Sex work and HIV incidence among people who inject drugs
Bibliographic record
Abstract
Objective—Although the global burden of HIV infection among sex workers (SW) has been well recognized, HIV-related risks among sex workers who inject drugs (SW-IDU) have received less attention. We investigated the relationship between sex work and HIV incidence among people who inject drugs (IDU) in a Canadian setting. Design—Prospective cohort study. Methods—Using Kaplan–Meier methods and the extended Cox regression, we compared HIV incidence among SW-IDU and non-SW-IDU in Vancouver, Canada, after adjusting for potential confounders. Results—Between 1996 and 2012, 1647 participants were included in the study, including 512 (31.1%) IDU engaged in sex work. At 5 years the HIV cumulative incidence was higher among SW-IDU in comparison to other IDU (12 vs. 7%, P = 0.001). In unadjusted Cox regression analyses, HIV incidence among SW-IDU was also elevated [relative hazard: 1.69; 95% confidence interval (CI): 1.13–2.53]. However, in a multivariable analysis, sex work did not remain associated with HIV infection (adjusted relative hazard: 0.74; 95% CI: 0.45–1.20), with cocaine injection appearing to account for the elevated risk for HIV infection among SW-IDU. Conclusion—These data suggest that local SW-IDU have elevated rates of HIV infection. However, our exploration of risk factors among SW-IDU demonstrated that drug use patterns and environmental factors, rather than sexual risks, may explain the elevated HIV incidence among SW-IDU locally. Our findings highlight the need for social and structural interventions, including increased access to harm reduction programs and addiction treatment.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".