The effect of opioid use on traffic fatalities
Bibliographic record
Abstract
We use a difference-in-differences design to study the effect of opioid use on traffic fatalities. Following Alpert et al., we focus on the 1996 introduction and marketing of OxyContin, and we examine its long-term impacts on traffic fatalities involving Schedule II drugs or heroin. Based on the national fatal vehicle crash database, we find that the states heavily targeted by the initial marketing of OxyContin (i.e., non-triplicate states) experienced 2.4 times more traffic fatalities (1.6 additional deaths per million individuals) involving Schedule II drugs or heroin during 2011-2019, when overdose deaths from heroin and fentanyl became more prominent. We find no difference in traffic fatalities until after the mid-2000s between states with and without a triplicate prescription program. The effect is mainly concentrated in fatal crashes with drug involvement of drivers ages between 25 and 44. Our results highlight additional long-term detrimental consequences of the introduction and marketing of OxyContin.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".