Potential impact of prison-based interventions on hepatitis C transmission among people who inject drugs in Montréal, Canada Insights from mathematical modelling
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".