Trends in knowledge of HIV status and efficiency of HIV testing services in Sub-Saharan Africa (2000-2020): a modelling study of survey and HIV testing program data
Bibliographic record
Abstract
Abstract Background Knowledge of HIV status (KOS) among people living with HIV (PLHIV) is essential for an effective national HIV response. This study estimates progress and gaps in reaching the UNAIDS 2020 target of 90% KOS, and the efficiency of HIV testing services (HTS) in sub-Saharan Africa (SSA), where two thirds of all PLHIV live. Methods We used data from 183 population-based surveys (N=2.7 million participants) and national HTS programs (N=315 country-years) from 40 countries as inputs into a mathematical model to examine trends in KOS among PLHIV, median time from HIV infection to diagnosis, HIV testing positivity, and proportion of new diagnoses among all positive tests, adjusting for retesting. Findings Across SSA, KOS steadily increased from 6% (95% credible interval [95%CrI]: 5% to 7%) in 2000 to 84% (95%CrI: 82% to 86%) in 2020. Twelve countries and one region, Southern Africa, reached the 90% target. In 2020, KOS was lower among men (79%) than women (87%) across SSA. PLHIV aged 15-24 years were the least likely to know their status (65%), but the largest gap in terms of absolute numbers was among men aged 35-49 years, with over 700,000 left undiagnosed. As KOS increased from 2000 to 2020, the median time to diagnosis decreased from 10 to 3 years, HIV testing positivity declined from 9% to 3%, and the proportion of first-time diagnoses among all positive tests dropped from 89% to 42%. Interpretation On the path towards the next UNAIDS target of 95% diagnostic coverage by 2030, and in a context of declining positivity and yield of first-time diagnoses, we need to focus on addressing disparities in KOS. Increasing KOS and treatment coverage among older men could be critical to reduce HIV incidence among women in SSA, and by extension, reducing mother-to-child transmission.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".