Prescription opioid use among drivers in British Columbia, 1997–2016
Bibliographic record
Abstract
BACKGROUND: Opioids increase the risk of traffic crash by limiting coordination, slowing reflexes, impairing concentration and producing drowsiness. The epidemiology of prescription opioid use among drivers remains uncertain. We aimed to examine population-based trends and geographical variation in drivers' prescription opioid consumption. METHODS: We linked 20 years of province-wide driving records to comprehensive population-based prescription data for all drivers in British Columbia (Canada). We calculated age- and sex-standardised rates of prescription opioid consumption. We assessed temporal trends using segmented linear regression and examined regional variation in prescription opioid use using maps and graphical techniques. RESULTS: A total of 46 million opioid prescriptions were filled by 3.0 million licensed drivers between 1997 and 2016. In 2016 alone, 14.7% of all drivers filled at least one opioid prescription. Prescription opioid use increased from 238 morphine milligram equivalents per driver year (MMEs/DY) in 1997 to a peak of 834 MMEs/DY in 2011. Increases in MMEs/DY were greatest for higher potency and long-acting prescription opioids. The interquartile range of prescription opioid dispensation by geographical region increased from 97 (Q1=220, Q3=317) to 416 (Q1=591, Q3=1007) MMEs/DY over the study interval. IMPLICATIONS: Patterns of prescription opioid consumption among drivers demonstrate substantial temporal and geographical variation, suggesting they may be modified by clinical and policy interventions. Interventions to curtail use of potentially impairing prescription medications might prevent impaired driving.
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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 | 0.000 | 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) | 0.001 | 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".