Drug driving: a secondary analysis of factors associated with driving under the influence of cannabis in Jamaica
Department of Community Health and Psychiatry, The University of the West Indies, Mona, Jamaica
Department of Medicine, The University of the West Indies, Mona, Jamaica
Department of Economics, Western Michigan University, Kalamazoo, Michigan, USA
Abstract
Objectives
To determine cannabis use patterns, the predictive sociodemographic correlates of driving under the influence of cannabis (DUIC) and the association between risk perception and cannabis dependence among vehicle drivers in Jamaica.
Design
Secondary data analysis.
Setting
Used the Jamaica National Drug Prevalence Survey 2016 dataset.
Participants
1060 vehicle drivers extracted from the population sample of 4623.
Primary and secondary outcome measures
Analysis used Pearson’s χ2 test and logistic regression. ORs and 95% CIs were recorded. A p<0.05 was considered statistically significant.
Results
More than 10% of Jamaican drivers admitted to DUIC in the past year. Approximately 43.3% of drivers who currently use cannabis reported DUIC only. Evidently, 86.8% of drivers who DUIC were heavy cannabis users. Approximately 30% of drivers with moderate to high-risk perception of smoking cannabis sometimes or often were dependent on cannabis. Notwithstanding, drivers with no to low-risk perception of smoking cannabis sometimes or often were significantly likelier to be dependent (p<0.001 and p<0.001, respectively). Logistic regression highlighted male drivers (OR 4.14, 95% CI 1.59 to 14.20, p=0.009) that were 34 years and under (OR 2.97, 95% CI 1.71 to 5.29, p<0.001) and were the head of the household (OR 2.22, 95% CI 1.10 to 4.75, p=0.031) and operated a machine as part of their job (OR 1.87, 95% CI 1.09 to 3.24, p=0.023) were more likely to DUIC, while those who were married (OR 0.42, 95% CI 0.22 to 0.74, p=0.004) and had achieved a tertiary-level education (OR 0.26, 95% CI 0.06 to 0.76, p=0.031) were less likely.
Conclusions
Two in five Jamaican drivers, who currently smoke cannabis, drive under its influence, with over 85% engaging in heavy use. Public health implications necessitate policy-makers consider mobile roadside drug testing and amending drug-driving laws to meet international standards.
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Keywords: Substance misuse, PUBLIC HEALTH, Health policy
Article notes
Series information
Original research
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Received 2023 Aug 2; Accepted 2024 Jan 3; Collection date 2024.
Boxed Text
- The use of the entire dataset population augmented the strength of the analysis.
- The use of logistic regression analysis allows for several sociodemographic factors to be simultaneously assessed to elicit appropriate risk and protective factors.
- The validated Cannabis Abuse Screening Test enhanced the reliability of the findings, given its frequent use in population sample studies.
- Driving under the influence information was self-reported, which can be affected by recall bias.
- This study could not establish causal relationships due to the nature of the cross-sectional data.
Introduction
The World Health Organization (WHO) highlights drug driving as a significant risk factor for road traffic accidents,1 and further acknowledges that vehicular crashes are not only among the top 10 causes of demise across all age groups but also the leading cause of death among young individuals under 30.2 Accordingly, driving under the influence (DUI) of drugs represents a massive public health concern for the global community.3–7 While DUI has historically referred to alcohol, cannabis is evidently the second leading drug detected among drivers involved in road traffic injuries and fatalities worldwide.8 9 This remains foreseeable, as it continues to be widely used globally, as stated by the latest World Drug Report.10
Legislative changes cultivate a growing concern that less restrictive laws might increase the prevalence of cannabis use in the general population and, thereby, its use among vehicle drivers. Several studies have confirmed this trend and denoted that cannabis prevalence among the populace is rising, as is the frequency of driving under its influence.11–14 Mounting data suggest that vehicular accidents due to impaired driving will increase15–17 as more countries decriminalise recreational and medicinal cannabis use. Moreover, the risk of cannabis dependence will likely escalate as a competitive market allows for easier access to more affordable and potent cannabis products.18
The impact of legalisation and decriminalisation has also likely contributed to changing risk perceptions regarding cannabis use19 and the influence it exudes on driving behaviours.20 The misconception that drug driving is not perceived to be risky behaviour21 and the contrasting belief that cannabis is the safest drug to use and drive22 as it improves driving capability,23 24 presents a significant challenge in discouraging DUI of cannabis (DUIC).
Despite the accumulated evidence over the preceding four decades, that suggests cannabis use impairs driver reaction time, visual acuity and spatial awareness, and encourages risky decision-making,25–28 cannabis users perceive DUIC as being less dangerous than DUI of alcohol (DUIA).29 30 Notwithstanding, extant literature highlights cannabis use is associated with higher risks of vehicular crash and fatality,31–33 which is exponentially elevated when used with alcohol.34–37 Contemporary research, however, is less conclusive as it pertains to the dose–response effects of delta-9-tetrahydrocannabinol (THC) on driving skills, with a number of studies showing a positive association between elevated levels of THC and impairment38 39 while others found no clear relationship.40
In Jamaica, a number of smaller-sized studies have reported an association between victims of vehicular crashes and evidence of cannabis use.41 42 Findings from these studies revealed victims were male drivers and young adults predominantly under the age of 30.41 42 However, the accrued data from these studies would have been prior to the legislative changes in 2015 that decriminalised and permitted the possession of two ounces of cannabis for personal use, and the establishment of a legal medicinal cannabis industry.43 44 It is suggested that the strong inclination for cannabis use among Jamaicans is enhanced due to recent amendments to the island’s Dangerous Drugs Act that endorsed decriminalisation,45 among other factors, including strong sociocultural practices and religious beliefs.46 47
That being the case, the rapidly evolving legal landscape surrounding cannabis and the resulting shifts in social norms regarding its use, necessitates a closer examination of the various factors associated with DUIC. To date, no studies in the Caribbean have used nationally representative data to determine cannabis use patterns, the sociodemographic characteristics, and the risk perception of using cannabis and its association with developing dependence in vehicle drivers that may DUIC.
