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Canadian Multidisciplinary Road Safety Conference  ‐ Montreal 2013  ATTENTION DEFICIT HYPERACTIVITY DISORDER,
ALCOHOL, DRUGS AND DRIVING: POPULATION-BASED
EXAMINATION IN A CANADIAN SAMPLE
Evelyn Vingilis, Western University
Robert E. Mann, Centre for Addiction and Mental Health
Patricia Erickson, University of Toronto
Maggie Toplak, York University
Umesh Jain, Centre for Addiction and Mental Health
Nathan Kolla, University of Toronto
Jane Seeley, Western University

ABSTRACT
Purpose:
To explore the relationships among self-reported screening measures of Attention
Deficit Hyperactivity Disorder (ADHD), other psychiatric problems, substance use/abuse and
driving-related outcomes among a provincially representative sample of adults 18 years and
older living in Ontario, Canada.

Method:
The Centre for Addiction and Mental Health (CAMH) Ontario Monitor is an ongoing
repeated cross-sectional telephone survey of Ontario adults (18 and over) which includes
validated measures: ADHD measures (ASRS-V1.1, previous ADHD diagnosis, ADHD
medication use); psychiatric distress; antisocial behaviour screen; pain, anxiety, depression
medication use; lifetime cannabis and cocaine use; Alcohol Use Disorders Identification Test
(AUDIT); Alcohol, Smoking and Substance Involvement Screening Test (ASSIST); driving-
related outcomes (driving after drinking, driving after cannabis use, street racing, collisions in
past year) and socio-demographics. This study presents statistically weighted results of the first
year survey sample of a 3-year study.
Results: A total of 1999 Ontario residents were sampled, of which 70 (3.5%) screened
positively for ADHD on the ASRS-V1.1 screening tool. Of those who screened positively for
ADHD, 54.4% were female and 45.6% were male. A significantly greater percentage of those
who screened ADHD positive (8.0%) reported at least one crash in the past year compared with
those who screened ADHD negative (3.3%), although there were no differences between the
ADHD positive and negative screened respondents on driving a motor vehicle after having two
or more drinks in the previous hour, within an hour of using cannabis, marijuana or hash or in a
race. When a sequential regression was conducted to predict self-reported crashes, only age,
antisocial personality screen and lifetime cannabis use predicted crashes.
Conclusion: This research is the first Canadian population-based study on adult ADHD using a
representative sample of adults 18 years and older living in Ontario, Canada. These early
results showed no relationship between the ADHD screen and crashes when age, sex and
kilometres driven are controlled for, while antisocial personality screen and lifetime cannabis
23e Conférence canadienne multidisciplinaire sur la sécurité routière 23rd Canadian Multidisciplinary Road Safety Conference Canadian Multidisciplinary Road Safety Conference  ‐ Montreal 2013  use were significant predictors. However, these analyses are based on self-report screeners and not psychiatric diagnoses and a small sample of ADHD respondents. Thus, these results should be interpreted with caution. RÉSUMÉ
23e Conférence canadienne multidisciplinaire sur la sécurité routière 23rd Canadian Multidisciplinary Road Safety Conference Canadian Multidisciplinary Road Safety Conference  ‐ Montreal 2013  INTRODUCTION
Attention Deficit Hyperactivity Disorder (ADHD) is a complex neurodevelopmental disorder [1].
According to the Diagnostic and Statistical Manual of Mental Disorders, 4th edition, Text
Revision (DSM-IV-TR), ADHD includes symptoms of inattention, hyperactivity and impulsivity
with “clear evidence of clinically significant impairment in social, academic or occupational
functioning”(p.66) and onset of symptoms by age seven [2]. Until recently, ADHD was viewed
as a diagnosis almost exclusively for children and adolescents as it was believed to diminish or
disappear before adulthood [3,4]. Diagnosis of ADHD is further complicated by the higher
presence of comorbidities, such as oppositional defiant disorder (ODD), conduct disorder (CD),
anxiety, depression, bipolar disorder and antisocial personality disorder (ASPD), in persons with
diagnosed ADHD when compared to normal controls [5-9]. “Current expert opinion is that it is
uncommon to find ‘pure’ AD/HD” (p.195) [4].
