Undiagnosed HIV Among Transgender Women in the United States: Implications for Testing Programs
Bibliographic record
Abstract
INTRODUCTION: Transgender women experience health care barriers that can impede HIV status awareness, increasing the risk of delayed diagnosis. We sought to characterize undiagnosed HIV among transgender women in the United States. METHODS: We used data from a hybrid cohort using digital and in-person strategies for transgender women in the United States who are not living with HIV. Assessments include a sociobehavioral questionnaire and HIV testing. Using baseline screening data, we identified undiagnosed HIV among transgender women who self-reported no HIV testing history or a negative result at last test. Bivariate and multivariable Firth-penalized logistic regression models were fit to assess correlates of undiagnosed HIV. RESULTS: A total of 2547 participants completed HIV testing. Forty-three participants tested positive, 15 of whom disclosed during post-test counseling they had been previously diagnosed and were excluded from this sample. Of the 2532 with no previous HIV diagnosis, 28 (1.1%) were estimated to have undiagnosed HIV at baseline. Correlates of undiagnosed HIV included identifying as a person of color (aOR = 4.8; 95% CI: 2.1 to 11.1) and past 6-month stimulant use (aOR = 2.8; 95% CI: 1.2 to 6.4). Among transgender women of color, correlates of undiagnosed HIV were past 6-month stimulant use (OR = 2.5; 95% CI: 0.9 to 6.7), no lifetime HIV testing history (OR = 3.8; 95% CI: 1.4 to 10.8), and no insurance (OR = 4.5; 95% CI: 1.0 to 20.5) or public health insurance (OR = 4.4; 95% CI: 1.1 to 17.1) vs. private insurance. CONCLUSIONS: Undiagnosed HIV among transgender women is concerning and disproportionately affects transgender women of color. Addressing stimulant use is important for HIV prevention. Findings underscore the urgent need for free, accessible HIV testing with linkage to HIV prevention and care to achieve HIV strategy goals.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".