Transgender women in Kenya experience greater stigma, depressive symptoms, alcohol and drug use and risky sexual practices than cis-gendered men who have sex with men
Bibliographic record
Abstract
BACKGROUND: Worldwide, sexual and gender minority individuals have disproportionate burden of HIV. There are limited quantitative data from sub-Saharan Africa on the intersection of risks experienced by transgender women (TGW) in comparison to cis-men who have sex with men (MSM). This analysis addresses this gap by comparing reported stigma, psychosocial measures of health, and sexual risk practices between TGW and cis-MSM in Kenya. METHODS: We analyzed data from the baseline visit of an ongoing prospective cohort study taking place in three diverse metropolitan areas. Eligible participants were HIV-negative, assigned male at birth, ages 18-29 years, and reported anal intercourse in the past 3 months with a man or TGW. Data collected by audio computer assisted self-interview included sociodemographic measures, and sexual practices occurring in the past 3 months. Multivariable regressions assessed differences between TGW and cis-MSM in selected sexual practices, depressive symptoms, alcohol and drug use, and stigma. RESULTS: From September, 2019, through May, 2021, 838 participants were enrolled: 108 (12.9%) TGW and 730 (87.1%) cis-MSM. Adjusting for sociodemographic variables, TGW were more likely than cis-MSM to report: receptive anal intercourse (RAI; adjusted prevalence ratio [aPR] = 1.59, 95% CI: 1.32 - 1.92), engaging in group sex (aPR = 1.15, 95% CI: 1.04 - 1.27), 4 or more male sex partners (aPR = 3.31, 95% CI: 2.52 - 4.35), and 3 or more paying male sex partners (aPR = 1.58, 95% CI: 1.04 - 2.39). TGW were also more likely to report moderate to severe depressive symptoms (aPR = 1.42, 95% CI: 1.01 - 1.55), and had similar alcohol and drug abuse scores as cis-MSM. In sensitivity analysis, similar to TGW, male-identifying individuals taking feminizing gender affirming therapy had an increased likelihood of reporting RAI and group sex, and greater numbers of male sex partners and paying male sex partners relative to cis-MSM. CONCLUSIONS: Across three metropolitan areas in Kenya, TGW were more likely to report depressive symptoms and increased sexual risk taking. We identified a need for research that better characterizes the range of gender identities. Our analysis affirms the need for programmatic gender-affirming interventions specific to transgender populations in Kenya and elsewhere in Africa.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".