Objectives
- To determine the prevalence of cannabis use patterns among vehicle drivers, including those who DUIC and those who are heavy cannabis users that DUIC.
- To examine the association between developing cannabis dependence and the risk perception of smoking cannabis among drivers.
- To investigate the sociodemographic factors associated with DUIC.
Methods
Study design, sample size and data source
This study is a secondary data analysis of the National Household Survey, Jamaica 2016. The target population comprised all respondents who identified as drivers of motorised vehicles. The sample analysed in this study was a subset of 1060 participants. This research extracted variables relevant to cannabis use patterns, dependence, risk perception and the associated sociodemographic characteristics of DUIC in vehicle drivers. This study contained no identifying data on respondents, and researchers made no direct or indirect contact with any respondents.
The original study was a cross-sectional survey of a national population sample investigating the prevalence and patterns of drug use among Jamaicans between the ages of 12 and 65.48 Data were collected between April and July 2016, targeting 357 households per parish, totalling a nationally representative sample of 4623 dwellings. The data were collected from a standardised questionnaire developed by the Inter-American Drug Abuse Control Commission and the Inter-American Observatory on Drugs, and conducted through a partnership with Jamaica’s National Council on Drug Abuse.
The survey employed a stratified multistage sampling design in which the primary sampling units were the Enumeration Areas (EAs). Jamaica comprises 14 parishes and 22 EAs per parish. In each EA, systematic random sampling was used to draw 16 households, from which one individual was randomly selected as the survey respondent. Sampling weights were calculated to account for selection probability, non-response, and population distribution and applied to ensure that the weighted sample distribution matched the population distribution of age and sex categories.48
Study variables
Cannabis use prevalence
Using the target variables, the prevalence of cannabis use was calculated for the periods of lifetime (ever use), past year (chronic) and past month (current) use. Responses were coded as 1=yes and 2=no. For heavy cannabis use, respondents were asked to state how many days they had smoked marijuana over the past 30 days. Responses indicating 20 days or more demonstrate heavy use as defined by the European Monitoring Centre for Drugs and Drug Addiction.49
Driving under the influence of cannabis
The National Household Survey questionnaire asked respondents, ‘Have you driven a vehicle in the past 12 months?’ and ‘During the past 12 months, have you driven a vehicle while you were under the influence of illegal drugs?’ The response options were 1=yes, 2=no. Using a method previously described in the literature,50 DUIC only was defined as an affirmative response to the survey question, ‘During the past 12 months, have you driven a vehicle while you were under the influence of illegal drugs?’ (limited to respondents who reported past-year cannabis use and no other illicit drug use). Individuals who reported using cannabis in the past year but did not report any other illegal drug use during that same time period, and also reported DUI of drugs in the past year, were considered to have driven under the influence of cannabis only in the past year. Statistical computations also extracted respondents who were past month (current) cannabis users that answered ‘yes’ to the question ‘During the past 12 months, have you driven a vehicle while you were under the influence of illegal drugs?’ to determine those vehicle drivers who were current users that DUIC only. The term illegal drugs included cannabis, crack, cocaine, heroin and 3,4-methylenedioxymethamphetamine (MDMA).
Cannabis dependence
Cannabis dependence was assessed using the Cannabis Abuse Screening Test (CAST). It is often used for the identification of cannabis use disorder.51 It encompasses a six-item scale in which a score of less than 3 indicates users with a low risk of dependence, a score of 3–6 represents users with a moderate risk of dependence, and a score of 7 or more determines users with a high risk of dependence.52 For this study, the CAST scores were recategorised into two groups where a score greater than or equal to 7 was defined as a high risk of dependence and a score of less than or equal to 6 was defined as not a high risk of cannabis dependence.
Risk perception
Perception of risk associated with frequent or infrequent use of marijuana was investigated in the initial survey by asking participants, ‘In your opinion, please indicate the risk level of smoking marijuana sometimes and smoking marijuana often’. Participants indicated their risk level along a Likert scale continuum: (1) no risk, (2) low risk, (3) moderate risk, (4) high risk and (5) I don’t know the risk. For the secondary analysis, response options were recategorised to reflect 0=no to low-risk and 1=moderate to high-risk to examine respondents who had indicated some level of perceived risk. The option ‘I don’t know the risk’ was excluded as an underrepresented category with insufficient frequencies that may introduce variability and bias in interpreting the results.53
Sociodemographic characteristics
Sociodemographic characteristics that might influence cannabis use in drivers were included as covariates. Respondents were asked to state their sex (1=male, 0=female) and geographical location (recategorised into 1=urban and 0=rural). Respondents’ age was categorised into two groups—1=34 years and under, representing young adults as suggested by Franssen et al 54 and 0=over 35 years. Respondents were asked ‘Are you the head of household’. The response options were 1=yes, 0=no. Educational status was assessed by asking respondents, ‘What is the highest educational level that you have achieved?’ Ten response options were recategorised into 1=tertiary level, 0=below tertiary level. The item corresponding to marital status had seven response options recategorised into two choices: 1=married, 0=unmarried. The respondents were asked to disclose the household’s total monthly income from a list of 15 possibilities. These were recategorised into 1=JA$50 000 and under or 0=over JA$50 000. The options ‘don’t know’ or ‘no response’ was considered missing variables. The item on religious affiliation had 26 response options that were recategorised as 1=Christian, 0=non-Christian. For occupation, respondents were asked to describe their job from a list of eleven options. These were recategorised into 1=machine operators or 0=non-machine operators.
