A Systematic Review of the Cost-Effectiveness of Biologics for Ulcerative Colitis
Notice bibliographique
Résumé
BACKGROUND: Ulcerative colitis (UC) is a chronic autoimmune inflammation of the colon. The condition significantly decreases quality of life and generates a substantial economic burden for healthcare payers, patients and the society in which they live. Some patients require chronic pharmacotherapy, and access to novel biologic drugs might be crucial for long-term remission. The analyses of cost-effectiveness for biologic drugs are necessary to assess their efficiency and provide the best available drugs to patients. OBJECTIVE: Our aim was to collect and assess the quality of economic analyses carried out for biologic agents used in the treatment of UC, as well as to summarize evidence on the drivers of cost-effectiveness and evaluate the transferability and generalizability of conclusions. METHODS: A systematic database review was conducted using MEDLINE (via PubMed), EMBASE, Cost-Effectiveness Analysis Registry and CRD0. Both authors independently reviewed the identified articles to determine their eligibility for final review. Hand searching of references in collected papers was also performed to find any relevant articles. The reporting quality of economic analyses included was evaluated by two reviewers using the International Society of Pharmacoeconomics and Outcomes Research (ISPOR) Consolidated Health Economic Evaluation Reporting Standards (CHEERS) statement checklist. We reviewed the sensitivity analyses in cost-effectiveness analyses to identify the variables that may have changed the conclusions of the study. Key drivers of cost-effectiveness were selected by identifying uncertain parameters that caused the highest change of the results of the analyses compared with base-case results. RESULTS: Of the 576 identified records, 87 were excluded as duplicates and 16 studies were included in the final review; evaluations for Canada, the UK and Poland were mostly performed. The majority of the evaluations revealed were performed for infliximab (approximately 75% of total volume); however, some assessments were also performed for adalimumab (50%) and golimumab (31%). Only three analyses were conducted for vedolizumab, whereas no relevant studies were found for etrolizumab and tofacitinib. The reporting quality of the included economic analyses was assessed as high, with an average score of 21 points per 24 maximum possible (range 14-23 points according to the ISPOR CHEERS statement checklist). In the case of most analyses, quality-adjusted life-years were used as a clinical outcome, and endpoints such as remission, response and mucosal healing were less common. The higher clinical effectiveness (based on response rates) of biological treatment over non-biological treatments was presented in revealed analyses. The incremental cost-utility ratios for biologics, compared with standard care, varied significantly between the studies and ranged from US$36,309 to US$456,979. The lowest value was obtained for infliximab and the highest for the treatment scheme including infliximab 5 mg/kg and infliximab 10 mg/kg + adalimumab. The change of utility weights and clinical parameters had the most significant influence on the results of the analysis; the variable related to surgery was the least sensitive. CONCLUSIONS: Limited data on the cost-effectiveness of UC therapy were identified. In the majority of studies, the lack of cost-effectiveness was revealed for biologics, which was associated with their high costs. Clinical outcomes are transferable to other countries and could be generalized; however, cost inputs are country-specific and therefore limit the transferability and generalizability of conclusions. The key drivers and variables that showed the greatest effect on the analysis results were utility weights and clinical parameters.
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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».