The continuum of HIV care in South Africa
Bibliographic record
Abstract
BACKGROUND: We characterize engagement with HIV care in South Africa in 2012 to identify areas for improvement towards achieving global 90-90-90 targets. METHODS: Over 3.9 million CD4 cell count and 2.7 million viral load measurements reported in 2012 in the public sector were extracted from the national laboratory electronic database. The number of persons living with HIV (PLHIV), number and proportion in HIV care, on antiretroviral therapy (ART) and with viral suppression (viral load <400 copies/ml) were estimated and stratified by sex and age group. Modified Poisson regression approach was used to examine associations between sex, age group and viral suppression among persons on ART. RESULTS: We estimate that among 6511 000 PLHIV in South Africa in 2012, 3300 000 individuals (50.7%) accessed care and 32.9% received ART. Although viral suppression was 73.7% among the treated population in 2012, the overall percentage of persons with viral suppression among all PLHIV was 23.8%. Linkage to HIV care was lower among men (38.5%) than among women (57.2%). Overall, 47.1% of those aged 0-14 years and 47.0% of those aged 15-49 years were linked to care compared with 56.2% among those aged above 50 years. CONCLUSION: Around a quarter of all PLHIV have achieved viral suppression in South Africa. Men and younger persons have poorer linkage to HIV care. Expanding HIV testing, strengthening prompt linkage to care and further expansion of ART are needed for South Africa to reach the 90-90-90 target. Focus on these areas will reduce the transmission of new HIV infections and mortality in the general population.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".