ADHD has been found to be a risk factor for motor vehicle crashes, with meta-analyses indicating relative risks (RRs) for crash involvement ranging from 1.54 (CI 1.12, 2.13) [10] to 1.88 (CI 1.42, 2.50) [11]. Both correlational and experimental studies have been conducted to assess whether adolescents and adults with ADHD have a higher propensity than controls to drive riskily, commit driving offences and be involved in crashes, although most studies suffer from serious methodological problems. Observational studies generally have shown higher rates of driving violations and crashes for persons with ADHD compared with controls [12-19]. However, most studies suffer from serious methodological problems, such as referral bias, self-reporting, inappropriate or undefined comparison groups, non-blinded test administrators, un-validated measures, participant attrition, small sample sizes, lack of adjustment for multiple comparisons, lack of statistical controls for age, sex and driving exposure, lack of control for ADHD medication use and comorbidities. Some studies have found that co-morbid externalizing disorders, such as CD, ODD or ASPD, partially or fully explained driving-related outcomes [14,15,18]. One study found that adolescents and adults with a history of ADHD were more likely to engage in motorsports (racing, four wheeling, motorcycle trail biking, all-terrain vehicle driving) compared to a control group [20]. Path analysis indicated that ADHD directly predicted engagement in motorsports but three mediational paths were statistically significant; one path was through impulsivity and heavy drinking, a second path was through CD/ASPD and heavy drinking and the third path was through CD/ASPD [20]. Studies of adults with ADHD have also found higher rates of alcohol and drug use problems when compared with control samples [3,4,6-7,9,21-22]. However, studies examining drinking driving behaviours have found mixed results. Barkley and colleagues [13] found that clinical ADHD and community control groups did not differ in the proportion that endorsed drinking driving or speeding as contributing factors to their crashes. Similarly, Thompson and colleagues [18] found no increased self-reported number of impaired driving occurrences or tickets between the ADHD and control groups, although associations were found between conduct problems and risky and drinking driving, even after controlling for hyperactivity-impulsivity. Yet two longitudinal studies of New Zealand children found that those with attentional difficulties or ADHD were significantly more likely to report driving after drinking, while drunk, and to be arrested for drinking driving [19,23]. Recent simulator studies also suggest that adults with ADHD may be more affected by alcohol use, monotonous driving environment and time of day [24-26]. For example, Weafer and colleagues [26] found that for a given level of intoxication, 23e Conférence canadienne multidisciplinaire sur la sécurité routière 23rd Canadian Multidisciplinary Road Safety Conference Canadian Multidisciplinary Road Safety Conference  ‐ Montreal 2013  community ADHD participants performed worse than control participants. However, no studies have examined cannabis use and driving in relation to ADHD. Research has generally found some evidence of improved driving among adolescent and adult drivers with ADHD medication use [17,22,27-31] although “previous research has shown poor concordance between laboratory- or clinic-based measures of response to methylphenidate and actual performance” (p. 754) [18]. One follow-up study of adults who had been diagnosed with ADHD as children, found that those who had received medication in childhood for their ADHD reported fewer crashes as adults compared to those who went untreated or non-ADHD controls, although self-reports of the cost or damage of the crashes, extent of bodily injury and use of alcohol, drugs or emotional states at time of the crash did not differ among groups [30]. Secnik and colleagues [7], using a large medical claims database that captured inpatient, outpatient and prescription drug services, examined individuals diagnosed with ADHD to a matched non-ADHD cohort. They found no difference in the prevalence of crashes/injuries between the two groups. A driving simulator study found no differences among placebo, low or high dosed drivers on 15 out of 18 driving measures; yet the authors conclude: “the results, when placed in the context of prior studies of stimulants on driving performance, continue to recommend their clinical use as one measures of reducing the driving risks in ADHD teens and adults” (p. 121) [32]. One important methodological challenge is the use of clinical samples [33]. ADHD samples are primarily drawn from treatment facilities but only a small and biased proportion seeks or is referred to treatment. For example, clinical samples have found higher prevalence for ADHD in boys than in girls; yet population-based studies have found no sex differences [6]. Some suggest that the less-violent ways of girls lead to fewer referrals than the attention-getting conduct of “bratty boys” [35]. Clinical samples have the advantage of extensive assessment but the disadvantage of a lack of representativeness of those with ADHD [36-38]. Population-based samples are generally based on less thorough assessments, but findings can be used for making inferences to the general population [36-37]. Thus, although a population-based study can contribute to our understanding of ADHD and risky driving, it is important to point out that large, population-based surveys rely on screening instruments and are limited by the measurements they contain. The purpose of this study is to explore the relationships among self-reported screening
measures of ADHD, other psychiatric problems, substance use/abuse and driving-related
outcomes of a provincially representative sample of adults 18 years and older living in Ontario,
Canada. This study is based on the first year survey sample of a 3-year study and thus is
exploratory because of its small sample size of ADHD cases; conclusions may be modified once
final data collection and analysis are complete.

