HIV prevalence in transgender women and cisgender men who have sex with men in sub-Saharan Africa 2010-2022: a meta-analysis
Bibliographic record
Abstract
Abstract Introduction The Global AIDS Strategy 2021-2026 calls for equitable access to HIV services for all populations. Transgender people have been marginalised and experience disproportionate risk of HIV infection in sub-Saharan Africa (SSA) and data to guide HIV programmes are severely limited. Surveillance data among cisgender men who have sex with men (cis-MSM) are comparatively abundant. We assessed whether HIV prevalence among cis-MSM was correlated with HIV prevalence among transgender women. Methods Data from key population surveys conducted in SSA between 2010-2022 were identified from existing databases and survey reports. Studies that collected HIV prevalence on both transgender women and cis-MSM populations were analysed with random effect meta-analysis to estimate the ratio of HIV prevalence among cis-MSM:transgender women. Results Twenty-one studies were identified encompassing 8,476 transgender women and 24,102 cis-MSM. Median HIV prevalence among transgender women was 23.5% (interquartile range [IQR] 11.5-39.8%) and 16.2% (IQR 8.1-26.8%) among cis-MSM. HIV prevalence among transgender women was 50% higher than in cis-MSM (prevalence ratio 1.48 95CI 1.25-1.76). HIV prevalence among transgender women was highly correlated with year/province-matched HIV prevalence among cis-MSM (R 2 =0.60), but poorly correlated with year/province-matched total population HIV prevalence (R 2 =0.01). Conclusion Transgender women experience a significantly greater HIV burden than cis-MSM in SSA, underscoring the need for HIV services addressing the disproportionate vulnerability experienced by transgender women. Further bio-behavioural surveys focused on determinants of HIV infection, treatment uptake, and risk behaviours among transgender people, distinct from cis-MSM, will improve understanding of HIV risk and vulnerabilities.
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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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.078 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".