Trends in HIV self-testing uptake in Africa: A modeling study of population-based surveys and HIV testing program data
Bibliographic record
Abstract
BACKGROUND: HIV self-testing (HIVST) can increase access to and uptake of HIV testing among people underserved by other HIV testing approaches. Several countries in Africa, the region most affected by HIV, have scaled-up HIVST. However, no comprehensive analysis has yet quantified HIVST uptake trends and how HIVST kits are used. We aimed to estimate 1) country-level and regional trends in HIVST uptake among adults by sex and age and 2) the proportion of distributed HIVST kits that are used and re-testing rates with HIVST. METHODS AND FINDINGS: Across African countries, we analyzed 1) data from national population-based surveys that included questions on previous HIVST use and 2) the number of HIVST kits distributed from nationally reported program data (2012-2024). We developed a hierarchical Bayesian compartmental model to estimate HIVST rates by triangulating surveys and program data. Random effects were used to pool information across countries. Data were available from 40 surveys in 27 countries and from 99 country-years of HIVST program data. The proportion of adults aged ≥15 years in Africa who have ever used an HIVST (HIVST uptake) steadily increased, from <1% in 2012 to almost 7% (6.8%; 95% credible interval [95%CrI] [5.8, 8.2]) in 2024. HIVST uptake was higher in eastern and southern Africa (10.2% in 2024, 95%CrI [8.5, 12.7]) compared to western and central Africa (2% in 2024; 95%CrI [1.7, 2.5]). The proportion of people who ever self-tested varied substantially across countries, reaching a maximum in 2024 of 45.4% (95%CrI [41.8, 51.5]) in Lesotho. Men (7.2% in 2024, 95%CrI [6.1, 8.8]) were slightly more likely than women to have ever used an HIVST (6.4% in 2024; 95%CrI [5.4, 7.8]). Compared to younger individuals (15-24 years), those aged 25-34 years had higher rates of self-testing (men: rate ratio [RR]=1.8, 95%CrI [1.5, 2.3]; women: RR = 1.4, 95%CrI [1.1, 1.6]). Individuals who previously self-tested may be more likely to self-test again (RR = 1.1, 95%CrI [0.8, 1.5]), although with substantial uncertainty. We estimated that 70% (95%CrI [60, 80]) of all HIVST distributed were used. Limitations of the study include challenges in precisely estimating some parameters, exclusion of countries without any HIVST distribution data and inability to model HIVST positivity. CONCLUSIONS: HIVST uptake has increased in Africa, with wide variation between countries. HIVST is more likely to engage 25-34-year-olds and men, who have historically been less likely to be aware of their HIV status. Our results can help understand patterns of use and support countries in optimizing their HIV testing services.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".