Background: In the last years were published many epidemiological articles aiming to link driving under the influence of cannabis (DUIC) with the risk of various unfavorable traffic events (UTEs), with sometimes contradictory results.
Aim: The primary objective of this study was to analyze whether there is a significant association between DUIC and UTEs.
Materials and Methods: We used two meta-analytical methods to assess the statistical significance of the effect size: random-effects model and inverse variance heterogeneity model.
Results: Twenty-four studies were included in the meta-analysis. We obtained significant increases in the effect size for DUIC tested through blood analysis, with an odds ratio (OR) of 2.27 and a confidence interval (CI) between 1.36 and 3.80; death as an outcome, with an OR of 1.56 and a CI between 1.16 and 2.09; and case–control as the type of study, with an OR of 1.99 and a CI between 1.05 and 3.80. Publication bias was very high.
Conclusion: Our analysis suggests that the overall effect size for DUIC on UTEs is not statistically significant, but there are significant differences obtained through subgroup analysis. This result might be caused by either methodological flaws (which are often encountered in articles on this topic), the indiscriminate employment of the term “cannabis use,” or an actual absence of an adverse effect. When a driver is found, in traffic, with a positive reaction suggesting cannabis use, the result should be corroborated by either objective data regarding marijuana usage (like blood analyses, with clear cut-off values), or a clinical assessment of the impairment, before establishing his/her fitness to drive.
In the last years were published numerous epidemiological studies that tried to link driving under the influence of cannabis (DUIC) with the risk of various unfavorable traffic events (UTEs) – collision, injury, or death. Most of them had important limitations (see e.g., Gerberich et al., 2003; Laumon et al., 2005; Asbridge et al., 2014). For example, some articles did not differentiate between testing for tetrahydrocannabinol (THC) and its core metabolite – 11-Nor-9-carboxy-Δ9-tetrahydrocannabinol (THC-COOH) (Laumon et al., 2005). THC-COOH is formed through the hepatic oxidation of the active metabolite, after which is conjugated with glucuronide (Skopp and Pötsch, 2002), resulting in a water-soluble substance that can be easily excreted (Law et al., 1984). Unlike THC, which has a half-life of about 7 h (Bédard et al., 2007), THC-COOH can be detected in body fluids and may give a positive test for cannabis use for several days (or even weeks in heavy users), even though the active component is absent (Ashton, 2001), leading to a false belief that the person is DUIC. Additionally, in the terminal elimination phase of the metabolite, a single subject may produce consecutive specimens that could be tested positive, negative, and again positive, making it very hard to differentiate a new episode of consumption from a previous cannabis exposure (Goodwin et al., 2008). There is always some delay between UTE and the moment of collecting biological samples, which makes the simple determination of the relationship between cannabis use and collision risk very difficult.
Many studies analyzed the association between cannabis use and UTEs through “self-reporting,” a method known to underestimate the actual proportion of cannabis users (Gerberich et al., 2003; Asbridge et al., 2014), as many users tend not to report consumption of an illegal substance. Also, it is possible that a driver may have a positive result for cannabis, be involved in a car crash, but not be in an impaired driving status. Some studies evaluated the association between DUIC and UTE through epidemiological surveys, other used public datasets, culpability studies, or case–control studies. Some articles analyzed the association between previous use of cannabis and the risk of traffic events, showing an increased risk (Blows et al., 2005; Mann et al., 2007; Terry-McElrath et al., 2014) while other reached inconclusive results (Gerberich et al., 2003; Asbridge et al., 2014).
Also, three recent meta-analyses tried to summarize the effect size of DUIC (Asbridge et al., 2012; Li et al., 2012; Rogeberg and Elvik, 2016) and suggested that the risk of UTEs is increased by cannabis (Asbridge et al., 2012; Li et al., 2012; Rogeberg and Elvik, 2016) used a random-effect model to assess the effect size of cannabis use on UTEs, but they failed to provide prediction intervals (PI) for their values. Rogeberg and Elvik (2016) used a meta-regression model for estimating the publication bias, which was one of the objectives of our study.
The primary objective of this study was to analyze whether there is a significant association between DUIC and UTEs.
(1) To test whether DUIC is associated with an increased risk of unfavorable driving-related outcomes compared to chronic cannabis use, based on recent published literature (after 2000).
(2) To test whether publishing bias is significant in studies dealing with cannabis use in drivers.
(3) To see whether the self-reported use of cannabis during driving leads to an under-reporting of the actual cannabis use while driving.