Does regulating drug precursors affect illicit drug markets? An expanded and updated systematic review
Bibliographic record
Abstract
BACKGROUND: Many countries are placing greater emphasis on regulating precursor chemicals used in illicit drug production. However, the latest review on this topic is 14 years old and limited to North American methamphetamine regulations. This review updates and expands on past work by assessing how precursor regulations affect illicit drug markets. METHOD: We conducted a systematic review following PRISMA guidelines, searching 13 databases and relevant organizational websites for grey literature. Eligible studies quantitatively assessed precursor regulations' impact on drug supply, demand, or related harms. Due to intervention variability, we used narrative synthesis. Bias risk was evaluated with the EPOC Risk of Bias Tool. RESULTS: Twenty-six studies met the inclusion criteria, published between 2003 and 2023, focusing on methamphetamine (n = 23), cocaine (n = 3), and heroin (n = 1). Most were from the USA (n = 20), with others from Canada (n = 1), Mexico (n = 1), Australia (n = 3), and the Czech Republic (n = 1). The studies assessed 12 outcomes across 37 interventions, 14 of which were effective and 23 ineffective. Effective interventions led to impacts such as a 100 % price increase, a 40 % purity reduction, and a 43 % drop in past-month drug use, lasting from months to seven years. Ineffective interventions shared three issues: targeting unused chemicals, focusing on small-scale operations, or failing as suppliers adapted to new sources or routes. CONCLUSIONS: Precursor regulations can reduce the supply, use, and harms of heroin, cocaine, and methamphetamine. However, they are not a one-size-fits-all solution. Their effectiveness depends on how they are designed and the context in which they are implemented.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".