Does heroin‐assisted treatment reduce crime? A review of randomized‐controlled trials
Bibliographic record
Abstract
BACKGROUND AND AIMS: Several randomized controlled trials (RCTs) conclude that heroin-assisted-treatment (HAT) has a larger benefit-cost ratio than oral methadone because HAT more reliably and substantially reduces participants' criminal activity. This review: (1) summarizes results from RCTs concerning the comparative effectiveness of HAT for reducing criminal activity and (2) examines the role of different mechanisms for explaining changes in crime. DESIGN: Systematic search of five databases for RCTs evaluating comparative effectiveness of HAT on participant crime outcomes and potential mediators of crime. Narrative synthesis with tabular comparisons of outcomes extracted across RCTs. SETTING: Europe and Canada. PARTICIPANTS: Twenty studies, spanning 10 RCTs with 2427 participants, met inclusion criteria. INTERVENTIONS: HAT compared to other treatments for opioid use disorder, primarily oral methadone. MEASUREMENTS: The primary outcome was criminal activity. Mediator outcomes included illicit heroin use, drug expenditures, employment and earnings and social functioning. FINDINGS: All trials found significantly reduced criminal activity among HAT participants, and four found significantly larger reductions for HAT compared to control condition [median odds ratios (ORs) = 0.45]. Reductions in crime are concentrated in drug-related and property offenses (ORs range from 0.14 to 0.90 and from 0.12 to 1.89, respectively). Comparative efficacy of HAT for reducing illicit heroin use probably explains reductions in drug possession offenses, but does not show consistent correlation with drug dealing or property offenses. While three trials showed reductions in drug expenditures as possibly driving crime reductions, others did not report expenditures. There is little evidence that treatment effects on economic and social functioning outcomes explain within-trial changes in criminal activity. CONCLUSIONS: Existing literature suggests that heroin-assisted treatment reduces criminal activity, but trials varied in whether these effects exceeded those from oral methadone treatment. Inconsistency in outcome measures across trials complicates understanding drivers of heterogeneity. More detailed information on legal and illegal income, drug expenditures and social interactions could improve our understanding of the causal mechanisms underlying the effect of heroin-assisted-treatment on crime.
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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 | 0.025 | 0.101 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.011 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".