Syringe Sharing and HIV Incidence Among Injection Drug Users and Increased Access to Sterile Syringes
Bibliographic record
Abstract
OBJECTIVES: We assessed the effects of syringe exchange program (SEP) policy on rates of HIV risk behavior and HIV incidence among injection drug users. METHODS: Using a multivariate generalized estimating equation and Cox regression methods, we examined syringe borrowing, syringe lending, and HIV incidence among a prospective cohort of 1228 injection drug users in Vancouver, British Columbia. RESULTS: We observed substantial declines in rates of syringe borrowing (from 20.1% in 1998 to 9.2% in 2003) and syringe lending (from 19.1% in 1998 to 6.8% in 2003) following SEP policy change. These declines coincided with a statistically significant increase in the proportion of participants accessing sterile syringes from nontraditional SEP sources (P < .001). In multivariate analyses, the period following the change in SEP policy was independently associated with a greater than 40% reduction in syringe borrowing (adjusted odds ratio [AOR] = 0.57; 95% confidence interval [CI] = 0.49, 0.65) and lending (AOR = 0.52; 95% CI = 0.45, 0.60), as well as declining HIV incidence (adjusted hazard ratio = 0.13; 95% CI = 0.06, 0.31). CONCLUSIONS: Widespread syringe distribution appears to be a more effective SEP policy than do more restrictive SEP policies that limit syringe access. Efforts should be made to ensure that SEP policies and program design serve to maximize rather than hinder syringe access.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".