Notice bibliographique
Résumé
In a recent Perspective article in this journal (1), Roux and Donaldson criticize the usefulness of cost-of-illness (COI) (1) studies, which they suggest are fraught with methodological flaws.Furthermore, they contend that COI studies should not be used to set priorities for government investment in the prevention of disease and injury.We acknowledge the validity of some of their arguments and wholeheartedly agree that scientifically rigorous economic evaluation (such as cost-benefit analysis and cost-effectiveness analysis) should be used in decisions for funding interventions.However, as we recently noted (2), COI studies can be valuable tools for promoting attention to the economic burden imposed by specific illnesses and are a crucial first step in the economic evaluation of prevention interventions.As suggested by Roux and Donaldson, COI data may assist in mobilizing interest and resources to a particular public health problem.We argue that this is how many COI studies are being used in the government: not to explicitly set health policy but rather to spotlight burden of disease that moves beyond traditional epidemiological estimates of mortality and morbidity.For example, a recent COI study by Finkelstein et al. (3) showed that the government pays for Ͼ50% of the health care costs associated with persons who are obese or overweight.These findings clarify the debate about whether obesity is a personal or societal issue and provide a clear motivation for government to try to reduce the costs of obesity.Because economic evaluation is useful only after interventions have been developed and evaluated, COI studies are an important part of the process for priority-setting in research on prevention.Many of the methodological "flaws" that Roux and Donaldson contend to be inherent in COI studies, such as overestimating productivity losses and double-counting costs for chronic diseases, can be overcome.For example, concerns about using earnings to measure productivity have led many prevalence-based COI studies to focus on direct costs of care only.Furthermore, whereas the human capital approach has limitations to valuing lost productivity, this is not specific to COI studies.Indeed, cost-benefit analyses in the health care field often use the human capital approach to calculate the economic benefits from preventing death and disability.Many recent COI studies rely on individual level data that allow for directly modeling the relationship between a particular disease and expenditures (4) and, therefore, do not suffer from problems of double-counting ex-penditures that were most likely to occur in epidemiological COI studies.As stated in our own critique of COI studies, the potential for misuse of these, and many other, studies is ever-present, and, therefore, authors need to clearly state how the results should and should not be used.We also believe that COI studies will continue to benefit from better data and methods.Further debate on this subject should focus on how to advance the state of science of COI studies and how to transfer the results of these studies to policy makers required to allocate our scarce public health resources.
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 | 0,008 | 0,064 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,005 | 0,005 |
| Science ouverte | 0,004 | 0,003 |
| Intégrité de la recherche | 0,034 | 0,055 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,012 | 0,011 |
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 ».