Seroprevalence of Hepatitis C in Ethiopia: First National Study Based on the 2016 Ethiopian Demographic and Health Survey
Bibliographic record
Abstract
Hepatitis C virus (HCV) is hypothesised to be a public health problem in Ethiopia, and systematic review evidence suggested 1%-3% seroprevalence. We aimed to estimate the seroprevalence of HCV overall and across regions of Ethiopia. We estimated HCV seroprevalence using the 2016 Ethiopian Demographic and Health Survey (EDHS-2016). EDHS-2016 is a nationwide household survey conducted using two-stage cluster sampling methods. We tested all 26,753 samples from participating adult women (15-49 years) and men (15-59 years) using HCV Enzyme Immunoassay. Descriptive analyses were performed based on the Guide to Demographic Health Survey statistics. We applied sample weighting to derive representative estimates. Of the total tested, more than half (54.40%) were aged 15-29 years and 51.59% were women. Overall HCV seroprevalence was 0.18% (95% Confidence Interval: 0.10-0.32). Higher seroprevalences were found in Afar (0.92%) and South Nations Nationality Peoples Region (0.43%); people living with HIV (PLWH) (0.62%); the poorest wealth index (0.35%); people having multiple lifetime sexual partners (0.31%); and widowed/divorced individuals (0.30%). In stratified analyses by sex and residency, we found higher seroprevalences in non-Christian and non-Muslim males (1.98%) and rural population (1.00%), male PLWH (1.67%), rural PLWH (1.45%), widowed/divorced males (0.97%), and in all groups from the Afar region: males (1.30%), females (0.61%), urban (1.07%), and rural (0.86%). HCV seroprevalence among the general population in Ethiopia is much lower than from previous estimates. General population screening is unlikely to be cost-effective, and so screening programs targeted to people at greater risk of HCV will be required.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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 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".