Potential impact of prison-based interventions on hepatitis C transmission among people who inject drugs in Montréal, Canada Insights from mathematical modelling
Notice bibliographique
Résumé
BackgroundThe Canadian burden of chronic hepatitis C (HCV) is highly concentrated among people who inject drugs (PWID), a population with high incarceration rates in the provincial prison system, where the duration of sentences is less than 2 years. PWID and people in prison have been identified as key populations for HCV transmission and to eliminate HCV as a public health threat by 2030. However, the treatment and care continuum for HCV in provincial prisons, as well as the link with community services still represent challenges because of high turnover rates, frequent prison transfers, and the lack of standardized care pathways. Because of short-course direct-acting antivirals (DAA) with high safety and tolerability, people in provincial prisons could be prioritized for treatment during or after their stay in prison. However, universal HCV screening has yet to be implemented in these settings, which precludes case identification. Further, the heightened risk of HCV acquisition and transmission post-release creates a potential for reinfection among individuals treated in prison. There is an urgent need to build the evidence base regarding prison-based interventions to reduce HCV transmission.AimsThis study aims to assess the potential population-level impact of prison-based intervention strategies on HCV transmission among PWID in Montréal.MethodsA dynamic compartmental model of HCV transmission among PWID in Montréal was developed. The model is stratified by sex (male, female), incarceration status (never, currently, recently or previously released), and injecting status (active, past, and on opioid agonist therapy). It was calibrated in a Bayesian framework to local epidemiological data from bio-behavioural surveys conducted annually among PWID (2003-2014) and twice among the general prison population (2003, 2014). Among other scenarios, three broad types of intervention strategies were explored: 1) prison-based test-and-treat (90% tested, and 75% treated in prison), 2) linkage to care post-release (90% tested in prison, and 75% treated post-release), and 3) risk reduction interventions, which halve the elevated post-release risk. The impact of these interventions was estimated over ten years from 2018 on prevalence (P), incidence (I), and prevented fraction of new infections (PF), as compared to a status quo counter-factual where no specific intervention is implemented in prison for HCV.ResultsThe model reproduces the HCV epidemic among PWID in Montréal, both inside and outside of prison settings. After ten years, prison-based test-and-treat (P: 27%(95%CrI = 20−34%); I: 19%(95%CrI = 9−28%); PF: 7%(95%CrI = 4−10%)) and linkage to care post-release (P: 30%(95%CrI = 22−38%); I: 23%(95%CrI = 11−33%); PF: 9%(95%CrI = 5−14%)) would both reduce prevalence and slow down HCV transmission among active PWID in Montréal. Combined with risk reduction, test-and-treat interventions (P: 32%(95%CrI = 25−41%); I: 30%(95%CrI = 17−41%); PF: 10%(95%CrI = 6−17%)), and linkage to care (P: 36%(95%CrI = 28−45%); I: 33%(95%CrI = 20−45%); PF: 13%(95%CrI = 7−20%)) would lead to a sustained impact.ConclusionThese results suggest that offering universal HCV testing in prison and increasing treatment for PWID in or upon release from provincial prisons could change the course of the HCV epidemic in Montéal. Among all scenarios, models of care that integrate risk reduction measures post-release have the greatest potential to reduce HCV transmission among PWID
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 | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 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 ».