Impact of HCV Testing and Treatment on HCV Transmission Among Men Who Have Sex With Men and Who Inject Drugs in San Francisco: A Modelling Analysis
Bibliographic record
Abstract
BACKGROUND: Men who have sex with men who ever injected drugs (ever MSM-IDU) carry a high hepatitis C virus (HCV) burden. We estimated whether current HCV testing and treatment in San Francisco can achieve the 2030 World Health Organization (WHO) HCV elimination target on HCV incidence among ever MSM-IDU. METHODS: A dynamic HCV/HIV transmission model among MSM was calibrated to San Francisco data, including HCV antibody (15.5%, 2011) and HIV prevalence (32.8%, 2017) among ever MSM-IDU. MSM had high HCV testing (79%-86% ever tested, 2011-2019) and diagnosed MSM had high HCV treatment (65% ever treated, 2018). Following coronavirus disease 2019 (COVID-19)-related lockdowns, HCV testing and treatment decreased by 59%. RESULTS: Among all MSM, 43% of incident HCV infections in 2022 were IDU-related. Among ever MSM-IDU in 2015, HCV incidence was 1.2/100 person-years (95% credibility interval [CrI], 0.8-1.6). Assuming COVID-19-related declines in HCV testing/treatment persist until 2030, HCV incidence among ever MSM-IDU will decrease by 84.9% (95% CrI, 72.3%-90.8%) over 2015-2030. This decline is largely attributed to HCV testing and treatment (75.8%; 95% CrI, 66.7%-89.5%). Slightly greater decreases in HCV incidence (94%-95%) are projected if COVID-19 disruptions recover by 2025 or 2022. CONCLUSIONS: We estimate that HCV incidence will decline by >80% over 2015-2030 among ever MSM-IDU in San Francisco, achieving the WHO target.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".