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Enregistrement W1991543216 · doi:10.1093/eurpub/ckp054

Health technology assessment and public health: a time for convergence

2009· editorial· en· W1991543216 sur OpenAlexaffabout
Renaldo N. Battista, Louise Lafortune

Notice bibliographique

RevueEuropean Journal of Public Health · 2009
Typeeditorial
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueHealth Systems, Economic Evaluations, Quality of Life
Établissements canadiensUniversité de Montréal
Organismes subventionnairesnon disponible
Mots-clésPublic healthConvergence (economics)Environmental healthMedicineNursingEconomicsEconomic growth

Résumé

récupéré en direct d'OpenAlex

Health Technology Assessment (HTA) and Public Health have more in common than meets the eye. Despite having distinct historical trajectories and organizational structures, these fields of applied research share several key defining features: the interdisciplinary nature of their core activities and required expertise; the use of a variety of methods to generate and synthesize evidence; and their enhanced focus on knowledge translation. In light of unprecedented technological innovation, population aging and economic concerns, HTA and Public Health also face the same difficult questions. How to prioritize interventions aimed at preventing, diagnosing and treating chronic diseases? How to account for the social, ethical and legal implications of increasingly expensive and complex interventions? What methodologies should be used to evaluate these interventions? How best to use available evidence when randomization is neither possible nor desirable? Fuelled by the evidenced-based paradigm and the call for increased accountability, these shared features and questions place HTA and Public Health on a naturally converging path. HTA is a policy-oriented research process aimed at informing decision making. Since its inception in the late 60s, the focus of evaluation has shifted from the technical performance of capital-intensive technologies, to diseases and clinical outcomes, and, recently, to service delivery models—testifying to the expansion of the breadth and scope of HTA.1 Conversely, Public Health is an action-oriented process with a population outlook, which has focused from its inception on both health states and interventions. Building from the study of the distribution, determinants and etiologies of health and illness, public health has striven to develop interventions and influence policies to prevent the occurrence of illness, and to maintain and restore health. HTA and Public Health draw on a variety of perspectives and methods from different disciplines, including epidemiology, health economics, biomedical engineering, the social sciences, ethics and law. As of now, two important and unifying challenges persist: the consolidation of an interdisciplinary research capacity and the development of knowledge translation tools targeted to a diversified audience. There is thus an opportunity to share methodologies, expertise and insights, refine evaluation tools, develop recommendations and improve the relevance of evidence across multiple levels of decision making in health. Moreover, important trends foster the convergence between HTA and Public Health. One pertains to the increased complexity of healthcare resulting from the growing burden of chronic diseases that accompany aging populations. As interventions become more complex, one must consider their impact across the policy, organizational, community, and individual levels, measure their effects on several intermediate outcomes, and examine how these effects are mediated through multiple causal pathways. This complexity calls for increasingly sophisticated definitions of what constitutes necessary and sufficient evidence as well as a willingness to settle for methodological pluralism. Another is technological innovation, which continues to expand the breadth of interventions to be evaluated and blur disciplinary boundaries. Notable examples include the development of vaccines for cancer and chronic diseases like diabetes; the development of genomics and accompanying screening tools for increased risk of unhealthy behaviours. Combined to increased consumer expectations and our greater understanding of health determinants across the life cycle, these developments raise for HTA and Public Health the same social, ethical and economic issues. In that context, the need to deliver scientifically sound evaluations of interventions whose promise to improve Public Health depends on shared accountability and equitable uptake creates favourable conditions for a greater convergence between HTA and Public Health. Historically, the proximity between HTA and Public Health was mainly disciplinary in that HTA is firmly rooted in the founding disciplines of Public Health. Yet, the institutional differentiation of HTA and Public Health was justified by the distinctive nature and scope of their activities—these differences remain and so should the organizational structures. Indeed, up until recently, a majority of HTA products focused on clinically oriented interventions,2 albeit adopting a population perspective. Nonetheless, the trends and challenges we highlighted announce a phase of convergence between Public Health and HTA. This should lead the way for the development of more scientifically grounded decision making in Public Health, and the production of contextualized HTA outputs that can better respond to the contingencies of everyday practice in Public Health.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,049
score de la tête « metaresearch » (Gemma)0,185
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,049
Score d'incertitude au seuil0,262

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0490,185
Méta-épidémiologie (sens strict)0,0030,002
Méta-épidémiologie (sens large)0,0080,004
Bibliométrie0,0070,006
Études des sciences et des technologies0,0040,017
Communication savante0,0190,021
Science ouverte0,0060,005
Intégrité de la recherche0,0300,056
Charge utile insuffisante (le modèle a refusé de juger)0,0060,003

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 Opus0,388
Tête enseignante GPT0,464
Écart entre enseignants0,076 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations8
Publié2009
Routes d'admission2
Résumé présentoui

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