A Systematic Review of Interventions and Programs Targeting Appropriate Prescribing of Opioids.
Notice bibliographique
Résumé
BACKGROUND: Canada and the United States have the highest levels of prescription opioid consumption in the world. In an attempt to curb the opioid epidemic, a variety of interventions have been implemented. Thus far, evidence regarding their effectiveness has not been consolidated. OBJECTIVES: The objectives of this study were to: 1) identify interventions that target opioid prescribing; 2) assess and compare the effectiveness of interventions on opioid prescription and related harms; 3) determine the methodological quality of evaluation studies. STUDY DESIGN: The study involved a systematic review of the literature including bibliographical databases and gray literature sources. SETTING: Systematic review including bibliographical databases and gray literature sources. METHODS: We searched MEDLINE, Embase, and LILACS databases from January 1, 2005 to September 23, 2016 for any intervention that targeted the prescription of opioids. We also examined websites of relevant organizations and scanned bibliographies of included articles and reviews for additional references. The target population was that of all health care providers (HCPs) or users of opioids with no restriction on indication. Endpoints were those related to process (implementation), outcomes (effectiveness), or impact. Sources were screened independently by 2 reviewers using pre-defined eligibility criteria. Synthesis of findings was qualitative; no pooling of results was conducted. RESULTS: Literature search yielded 12,278 unique sources. Of these, 142 were retained. During full-text review, 75 were further excluded. Searches of the gray literature and bibliographies yielded 49 additional sources. Thus, a total of 95 distinct interventions were identified. Over half consisted of prescription monitoring programs (PMPs) and mainly targeted HCPs. Evaluation studies addressed mainly opioid prescription rate (30.6%), opioid use (19.4%), or doctor shopping or diversion (9.7%). Fewer studies considered overdose death (9.7%), abuse (9.7%), misuse (4.2%), or diversion (5.6%). Study designs consisted of cross-sectional surveys (23.3%), pre-post intervention (26.7%), or time series without a comparison group (13.3%), which limit the robustness of the evidence. Although PMPs and policies have been associated with a reduction in opioid prescription, their impact on appropriateness of use according to clinical guidelines and restriction of access to patients in need is inconsistent. Continuing medical education (CME) and pain management programs were found effective in improving chronic pain management, but studies were conducted in specific settings. The impact of interventions on abuse and overdose-death is conflicting. LIMITATIONS: Due to the very large number of publications and programs found, it was difficult to compare interventions owing to the heterogeneity of the programs and to the methodologies of evaluation studies. No assessment of publication bias was done in the review. CONCLUSIONS: Evidence of effectiveness of interventions targeting the prescription of opioids is scarce in the literature. Although PMPs have been associated with a reduction in the overall prescription rates of Schedule II opioids, their impact on the appropriateness of use taking into consideration benefits, misuse, legal and illegal use remains elusive. Our review suggests that existing interventions have not addressed all determinants of inappropriate opioid prescribing and usage. A well-described theoretical framework would be the backdrop against which targeted interventions or policies may be developed. KEY WORDS: Opioid, prescription, abuse, misuse, diversion, interventions, prescription monitoring programs.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,005 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».