Needle and syringe programmes and opioid substitution therapy for preventing HCV transmission among people who inject drugs: findings from a Cochrane Review and meta‐analysis
Bibliographic record
Abstract
Abstract Aims To estimate the effects of needle and syringe programmes (NSP) and opioid substitution therapy (OST), alone or in combination, for preventing acquisition of hepatitis C virus (HCV) in people who inject drugs (PWID). Methods Systematic review and meta‐analysis. Bibliographic databases were searched for studies measuring concurrent exposure to current OST (within the last 6 months) and/or NSP and HCV incidence among PWID. High NSP coverage was defined as regular NSP attendance or ≥ 100% coverage (receiving sufficient or greater number of needles and syringes per reported injecting frequency). Studies were assessed using the Cochrane risk of bias in non‐randomized studies tool. Random‐effects models were used in meta‐analysis. Results We identified 28 studies (n = 6279) in North America (13), United Kingdom (five), Europe (four), Australia (five) and China (one). Studies were at moderate (two), serious (17) critical (seven) and non‐assessable risk of bias (two). Current OST is associated with 50% [risk ratio (RR) =0.50, 95% confidence interval (CI) = 0.40–0.63] reduction in HCV acquisition risk, consistent across region and with low heterogeneity (I2 = 0, P = 0.889). Weaker evidence was found for high NSP coverage (RR = 0.79, 95% CI = 0.39–1.61) with high heterogeneity (I2 = 77%, P = 0.002). After stratifying by region, high NSP coverage in Europe was associated with a 56% reduction in HCV acquisition risk (RR = 0.44, 95% CI = 0.24–0.80) with low heterogeneity (I2 = 12.3%, P = 0.337), but not in North America (RR = 1.58, I2 = 89.5%, P = < 0.001). Combined OST/NSP is associated with a 74% reduction in HCV acquisition risk (RR = 0.26, 95% CI = 0.07–0.89, I2 = 80% P = 0.007). According to Grades of Recommendation Assessment, Development and Evaluation (GRADE) criteria, the evidence on OST and combined OST/NSP is low quality, while NSP is very low. Conclusions Opioid substitution therapy reduces risk of hepatitis C acquisition and is strengthened in combination with needle and syringe programmes (NSP). There is weaker evidence for the impact of needle syringe programmes alone, although stronger evidence that high coverage is associated with reduced risk in Europe.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.038 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".