and quantified, or the impact that the choice of methods used to measure spillover effects has on economic analyses. What Does This Mean? There has been significant progress in recent years in the development of instruments and methods for measuring and valuing child health. These advances are vital steps toward supporting the assessment of health technologies targeting pediatric conditions and resource allocation decisions about these technologies. However, there is a need for additional research comparing the impact of using child versus adult health-state preferences in cost-utility analyses; whether the same preference-based instruments should be used for children and adults; and the impact that different approaches to handling age transitions have when modelling over a lifetime horizon. The small number of identified studies precludes a robust discussion of the impact of spillover effects on economic evaluations, and there is currently no consensus in the literature as to best practices. Additional research is needed into the number and type of caregivers that could be included in the assessment of spillover effects and the ethical implications of including spillover in economic evaluations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".