The effect of needle and syringe program and opioid agonist therapy on the risk of HIV, hepatitis B and C virus infection for people who inject drugs in Amsterdam, the Netherlands: findings from an emulated target trial
Bibliographic record
Abstract
BACKGROUND AND AIMS: Major declines in HIV and hepatitis C and B virus (HCV/HBV) incidence among people who inject drugs (PWID) have been attributed to early implementation of harm-reduction programs (HRP) in the Netherlands, but alternative factors such as selective mortality and demographic and drug market shifts over time probably contributed to observed incidence declines. We quantified and tested the effect of HRP participation on risk of these infections among PWID in Amsterdam, the Netherlands. DESIGN: We emulated the design of a hypothetical, ideal randomized trial using observational data from the Amsterdam Cohort Studies (1985-2014). SETTING: Amsterdam, the Netherlands. PARTICIPANTS: We included PWID who ever used opioids, had a recent history of injecting drug use (IDU) and tested negative for HIV, HCV or HBV. Of 983 participants, 640, 137 and 308 were included for the HIV, HCV and HBV analyses and 59, 45 and 49 seroconversions were observed, respectively. INTERVENTIONS: Intervention arms were: complete HRP participation [≥ 60 mg/day methadone and 100% needle and syringe program (NSP) coverage, or any methadone dose if no recent injection drug use] versus no HRP and partial HRP participation combined (< 60 methadone mg/day and/or < 100% NSP coverage). CONCLUSIONS: Complete participation in harm reduction programs appears to have led to substantial decreases in HIV and hepatitis C and B virus acquisition risk among people who inject drugs in the Netherlands. MEASUREMENTS: Separately for each infection, we estimated the hazard ratios (HR) comparing HRP arms using marginal structural models. FINDINGS: Compared with no/partial HRP participation, complete HRP participation led to lower risk of HIV [HR = 0.54, 95% confidence interval (CI) = 0.27-1.08], HCV (HR = 0.16, 95% CI = 0.06-0.40) and HBV (HR = 0.28, 95% CI = 0.13-0.61) acquisition.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".