Statistical analysis
Descriptive statistical analysis was performed to determine the prevalence of cannabis use among vehicle drivers and to describe the associated sociodemographic characteristics. These were represented in frequencies, means and percentages. Bivariate analysis examining the association between the risk perception of smoking cannabis sometimes or often and the risk of dependence among vehicle drivers was done using Pearson’s χ2 test. Logistic regression analyses were done to identify sociodemographic protective and risk factors for DUIC. Covariates included in the model were age (over 35 years as the reference category), occupation (non-machine operators as the reference category), marital status (unmarried as the reference category), sex (female as the reference category), education (below tertiary level as the reference category), religion (non-Christian as the reference category), household income (over JA$50 000 as the reference category), head of household (‘no’ responses as the reference category) and geographical location (rural as the reference category). Statistical analyses were conducted using R software, V.4.2.0. Multicollinearity between the study variables was explored using variance inflation factor (VIF) (with multicollinearity being defined as VIF>2.5).The Hosmer-Lemeshow statistic tested for goodness of fit of the regression model. The data were presented in the form of tables and text. ORs and 95% CIs were recorded. A p<0.05 was considered statistically significant.48
Results
Prevalence
Table 1 demonstrates the prevalence of cannabis use in the total population (n=4623) and among the vehicle driver population (n=1060). Approximately one in four individuals was a vehicle driver (23%). Among those in the population who reported lifetime use of cannabis, 60.1% reported use in the past year and 53.9% reported use in the past month. Of the 1060 vehicle drivers, 125 (11.8%) admitted DUI of illegal drugs (DUI-ID) in the past year. Of those who admitted to DUI-ID, 110 drivers were cannabis users only (92%), indicating that 10.4% of drivers admitted to DUIC in the past year.
| Cannabis use | Frequency | Percentage |
| Population lifetime use | 1307 | 28.3 |
| Population past year use | 786 | 60.1 |
| Population past month use | 704 | 53.9 |
| Current users who drive | 245 | 23.1 |
| Current users who DUIC only | 106 | 43.3 |
| Current and heavy users who DUIC only | 92 | 86.8 |
Of the 1060 vehicle drivers, 245 vehicle drivers admitted to being current cannabis users, of which 111 drivers admitted to DUI-ID. Of the 111 drivers who admitted to DUI-ID, five persons used drugs other than cannabis (one person each used heroin, crack and cocaine and two persons used MDMA). Excluding these 5 individuals, 106 drivers who admitted DUI-ID used cannabis only. This is approximately 43.3% and indicates that two in five Jamaicans, who are drivers and are current cannabis users, operate a vehicle under the influence of cannabis only.
Of the 106 drivers who admitted to DUIC only, 92 were heavy cannabis users (use 20 days or more in a month). This is 86.8% and indicates that 9 out of 10 Jamaican drivers, who were current cannabis users and admitted to DUI, were heavy users.
Sociodemographic factor findings
Table 2 shows the sociodemographic characteristics of population vehicle drivers. The mean age of respondents was 36.56 years (SD±12.582). Most drivers were male (70.6%), employed (72.6%), unmarried (61.0%), of Christian beliefs (72.8%), living in rural areas (54.5%) and with a less than tertiary-level education achievement (83.3%).
| Variable | Vehicle drivers (population) | Vehicle drivers (DUIC) |
| Frequency | Frequency | |
| Sex | ||
| Male | 748 (70.6%) | 99 (93.4%) |
| Female | 312 (29.4%) | 07 (06.6%) |
| Age | ||
| 34 years and under | 551 (52.0%) | 67 (63.2%) |
| Over 35 years | 509 (48.0%) | 39 (36.8%) |
| Education | ||
| Tertiary level | 177 (16.7%) | 07 (06.6%) |
| Below tertiary level | 883 (83.3%) | 99 (93.4%) |
| Religion | ||
| Christian | 772 (72.8%) | 65 (61.3%) |
| Non-Christian | 288 (27.2%) | 41 (38.7%) |
| Employment | ||
| Employed | 770 (72.6%) | 78 (73.6%) |
| Unemployed | 290 (27.4%) | 28 (26.4%) |
| Marital status | ||
| Married | 413 (39.0%) | 29 (27.4%) |
| Unmarried | 647 (61.0%) | 77 (72.6%) |
| Geographical location | ||
| Urban | 482 (45.5%) | 47 (44.3%) |
| Rural | 578 (54.5%) | 59 (55.7%) |
| Head of household | ||
| Yes | 700 (66.0%) | 82 (77.4%) |
| No | 360 (34.0%) | 24 (22.6%) |
| Household income | ||
| JA$50 000 and under | 416 (45.1%) | 46 (51.1%) |
| Over JA$50 000 | 507 (54.9%) | 44 (48.9%) |
| Occupation | ||
| Machine operators | 267 (34.7%) | 43 (55.1%) |
| Non-machine operators | 503 (65.3%) | 35 (44.9%) |
Table 2 also displays demographics for drivers who admit to being a current cannabis user who DUIC only. Most were male (93.4%), the head of household (77.4%) and operated machinery (55.1%) as part of their job description. Most drivers had achieved less than tertiary-level education achievement (93.4%). Household income is the sole variable where data were not recorded in 137 cases of drivers in the population, and 16 cases of drivers who are current cannabis users who DUIC only.
Risk perception and cannabis dependence findings
Table 3 summarises the risk perception of smoking cannabis sometimes or often and level of use according to the CAST questionnaire among vehicle drivers. Approximately 54% and 29% of drivers reported no to low-risk perception to smoking cannabis sometimes and often, respectively.
| Smoking cannabis sometimes | ||
| No to low-risk | Moderate to high-risk | |
| CAST≤6 | 132 | 147 |
| CAST≥7 | 103 | 57 |
| χ2=11.226, df=1, p<0.001*** | ||
| Smoking cannabis often | ||
| No to low-risk | Moderate to high-risk | |
| CAST≤6 | 61 | 218 |
| CAST≥7 | 67 | 93 |
| χ2=18.757, df=1, p<0.001*** | ||
Forty-four per cent of drivers who reported no to low-risk perception to smoking cannabis sometimes had a high risk of cannabis dependence (CAST≥7), whereas 28% of drivers who reported moderate to high-risk perception to smoking cannabis sometimes had a high risk of cannabis dependence (CAST≥7). This indicates that drivers with a no to low-risk perception to smoking cannabis sometimes were more likely to be dependent on cannabis and this difference was statistically significant (p<0.001).
