Systematic review and meta-analysis of the adolescent HIV continuum of care in South Africa: the Cresting Wave
Bibliographic record
Abstract
CONTEXT: South Africa has the most HIV infections of any country in the world, yet little is known about the adolescent continuum of care from HIV diagnosis through viral suppression. OBJECTIVE: To determine the adolescent HIV continuum of care in South Africa. DATA SOURCES: We searched PubMed, Google Scholar and online conference proceedings from International AIDS Society (IAS), International AIDS Conference (AIDS) and Conference on Retrovirology and Opportunistic Infections (CROI) from 1 January 2005 to 31 July 2015. DATA EXTRACTION: We selected published literature containing South African cohorts and epidemiological data reporting primary data for youth (15-24 years of age) at any stage of the HIV continuum of care (ie, diagnosis, treatment, retention, viral suppression). For the meta-analysis we used six sources for retention in care and nine for viral suppression. RESULTS: Among the estimated 867 283 HIV-infected youth from 15 to 24 years old in South Africa in 2013, 14% accessed antiretroviral therapy (ART). Of those on therapy, ∼83% were retained in care and 81% were virally suppressed. Overall, we estimate that 10% of HIV-infected youth in South Africa in 2013 were virally suppressed. LIMITATIONS: This analysis relies on published data from large mostly urban South Africa cohorts limiting the generalisability to all adolescents. CONCLUSIONS: Despite a large increase in ART programmes in South Africa that have relatively high retention rates and viral suppression rates among HIV-infected youth, only a small percentage are virally suppressed, largely due to low numbers of adolescents and young adults accessing ART.
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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 | 0.024 | 0.076 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.029 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".