Evaluating the impact of the risk-mitigation guidance for opioid prescribing in British Columbia, Canada using a cross-model comparison approach: study protocol
Notice bibliographique
Résumé
INTRODUCTION: Drug poisoning, caused predominantly by fentanyl in the unregulated drug supply, is the leading cause of death among persons 10-59 years in British Columbia (BC), Canada. In March 2020, in response to the emergence of the COVID-19 pandemic, the province of BC released the Risk Mitigation Guidance (RMG) as a clinical tool for physicians and nurse practitioners, allowing prescribers to provide selective withdrawal management medications, such as hydromorphone, dextroamphetamine, diazepam and others, as a legal and regulated supply of pharmaceutical alternatives to individuals who were at-risk of COVID-19 and overdose. In July 2021, the government of BC released the prescribed safer supply (PSS) policy, extending the scope beyond the COVID-19 pandemic and initial medications offered under the RMG. Recent studies have shown clear benefits among people with a diagnosed opioid use disorder who were prescribed PSS, in reducing mortality, as well as improving retention on opioid agonist treatment for those who were coprescribed PSS medications. The objective of the analysis detailed in this protocol is to use a cross-model comparison approach, comparing two independently developed models which are currently used in public health institutions in BC, to estimate the impact of this policy on opioid overdose-related mortality, while also considering the potential negative impacts of PSS medication diversion to those who are opioid naïve. This project will add to the limited evidence-base on the population-level impact of pharmaceutical alternatives interventions to date. METHODS AND ANALYSIS: We have identified two appropriate mathematical models to evaluate the impact of PSS on the number of opioid overdose-related deaths within BC from the inception of the programme (March 2020) until December 2022. We will use recently established guidelines on conducting a cross-model comparison to identify structural and parameter differences between the models and perform adaptation steps to generate the counterfactual scenarios. These will include creating additional health states for the population representing individuals receiving PSS, and parameterising the overdose risk, mortality and retention in the new compartments from a comprehensive population-level data set. Harmonisation will be conducted to ensure that both models evaluate the same scenarios with the same data. Further sensitivity analyses will be conducted to consider alternative counterfactual scenarios and changes to the population following the implementation of the intervention. ETHICS AND DISSEMINATION: This study is exempt from research ethics board review, as outlined in the Tri-Council Policy Statement, because it relies on data that is available in the public domain and there is no possibility of identifying individual persons. Results of the model validation analysis will be distributed through peer-reviewed journals and knowledge translation materials posted on the websites of the BC Centre for Disease Control and Centre for Advancing Health Outcomes. REGISTRATION: https://osf.io/kju2p/overview.
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 | 0,067 | 0,093 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,002 |
| Méta-épidémiologie (sens large) | 0,005 | 0,010 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,006 | 0,003 |
| Intégrité de la recherche | 0,004 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,022 | 0,002 |
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 ».