Migration is Associated With Increased HIV Vulnerability Among Young Transgender Women in Lima, Peru
Bibliographic record
Abstract
BACKGROUND: Latin America-amid its largest mass migration-has seen minimal progress in curbing new HIV infections. Transgender women (TW) in the region are disproportionately affected, but scant data examine HIV vulnerabilities alongside migration. METHODS: Between February and July 2022, 211 young TW ages 16-24 in Lima participated in a cross-sectional quantitative study accompanied by serological testing (HIV, syphilis, chlamydia, gonorrhea, hepatitis B). Bivariate tests compared HIV and STI prevalence and related vulnerabilities among migrants to nonmigrants. Poisson regression modeling estimated the association between time in Lima (nonmigrant, 0-1 years, 2-5 years, ≥6 years) and HIV vulnerabilities (condom use). FINDINGS: Of 204 young TW, 110 were migrants to Lima (54%); 45% arrived in Lima ≤5 years ago. Most migrants were Peruvian (70% from Jungle regions); 14% were from Venezuela. HIV prevalence was 44% among migrants and 39% among nonmigrants ( P = 0.67). Compared with nonmigrants, migrants had higher prevalence of lifetime syphilis (65% vs 41%; P < 0.01) and poor health care access (29% vs 12% no medical insurance), lifetime sex work (78% vs 55%; P < 0.01) and sex work in past 30 days, (42% vs 8%; P < 0.01), and client violence (23% vs 6%; P < 0.01). Migrants arriving in Lima 0-1 years ago were more likely to report past 6-month condomless anal sex compared with nonmigrants (adjusted prevalence ratio = 1.54; 95% confidence interval = 1.02 to 2.32). CONCLUSIONS: Young TW face high rates of HIV and STIs, with vulnerabilities persisting even after resettlement for migrants. There is an urgent need for expanded HIV prevention and care for these women and sustained health and social services for migrants in urban centers postmigration.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".