A Systematic Review and Meta‐Analysis of HIV/AIDS Prevalence Among Transgender Populations Worldwide
Bibliographic record
Abstract
ABSTRACT Background and Aims The study's goal was to assess the global prevalence of HIV/AIDS infection in transgender people and to provide light on regional variances. Methods A systematic search was conducted across multiple databases, including PubMed (Medline), Scopus, Web of Science, Embase, Ovid, and PsycINFO, from inception until September 2023. Studies were selected based on predefined inclusion and exclusion criteria, and quality was assessed using the Newcastle–Ottawa Scale (NOS). Heterogeneity was evaluated using Cochran's Q and I ‐squared statistics. A meta‐analysis was performed using the random‐effects model in Stata 17.0 (StataCorp, Texas, USA). A total of 3125 articles were identified, of which 37 studies met the inclusion criteria and were included in the systematic review and meta‐analysis. Results The global prevalence of HIV/AIDS among transgender populations was 24% (CI: 12%–39%). Regional analysis revealed higher rates of HIV/AIDS among male‐to‐female (MTF) transgender individuals in Asia (30%, CI: 5%–54%) and Africa (35%, CI: 13%–57%) compared to other regions. In contrast, female‐to‐male (FTM) transgender individuals in Asia had a higher prevalence (23%, CI: 9%–31%) than those in the Americas (11%, CI: 3%–23%). Conclusion Given the high prevalence of HIV/AIDS in the TG community worldwide and the significant variance within communities, it is critical to execute a comprehensive set of interventions to successfully prevent HIV/AIDS in transgender people.
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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 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.035 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".