Medications and risk of motor vehicle collision responsibility in British Columbia, Canada: a population-based case-control study
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».