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Enregistrement W4387930906 · doi:10.1111/add.16372

How research and policy can shape driving under the influence of cannabis

2023· editorial· en· W4387930906 sur OpenAlexaboutno aff
Jane Metrik, Denis M. McCarthy

Notice bibliographique

RevueAddiction · 2023
Typeeditorial
Langueen
DomaineMedicine
ThématiqueCannabis and Cannabinoid Research
Établissements canadiensnon disponible
Organismes subventionnairesNational Institute on Drug Abuse
Mots-clésCannabisMarijuana smokingPsychologyPoison controlMedicineMedical emergencyPsychiatrySubstance abusePolysubstance dependence

Résumé

récupéré en direct d'OpenAlex

In the wake of widespread cannabis legalization in the United States (US) and internationally, law enforcement and policy makers are at a standstill on solutions to deter people from driving under the influence of cannabis (DUIC). As the prevalence of cannabis use increases, the public perception of DUIC as safe and devoid of consequence is growing. Shifting this perception and preventing DUIC will require clear messaging about risk, development of a consistent DUIC impairment standard and DUIC-specific statutes and law enforcement efforts. There has been an increase in prevalence of driving under the influence of cannabis (DUIC) and in fatal motor vehicle collisions in US states [1] and other countries following recreational cannabis legalization (e.g. Uruguay [2]; Canada [3]). Studies have found that acute cannabis intoxication is associated with a statistically significant increase in motor vehicle collision risk [4]. Cannabis impairs psychomotor skills critical to driving in both occasional and heavy users [5]. This is particularly concerning because of the increasing potency of Δ9-tetrahydrocannabinol (THC) concentration linked with more severe withdrawal and motor impairment [6]. Despite the unequivocal evidence that cannabis acutely impairs driving-related skills and increases risk, public attitudes toward DUIC are highly permissive in the United States and in Australia, particularly among medical cannabis users [7]. DUIC is perceived as safe, normative and associated with fewer consequences than alcohol-impaired driving [8]. However, this may not extend to other countries with high prevalence of cannabis use [9-11]. As the prevalence of cannabis use and DUIC increases, challenging and correcting these perceptions is imperative for the new generations of drivers who also use cannabis. To this end, we need universal objective standards for DUIC, combined with consistent DUIC-specific offenses and sanctions, to ensure highway safety [5]. Many countries have achieved significant reductions in alcohol-impaired driving and fatalities through a combination of policy, law enforcement and public awareness campaigns [12]. Of these, perhaps the most successful has been per se blood alcohol concentration (BAC) legal limits, currently 0.08 in 49 US states and 0.05 in many industrialized nations [13]. Per se laws provide a clear, consistent standard for defining prohibited levels of alcohol-based impairment for driving and are thought to reduce alcohol-impaired driving by increasing the perceived risk of arrest [14], particularly when combined with visible enforcement. Unfortunately, replicating this effective policy/enforcement combination for DUIC is complicated by differences in pharmacology and impairment indicators between the two drugs. Currently, there are no reliable and practical biochemical or behavioral on-the-road methods to establish cannabis-induced impairment. In contrast to alcohol, there is poor correspondence between levels of THC in biological specimens (e.g. blood, saliva) and psychomotor impairment [15]. THC-induced impairment continues well after the decline of THC in blood and oral fluid. Maximal impairment is typically observed during the first hour after inhalation, with subsequent declines over 3 to 4 h [15, 16] and recovery of most driving-related skills within 5 h [17]. However, there is a substantial delay in the time course for impairment following oral ingestion, with at least 8 h of driving-related cognitive impairment [17] and substantial individual variability in THC's pharmacokinetic profile. Such poor correspondence produces significant challenges for DUIC policy and prevention efforts. These challenges underscore the complexities in developing clear, consistent and enforceable policies to limit DUIC. The most promising approach would be behavioral assessment of impairment combined with a positive biomarker test [18]. Ideally, this combination would use a “successive hurdles” approach, where an initial step with high sensitivity to detect recent cannabis use would be followed by a more thorough assessment with high specificity to detect impairment. Although there are promising methods for such an approach, there are several problems that would need to be resolved prior to implementation. Oral fluid (OF) tests are likely the best candidates for detecting recent use. OF screening is non-invasive, carries minimal risk of adulteration, can be conducted in proximity to the time of driving and has reduced interindividual variability and reduced variability between THC doses compared to blood [19]. At very low thresholds (e.g. ≤1 ng/mL), OF testing detects recent (past 3 h) use of smoked THC with very high sensitivity, but has modest specificity and longer detection windows, which may lead to positive tests outside of the typical time course of impairment [19]. A higher cut-off of 10 ng/mL has better specificity for detecting recent use, although THC remains detectable in a small proportion of users long term [20]. Higher cut-offs also risk missing occasional users who may be impaired. Further complicating the issue is the difference in impairment time course between THC administration routes (i.e. inhaled vs oral), and research on OF testing and edibles is lacking. Current OF screening devices cannot serve as evidence of per se impairment, but can serve as a first pass screener of recent use for follow-up behavioral assessment. The Drug Evaluation and Classification Program (DECP), developed by the US Department of Transportation National Highway Traffic Safety Administration and the International Association of Chiefs of Police, certifies law enforcement officers as Drug Recognition Experts (DREs) to conduct psychophysical tests and recognize signs of drug-related impairment. In a comprehensive study of this program, the most reliable cannabis impairment indicators included elevated pulse, dilated pupils and other eye exam markers and impairment on the standardized field sobriety test [21]. Despite its demonstrated validity and reliability, obtaining and maintaining this certification is onerous, resulting in a limited number of certified experts. Given the limited specificity of the biomarker cut-off in screening and limited availability of DREs, further research is needed to support DUIC-specific statutes and the determination of DUIC events. First, controlled research with oral administration and varying THC concentrations is needed to determine OF cut-off levels that correspond to the time course of behavioral impairment. Second, the development and validation of technological tools that can reliably detect impairment, supplement DRE evaluations and increase the availability of behavioral assessments is crucial. This is particularly critical for individuals who use cannabinoids for medical indications with a consistent dosing regimen, who may develop tolerance to THC effects. These individuals may be less impaired when driving [22] and can test positive on OF screening, therefore, could benefit from sensitive behavioral assessment. In fact, medical cannabis legalization may be associated with reduced motor vehicle collisions and related mortality [1]. Finally, research on combining OF testing and behavioral assessment, currently used in some countries (e.g. Australia), and their sensitivity and specificity is indicated. Such research is needed to develop a consistent impairment standard for DUIC-specific statutes and enforcement efforts. Doing so can provide a clear message about the risk of DUIC, help shift inaccuracies in public perception and aid individuals in making safer driving decisions. Jane Metrik: Conceptualization (equal); data curation (equal); funding acquisition (lead); methodology (equal); resources (equal); writing—original draft (equal); writing—review and editing (equal). Denis M. McCarthy: Conceptualization (equal); data curation (equal); methodology (equal); resources (equal); writing—original draft (equal); writing—review and editing (equal). This work was supported by the National Institute on Drug Abuse (R01DA055654). None.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,004
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,281
Score d'incertitude au seuil0,994

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,004
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,001
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,026
Tête enseignante GPT0,357
Écart entre enseignants0,330 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2023
Routes d'admission1
Résumé présentoui

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