The burden of hepatitis C virus in Cameroon: Spatial epidemiology and historical perspective
Bibliographic record
Abstract
Cameroon is thought to have one of the highest prevalences of hepatitis C virus (HCV) infection in the world (4.9% among adults). A marked cohort effect exists in several communities where ≈50% of the elderly are infected. Better assessment of HCV distribution is needed for planning treatment programmes. We tested for HCV antibodies 14 150 capillary blood samples collected during the 2011 Demographic and Health Survey, whose participants were representative of the Cameroonian population aged 15-49 (both genders) and 50-59 years (men only). Historical data on exposure to medical care were collected and factors associated with HCV assessed through logistic regression and geospatial analyses. To estimate prevalence in all persons aged ≥15 years, we used data from the survey for the 15-59 years fraction and modelled a cohort effect for older individuals. The nationwide HCV prevalence was 0.81% for the 15-49 years group, and 2.51% for all individuals aged ≥15 years. Only 0.2% of individuals aged 15-19 were seropositive. Among participants aged 15-44 years, HCV was associated with age, rural residence and, for males, with ritual circumcision. For those aged 45-59 years, HCV was associated with age and access to medical care in the late 1950s. Prevalence of HCV seropositivity in Cameroon is half of previous estimates. Nationwide surveys are essential to rationalize resources allocation. The high prevalence among older cohorts, a colonial legacy, has had little spillover into younger cohorts. HCV-free generations might be attainable in countries not plagued with intravenous drug abuse.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".