Determining reinfection rates by hepatitis C testing interval among key populations: A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND & AIMS: Detecting hepatitis C virus (HCV) reinfection among key populations helps prevent ongoing transmission. This systematic review aims to determine the association between different testing intervals during post-SVR follow-up on the detection of HCV reinfection among highest risk populations. METHODS: We searched electronic databases between January 2014 and February 2023 for studies that tested individuals at risk for HCV reinfection at discrete testing intervals and reported HCV reinfection incidence among key populations. Pooled estimates of reinfection incidence were calculated by population and testing frequency using random-effects meta-analysis. RESULTS: Forty-one single-armed observational studies (9453 individuals) were included. Thirty-eight studies (8931 individuals) reported HCV reinfection incidence rate and were included in meta-analyses. The overall pooled estimate of HCV reinfection incidence rate was 4.13 per 100 per person-years (py) (95% confidence interval [CI]: 3.45-4.81). The pooled incidence estimate among people who inject drugs (PWID) was 2.84 per 100 py (95% CI: 2.19-3.50), among men who have sex with men (MSM) 7.37 per 100 py (95% CI: 5.09-9.65) and among people in custodial settings 7.23 per 100 py (95% CI: 2.13-16.59). The pooled incidence estimate for studies reporting a testing interval of ≤6 months (4.26 per 100 py; 95% CI: 2.86-5.65) was higher than studies reporting testing intervals >6 months (5.19 per 100 py; 95% CI: 3.92-6.46). CONCLUSIONS: HCV reinfection incidence was highest in studies of MSM and did not appear to change with retesting interval. Shorter testing intervals are likely to identify more reinfections, help prevent onward transmission where treatment is available and enable progress towards global HCV elimination, but additional comparative studies are required.
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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 | 0.015 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.043 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".