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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".