Cost-Effectiveness and Social Values in Health Care Priority Setting: Normative Reasons and Public Deliberation
Notice bibliographique
Résumé
Paper 1 discusses the question of whether health policy should treat drugs for rare diseases (“orphan drugs”) differently from drugs for common diseases. Due to the small number of potential patients, orphan drugs are less likely to be developed and tend to be expensive when they come to market. To promote development of and access to these treatments, many countries have instituted policies that give special consideration to orphan drugs in a variety of ways. This paper asks whether disease prevalence is a morally relevant characteristic that warrants such special treatment. After examining reasons given in the literature for distinguishing between rare and common diseases, I argue that it is not. It is the severity of orphan diseases that drives our judgments that important claims are being overlooked when orphan treatments are unavailable, not their prevalence. Unlike prevalence, severity is an appropriate consideration for priority setting. Therefore, policies aiming to treat all claims equally should make prevalence irrelevant rather than making it the basis of differential treatment.\nPaper 2 is a qualitative study of public values on the question of how the severity of a condition and its prevalence should affect priority setting. As part of two citizens’ juries in Alberta, Canada, participants engaged in a deliberative exercise designed to elicit trade-offs between helping small groups with severe conditions and larger groups with less severe conditions. A thematic analysis of transcripts of the deliberations indicates that the public would support funding high-cost drugs to meet the needs of a few when the interventions for rare conditions are life-saving; extend life enough to give hope of future improvement; and relieve otherwise intractable symptoms, especially pain. Considerations of whether a treatment manages symptoms or alters the underlying condition take low priority. These findings can inform Canada’s current drive to establish a national orphan drug policy.\nPaper 3 examines the use of cost-effectiveness thresholds in the British National Health System (NHS). A recent report states that the NHS’ cost-effectiveness threshold should be lowered from £30,000 to £12,936, to reflect the opportunity cost of a quality-adjusted life-year (QALY) rather than a measure of societal willingness to pay for a QALY. This paper argues that if the threshold is to be determined by opportunity costs, then the understanding of opportunity cost needs to be broadened to include not only QALYs foregone, but also effects on equity, financial protection, and other social values. Even if a broader notion of opportunity cost is considered, deriving the threshold from these costs raises a question of justification: opportunity costs are determined by political factors and facts about the health care system that are unrelated to patients’ claims on the NHS, and may seem arbitrary from their point of view.
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 | 0,172 | 0,177 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,003 |
| Études des sciences et des technologies | 0,020 | 0,157 |
| Communication savante | 0,023 | 0,021 |
| Science ouverte | 0,004 | 0,019 |
| Intégrité de la recherche | 0,018 | 0,017 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».