Characterizing the Human Immunodeficiency Virus Care Continuum Among Transgender Women and Cisgender Women and Men in Clinical Care: A Retrospective Time-series Analysis
Bibliographic record
Abstract
BACKGROUND: Prior studies suggest that transgender women (TW) with human immunodeficiency virus (HIV) are less likely to be virally suppressed than cisgender women (CW) and cisgender men (CM). However, prior data are limited by small sample sizes and cross-sectional designs. We sought to characterize the HIV care continuum comparing TW to CW and CM in the United States and Canada. METHODS: We analyzed annual HIV care continuum outcomes by gender status from January 2001 through December 2015 among adults (aged ≥18 years) in 15 clinical cohorts. Outcomes were retention in care and viral suppression. RESULTS: The study population included TW (n = 396), CW (n = 14 094), and CM (n = 101 667). TW had lower proportions retained in care than CW and CM (P < .01). Estimates of retention in care were consistently lower in TW, with little change over time within each group. TW and CW had similar proportions virally suppressed over time (TW, 36% in 2001 and 80% in 2015; CW, 35% in 2001 and 83% in 2015) and were lower than CM (41% in 2001 and 87% in 2015). These differences did not reach statistical significance after adjusting for age, race, HIV risk group, and cohort. CONCLUSIONS: TW experience challenges with retention in HIV care. However, TW who are engaged in care achieve viral suppression that is comparable to that of CW and CM of similar age, race, and HIV risk group. Further research is needed to understand care engagement disparities.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".