Incidence of new hepatitis C virus infection is still increasing in French MSM living with HIV
Bibliographic record
Abstract
OBJECTIVE: High hepatitis C virus (HCV) treatment uptake combined with effective direct-acting antiviral-based regimens resulted in a dramatic decline of HCV infection in French people living with HIV (PLWH). We assessed the yearly incidence of new HCV infection in PLWH enrolled in the large French Dat'AIDS cohort from 2012 to 2016 with a specific focus on MSM. METHODS: The incidence of new HCV infection was determined yearly in HCV-negative PLWH with serological follow-up during 2012-2016. The incidence of HCV reinfection was determined in patients who were cured of a previous infection. RESULTS: Among 40 714 PLWH, HCV status was available in 38 217 (94%). A total of 5557 PLWH (15%) were HCV infected at first time-point, 82% of whom were cured of HCV by the end of 2016. Among 21 519 HCV-negative PLWH with serological follow-up (63 449 patient-years), 219 first HCV infections occurred (MSM: 188, others: 31). Similarly, among 3406 patients who were cured of a previous infection (10 602 patient-years), 73 reinfections occurred (MSM: 51, others: 22). From 2012 to 2016, the incidence of a first infection in MSM rose from 0.5 to 0.92% patient-years, whereas the incidence or reinfection remained stable (2.52-2.90% patient-years). CONCLUSION: Despite a high HCV treatment uptake and cure rate, the incidence of first HCV infection regularly increased in French HIV-positive MSM between 2012 and 2016. The incidence of reinfection fluctuated but remained constantly higher than the incidence of first infection, suggesting that a subgroup of MSM pursued high-risk practices following cure of a first infection.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".