A systematic review of hepatitis C virus epidemiology in Europe, Canada and Israel
Bibliographic record
Abstract
BACKGROUND AND AIM: Decisions on public health issues are dependent on reliable epidemiological data. A comprehensive review of the literature was used to gather country-specific data on risk factors, prevalence, number of diagnosed individuals and genotype distribution of the hepatitis C virus (HCV) infection in selected European countries, Canada and Israel. METHODOLOGY: Data references were identified through indexed journals and non-indexed sources. In this work, 13,000 articles were reviewed and 860 were selected based on their relevance. RESULTS: Differences in prevalence were explained by local and regional variances in transmission routes or different public health measures. The lowest HCV prevalence (≤ 0.5%) estimates were from northern European countries and the highest (≥ 3%) were from Romania and rural areas in Greece, Italy and Russia. The main risk for HCV transmission in countries with well-established HCV screening programmes and lower HCV prevalence was injection drug use, which was associated with younger age at the time of infection and a higher infection rate among males. In other regions, contaminated glass syringes and nosocomial infections continue to play an important role in new infections. Immigration from endemic countries was another factor impacting the total number of infections and the genotype distribution. Approximately 70% of cases in Israel, 37% in Germany and 33% in Switzerland were not born in the country. In summary, HCV epidemiology shows a high variability across Europe, Canada and Israel. CONCLUSION: Despite the eradication of transmission by blood products, HCV infection continues to be one of the leading blood-borne infections in the region.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.031 | 0.039 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".