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Enregistrement W2087605942 · doi:10.1111/add.12612

Response to <scp>H</scp>ser <i>et al</i>. (2014): The necessity for more and better data on the global epidemiology of opioid dependence

2014· letter· en· W2087605942 sur OpenAlexaboutno aff
Louisa Degenhardt, Bradley Mathers, Wayne Hall

Notice bibliographique

RevueAddiction · 2014
Typeletter
Langueen
DomaineMedicine
ThématiqueOpioid Use Disorder Treatment
Établissements canadiensnon disponible
Organismes subventionnairesNational Health and Medical Research CouncilBill and Melinda Gates Foundation
Mots-clésEpidemiologyBurden of diseaseDisease burdenPublic healthMedicineGlobal healthDiseaseIncidence (geometry)Environmental healthDemographyPsychiatryPopulationSociology

Résumé

récupéré en direct d'OpenAlex

We agree with Hser et al. 1 on the importance of renewed efforts to address the major public health burden of opioid dependence 2. Opioid dependence made the greatest by far estimated contribution to disease burden via premature death and disability of all illicit drugs considered in the 2010 Global Burden of Disease Study 3. The need for a concerted, evidence-based response is heightened by the estimated increase in the burden over the past two decades, due largely to increased prevalence of these disorders 2. We also agree with Hser et al. 1 about the major limitations of existing global data on the epidemiology of this disorder, especially in regions other than western Europe, North America and Australia. As we documented in the systematic reviews that informed our modelling 4-6, and have noted elsewhere 7, 8, there are major gaps in the data that vary with the parameter being estimated. There are many more estimates of the prevalence and mortality of opioid dependence than there are of incidence and remission of the disorder. These estimates are also geographically limited, as indicated above. Hser et al. suggest that it is counter-intuitive that disability adjusted life year (DALY) rates were highest in North America and Australasia because treatment and health services are most developed and accessible in these countries. We disagree for two reasons: first, the DALY burden is directly related to the prevalence of the disorder, which was much higher in these countries than most others; and second, even in high-income countries such as the United States, only a minority of opioid-dependent people are offered evidence-based treatment 9. Hser et al. 1 observed that our point estimate of the prevalence of opioid dependence in GBD 2010 was similar to the estimated prevalence of opiate use in the past year (excluding pharmaceutical opioids) in the United Nations Office on Drugs and Crime's World Drug Report 10. It is important to note that both estimates have wide uncertainty intervals around them, and we encourage all readers to take this into account when interpreting our findings, especially in developing countries. As noted by Hser et al. 1, the sources of our estimates of opioid dependence in global burden of disease (GBD) 2010 did not exclude problems related to pharmaceutical opioids. These opioids are important to consider in the United States, Canada and Australia and are important drivers of opioid burden in some countries in South Asia and eastern Europe. Despite these acknowledged limitations, there is public health value in producing estimates of global burden that are based upon conservative assumptions and include clearly described levels of uncertainty. First, GBD 2010 has an important role in heightening attention to opioid dependence at global, regional and national levels. The updates that will be provided through GBD 2.0 11 will improve upon these estimates by estimating the epidemiology and health burden of a full range of injuries and diseases, using comparable metrics and a consistent estimation framework for each disorder. If no estimates are made while we await better data to be collected, then we run the risk that the burden of opioid dependence will be ignored by policy makers. Secondly, the quantification of the uncertainty around our estimates also serves to highlight the gaps in epidemiological data. This should stimulate the efforts to undertake better studies that will produce more reliable and less uncertain estimates of the epidemiological parameters needed to estimate burden, prevalence, incidence, mortality and remission. L.D. has received untied educational grants from Reckitt Benckiser for the post-marketing surveillance of opioid substitution therapy medications in Australia. All such studies' design, conduct and interpretation of findings are the work of the investigators; the funders had no role in those studies.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,004
Version: codex-gemma-dda1882f352aStatut 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: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,073
Score d'incertitude au seuil0,794

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,004
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,040
Tête enseignante GPT0,333
Écart entre enseignants0,292 · 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 tête enseignante, 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
GenreCommentaire

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

En bref

Citations4
Publié2014
Routes d'admission1
Résumé présentoui

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