Notice bibliographique
Résumé
In the pharmaceutical field, the term ‘fourth hurdle’ has become common currency in many health systems. It means that only products that can show their cost-effectiveness will gain unrestricted access to patients. The preceding hurdles of safety, efficacy and quality have existed for several decades and have been harmonized within the European Union years ago. In the early 1990s, Australia and Canada pioneered the integration of cost-effectiveness into the regulation of drug reimbursement; by 2005, a survey identified 10 more European countries to consider this aspect in drug regulation.1 Adding France and Germany leads to at least a dozen European countries going this way today. The emphasis of the ‘fourth hurdle’ is on individual clinical care. ‘Concentrating on drugs and clinical procedures does not really have a great influence on population health’2 Holland criticized after having reviewed the first experience of health technology assessment in Europe. Banta and de Wit3 in a recent review also emphasize that economic evidence plays a minor role outside pharmaceutical care. To improve decision making, they suggest that researchers should economically study portfolios of curative and preventive interventions. Holland2 had argued that public-health interventions often are too complex to be assessed adequately. Indeed the evaluation of public-health intervention may be more difficult than that of drugs as experimental designs may not be possible, and as long-term time frames and reference to heterogeneous target populations may require more comprehensive evaluation approaches. The population investigated may furthermore feature medical and economic characteristics that are both relevant and specific to the decision context at stake. In consequence, assessments have to be tailor-made, and good transferability of economic results from other study contexts is less likely than it is for clinical studies. On an international scale, more efforts and resources may thus be needed to show the cost-effectiveness of public-health intervention as compared with clinical care strategies. This does certainly not mean that basing decisions on effectiveness and cost-effectiveness should be restricted to individual clinical care. The benefits of systematically integrating the economic aspects into evidence-based decision making apply to public-health interventions as well: more cost-effectiveness will produce more health with a given budget, or given health benefits may be reached by less-resource input. Acknowledging this, UK health policy has extended the responsibility of the much respected National Institute of Health and Clinical Excellence (NICE) to also develop cost-effective guidance for public health.4 Such guidance might move the benefits of public-health intervention more into the focus of the public. However, the adequate use of such economic evidence also poses significant challenges. The responsibility for public-health action is typically attributed to a number of institutions at the national, regional and local level. In order to increase efficiency in public health intervention, more decision makers have to be provided with advanced knowledge of how to best make use of the economic evidence. In practice, the role and use of economics in public-health decision making may significantly differ by sector, as Grosse et al.5 found in their review on the situation in the United States. They had included such diverse areas as environmental regulation, injury prevention and screening issues from newborns up to cancer. Behavioural prevention is a key topic for public-health research. Life style may substantially influence the large-scale health problems such as smoking, obesity, heart disease and diabetes. The knowledge on genetic risks is quickly expanding. Humphries et al.6 suggest that the combination of genetics with conventional risk factors, including life style, may improve the understanding of disease. If future screening approaches shall include early recognition of individual disease risk, public health and clinical medicine will move closer together. Intervention strategies may then integrate individual and population aspects in disease management—provided they are effective and cost-effective. Promoting the economic value of public-health intervention makes up a Sisyphus task, but it also provides a measure to further improve population health. Today, decision making based on economic assessments primarily focuses on individual clinical care. Only adding the population perspective and public-health intervention will provide the full health benefit that evidence-based decision making is able to achieve.
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 | 0,021 | 0,081 |
| Méta-épidémiologie (sens strict) | 0,004 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,002 |
| Bibliométrie | 0,004 | 0,003 |
| Études des sciences et des technologies | 0,003 | 0,012 |
| Communication savante | 0,013 | 0,013 |
| Science ouverte | 0,005 | 0,004 |
| Intégrité de la recherche | 0,032 | 0,044 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,002 |
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 ».