Medications and risk of motor vehicle collision responsibility in British Columbia, Canada: a population-based case-control study
Bibliographic record
Abstract
BACKGROUND: Many medications impair driving skills yet their influence on collision risk remains uncertain. We aimed to systematically investigate the risk of collision responsibility associated with common classes of prescription medications. METHODS: In this population-based case-control study we analysed linked driving and health records in British Columbia, Canada from Jan 1, 1997, to Dec 31, 2016. The study cohort included all drivers involved in an incident collision (defined as first collision after 3 collision-free years) that resulted in a police report. We scored police collision reports and classified drivers as responsible for the collision (cases) or not responsible (controls); drivers with indeterminate scores were excluded. We used logistic regression to determine odds of collision responsibility in drivers with current prescriptions for medications of interest versus drivers without prescriptions. To explore whether risk of collision responsibility was related to medication effect or driver factors, we compared risk in current medication users versus past users. To study whether drivers developed tolerance to medication effects, we compared risk in new (first 30 days of a prescription) versus established users. FINDINGS: During the study period, 4 906 925 drivers had their driving licence linked to health records; of these drivers, 747 662 unique drivers were involved in 837 919 incident collisions between Jan 1, 2000, and Dec 31, 2016. 382 685 drivers responsible for the collision (cases) and 332 259 drivers not responsible (controls) were included in the final analysis; 122 975 drivers with indeterminate responsibility were excluded. We found increased risk of collision responsibility in drivers prescribed sedating antipsychotics (adjusted odds ratio [aOR] 1·35 [98·75% CI 1·25-1·46]), long-acting benzodiazepines (aOR 1·30 [1·22-1·38]), short-acting benzodiazepines (aOR 1·25 [1·20-1·31]), and high-potency opioids (aOR 1·24 [1·17-1·30]). Among medications used for medical indications, the highest risk was seen in drivers prescribed neurological medications: cholinergic drugs (aOR 1·83 [1·39-2·40]), anticholinergic agents for Parkinson's disease (aOR 1·45 [1·08-1·96]), dopaminergic agents (aOR 1·20 [1·04-1·38]), and anticonvulsants (aOR 1·20 [1·14-1·26]). People currently taking benzodiazepines, non-sedating antidepressants, high-potency opioids, and anticonvulsants had increased risk compared with past users, and we did not find increased risk in new compared with established users of these drugs. INTERPRETATION: Drivers prescribed benzodiazepines or high-potency opioids are at increased risk of being responsible for collisions and this risk does not decrease over time. Several other classes of medications are associated with increased risk, but this association might be independent of medication effect. These findings can guide medication warnings and prescription choices and inform public education campaigns targeting impaired driving. FUNDING: Canadian Institutes of Health Research.
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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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".