METHODS

The data are based on telephone interviews with 1999 respondents (effective response rate =
51%) from the 2011 cycle of the Centre for Addiction and Mental Health (CAMH) Monitor, an
ongoing cross-sectional telephone survey (landline and cell phones) of Ontario adults (ages 18
or older) using a stratified two-stage probability selection procedure occurring each quarter.
The survey is conducted by CAMH and administered by the Institute for Social Research at
York University (see [39] for details). Results are based on “valid” responses; responses such
as “don’t know” and refusals were considered missing data and excluded from analyses. The
23e Conférence canadienne multidisciplinaire sur la sécurité routière 23rd Canadian Multidisciplinary Road Safety Conference Canadian Multidisciplinary Road Safety Conference  ‐ Montreal 2013  percentages reported are based on the weighted sample size and are considered representative for the population surveyed. Measures
ADHD measures: 1) Adult ADHD Self-Report Scale-V1.1 (ASRS-V1.1) was developed by
Kessler and colleagues [39] in conjunction with a revision of the World Health Organization
(WHO) Composite Diagnostic Interview. The validated screener consists of the six items found
to be most predictive of a DSM IV-based diagnosis of ADHD, out of a total of 18 items included
that typified ADHD symptoms [40-43]. Participants respond to each question on a 5-point Likert
scale, and those who score four or more positive responses based on the ranges of the items
are considered positive for ADHD. Chronbach’s α for current study = .75; 2) Previous ADHD
diagnosis
was assessed by the item have you ever been diagnosed with Attention Deficit
Disorder (ADD) or Attention Deficit Hyperactivity Disorder (ADHD) by a doctor or health care
professional?’ Validity was assessed through criterion validity [44]. 3) ADHD Medication use
was assessed by items querying on if and when they had ever been treated with medication for
ADHD or ADD by a doctor or health care professional?’ (adapted from Ontario Student Drug
Use and Health Survey [45]).
Psychiatric distress and medication use measures: 1) General Health Questionnaire (GHQ12) is
a widely used, well validated screening instrument for current psychiatric distress, and captures
depression/anxiety and problems with social functioning [46-48]. Chronbach’s α for current
study = .82; 2) pain/anxiety/depression medication use.
Antisocial behaviour measure: 1) The Antisocial Personality Disorder (ASPD) scale from the
Mini-International Neuropsychiatric Interview was designed to provide a short clinical screening
tool that could be used in a detailed, academic, research-oriented interview. Reliability and
validity of the full instrument has been established [49-51]. Chronbach’s α for current study =
.73. The ASPD cutoff is calculated in two steps: first, if respondents scored 3 or more on the
first 5 items, they were then asked the next 6 items. Those respondents with a sum score of 3
or higher on the second set of items were scored positively on the ASPD screen.
Substance use and abuse measures: 1) Lifetime cannabis and cocaine use; 2) Alcohol Use
Disorders Identification Test (AUDIT) is a validated screening instrument developed by the
WHO, to detect individuals at the less severe end of the spectrum of alcohol problems [52,53].
Chronbach’s α for current study = .78; 3) Alcohol, Smoking and Substance Involvement
Screening Test (ASSIST) is a screening instrument, with good validity and reliability [54]
designed to assess for users of specific substances, the risk of experiencing health and other
problems (e.g. social, financial, legal, relationship) from their current pattern of use.
