Estimating the annual number of hepatitis C virus infections through vertical transmission at country, regional, and global levels: a data synthesis study
Bibliographic record
Abstract
BACKGROUND: The burden of hepatitis C virus (HCV) among women of childbearing age remains high globally. Studies have estimated that 7-12% of children born to women with HCV infection will acquire HCV, although around two-thirds of children will then clear their HCV infection by 5 years of age. We aimed to estimate the annual number of vertically transmitted HCV infections and how many cases remain at 5 years of age at the country or territory, regional, and global levels. METHODS: In this data synthesis study, we produced estimates of vertical HCV transmission by combining data from several sources: data on the number of women, age-specific fertility rates, mortality rates among children aged 0-5 years, and HIV prevalence among women aged 15-49 years from the UN; modelled data on HCV prevalence among women aged 15-49 years; meta-analysis data on HCV-HIV co-infection prevalence; and recent estimates of the probabilities of vertical HCV transmission and subsequent clearance by age 5 years. The annual number of births with HCV was estimated by multiplying the number of women with HCV in 5-year age bands by age band-specific birth rates, separately by HIV status, and multiplying by HIV status-specific HCV vertical transmission probabilities. The number of births with HCV was multiplied by the probability of spontaneous clearance of HCV by 5 years of age, accounting for mortality. All estimates were sampled 1000 times from their uncertainty intervals (UIs) to produce 95% UIs. FINDINGS: The estimated annual global number of new HCV infections occurring through vertical transmission was 73 862 (95% UI 69 808-78 279). Southern Asia (21 245 [18 095-24 847]), western Africa (16 482 [14 873-18 283]), and eastern Africa (8182 [7479-9085]) were the regions with the most infections. Pakistan (16 350 [13 325-19 844]) and Nigeria (8483 [6944-10 184]) had the largest burden and together accounted for around a third of new infections. We estimated that 23 120 (20 596-25 813) of these children would be alive and still have HCV when aged 5 years. INTERPRETATION: Targeted screening policies that test and treat pregnant women with HCV could prevent substantial numbers of new HCV infections; however, data on the safety of HCV treatments in pregnant women are required. FUNDING: None.
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 | 0.062 | 0.221 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.032 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".