An empirical study looking at the potential impact of increasing cost-effectiveness threshold on reimbursement decisions in Thailand
Notice bibliographique
Résumé
• Economic evidence (cost-effectiveness information) can be used to inform policy-making process in supporting the movement towards universal health coverage. Published literature has focused on methods to set a cost-effectiveness threshold (CET) which can be used to guide the cost-effectiveness of a health technology. Higher CET could increase the opportunity that health technologies will be reimbursed by a healthcare payer. Other published literature focused on the discussion of whether CET should be increased. • Although there has been a debate around an optimal CET and a significant development of methodologies for estimating CET, to our knowledge, no other country has changed their existing explicit CET. Thailand is in a unique position to help answer the question of what happened when CET was increased. The objectives were to explore the impact of increasing CET on the submitted medicine price and the reimbursement decision to the National List of Essential Medicine. • The current findings showed that a change in CET did not significantly influence the likelihood of a positive benefit package listing recommendation, or the medicine prices set by manufacturers for public payers. The findings shed light to the potential impact of increasing a CET and highlighted a need for further research into the role of CET in informing policy decisions (with a qualitative approach), to better guide CET policy in Thailand and globally. There has been lots of debate regarding an appropriate value of cost-effectiveness threshold (CET). To our knowledge, Thailand is the only country which has explicit CET and has increased the CET. Therefore, Thailand is in a unique position to help answer the question of what happened when CET was increased. The study objectives were to explore the impact of increasing CET on the submitted medicine price by industry and the decision to be included in the National List of Essential Medicine in Thailand. Retrospective secondary data analyses were conducted using data from economic evaluation reports being reviewed by the National Drug Subcommittee. In total, 55 reports were included in the analysis, which represented 295 observations as each report could have more than one medicine for different indication and/or target population. The intervention of interest was the change in CET policy from 100,000 THB/QALY in 2008 to 120,000 THB/QALY in 2010 to 160,000 THB/QALY in 2013. There is no evidence suggesting the increase in CET affected the submitted medicine prices (price change=19%, p-value=0.457) or increased the likelihood of a positive reimbursement decision (OR=1.596, p-value=0.532). There were other factors which may influence medicine prices and reimbursement decision. The change in the CET did not significantly affect health resource allocation. The findings do not support whether the current CET value in Thailand should be increased. Future research should continue to monitor the submission and re-analyse the current work as more data become available using both quantitative and qualitative approaches.
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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 | 0,037 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 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 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 ».