Health Gain Maximization - An Ethically Defendable Criterion for Decisions on How to Allocate Health Care Resources?
Notice bibliographique
Résumé
Rationale: In the health economic literature, many statements on the allocation of health care resources can be found showing an appreciation of the two criteria of efficiency and justice or fairness as two values which are to weighed against each other in case of conflict. Objective: This paper provides a critique of this model which is based on various arguments challenging the position that health gain maximization may be considered an ethically defendable value. Methodology: First, by returning to the survival lottery proposed by John Harris (1975), I will demonstrate that an ethically unbounded application of the health gain maximization rule may come into conflict with deeply rooted ethical norms such as the strict obligation not to kill innocent persons. Secondly, even an ethically controlled utilitarian position is confronted with John Taurek's no-worse-claim (Taurek 1977). In essence, Taurek argues that if life and death are at stake, it is the loss or gain to individuals that matters, not the loss or gain of individuals. As the sum of losses or gains resulting from aggregating individual losses or gains is nobody's loss or gain, interpersonally aggregated (dis)utilities of individuals do not have any moral relevance. Thus, maximization of health gain achievable with a given amount of resources cannot be regarded as an ethical principle on which a normative ranking of alternative allocations of resources could be based. Thirdly, one way of reconstructing our intuition of efficiency as a value might be to derive the principle of health gain maximization from a virtual social contract reflecting the societal preferences over the allocation of health care resources. In extreme cases of deadly decisions one could expect an ex-ante consent to the maximization rule, as its application would maximize everybody's probability to survive. In the case of less dramatic choices, however,it is doubtful whether a general agreement with a maximization rule can be assumed, as well. Particularly for QALY maximization which is the reference model of standard economic evaluation in health care, there is enough empirical evidence against this assumption. In a recent systematic review of studies reporting empirical data on public preferences, Dolan et al. (2005) demonstrate that the literature clearly suggests that QALY maximization is descriptively flawed: Contrary to the implications of QALY maximization, the social value of health gain is no linear function of changes in quality and length of life, is dependent of the age and other characteristics (e.g., having children or not) of the recipient, is affected by inequalities in health, and is dependent of how a fixed health gain is distributed. Thus, one cannot reasonable argue that QALY maximization can be justified by an implicit ex-ante consent. Result: In sum, theoretical as well as empirical arguments suggest that health gain maximization cannot be considered to be an ethically well-founded moral value. Conclusion: The normative importance of health gain maximization in the search for the best allocation of health care resources remains unclear.
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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 | 0,028 | 0,030 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,048 |
| Communication savante | 0,008 | 0,010 |
| Science ouverte | 0,003 | 0,006 |
| Intégrité de la recherche | 0,014 | 0,009 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 ».