Prevalence of hepatitis B and C infection and linkage to care among patients with Non-Communicable Diseases in three rural Rwandan districts: a retrospective cross-sectional study
Bibliographic record
Abstract
INTRODUCTION: Rwanda's Hepatitis C elimination campaign has relied on mass screening campaigns. An alternative "micro-elimination" strategy focused on specific populations, such as non-communicable disease (NCD) patients, could be a more efficient approach to identifying patients and linking them to care. METHODS: This retrospective cross-sectional study used routine data collected during a targeted screening campaign among NCD patients in Kirehe, Kayonza, and Burera districts of Rwanda and patients receiving oncology services from the Butaro District Hospital. The campaign used rapid diagnostic tests to screen for Hepatitis B surface antigen (HBsAg) and Hepatitis C antibody (anti-HCV). We reported prevalences and 95% confidence intervals for HBsAg and anti-HCV, assessed for associations between patients' clinical programs and hepatitis B and C, and reported cascade of care for the two diseases. RESULTS: Out of 7,603 NCD patients, 3398 (45.9%) self-reported a prior hepatitis screening. Prevalence of HBsAg was 2.0% (95% CI: 1.7%-2.3%) and anti-HCV was 6.7% (95% CI: 6.2%-7.3%). The prevalence of HBsAg was significantly higher among patients < 40 years (2.4%). Increased age was significantly associated with anti-HCV (12.0% among patients ≥ 70 years). Of the 148 individuals who screened positive for HbsAg, 123 had viral load results returned, 101 had detectable viral loads (median viral load: 451 UI/mL), and 12 were linked to care. Of the 507 individuals who screened positive for anti-HCV, 468 had their viral load results returned (median viral load: 1,130,000 UI/mL), 304 had detectable viral loads, and 230 were linked to care. CONCLUSION: Anti-HCV prevalence among Rwandan patients with NCD was high, likely due to their older age. NCD-HCV co-infected patients had high HCV viral loads and may be at risk of poor outcomes from hepatitis C. Hepatitis C micro-elimination campaigns among NCD patients are a feasible and acceptable strategy to enhance case detection in this high-prevalence population with elevated viral loads and may support linkage to care for hepatitis C among elderly populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".