Chronbach’s α for current study = .72.
Driving-related problem behaviours: 1) in last 12 months driving after having two or more drinks
in the previous hour; 2) in last 12 months driving after having used cannabis in previous hour; 3)
in the past 12 months driven a vehicle in a street race; 4) crash involvement in past year.
Socio-demographics: 1) sex; 2) age (2006 Census categories); 3) driving exposure (estimated
km driven per week categorized into quartiles).
Statistical Analysis
X2s were used for the bivariate statistics. As there is still debate regarding adjustments for multiple comparisons [57-59], the results are not interpreted with a correction for multiple 23e Conférence canadienne multidisciplinaire sur la sécurité routière 23rd Canadian Multidisciplinary Road Safety Conference Canadian Multidisciplinary Road Safety Conference  ‐ Montreal 2013  comparisons. However, the p-values are reported so that readers can adjust for multiple comparisons using Bonferroni correction of p = .0025 for 20 comparisons. A sequential logistic regression was performed considering self-reported crashes as the dependent variable, and entering age (18-24, 25-44, 45-64, ≥65), sex and driving exposure as control variables in the first block, ADHD screener status in the second block, ASPD screener status in the third block, and cannabis and cocaine use in the fourth block in order to examine the direct and indirect effects of the predictor variables on the dependent variable. Sequential logistic regression is a commonly used statistic that allows the researcher to assign order of entry of variables based on logical or theoretical considerations and to determine whether prediction of the dependent variable improves with the additional independent variables added to the equation [59]. The ordering of variables for the sequential logistic regression reflected the conceptualization and findings of the ADHD and comorbidity literature [3,5,7-9,20,22,30]. The annualized weight, based on selection weight and a post-stratification adjustment, was applied to all analyses. IBM SPSS Statistics 20 [61] software was used in all analyses. Overall, 70 (3.47%, CI: 2.73, 4.40) of respondents screened positively for ADHD. Table 1 presents the descriptive statistics, disaggregated by ADHD screener status. The results indicate that a significantly higher percentage of those who screened ADHD positive had at least one crash in the previous year and had risk factors for increased crash involvement, namely had taken medication for pain, anxiety and depression, screened positively for psychiatric distress, ASPD, the AUDIT, the ASSIST, cannabis and cocaine use. Variables
ADHD+ Screen
ADHD- Screen
Ever treated with
Distress (GHQ)
23e Conférence canadienne multidisciplinaire sur la sécurité routière 23rd Canadian Multidisciplinary Road Safety Conference Canadian Multidisciplinary Road Safety Conference  ‐ Montreal 2013  Pain meds
Anti-depressants
Anti-anxiety meds
ASPD screen
Lifetime used
cannabis
Lifetime used cocaine
Crashed in past yr
Drinking driving past
Cannabis driving past
Racing past yr
23e Conférence canadienne multidisciplinaire sur la sécurité routière 23rd Canadian Multidisciplinary Road Safety Conference Canadian Multidisciplinary Road Safety Conference  ‐ Montreal 2013  * Small cell size
Table 1 ADHD screener status by socio-demographic, previous ADHD diagnosis and
medication use, ASPD screener status, substance use/abuse and driving variables.


Table 2 presents the variables associated with crash involvement. Age, ADHD positive screen,
and substance use and abuse were the only variables significantly associated with at least one
self-reported crash in past 12 months.
