Hepatitis B Virus Care Cascade in Rwanda: A Population-based Study From 2016–2023
Bibliographic record
Abstract
INTRODUCTION: In sub-Saharan Africa, data on the hepatitis B virus (HBV) care cascade are limited. We assessed Rwanda's HBV care cascade. METHODS: We analyzed data from the District Health Information System 2, capturing 4.6 million individuals (≥2 years) screened for HBV from January 2016 to June 2023. The HBV care cascade included 6 stages: (1) chronic HBV prevalence, (2) diagnosis, (3) care enrollment, (4) treatment eligibility, (5) treatment initiation, and (6) treatment continuation. HBV infection was defined as a reactive hepatitis B surface antigen test. Hierarchical logistic regression identified factors influencing progression through the cascade. FINDINGS: Among 4 604 468 screened individuals, 138 512 were hepatitis B surface antigen-positive. District Health Information System 2 data included 57 520 cases, of which 52 827 (91.8%) had HBV monoinfection. Of those diagnosed, 21 247 (37.0%) were enrolled in care and 6429 (30.3%) were eligible for treatment. Among eligible individuals, 4893 (76.1%) initiated treatment, with 4839 (98.9%) retained on treatment 1-year after initiation. Individuals aged 35-54 years were more likely to engage in care (adjusted odds ratio [aOR]: 1.19; 95% confidence interval [CI]: 1.12-1.27), initiate treatment (aOR: 1.45; 95% CI: 1.14-1.86), and remain on treatment (aOR: 2.88; 95% CI: 1.09-7.63) compared to those <35 years. Individuals living 30-60 minutes from health facilities were less likely to engage in care or continue treatment compared to those living at a shorter distance. CONCLUSIONS: Engagement in the HBV care cascade is low, particularly among younger individuals and those living farther from health facilities. Efforts to improve awareness and accessibility are essential to strengthen HBV prevention and care.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".