Public health interventions, priority populations, and the impact of COVID-19 disruptions on hepatitis C elimination among people who have injected drugs in Montreal (Canada): A modeling study
Bibliographic record
Abstract
BACKGROUND: In Montreal (Canada), high hepatitis C virus (HCV) seroincidence (21 per 100 person-years in 2017) persists among people who have injected drugs (PWID) despite relatively high testing rates and coverage of needle and syringe programs (NSP) and opioid agonist therapy (OAT). We assessed the potential of interventions to achieve HCV elimination (80% incidence reduction and 65% reduction in HCV-related mortality between 2015 and 2030) in the context of COVID-19 disruptions among all PWID and PWID living with HIV. METHODS: Using a dynamic model of HCV-HIV co-transmission, we simulated increases in NSP (from 82% to 95%) and OAT (from 33% to 40%) coverage, HCV testing (every 6 months), or treatment rate (100 per 100 person-years) starting in 2022 among all PWID and PWID living with HIV. We also modeled treatment scale-up among active PWID only (i.e., people who report injecting in the past six months). We reduced intervention levels in 2020-2021 due to COVID-19-related disruptions. Outcomes included HCV incidence, prevalence, and mortality, and proportions of averted chronic HCV infections and deaths. RESULTS: COVID-19-related disruptions could have caused temporary rebounds in HCV transmission. Further increasing NSP/OAT or HCV testing had little impact on incidence. Scaling-up treatment among all PWID achieved incidence and mortality targets among all PWID and PWID living with HIV. Focusing treatment on active PWID could achieve elimination, yet fewer projected deaths were averted (36% versus 48%). CONCLUSIONS: HCV treatment scale-up among all PWID will be required to eliminate HCV in high-incidence and prevalence settings. Achieving elimination by 2030 will entail concerted efforts to restore and enhance pre-pandemic levels of HCV prevention and care.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".