O2-S5.04 Outlier populations: heightened risk for HIV, HCV and HIV/HCV co-infection among solvent-using injection drug users
Bibliographic record
Abstract
Introduction Globally, substantial heterogeneity in the prevalence of HIV and other sexually transmitted infections (STIs) among most at-risk populations (MARPs) has been demonstrated. Examining factors related to heterogeneity can inform targeted programming. In Winnipeg, Canada, particularly high risk for HIV and hepatitis C (HCV) has been observed in injection drug users (IDUs) with a history of solvent use (S-IDUs). However, comparisons to other MARPs have been limited. Thus this study examined the association between HIV/STIs and S-IDUs in comparison to IDUs and other MARPs. Methods Data were from a 2008 to 2009 cross-sectional study of Winnipeg MARPs (IDUs, sex work- and street-involved individuals); subjects were recruited through respondent-driven sampling (RDS) methods. Adjusted ORs (AORs) from multivariable logistic regression models were estimated, examining the risk of HIV, HCV and HIV/HCV co-infection, and corrected for RDS-chain clustering using generalised estimating equations. Results Total sample was 499, of which 13% recently injected drugs (ie, last 6 months), 5% recently inhaled solvents, 6% were recent S-IDUs, and 76% did not inject drugs or inhale solvents. HIV and HCV prevalence among recent S-IDU was 21% and 79%, respectively; HIV/HCV co-infection was 18%. In multivariable models, S-IDUs were at highest risk of HIV (AOR: 3.6, 95% CI: 1.6% to 7.9%; p<0.001), HCV (AOR: 19.3, 95% CI: 6.8% to 58.3%; p<0.001) and HIV/HCV co-infection (AOR: 6.0, 95% CI:2.5% to 14.7%; p<0.001). Comparatively, AORs for IDU-only were 3.3 (95% CI: 1.3% to 7.6%), 3.8 (95% CI: 2.3% to 7.5%) and 4.9 (95% CI: 2.1% to 14.7%). Among lifetime S-IDUs, elevated risk for HIV (AOR: 7.4, 95% CI: 2.3% to 26.2%) and HCV (AOR: 22.7, 95% CI: 11.0% to 47.0%) was observed, but not for HIV/HCV co-infection. Conclusions Solvent use occurs among the most marginalised of MARPs, representing unique and complicated drug use trajectories. As the HIV epidemic in Canada becomes increasingly complex, examination of outlier populations such as S-IDU can inform public health by elucidating important pathways by which structural, environmental and individual factors interact to create the highest risk for HIV/STIs and other bloodborne pathogens.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".