Sex Work as an Emerging Risk Factor for Human Immunodeficiency Virus Seroconversion Among People who Inject Drugs in the SurvUDI Network
Bibliographic record
Abstract
BACKGROUND: Recent analyses have shown an emerging positive association between sex work and human immunodeficiency virus (HIV) incidence among people who inject drugs (PWIDs) in the SurvUDI network. METHODS: Participants who had injected in the past 6 months were recruited across the Province of Quebec and in the city of Ottawa, mainly in harm reduction programs. They completed a questionnaire and provided gingival exudate for HIV antibody testing. The associations with HIV seroconversion were tested with a Cox proportional hazard model using time-dependent covariables including the main variable of interest, sexual activity (sex work; no sex work; sexually inactive). The final model included significant variables and confounders of the associations with sexual activity. RESULTS: Seventy-two HIV seroconversions were observed during 5239.2 person-years (py) of follow-up (incidence rates: total = 1.4/100 py; 95% confidence interval [CI], 1.1-1.7; sex work = 2.5/100 py; 95% CI, 1.5-3.6; no sex work = 0.8/100 py; 95% CI, 0.5-1.2; sexually inactive = 1.8/100 py; 95% CI, 1.1-2.5). In the final multivariate model, HIV incidence was significantly associated with sexual activity (sex work: adjusted hazard ratio [AHR], 2.19; 95% CI, 1.13-4.25; sexually inactive: AHR, 1.62; 95% CI, 0.92-2.88), and injection with a needle/syringe used by someone else (AHR, 2.84; 95% CI, 1.73-4.66). CONCLUSIONS: Sex work is independently associated with HIV incidence among PWIDs. At the other end of the spectrum of sexual activity, sexually inactive PWIDs have a higher HIV incidence rate, likely due to more profound dependence leading to increased vulnerabilities, which may include mental illness, poverty, and social exclusion. Further studies are needed to understand whether the association between sex work and HIV is related to sexual transmission or other vulnerability factors.
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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 | 0.001 | 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 | 0.001 |
| 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".