Fifty-two per cent of drivers who reported no to low-risk perception to smoking cannabis often had a high risk of cannabis dependence (CAST≥7), whereas 30% of drivers who reported moderate to high-risk perception to smoking cannabis often had a high risk of cannabis dependence (CAST≥7). This indicates that drivers with a no to low-risk perception to smoking cannabis often were more likely to be dependent on cannabis and this difference was statistically significant (p<0.001).
Estimation of ORs for sociodemographic factors associated with DUIC
Table 4 shows the results of a logistic regression analysis performed to assess the associations between DUIC in the past year and select sociodemographic factors. There was no multicollinearity found among the independent variables used in the analysis. The Hosmer-Lemeshow test shows the p value at 0.497 (p>0.05), which indicates the model fits the data. The model illustrates statistically significant relationships for several factors.
| Variables | Estimate | OR | 95% CI | P value |
| Age (over 35 years) | 1 | |||
| Age (34 years and under) | 1.0897 | 2.97 | 1.71 to 5.29 | <0.001*** |
| Occupation (non-machine operators) | 1 | |||
| Occupation (machine operators) | 0.6280 | 1.87 | 1.09 to 3.24 | 0.023* |
| Marital status (unmarried) | 1 | |||
| Marital Status (married) | −0.8794 | 0.42 | 0.22 to 0.74 | 0.004** |
| Sex (female) | 1 | |||
| Sex (male) | 1.4197 | 4.14 | 1.59 to 14.2 | 0.009** |
| Education (below tertiary level) | 1 | |||
| Education (tertiary level) | −1.3568 | 0.26 | 0.06 to 0.76 | 0.031* |
| Religion (non-Christian) | 1 | |||
| Religion (Christian) | −0.4761 | 0.62 | 0.36 to 1.08 | 0.088 |
| Household income (over JA$50 000) | 1 | |||
| Household income (JA$50 000 and under) | −0.2079 | 0.81 | 0.47 to 1.40 | 0.460 |
| Head of household (no) | 1 | |||
| Head of household (yes) | 0.7962 | 2.22 | 1.10 to 4.75 | 0.031* |
| Geographical location (rural) | 1 | |||
| Geographical location (urban) | 0.1294 | 1.14 | 0.66 to 1.97 | 0.640 |
Drivers 34 years and under were 2.97 times (95% CI 1.71 to 5.29, p<0.001) more likely than those 35 years and older to report DUIC in the past year. Drivers who were the head of household and operated machinery as part of their job were 2.22 times (95% CI 1.10 to 4.75, p=0.031) and 1.87 times (95% CI 1.09 to 3.24, p=0.023) more likely to report DUIC in the past year than those who were not the head of household or in non-machinery operated positions. Similarly, male drivers were 4.14 times (95% CI 1.59 to 14.20, p=0.009) more likely than females drivers to report DUIC in the past year.
The model also indicated that there were statistically significant inverse relationships for two of the factors. Married drivers were 58% less likely than unmarried drivers to report DUIC in the past year. Alternately, the result can also be interpreted that the odds of DUIC are 2.38 times higher among unmarried drivers. Similarly, drivers who matriculated up to a tertiary-level education were 74% less likely to report DUIC in the past year than drivers with less than a tertiary-level education. Alternately, the result can also be interpreted that the odds of DUIC are 3.85 times higher among drivers with less than a tertiary education. Drivers who were Christian-affiliated and living in a household that earned JA$50 000 or lower per month reduced the risk of DUIC in the past year. These inverse associations were, however, not statistically significant.
Discussion
This study is the first of its kind in the Caribbean region and sheds light on the impact of DUIC on a national population. The findings reveal that a significant number of Jamaican drivers who use cannabis, operate a vehicle under its influence, with more than 10% conceding to this unsafe and risky practice. It is worth noting the occurrence is eight times greater than the reported mean prevalence of 1.32% (range 0.0%–5.99%) in general driving populations across 13 participant countries of The European Union’s Driving under the Influence of Drugs, Alcohol and Medicines project.8 The high proclivity towards its use among Jamaicans, no doubt as a result of social and cultural influence,46 47 represents a worrying proposition given that extensive research demonstrates that cannabis use while driving, or under its influence, may impair driving ability.55
The second key finding is that among past-month cannabis users, more than 2 in 5 (43.3%) drivers report driving a vehicle while under the influence of cannabis (DUIC). This represents a marked difference compared with similar respondents in a previous study who admitted to DUI of alcohol (18%).56 Indeed, in terms of exposure, DUIC was far more commonplace among the Jamaican driving population than DUI of alcohol. A plausible rationale is that the effects of DUIC have not received the same level of public awareness as DUI of alcohol. Moreover, current legislation provides minimal legal deterrence to DUIC, lacking an effective roadside drug driving testing strategy and comprising a fine of approximately US$65 that is unlikely to dissuade offender motorists.57
The third critical finding is that the more frequent cannabis users are more likely to report DUIC. This study denoted that approximately 9 out of 10 Jamaicans who were past-month cannabis users and reported DUIC admitted to being heavy users (use 20 days or more in a month). The compelling significance of this finding is that a number of studies highlight the increased odds of motor vehicle crashes associated with acute cannabis intoxication,31 32 58 mainly because the psychoactive effects may persist for a significant period of time.59
The fourth key finding demonstrated a significant association between risk perception and the risk of developing cannabis dependence among vehicle drivers. Expectedly, drivers with no to low-risk perceptions of smoking cannabis showed higher risks of cannabis dependence. However, a salient finding is that approximately 30% of drivers with moderate to high-risk perception of smoking cannabis, whether sometimes or often, were also associated with an increased risk of cannabis dependence. Indeed, it may be beneficial to address the misperception that smoking cannabis is not dangerous among drivers who perceive it as less risky. Additionally, directing drivers who have been convicted or suspended for this behaviour towards a remedial programme, similar to those mandated for drunk driving, could prove effective.60 However, the high prevalence of cannabis dependence among drivers who acknowledge smoking cannabis as being harmful further underscores the fact that DUIC is becoming more socially acceptable,61 normative62 and less perceived as a risky behaviour.21