Variables Crashed
ADHD screen
ASPD screen
Pain meds
Anti-anxiety meds
Lifetime used cannabis
Lifetime used cocaine
Racing past yr
23e Conférence canadienne multidisciplinaire sur la sécurité routière 23rd Canadian Multidisciplinary Road Safety Conference Canadian Multidisciplinary Road Safety Conference  ‐ Montreal 2013  Table 3 provides the results of the sequential logistic regression. In block 1 the control variables of age, sex and driving exposure provided statistically significant improvement over the constant only model (X2=13.349, df=3, p=.004). Only the odds ratio for age showed significance (OR=.615, CI .470, .805). In block 2, the entry of ADHD screener status did not significantly improve the model (block X2=.097, df=1, p=.756) over and above that accounted for by the control variables. When ASPD screener status was added in block 3, the model showed a near significant improvement (block X2=3.202, df=1, p=.074). The odds ratio for the ASPD screen was significant (OR=6.937, CI 1.044, 46.085), although the small cell size and large CIs suggest a low level of precision. In block 4, the entry of the AUDIT, use of cannabis in lifetime and use of cocaine in lifetime showed a significant model improvement (block X2=11.358, df=3, p=.010). The final model correctly classified 100% of no crash status, 0% of crash status and 93.8% of all cases overall at a cut-off value of .500 and no improvement in classification was found from the first to the last block. Examination of odds ratios showed that those who reported having used cannabis in his/her lifetime had significantly higher odds of reporting at least one crash in the past 12 months (OR=1.869, CI 1.123, 3.108). Model X2 =13.349, df=3, p=.004 -2 Log likelihood=605.909 Hosmer and Lemeshow Test X2=9.203, df=8, sig.=.325 Block 2 Model X2=13.446, df=4, p=.009 -2 Log likelihood=605.812 Hosmer and Lemeshow Test X2=4.976, df=7, sig.=.663 Block 3 Table 2 Crash status by age, ADHD screener status, substance use and abuse variables
23e Conférence canadienne multidisciplinaire sur la sécurité routière 23rd Canadian Multidisciplinary Road Safety Conference Canadian Multidisciplinary Road Safety Conference  ‐ Montreal 2013  Model X2=16.648, df=5, p=.005 -2 Log likelihood=602.610 Hosmer and Lemeshow Test X2=8.807, df=8, sig.=.359 Block 4
Model X2=28.007, df=8, p=.000
-2 Log likelihood=591.251
Hosmer and Lemeshow Test X2=8.279, df=8, sig.=.407
Table 3 Sequential Logistic regression for self-reported crash involvement in past 12
months

DISCUSSION
There are limitations with this study and the results should be framed keeping these cautions in
mind. These data are based on self-report screeners from a telephone survey, and therefore do
not reflect diagnoses that would be elicited in an in-person clinical assessment. Additionally, this
study included only the first year’s data of a 3-year study which meant small cell sizes for key
variables. Despite these caveats, this is the first population-based study in Ontario to explore
the relationship between ADHD screener status and self-reported driving outcomes.
ADHD has been found to be a significant risk factor for traffic violations and crashes [10-11].
Attention regulation and impulsivity, two key symptoms of ADHD, have been associated with
self-reported violations [62]. However, studies have also found that co-morbid externalizing
disorders, such as CD, ODD or ASPD, partially or fully explained driving-related outcomes
[14,15,18]. Moreover, virtually all studies have used clinical populations and have often used
inappropriate control groups; for example, Richards and colleagues compared a clinical ADHD
sample to participants recruited from a medical school [63].
The current results suggest that a larger percent of those who screened positively for ADHD
reported at least one crash as a driver in the past year compared with those who screened
negatively, although there were no differences between groups on driving after drinking or
cannabis use. Yet the regression analysis showed that when age, gender and driving exposure
were controlled for, no relationship was found for ADHD screener status and crashes. ASPD
screener status and any history of cannabis use were significant predictors, but the large CIs for
ASPD screener status suggest a low level of precision for this estimate. These results are
consistent with other studies regarding externalizing disorders explaining negative driving-
related outcomes [14-15,18,20]. Future research with larger sample sizes, and possibly a case-
control design, needs to re-examine the relationships between the ADHD screener status and
other variables to further examine these dimensions in relation to driving outcomes. Moreover,
23e Conférence canadienne multidisciplinaire sur la sécurité routière 23rd Canadian Multidisciplinary Road Safety Conference Canadian Multidisciplinary Road Safety Conference  ‐ Montreal 2013  further research is required to assess the sensitivity and specificity of the screener with actual ADHD diagnosis. ACKNOWLEDGEMENTS
This study was supported by a Canadian Institutes for Health Research operating grant (#MOP 102537). REFERENCES
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