Risk factors for viral hepatitis C infection in Rwanda: results from a nationwide screening program
Bibliographic record
Abstract
BACKGROUND: The epidemiology and risk factors for hepatitis C virus (HCV) infection in Rwanda are not well known; however, this information is crucial to shaping the country's public health approach to hepatitis C control. METHODS: A HCV screening campaign was conducted in the general population in 24 districts previously identified to have a high HCV disease burden. At the time of sample collection, sociodemographic information and self-reported risk factors were collected. Bivariate and multivariate logistic regressions were conducted to assess risk factors independently associated with hepatitis C antibodies (HCVAb) seroprevalence. RESULTS: Out of a total of 326,263 individuals screened for HCVAb, 22,183 (6.8%) were positive. In multivariate analysis, risk factors identified as statistically associated with HCVAb Seroprevalence include history of traditional operation or scarification (OR = 1.09, 95% CI: 1.05-1.14), presence of viral hepatitis in the family (OR = 1.27, 95% CI: 1.15-1.40), widowed or separated/divorced (OR = 1.36, 95% CI: 1.26-1.47), Southern province (OR = 1.98, 95% CI: 1.88-2.08) and aged 65 years and older (OR = 4.86, 95% CI: 4.62-5.11). Ubudehe category 3 (OR = 0.97, 95% CI: 0.93-1.01) and participants using RAMA (Health insurances for employees of public and private sectors) insurance (OR = 0.76, 95% CI: 0.70-0.85) had lower odds of HCV seroprevalence. CONCLUSIONS: Our findings provide important information for Rwanda's strategy on prevention and case-finding. Future prevention interventions should aim to reduce transmission through targeted messaging around traditional healing practices and case-finding targeting individuals with a history of exposure or advanced age.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".