Hepatitis B Virus (HBV) prevalence and characteristics in HIV-transmitting mothers and their infants in KwaZulu-Natal, South Africa
Bibliographic record
Abstract
Abstract Background HIV and hepatitis B virus (HBV) prevalence are high in KwaZulu-Natal (KZN), South Africa. HIV co-infection negatively impacts HBV prognosis, and can increase likelihood of HBV mother-to-child-transmission (MTCT). In an established early treatment intervention cohort of HIV-transmitting mother-child pairs in KZN, we characterised HBV serological makers in mothers, and screened at-risk infants for HBV. Methods Maternal samples (n=175) were screened for HBV infection (HBsAg), exposure to HBV (anti-HBc) and vaccination responses (anti-HBs-positive without other HBV markers). Infants of HBV-positive mothers were screened for HBsAg at 1 and 12 months. Results HBV infection was present in 8.6% (15/175) of mothers. Biomarkers for HBV exposure were present in 31.4% (55/175), but absent in 53.3% (8/15) maternal HBV-positive cases. Maternal HBV vaccination appeared rare (8.0%; 14/175). Despite prescription of antiretroviral therapy (ART) active against HBV, HBV DNA was detectable in 46.7% (7/15) HBsAg-positive mothers, with (5/7) also viraemic for HIV. Three mothers had HBV viral loads >5.3log 10 IU/ml, making them high-risk for HBV MTCT. Screening of available infant samples at one month of age (n=14) found no cases of HBV MTCT, and at 12 months (n=13) identified one HBV infection. Serological vaccination evidence was present in 53.8% (7/13) infants tested. Discussion This vulnerable cohort of HIV-transmitting mothers had a high undiagnosed HBV prevalence. Early infant ART may have reduced risk of MTCT in high-risk cases. Current HBV guidelines recommend antenatal antiviral prophylaxis but these data underline a potential role for infant post-exposure prophylaxis in high-risk MTCT pairs, warranting further investigation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".