High incidence and persistence of hepatitis B virus infection in individuals receiving HIV care in KwaZulu-Natal, South Africa
Bibliographic record
Abstract
BACKGROUND: Hepatitis B virus (HBV), Human Immunodeficiency virus (HIV) and Tuberculosis (TB) are common infections in South Africa. We utilized the opportunity of care provision for HIV-TB co-infected patients to better understand the relationship between these coinfections, determine the magnitude of the problem, and identify risk factors for HBV infection in HIV infected patients with and without TB in KwaZulu-Natal, South Africa. METHODS: This retrospective cohort analysis was undertaken in 2018. In-care HIV infected patients were included in the analysis. Results from clinical records were analysed to determine the prevalence, incidence, persistence and factors associated with HBsAg positivity in HIV-infected patients with or without TB co-infection. RESULTS: A total of 4292 HIV-infected patients with a mean age of 34.7 years (SD: 8.8) were included. Based on HBsAg positivity, the prevalence of HBV was 8.5% (363/4292) [95% confidence interval (CI): 7.7-9.3] at baseline and 9.4% (95%CI: 8.6-10.3%) at end of follow-up. The HBV incidence rate was 2.1/100 person-years (p-y). Risk of incident HBV infection was two-fold higher among male patients (HR 2.11; 95% CI: 1.14-3.92), while severe immunosuppression was associated with a greater than two-fold higher risk of persistent infection (adjusted risk ratio (RR) 2.54; 95% CI 1.06-6.14; p = 0.004. Additionally, active TB at enrolment was associated with a two-fold higher risk of incident HBV infection (aHR 2.38; 95% CI: 0.77-7.35). CONCLUSION: The provision of HIV care and treatment in high HBV burden settings provide a missed opportunity for HBV screening, immunization and care provision.
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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 | 0.002 |
| 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 | 0.001 | 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".