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Enregistrement W4388203771 · doi:10.1200/op.2023.19.11_suppl.13

Evaluating the impact of novel oncology drug coverage through patient assistance programs in British Columbia (BC), Canada.

2023· article· en· W4388203771 sur OpenAlexaffabout
Vanessa Samuel, Megan Chan, Mina Huang, Brooke Cheng, Longlong Huang, Shaun Zheng Sun, Chris Jensen, Dennis Jang, Megan Darbyshire, Jenny J. Ko

Notice bibliographique

RevueJCO Oncology Practice · 2023
Typearticle
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueHealth Systems, Economic Evaluations, Quality of Life
Établissements canadiensPositive Living NorthUniversity of the Fraser ValleyUniversity of British ColumbiaUniversity of Calgary
Organismes subventionnairesnon disponible
Mots-clésMedicineCohortHazard ratioInternal medicineClinical trialFamily medicineOncologyConfidence interval

Résumé

récupéré en direct d'OpenAlex

13 Background: Despite a universal public healthcare system, Canadian oncology patients often enroll in patient assistance programs (PAPs) through drug manufacturers to access oncology drugs that are awaiting funding decisions by national and provincial regulatory bodies. Our multicenter study evaluated the pharmacoeconomic and clinical impact of PAPs for cancer patients in BC. Methods: Eligible patients were diagnosed with cancer and enrolled in a PAP for an oncology drug between January 1, 2016 - December 31, 2019 in 3 BC centres. Charts were reviewed for treatment details and survival data. We referenced median overall survival (mOS) or median progression-free survival (mPFS) data from phase III trials. mPFS data was used if mOS was unavailable or not statistically significant. For each drug indication, the hazard ratio from trial data was multiplied by the mOS or mPFS of our cohort to estimate the mOS or mPFS if the drug was not received. Life-years gained (LYG) was defined as the difference between the actual mOS or mPFS in our cohort and the estimated mOS or mPFS if the drug was not received. Mean OS was used if median OS was not reached by the cut-off date January 1, 2021. Quality-adjusted life years (QALY) and cost per drug were obtained from the Canadian Agency for Drugs and Technologies in Health (CADTH). QALY gained per drug was calculated as QALY multiplied by LYG. Assuming $100,000 Canadian dollars (CAD) for 1 QALY gained (based on prior studies), the economic value of QALY gained was calculated as $100,000 CAD multiplied by QALY gained per drug. Results: Our cohort consisted of 1025 patients that accessed 40 oncology drugs via PAP. By the cut-off date, 290 patients continued on treatment while 735 had stopped, most often due to disease progression (69%) or toxicity (14%). 74 patients then accessed another drug in their second exposure to PAP, and 5 patients accessed drugs in their third exposure to PAP. Median time from Health Canada approval to public funding was 2.04 years (IQR 1.53 – 2.34). QALY gained per drug ranged from 0.007-2.997 years for OS and 0.001-1.339 years for PFS. This translated to a total of 268.3 QALY gained in OS and 117.6 QALY gained in PFS. In the first exposure group, total economic value gained from PAP was $26,788,512 for those with OS benefits, $11,763,571 for those with PFS benefits, and total drug costs were $94,150,774. In the second exposure group, total economic value gained was $566,253 for those with OS benefits, $947,134 for those with PFS benefits, and total costs were $4,917,017. In the third exposure group, $44,754 total economic value was gained and total costs were $931,207. Conclusions: Nearly $100 million CAD of non-public funding was required to bridge gaps between regulatory approval and public funding. The economic value and QALY gained from PAPs are substantial. Future studies should focus on strategies to optimize the funding process for novel cancer therapies.

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)0,025
score de la tête « metaresearch » (Gemma)0,021
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche, Méta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,045
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0250,021
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
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 Opus0,446
Tête enseignante GPT0,512
Écart entre enseignants0,066 · 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.

Devis d'étudeSans objet
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

Citations0
Publié2023
Routes d'admission2
Résumé présentoui

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