Finally, this study also revealed several sociodemographic factors associated with DUIC. Regression analysis indicated that being a young adult, employed in a job operating machinery, of the male sex, and the head of the household were predictive of DUIC, whereas being married and having a tertiary-level education were protective factors. Interestingly, the risk factors accrued were similar to those observed for DUIA among Jamaican drivers demonstrated in previous research.56 As such, the opportunity for legislators to include DUIC as part of an overall national DUI interventional effort, allow for comprehensive and collaborative efforts that a siloed approach to policymaking is unlikely to foster. Accordingly, a further call to improve data collection protocols regarding DUI behaviours and increase sobriety checkpoints in Jamaica, represent replicable and cost-effective strategies that have found favour in other low-income and middle-income countries (LMICs).63
Cumulatively, the findings of this study have heightened concerns regarding DUIC among Jamaican drivers, particularly in light of the island’s increasing annual road fatality rate.64 The legislation regarding drug driving must comply with global benchmarks. International policy-makers have sought to model drug-driving laws on those that have proven effective for DUI of alcohol, inclusive of a strategy that encourages roadside drug testing. DUIC can be measured through observed impairment in a Standardised Field Sobriety Test65–67 or Drug Evaluation and Classification68 programme by a police officer or by using point‐of‐collection testing devices.69 These strict policies use a per se or zero-tolerance approach, meaning that if THC is detected at or above a specific concentration in a biological sample, the driver is considered to have committed a DUIC offence, regardless of their level of impairment. Several countries, including Norway and Denmark, alongside a number of states in the USA,69 70 have adopted per se limits ranging from 2 to 5 ng/mL THC on oral fluid and blood tests, where studies demonstrate the first indications of impairment may occur, while other nations such as Australia endorse the de-facto zero-tolerance approach.71 While some studies establish that a level of 3.7 ng/mL THC is equivalent to the legal blood alcohol concentration of 0.05 g/dL accepted in a number of countries,72 73 other studies in the USA suggest an absence of a dose-dependent relationship between THC level and driving impairment, especially among frequent users where tolerance may be a factor.40 74 Notably, the accumulated literature regarding the impact of roadside drug testing remains in its infancy, given that most countries would have recently embarked on legislative reform. Notwithstanding, a number of contemporary research have yielded promising results that endorse the importance of roadside testing as part of a DUIC deterrence initiative.75–77
Driving necessitates a higher level of mental and motor dexterity, making it an intrinsically complex undertaking.78 The misperception that DUIC is not risky and that cannabis use adjunctly improves driving skills21 23 is further highlighted by this study’s prevailing finding that a substantial number of drivers (between approximately 30% and 50%) demonstrated a no to low-risk perception of smoking cannabis. This notable risk tolerance or lack thereof may also represent a concern in the workplace, given that DUIC drivers were predominantly employed in machine-operated jobs. Notwithstanding, cannabis-impaired driving, certainly within the Jamaican context, is likely also influenced by recent decriminalisation and medicalisation44 policies as seen elsewhere that have adopted a similar stance,79 and undoubtedly enhanced by sociocultural acceptance,47 easy access48 and availability of higher potency products.80 Accordingly, a calculated national initiative should seek to review current cannabis policy, include more robust regulatory mechanisms and disseminate a public education programme on the risks associated with cannabis use and driving as part of an overall prevention and deterrence effort. Furthermore, the data gleaned may provide the groundwork for policy change in reducing DUIC behaviours per recommendations established in other territories that have similarly endorsed decriminalisation or legalisation.81–85
One of the study’s limitations was that DUI information was self-reported, which can be affected by recall bias. Moreover, the inclination of many of the survey participants to give ‘socially acceptable’ answers and not admit any illegal activities might also introduce a response bias. Notably, this research could not establish causal relationships due to the nature of the cross-sectional data. However, one of the study’s strengths is that the validated CAST enhanced the reliability of the findings given that it is frequently used worldwide in national population sample studies.52 Although the data analysed in this study were collected in 2016, the findings are of immense value for national policy, providing a foundation for further research to build on and vitally fill a knowledge gap, especially in LMICs where global research is scarce.
Conclusion
Two in five Jamaican drivers, who currently smoke cannabis, drive under its influence, with over 85% engaging in heavy use. Public health implications necessitate stakeholders consider roadside drug testing of drivers as part of developing evidence-based policy in mitigating the safety risks posed by DUIC. Moreover, the rising prevalence of cannabis use and availability of higher potency products in a legally and socially tolerant setting, position Jamaica as a key research collaborator in developing policies dissuading cannabis-impaired driving, which may be applicable and replicated in other sister LMICs.
Supplementary Material
Footnotes
Footnote Group
Data availability statement
Data are available on reasonable request. The data that support the findings of this study are available from the National Council on Drug Abuse, Jamaica, and the Inter-American Drug Abuse Control Commission (CICAD) but restrictions apply to the availability of these data, which were used under licence for the current study and are not publicly available. For access to the database, contact Mrs Uki Atkinson, research analyst, at uatkinson@ncda.org.jm
Ethics statements
Patient consent for publication
Not applicable.
Ethics approval
This study involves human participants and the Ministry of National Security in Jamaica approved the National Drug Use Prevalence Survey. The secondary data analysis was approved by the University of the West Indies Ethics Committee, Mona (Ref: CREC-MN.8,2021/2022). Participants gave informed consent to participate in the study before taking part.
References
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References
- 1. Drug use and road safety: a policy brief. Geneva, Switzerland: World Health Organization; 2016.
- 2. Save LIVES - A road safety technical package. licence: CC BY-NC-SA 3.0 IGO. Geneva: World Health Organization; 2017.
- 3. Brady JE, Li G. Trends in alcohol and other drugs detected in fatally injured drivers in the United States, 1999–2010. Am J Epidemiol 2014;179:692–9. 10.1093/aje/kwt327
- 4. Fell JC, Romano E. Alcohol and other drugs involvement in fatally injured drivers in the United States. In: Proceedings - 20th International Council on Alcohol, Drugs and Traffic Safety Conference 2013 Aug 22. n.d.: 22–5.
