Authors
Michael A. White, Nicholas R. Burns
Published
December 13, 2021
The development of drug driving policies should rest on sound epidemiological evidence as to the crash risks of driving after using psychoactive drugs. The findings from individual studies of the increased risk of crashing from the acute use of cannabis range in size from no increase (and perhaps even a protective effect) to a 10-fold increase. Coherent cannabis-driving policies cannot readily be developed from such an incoherent evidence base. A weighted average measure of risk, as provided by a meta-analysis, might be useful. However, if the range of risks found in the cannabis-crash studies reflects the different ways that a variety of biases are being expressed, then the simple application of a meta-analysis might provide little more than an average measure of bias. In other words, if the biases were predominantly inflationary, the meta-analysis would give an inflated estimate of crash risk; and if the biases were predominantly deflationary, the meta-analysis would give a deflated estimate of risk.
The authors undertook a systematic search of electronic databases, and identified 13 culpability studies and 4 case–control studies from which cannabis-crash odds ratios could be extracted. Random-effects meta-analyses gave summary odds ratios of 1.37 (1.10–1.69) for the culpability studies and 1.45 (0.94–2.25) for the case–control studies. A tool was designed to identify and score biases arising from: confounding by uncontrolled covariates; inappropriate selection of cases and controls; and the inappropriate measurement of the exposure and outcome variables. Each study was scrutinised for the presence of those biases, and given a total ‘directional bias score’. Most of the biases were inflationary. A meta-regression against the total directional bias scores was performed for the culpability studies, giving a bias-adjusted summary odds ratio of 0.68 (0.45–1.05). The same analysis could not be performed for the case–control studies because there were only four such studies. Nonetheless, a monotonic relationship was found between the total bias scores and the cannabis-crash odds ratios, with Spearman’s rho = 0.95, p = 0.05, indicating that the summary odds ratio of 1.45 is an overestimate. It is evident that the risks from driving after using cannabis are much lower than from other behaviours such as drink-driving, speeding or using mobile phones while driving. With the medical and recreational use of cannabis becoming more prevalent, the removal of cannabis-presence driving offences should be considered (while impairment-based offences would remain).
Update:
Jeff Brubacher was the corresponding author for one (Brubacher et al., 2019) of the 13 papers that we covered in our review of the relationship between the prior use of cannabis and the risk of being culpable for a road crash (White and Burns, 2021). Jeff identified an error in the value we selected to represent Brubacher et al’s adjusted cannabis-culpability odds ratio (OR) in our cannabis-culpability meta-analysis and meta-regression. We selected the value of 1.07 (0.98-1.20) that Brubacher et al. provided in their Table 3 for the “Model with THC in ng/ml”. Unfortunately, that value was for the slope of the (non-significant) linear relationship between the concentration of THC (in ng/ml) and the risk of being culpable for a crash, and not for the relationship of interest: the effect of the presence vs absence of THC on the risk of being culpable for a crash. Surprisingly, the value that we sought for the dichotomous cannabis variable was not provided anywhere in Brubacher et al’s paper.
However, Jeff has now generously run the adjusted model for THC > 0.00 ng/ml, which delivered a cannabis-culpability OR of 1.18 (0.80-1.76). So, the cannabis-culpability OR of 1.07 (0.98-1.20) that we reported for Brubacher et al. in our Table 1 should have been 1.18 (0.80-1.76). All of the other information for Brubacher et al’s study that we reported in our Tables 1 and 2 remains correct.
We have re-run our cannabis-culpability meta-analysis and meta-regression, and the results are provided below.
The cannabis-culpability ORs from the 13 studies included in our meta-analysis are given in Corrected Figure 2. The meta-analytic summary OR for the corrected model is 1.41 (1.14-1.74), which is only marginally higher than the uncorrected value of 1.37 (1.10-1.69).
The 13 effect sizes remain heterogeneous Q (12) = 21.9, p = .038; I2 = 45.3%, with moderate-to-high heterogeneity.
A meta-regression assessed our directional bias score as a moderator of the reported cannabis-culpability ORs. For our corrected meta-regression model, the directional bias score was significantly associated with the cannabis-culpability ORs (Q (1) = 8.22, p = .004), and explained 79.6% of the heterogeneity. The intercept of the corrected model (i.e., for a directional bias score of zero) expressed as an OR was 0.75 (0.48-1.19) (see Corrected Figure 3). That value is only marginally higher than the value of 0.68 (0.45-1.05) for the uncorrected model.
It is concluded that the changes required to accommodate our error are marginal, and of no consequence for our overall review.
This research was published in the Drug Science, Policy and Law Journal the definitive source of evidence-based information and comment for academics, scientists, policymakers, frontline workers and the general public on drugs and related issues
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