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Enregistrement W4398785298 · doi:10.2147/ceor.s462872

Timeliness of Health Technology Assessments and Price Negotiations for Oncology Drugs in Canada

2024· article· en· W4398785298 sur OpenAlexaffabout
Nigel S. B. Rawson, David J. Stewart

Notice bibliographique

RevueClinicoEconomics and Outcomes Research · 2024
Typearticle
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueHealth Systems, Economic Evaluations, Quality of Life
Établissements canadiensOttawa HospitalArthur B. McDonald-Canadian Astroparticle Physics Research InstituteUniversity of OttawaWilfrid Laurier UniversityFraser InstituteFraser HealthCanadian Institute for Health Information
Organismes subventionnairesnon disponible
Mots-clésTimelineNegotiationReimbursementMedicineAllianceFamily medicineAgency (philosophy)Public relationsHealth carePolitical scienceLaw

Résumé

récupéré en direct d'OpenAlex

Purpose: To evaluate whether time targets for Canadian Agency for Drugs and Technologies in Health (CADTH) reimbursement reviews and pan-Canadian Pharmaceutical Alliance (pCPA) price negotiations are being achieved for oncology drugs. Materials and Methods: Recommendations, dates of submission and publication, and indications for oncology medicines issued between January 2014 and December 2023 were recorded from CADTH’s reimbursement reports webpage. The date any negotiation began and the date it was completed (successfully or not), or when a decision was made not to pursue negotiation was extracted from the pCPA’s webpage. The duration of each CADTH review and pCPA negotiation was calculated, together with time between CADTH’s recommendation and start of the pCPA negotiation or a decision not to negotiate. Percentages of reviews completed within CADTH’s target and of times taken by the pCPA to decide whether to negotiate and by its price negotiations completed within the relevant targets were calculated. Results: CADTH achieved its 270-days target in 88.2% to 100% of reviews issued between 2015 and 2019 but only in 65.9% to 73.1% of reviews issued in the last three years of the decade. CADTH’s “typical timeline” of 180 days was achieved in under 40% of reviews issued in 2015 and not attained in any review in 2021, 2022 or 2023. The pCPA’s target of 60 days for deciding whether to negotiate was achieved for all recommendations issued in 2014 but dropped below 40% for the last seven years of the decade; its target of 130 days for negotiations was achieved for over 85% of the recommendations in 2014 but decreased to only 14.3% in 2016 and then gradually increased to 61.5% in 2023. Conclusion: CADTH’s “typical timeline” and the pCPA’s targets were not met sufficiently to be meaningful. Their processes take too long for cancer drugs. Plain Language Summary: Canadian patients and providers are often frustrated and concerned about the timeliness of the country’s health technology assessment (HTA) and price negotiation processes, especially for cancer drugs. HTAs are carried out to evaluate the benefit of a medicine in comparison with its cost to see whether the drug is of sufficient value to add it to the benefit lists of government drug plans. HTAs are performed by the Canadian Agency for Drugs and Technologies in Health (CADTH) for all of Canada, except the province of Quebec, and price negotiations with drug developers are carried out by the pan-Canadian Pharmaceutical Alliance (pCPA) on behalf of all government drug plans. We used data from the websites of CADTH and the pCPA on HTA reviews of cancer drugs issued between January 2014 and December 2023 and price negotiations for these drugs to assess whether CADTH and the pCPA complied with their stated target times for completing their processes. We found that CADTH’s reviews and the pCPA’s price negotiations failed to meet their targets for cancer drugs in the past 10 years and that the timeliness of their performance has, in most cases, deteriorated. HTA and price negotiation processes for cancer drugs take too long in Canada. Keywords: oncology drugs, health technology assessment, drug prices, Canada

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,024
score de la tête « metaresearch » (Gemma)0,002
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,318
Score d'incertitude au seuil0,819

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0240,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,531
Tête enseignante GPT0,626
Écart entre enseignants0,095 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations6
Publié2024
Routes d'admission2
Résumé présentoui

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