- 5. Brubacher JR, Chan H, Martz W, et al. Prevalence of alcohol and drug use in injured British Columbia drivers. BMJ Open 2016;6:e009278. 10.1136/bmjopen-2015-009278
- 6. DiRago M, Gerostamoulos D, Morris C, et al. Prevalence of drugs in injured drivers in Victoria, Australia. Aust J Forensic Sci 2021;53:166–80. 10.1080/00450618.2019.1687753
- 7. Elvik R. Risk of road accident associated with the use of drugs: a systematic review and meta-analysis of evidence from epidemiological studies. Accid Anal Prev 2013;60:254–67. 10.1016/j.aap.2012.06.017
- 8. Schulze H, Schumacher M, Urmeew R, et al. Driving Under the Influence of Drugs, Alcohol and Medicines in Europe — findings from the DRUID project. European Monitoring Centre for Drugs and Drug Addiction, 2012: 58.
- 9. Canadian Centre on Substance Use and Addiction . Drugs, driving and youth highlights; drug-impaired driving in Canada – educator Toolkit; 2016.
- 10. UNODC . World drug report 2022. United Nations publication; 2022.
- 11. Hasin DS, Saha TD, Kerridge BT, et al. Prevalence of marijuana use disorders in the United States between 2001-2002 and 2012-2013. JAMA Psychiatry 2015;72:1235–42. 10.1001/jamapsychiatry.2015.1858
- 12. Rotermann M. Looking back from 2020, how cannabis use and related behaviours changed in Canada. Health Rep 2021;32:3–14. 10.25318/82-003-x202100400001-eng
- 13. Compton WM, Han B, Jones CM, et al. Marijuana use and use disorders in adults in the USA, 2002–14: analysis of annual cross-sectional surveys. Lancet Psychiatry 2016;3:954–64. 10.1016/S2215-0366(16)30208-5
- 14. Fink DS, Stohl M, Sarvet AL, et al. Medical marijuana laws and driving under the influence of marijuana and alcohol. Addiction 2020;115:1944–53. 10.1111/add.15031
- 15. Huestis MA. Deterring driving under the influence of cannabis. Addiction 2015;110:1697–8. 10.1111/add.13041
- 16. Salomonsen-Sautel S, Min SJ, Sakai JT, et al. Trends in fatal motor vehicle crashes before and after marijuana commercialization in colorado. Drug Alcohol Depend 2014;140:137–44. 10.1016/j.drugalcdep.2014.04.008
- 17. Gjerde H, Mørland J. Risk for involvement in road traffic crash during acute cannabis intoxication. Addiction 2016;111:1492–5. 10.1111/add.13435
- 18. Hall W, Stjepanović D, Caulkins J, et al. Public health implications of legalising the production and sale of cannabis for medicinal and recreational use. Lancet 2019;394:1580–90. 10.1016/S0140-6736(19)31789-1
- 19. Pacek LR, Mauro PM, Martins SS. Perceived risk of regular cannabis use in the United States from 2002 to 2012: differences by sex, age, and race/ethnicity. Drug Alcohol Depend 2015;149:232–44. 10.1016/j.drugalcdep.2015.02.009
- 20. Donnan JR, Drakes DH, Rowe EC, et al. Driving under the influence of cannabis: perceptions from Canadian youth. BMC Public Health 2022;22. 10.1186/s12889-022-14658-9
- 21. Hasan R, Watson B, Haworth N, et al. A systematic review of factors associated with illegal drug driving. Accident Analysis & Prevention 2022;168:106574. 10.1016/j.aap.2022.106574
- 22. Arkell TR, Lintzeris N, Mills L, et al. Driving-related behaviours, attitudes and perceptions among Australian medical cannabis users: results from the CAMS 18-19 survey. Accid Anal Prev 2020;148:105784. 10.1016/j.aap.2020.105784
- 23. Holmes EA, Vanlaar W, Robertson RD. The problem of youth drugged driving and approaches to prevention. desLibris, 2014.
- 24. Robertson RD, Woods-Fry H, Morris K. Traffic Injury Research Foundation. Ottawa, Ontario, 2016.
- 25. Hartman RL, Brown TL, Milavetz G, et al. Cannabis effects on driving lateral control with and without alcohol. Drug Alcohol Depend 2015;154:25–37. 10.1016/j.drugalcdep.2015.06.015
- 26. Bosker WM, Kuypers KPC, Theunissen EL, et al. Medicinal Δ9‐tetrahydrocannabinol (dronabinol) impairs on‐the‐road driving performance of occasional and heavy cannabis users but is not detected in standard field sobriety tests. Addiction 2012;107:1837–44. 10.1111/j.1360-0443.2012.03928.x
- 27. Kurzthaler I, Hummer M, Miller C, et al. Effect of cannabis use on cognitive functions and driving ability. J Clin Psychiatry 1999;60:395–9. 10.4088/jcp.v60n0609
- 28. Moskowitz H. Marihuana and driving. Accid Anal Prev 1985;17:323–45. 10.1016/0001-4575(85)90034-x
- 29. Greene KM. Perceptions of driving after marijuana use compared to alcohol use among rural American young adults. Drug Alcohol Rev 2018;37:637–44. 10.1111/dar.12686
- 30. Swift W, Jones C, Donnelly N. Cannabis use while driving: a descriptive study of Australian cannabis users. Drugs Educ Prev Policy 2010;17:573–86. 10.3109/09687630903264286
- 31. Li MC, Brady JE, DiMaggio CJ, et al. Marijuana use and motor vehicle crashes. Epidemiol Rev 2012;34:65–72. 10.1093/epirev/mxr017
- 32. Asbridge M, Hayden JA, Cartwright JL. Acute cannabis consumption and motor vehicle collision risk: systematic review of observational studies and meta-analysis. BMJ 2012;344:e536. 10.1136/bmj.e536
- 33. Starkey NJ, Charlton SG. The prevalence and impairment effects of drugged driving in New Zealand; 2017.
