Implementation of Refugees' Inclusion in National Viral Hepatitis B and Hepatitis C Screening Campaign in Mahama Refugee Camp, Rwanda
Bibliographic record
Abstract
INTRODUCTION: The World Health Organization has called for the elimination of hepatitis B virus (HBV) and hepatitis C virus (HCV) as public health threats by 2030. In response to the United Nations High Commissioner for Refugees requests, Rwanda became the first country to include refugees in its national viral hepatitis prevention and management program in 2019. We used secondary data to describe the implementation of the first HBV and HCV screening program among refugees in Rwanda. METHODS: Rapid diagnostic tests were used to screen for HBV surface antigen (HBsAg) and HCV antibody (anti-HCV). We used routine data collected during the HBV and HCV mass screening campaign among Burundian refugees living in Mahama camp and program records to estimate the screening coverage, the prevalence of HBV and HCV, and the cost of the campaign. RESULTS: Over 28 days in February and March 2020, 26,498 unique individuals were screened for HBV and HCV, reflecting a screening coverage of 77.9% (95% confidence interval [CI]=76.5%, 78.4%). Coverage was greater than 90% among women aged 30-64 years, but younger age groups and men were less likely to be screened. On average, 946 clients were screened per day. The prevalence of anti-HCV was 1.1% (95% CI=1.0%, 1.3%), and the prevalence of HBsAg was 3.8% (95% CI=3.6%, 4.0%). We estimate that the total cost of the campaign was US$177,336.60, reflecting a per-person-screened cost of US$6.69. CONCLUSION: Conducting a mass screening was a feasible and effective strategy to achieve high screening coverage and identify refugees who were eligible for HBV and HCV treatment. This screening program in the Mahama refugee camp can serve as a reference for other refugee camps in Rwanda and elsewhere.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".