Pathways to HIV risk and vulnerability among lesbian, gay, bisexual, and transgendered methamphetamine users: a multi-cohort gender-based analysis
Bibliographic record
Abstract
BACKGROUND: Methamphetamine (MA) use continues to be a major public health concern in many urban settings. We sought to assess potential relationships between MA use and individual, social, and structural HIV vulnerabilities among sexual minority (lesbian, gay, bisexual or transgendered) drug users. METHODS: Beginning in 2005 and ending in 2008, 2109 drug users were enrolled into one of three cohort studies in Vancouver, Canada. We analysed longitudinal data from all self-identified sexual minority participants (n = 248). Logistic regression using generalized estimating equations (GEE) was used to examine the independent correlates of MA use over time. All analyses were stratified by biological sex at birth. RESULTS: At baseline, 104 (7.5%) males and 144 (20.4%) females reported sexual minority status, among whom 64 (62.1%) and 58 (40.3%) reported MA use in the past six months, respectively. Compared to heterosexual participants, sexual minority males (odds ratio [OR] = 3.74, p < 0.001) and females (OR = 1.80, p = 0.003) were more likely to report recent MA use. In multivariate analysis, MA use among sexual minority males was associated with younger age (adjusted odds ratio [AOR] = 0.93 per year older, p = 0.011), Aboriginal ancestry (AOR = 2.59, p = 0.019), injection drug use (AOR = 3.98, p < 0.001), having a legal order or area restriction (i.e., "no-go zone") impact access to services or influence where drugs are used or purchased (AOR = 4.18, p = 0.008), unprotected intercourse (AOR = 1.62, p = 0.048), and increased depressive symptoms (AOR = 1.67, p = 0.044). Among females, MA use was associated with injection drug use (AOR = 2.49, p = 0.002), Downtown South residency (i.e., an area known for drug use) (AOR = 1.60, p = 0.047), and unprotected intercourse with sex trade clients (AOR = 2.62, p = 0.027). CONCLUSIONS: Methamphetamine use was more prevalent among sexual minority males and females and was associated with different sets of HIV risks and vulnerabilities. Our findings suggest that interventions addressing MA-related harms may need to be informed by more nuanced understandings of the intersection between drug use patterns, social and structural HIV vulnerabilities, and gender/sexual identities. In particular, MA-focused prevention and treatment programs tailored to disenfranchised male and female sexual minority youth are recommended.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".