117 Prevalence of Hepatitis C among migrants: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract OP 32: Health Status 1, B210 (FCSH), September 5, 2025, 09:00 - 10:00 Aim This systematic review aimed to estimate Hepatitis C virus (HCV) infection prevalence among migrants residing in high-income countries with low or intermediate HCV prevalence. Methods We searched Scopus, PubMed, PsycINFO, and Cochrane Library for peer-reviewed articles published in English between 2015 and 2024. Studies from European Economic Area (EEA) countries, Switzerland, the United Kingdom (UK), the United States of America (USA), Canada, New Zealand, and Australia were included. The studies’ quality was assessed using The Joanna Briggs Institute (JBI) Critical Appraisal Tools. A proportional meta-analysis was used to estimate HCV prevalence. Results A total of 1302 studies were screened. Thirty-six studies were included in this review. Most of the studies (n:14) were from Italy. Seventeen studies included both people <18 and ≥18 years old, 16 studies only included people ≥18 years old, and three studies included people aged 18 and younger. Thirty-five studies reported results of anti-HCV and 20 HCV-RNA tests. The pooled prevalence of HCV antibody (anti-HCV) and RNA (HCV-RNA) was 1.5% (95% CI, 1.1-2.0%) and 0.6% (95% CI, 0.4-0.9%), respectively. The prevalence of anti-HCV was higher among males (1.9%) than females (0.6%). Among those aged 18 and older, the prevalence of anti-HCV and HCV-RNA was 1.5% (95% CI: 0.9-2.4%) and 0.5% (95% CI: 0.2-0.8%), respectively. Among refugees and asylum seekers, the prevalence of anti-HCV and HCV-RNA was 1.4% and 0.7%, respectively. Conclusion The prevalence of HCV among migrants is comparable with that among the general population of the destination countries. Given the barriers migrants, especially refugees and asylum seekers, face in accessing health services, their access to HCV information, testing, and treatment should be facilitated.
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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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.034 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".