The relationship between <scp>US</scp> heroin market dynamics and heroin‐related overdose, 1992–2008
Bibliographic record
Abstract
BACKGROUND AND AIMS: Heroin-related overdose is linked to polydrug use, changes in physiological tolerance and social factors. Individual risk can also be influenced by the structural risk environment including the illicit drug market. We hypothesized that components of the US illicit drug market, specifically heroin source/type, price and purity, will have independent effects on the number of heroin-related overdose hospital admissions. METHODS: Yearly, from 1992 to 2008, Metropolitan Statistical Area (MSA) price and purity series were estimated from the US Drug Enforcement Administration data. Yearly heroin overdose hospitalizations were constructed from the Nationwide Inpatient Sample. Socio-demographic variables were constructed using several databases. Negative binomial models were used to estimate the effect of price, purity and source region of heroin on yearly hospital counts of heroin overdoses controlling for poverty, unemployment, crime, MSA socio-demographic characteristics and population size. RESULTS: Purity was not associated with heroin overdose, but each $100 decrease in the price per pure gram of heroin resulted in a 2.9% [95% confidence interval (CI) = 4.8%, 1.0%] increase in the number of heroin overdose hospitalizations (P = 0.003). Each 10% increase in the market share of Colombian-sourced heroin was associated with a 4.1% (95% CI = 1.7%, 6.6%) increase in number of overdoses reported in hospitals (P = 0.001) independent of heroin quality. CONCLUSIONS: Decreases in the price of pure heroin in the United States are associated with increased heroin-related overdose hospital admissions. Increases in market concentration of Colombian-source/type heroin is also associated with an increase in heroin-related overdose hospital admissions. Increases in US heroin-related overdose admissions appear to be related to structural changes in the US heroin market.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".