- 34. Beirness DJ, Beasley EE, Boase P. A comparison of drug use by fatally injured drivers and drivers at risk. In International Conference on Alcohol, Drugs and Traffic Safety (T2013), 20th; Brisbane, Queensland, Australia, 2013.
- 35. Capler R, Bilsker D, Van Pelt K, et al. Cannabis use and driving: evidence review. Burnaby (BC): Simon Fraser University, Canadian Drug Policy Coalition, 2017.
- 36. Lenné MG, Dietze PM, Triggs TJ, et al. The effects of cannabis and alcohol on simulated arterial driving: influences of driving experience and task demand. Accid Anal Prev 2010;42:859–66. 10.1016/j.aap.2009.04.021
- 37. Brubacher JR, Chan H, Erdelyi S, et al. Cannabis use as a risk factor for causing motor vehicle crashes: a prospective study. Addiction 2019;114:1616–26. 10.1111/add.14663 Available: https://onlinelibrary.wiley.com/toc/13600443/114/9
- 38. Verster JC, Pandi-Perumal SR, Ramaekers JG, et al. Dose related risk of motor vehicle crashes after cannabis use: An update. Drugs, Driv Traffic Saf. Basel, 2009: 477–99. 10.1007/978-3-7643-9923-8
- 39. Vindenes V, Strand DH, Kristoffersen L, et al. Has the intake of THC by cannabis users changed over the last decade? Evidence of increased exposure by analysis of blood THC concentrations in impaired drivers. Forensic Sci Int 2013;226:197–201. 10.1016/j.forsciint.2013.01.017
- 40. Wurz GT, DeGregorio MW. Indeterminacy of cannabis impairment And∆ 9-Tetrahydrocannabinol (∆ 9-THC) levels in blood and breath. Sci Rep 2022;12. 10.1038/s41598-022-11481-5
- 41. Francis M, Eldemire D, Clifford R. A pilot study of alcohol and drug-related traffic accidents and death in two Jamaican parishes, 1991. West Indian Med J 1995;44:99–101.
- 42. McDonald A, Duncan ND, Mitchell DI. Alcohol, cannabis and cocaine usage in patients with trauma injuries. West Indian Med J 1999;48:200–2.
- 43. Rychert M, Emanuel MA, Wilkins C. Foreign investment in emerging legal medicinal cannabis markets: the Jamaica case study. Global Health 2021;17:38. 10.1186/s12992-021-00687-3
- 44. Golding M. Jamaica’s dangerous drugs amendment act 2015. EU-LAC Found Newsl; 2016.
- 45. Spencer N, Strobl E. The impact of decriminalization on marijuana and alcohol consumption in Jamaica. Health Policy Plan 2020;35:180–5. 10.1093/heapol/czz149
- 46. Hanson VJ. Cannabis policy reform: Jamaica’s experience. In: Legalizing Cannabis. Routledge, 2020: 375–89. 10.4324/9780429427794
- 47. Chevannes B. Criminalizing cultural practice, the case of Ganja in Jamaica. In: Klein A, Day M, et Harriott A, eds. Caribbean Drugs, From Criminalization to Harm Reduction, Kingston. London: Ian Randle Publishers, Zed books, 2004: 67–81.
- 48. Younger-Coleman N, Cumberbatch C, Campbell J, et al. Jamaica National drug use prevalence survey 2016. Kingston: Ian Randle Publishers, 2017.
- 49. EMCDDA . Characteristics of frequent and high-risk Cannabis users European monitoring centre for drugs and drug addiction; 2013.
- 50. Azofeifa A, Mattson ME, Lyerla R. Driving under the influence of alcohol, marijuana, and alcohol and marijuana combined among persons aged 16–25 years—United States, 2002–2014. MMWR Morb Mortal Wkly Rep 2015;64:1325–9. 10.15585/mmwr.mm6448a1
- 51. Spilka S, Janssen E, Legleye S. Detection of problem cannabis use: The cannabis abuse screening test (CAST). Saint-Denis (FR): CAST, 2013.
- 52. Legleye S, Karila L, Beck F, et al. Validation of the CAST, a general population cannabis abuse screening test. J Subst Use 2007;12:233–42. 10.1080/14659890701476532
- 53. Ranganathan P, Pramesh CS, Aggarwal R. Common pitfalls in statistical analysis: logistic regression. Perspect Clin Res 2017;8:148–51. 10.4103/picr.PICR_87_17
- 54. Franssen T, Stijnen M, Hamers F, et al. Age differences in demographic, social and health-related factors associated with loneliness across the adult life span (19–65 years): a cross-sectional study in the Netherlands. BMC Public Health 2020;20:1118. 10.1186/s12889-020-09208-0
- 55. Bédard M, Dubois S, Weaver B. The impact of cannabis on driving. Can J Public Health 2007;98:6–11. 10.1007/BF03405376
- 56. Lalwani K, Sewell C, Frazier G, et al. Drunk driving: a secondary analysis of factors associated with driving under the influence of alcohol in Jamaica. BMJ Open 2023;13:e073529. 10.1136/bmjopen-2023-073529
- 57. Road traffic (amendment) act 2018. The Jamaica Gazette supplement proclamations, rules and regulation. Kingston: Jamaica Printing Service; 2018.
