Initiation of hepatitis C treatment in two rural Rwandan districts: a mobile clinic approach
Bibliographic record
Abstract
BACKGROUND: To eliminate hepatitis C, Rwanda is conducting national mass screenings and providing to people with chronic hepatitis C free access to Direct Acting Antivirals (DAAs). Until 2020, prescribers trained and authorized to initiate DAA treatment were based at district hospitals, and access to DAAs remains expensive and geographically difficult for rural patients. We implemented a mobile clinic to provide DAA treatment initiation at primary-level health facilities among people with chronic hepatitis C identified through mass screening campaigns in rural Kirehe and Kayonza districts. METHODS: The mobile clinic team was composed of one clinician authorized to manage hepatitis, one lab technician, and one driver. Eligible patients received same-day clinical consultations, counselling, laboratory tests and DAA initiation. Using clinical databases, registers, and program records, we compared the number of patients who initiated DAA treatment before and during the mobile clinic campaign. We assessed linkage to care during the mobile clinical campaign and assessed predictors of linkage to care. We also estimated the cost per patient of providing mobile services and the reduction in out-of-pocket costs associated with accessing DAA treatment through the mobile clinic rather than the standard of care. RESULTS: Prior to the mobile clinic, only 408 patients in Kirehe and Kayonza had been initiated on DAAs over a 25-month period. Between November 2019 and January 2020, out of 661 eligible patients with hepatitis C, 429 (64.9%) were linked to care through the mobile clinic. Having a telephone number and complete address recorded at screening were strongly associated with linkage to care. The cost per patient of the mobile clinic program was 29.36 USD, excluding government-provided DAAs. Providing patients with same-day laboratory tests and clinical consultation at primary-level health facilities reduced out-of-pocket expenses by 9.88 USD. CONCLUSION: The mobile clinic was a feasible strategy for providing rapid treatment initiation among people chronically infected by hepatitis C, identified through a mass screening campaign. Compared to the standard of care, mobile clinics reached more patients in a much shorter time. This low-cost strategy also reduced out-of-pocket expenditures among patients. However, long-term, sustainable care would require decentralization to the primary health-centre level.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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 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".