Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,010 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,004 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».