Estimating changes in overdose death rates from increasing methamphetamine supply in Ohio: Evidence from crime lab data
Bibliographic record
Abstract
Background: We investigate the relationship between the supply of methamphetamine and overdose death risk in Ohio. Ohio and the overall US have experienced a marked increase in overdose deaths from methamphetamine combined with fentanyl over the last decade. The increasing use of methamphetamine may be increasing the risk of overdose death. However, if people are using it to substitute away from more dangerous synthetic opioids, it may reduce the overall risk of overdose death. Methods: Ohio's Bureau of Criminal Investigation's crime lab data include a detailed list of the content of drug samples from law enforcement seizures, which are used as a proxy for drug supply. We use linear regressions to estimate the relationship between the proportion of methamphetamine in lab samples and unintentional drug overdose death rates from January 2015 through September 2021. Results: Relatively more methamphetamine in crime lab data in a county-month has either no statistically significant relationship with overdose death rates (in small and medium population counties) or a negative and statistically significant relationship with overdose death rates (in large population counties). Past overdose death rates do not predict future increases in methamphetamine in crime lab data. Conclusions: The results are consistent with a relatively higher supply of methamphetamine reducing the general risk of overdose death, possibly due to substitution away from more dangerous synthetic opioids. However, the supply of methamphetamine appears unrelated to the past illicit drug risk environment. The non-lethal and yet serious health effects of MA use were not explored and, thus, even if the presence of MA reduces the population-level overdose mortality rate, the rise of other adverse health effects may counteract any public health benefits of fewer deaths.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".