Buprenorphine Maintenance for Opioid Dependence in Public Sector Healthcare: Benefits and Barriers
Notice bibliographique
Résumé
Background: Since its U.S. FDA approval in 2002, buprenorphine has been available for maintenance treatment of opiate dependence in primary care physicians' offices.Though buprenorphine was intended to facilitate access to treatment, disparities in utilization have emerged; while buprenorphine treatment is widely used in private care setting, public healthcare integration of buprenorphine lags behind.Results: Through a review of the literature, we found that U.S. disparities are partly due to a shortage of certified prescribers, concern of patient diversion, as well as economic and institutional barriers.Disparity of buprenorphine treatment dissemination is concerning since buprenorphine treatment has specific characteristics that are especially suited for low-income patient population in public sector healthcare such as flexible dosing schedules, ease of concurrently treating co-morbidities such as HIV and hepatitis C, positive patient attitudes towards treatment, and the potential of reducing addiction treatment stigma.Conclusion: As the gap between buprenorphine treatment in public sector settings and private sector settings persists in the U.S., current research suggests ways to facilitate its dissemination.largest opiate dependent population, confirmed higher prescription rates in high-income residential areas with low percentages of African American and Hispanic residents [11].Treatment rate disparities have been fueled by the focus of buprenorphine marketing on the private sector [12] and by the perception that office-based buprenorphine treatment is most appropriate for employed, and therefore "stable," patients [14,15].Buprenorphine has been increasingly prescribed by primary care physicians; primary care physicians compose 63.5% of buprenorphine maintenance treatment providers in 2013 [5].Despite an increase in buprenorphine maintenance providers, Stein et al found that 43% of U.S. counties have zero buprenorphine providers [15].Buprenorphine's comparable effectiveness to methadone in treating opioid addiction [16] and its tested suitability for varying therapeutic settings should be highlighted to promote implementation in public healthcare settings [17].Buprenorphine maintenance treatment has additional characteristics that make it useful in the public sector, such as: 1) enhanced accessibility due to multiple venues for treatment, 2) flexible dosing that requires less institutional oversight than methadone, 3) demonstrated effectiveness among populations that heavily rely on public healthcare systems, such as the formerly incarcerated, and the homeless, 4) the potential to treat comorbid chronic conditions prevalent among opiate dependent people such as HIV, and 5) the potential to lessen the stigma correlated with drug dependency among low income patients and ethnic minorities who already experience other forms of culturally defined social stigmatization [18,19].This accumulated data can be used to improve the accessibility of buprenorphine as a first line treatment for heroin and opioid dependence for patients in public clinics.
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 | 0,009 | 0,027 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
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.
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 ».