- 58. Romano E, Voas RB, Camp B. Cannabis and crash responsibility while driving below the alcohol per se legal limit. Accid Anal Prev 2017;108:37–43. 10.1016/j.aap.2017.08.003
- 59. Borodovsky JT, Crosier BS, Lee DC, et al. Smoking, vaping, eating: is legalization impacting the way people use cannabis. Int J Drug Policy 2016;36:141–7. 10.1016/j.drugpo.2016.02.022
- 60. Stoduto G, Mann RE, Flam-Zalcman R, et al. Impact of Ontario’s remedial program for drivers convicted of drinking and driving on substance use and problems. Can J Criminol Crim Justice 2014;56:201–17. 10.3138/cjccj.2014.ES04
- 61. Colonna R, Hand CL, Holmes JD, et al. Exploring youths’ beliefs towards Cannabis and driving: A mixed method study. Transportation Research Part F: Traffic Psychology and Behaviour 2021;82:429–39. 10.1016/j.trf.2021.09.013
- 62. Wickens CM, Watson TM, Mann RE, et al. Exploring perceptions among people who drive after cannabis use: collision risk, comparative optimism and normative influence. Drug Alcohol Rev 2019;38:443–51. 10.1111/dar.12923
- 63. C Fell J, Achoki T, DeJong W, et al. Implementation of road safety countermeasures in four cities to reduce the harmful effects of alcohol: a progress report. Arch Clin Biomed Res 2022;06:606–11. 10.26502/acbr.50170271
- 64. National road safety Council Jamaica. n.d. Available: http://www.nationalroadsafetycouncil.org.jm/
- 65. Bosker WM, Theunissen EL, Conen S, et al. A placebo-controlled study to assess standardized field sobriety tests performance during alcohol and cannabis intoxication in heavy cannabis users and accuracy of point of collection testing devices for detecting THC in oral fluid. Psychopharmacology (Berl) 2012;223:439–46. 10.1007/s00213-012-2732-y
- 66. Papafotiou K, Carter JD, Stough C. An evaluation of the sensitivity of the standardised field sobriety tests (SFSTs) to detect impairment due to marijuana intoxication. Psychopharmacology (Berl) 2005;180:107–14. 10.1007/s00213-004-2119-9
- 67. Stough C, Boorman M, Ogden E, et al. An evaluation of the Standardised Field Sobriety Tests for the detection of impairment associated with cannabis with and without alcohol. Canberra, Australia: National Drug Law Enforcement Research Fund, 2006.
- 68. Beirness DJ, Porath AJ. Clearing the smoke on Cannabis: Cannabis use and driving – an update; 2017.
- 69. Hermansen SK, Pedersen TR, Christoffersen DJ. THC-influenced drivers in the new Danish 3-level offense system. Traffic Inj Prev 2020;21:13–7. 10.1080/15389588.2019.1679799
- 70. Pearlson GD, Stevens MC, D’Souza DC. Cannabis and driving. Front Psychiatry 2021;12:689444. 10.3389/fpsyt.2021.689444
- 71. Quilter J, McNamara L. Zero tolerance'drug driving laws in Australia: a gap between rationale and form. Int J for Crime, Justice & Social Democracy 2017;6:47–71. 10.5204/ijcjsd.v6i3.416
- 72. Ramaekers JG, Moeller MR, van Ruitenbeek P, et al. Cognition and motor control as a function of Δ9-THC concentration in serum and oral fluid: limits of impairment. Drug Alcohol Depend 2006;85:114–22. 10.1016/j.drugalcdep.2006.03.015
- 73. Berghaus G, Sticht G, Grellner W, et al. Meta-analysis of empirical studies concerning the effects of medicines and illegal drugs including pharmacokinetics on safe driving. In: DRUID Deliverable 1.1.2b. 2011.
- 74. Ramaekers JG, Kauert G, Theunissen EL, et al. Neurocognitive performance during acute THC intoxication in heavy and occasional cannabis users. J Psychopharmacol 2009;23:266–77. 10.1177/0269881108092393
- 75. Fierro I, González-Luque JC, Seguí-Gómez M, et al. Alcohol and drug use by Spanish drivers: comparison of two cross-sectional road-side surveys (2008–9/2013). Int J Drug Policy 2015;26:794–7. 10.1016/j.drugpo.2015.04.021
- 76. Watson TM, Mann RE. International approaches to driving under the influence of cannabis: a review of evidence on impact. Drug Alcohol Depend 2016;169:148–55. 10.1016/j.drugalcdep.2016.10.023
- 77. Anderson L, Love S, Freeman J, et al. Hit and miss: a comparison of targeted and randomised roadside drug testing (RDT). PIJPSM 2021;44:1154–67. 10.1108/PIJPSM-07-2021-0090
- 78. Walshe EA, Ward McIntosh C, Romer D, et al. Executive function capacities, negative driving behavior and crashes in young drivers. Int J Environ Res Public Health 2017;14:1314. 10.3390/ijerph14111314
- 79. Windle SB, Socha P, Nazif-Munoz JI, et al. The impact of cannabis decriminalization and legalization on road safety outcomes: a systematic review. Am J Prev Med 2022;63:1037–52. 10.1016/j.amepre.2022.07.012
- 80. Lindsay CM, Bernard KK, Hammond AM, et al. Potency trends of Cannabis in Jamaica during the period of 2014 to 2020. In: Drug testing and analysis. 2023.
- 81. Maguire R. Developing Ireland’s policy on Cannabis and driving. Third International Symposium on Drug-Impaired Driving; Lisbon: European Monitoring Centre for Drugs and Drug Addiction, 2017.
- 82. Ramaekers J. Dutch policy on Cannabis and driving. Third International Symposium on Drug-Impaired Driving; Lisbon: European Monitoring Centre for Drugs and Drug Addiction, 2017.
- 83. Wolff K. Informing the development of Cannabis driving policy: reflections on developments in the UK. Third International Symposium on Drug-Impaired Driving; Lisbon: European Monitoring Centre for Drugs and Drug Addiction, 2017.
- 84. Davis G. Colorado policy. Third International symposium on drug-impaired driving. European Monitoring Centre for Drugs and Drug Addiction, 2017.Maguire R; Lisbon: Developing Irelands New Policy, 2017.
- 85. Davey J, Armstrong K, Freeman J, et al. Roadside drug testing scoping study. Kelvin Grove, QLD, Australia: CARRS-Q, 2017.
Associated Data
Supplementary Materials
Data Availability Statement
Data are available on reasonable request. The data that support the findings of this study are available from the National Council on Drug Abuse, Jamaica, and the Inter-American Drug Abuse Control Commission (CICAD) but restrictions apply to the availability of these data, which were used under licence for the current study and are not publicly available. For access to the database, contact Mrs Uki Atkinson, research analyst, at uatkinson@ncda.org.jm