Health state utility values: A description of their development and application for rheumatic diseases
Notice bibliographique
Résumé
Introduction The primary outcomes of most clinical studies in rheumatology are chosen as the most relevant and meaningful for the clinical community. However, they have restricted value in assisting policymakers considering resource-allocation decisions. Although clinical outcomes might constitute important results in a rheumatology trial, their use in economic evaluation is confined to cost-effectiveness analysis where outcomes are measured in units that are relevant to the condition under investigation. Comparisons between cost-effectiveness studies are restricted because the outcomes are typically measured in units that differ from study to study. Comparison across therapeutic areas becomes almost impossible. Therefore, cost-utility analysis (CUA; a distinct form of cost-effectiveness analysis) in which outcomes are measured in terms of a standard unit metric that combines information on the quantity and quality of life, the quality-adjusted life year (QALY), is important (1,2). The QALY requires data that express health-related quality of life (HRQOL) in the form of a single value, known as a health state utility value (HSUV), which is scored on a scale that assigns a value of 1 to a state equivalent to full health and 0 to a state equivalent to death (3). Although the most recent studies in rheumatology have used some form of an HRQOL questionnaire, such as the Short Form 36, Health Assessment Questionnaire (HAQ), or Western Ontario and McMaster Universities Osteoarthritis Index, these questionnaires typically measure and summarize a number of aspects of quality of life as a profile based on the responses. However, none of these instruments alone can be used to obtain an HSUV and therefore they are not amenable for economic evaluation. To obtain an HSUV requires the incorporation of a preference weight (3). The resulting values can be used to compare the general population preferences for different disease states both within and across diseases. When this is linked to the effect of an intervention, policymakers tasked with improving the outcomes of the whole population can allocate resources accordingly. It is from this context that economic evaluation becomes important: by identifying what gains in HSUVs (and life years) can be achieved by new interventions and at what additional cost. HSUVs have been described, analyzed, and reported in the rheumatology literature for more than 10 years (4). With the rising cost of health care (5), the use of economic evaluations has escalated, hence the rising interest in the methods and results of HSUV measures. However, although the motivation for using HSUVs is clear, the issues surrounding their development and use, predominantly the research of economists, is less well understood. Policies informed by these methods can impact the treatments available to physicians, and ultimately the patients’ wellbeing. We consequently reviewed the rheumatologic literature with the objective of identifying and addressing key issues and concepts in the valuation of HSUVs and then reported their application in rheumatology. We make recommendations for persons wanting to obtain values in the future and highlight issues requiring further research.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,022 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,003 |
| Bibliométrie | 0,006 | 0,010 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,016 | 0,005 |
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 source (Gemma direct ou Codex distillé), 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 ».