Proximity to the US–Mexico border: a key to explaining geographic variation in US methamphetamine, cocaine and heroin purity
Bibliographic record
Abstract
AIMS: Although illicit drug purity is a widely discussed health risk, research explaining its geographic variation within a country is rare. This study examines whether proximity to the US-Mexico border, the United States' primary drug import portal, is associated with geographic variation in US methamphetamine, heroin and cocaine purity. DESIGN: Distances (proximity) between the US-Mexico border and locations of methamphetamine, cocaine and heroin seizures/acquisitions (n = 239,070) recorded in STRIDE (System to Retrieve Information from Drug Evidence) were calculated for the period of 1990-2004. The association of drug purity with these distances and other variables, including time and seizure/acquisition size, was examined using hierarchical multivariate linear modeling (HMLM). SETTING: Coterminous United States. FINDINGS: Methamphetamine, cocaine and heroin purity generally decreased with distance from the US-Mexico border. Heroin purity, however, after initially declining with distance, turned upwards-a U-shaped association. During 2000-04, methamphetamine purity also had a U-shaped association with distance. For each of the three drugs, temporal changes in the purity of small acquisitions (<10 g) were typically more dynamic in areas closer to the US-Mexico border. CONCLUSIONS: Geographic variance in methamphetamine, cocaine and heroin purity throughout the coterminous United States was associated with US-Mexico border proximity. The U-shaped associations between border-distance and purity for heroin and methamphetamine may be due to imports of those drugs via the eastern United States and southeast Canada, respectively. That said, areas closer to the US-Mexico border generally had relatively high illicit drug purity, as well as more dynamic change in the purity of small ('retail level') drug amounts.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".