J. DRUG EDUCATION, Vol. 43(2) 183-201, 2013

DRUGGED DRIVING: INCREASED TRAFFIC RISKS INVOLVING LICIT AND ILLICIT SUBSTANCES*

MELINDA W. PILKINTON, PH.D., LCSW ANGELA ROBERTSON, PH.D. Mississippi State University, Starkville D. LEE MCCLUSKEY, M.S. West Virginia University, Morgantown

ABSTRACT

Driving under the influence of drugs poses risks for traffic safety. Most research attention has been focused on the most prevalent drugs of abuse, such as alcohol, illegal drugs, and prescription drugs with high abuse potential. The objectives of this study were to determine the types of drugs used by convicted DUI offenders on the day of their arrest, prevalence of poly-substance use, and offender characteristics associated with different drug use patterns. Data were collected from 6,339 individuals enrolled in the court-mandated Mississippi Alcohol Safety Education Program. After alcohol, cannabis was the most frequently used substance, followed by sedative medications and prescription analgesics. Among poly-substance users, 78.4% reported combining alcohol with other drugs. Findings could be used to inform public education campaigns, law enforcement training, and highway safety policies about the prevalence of combining alcohol with other drugs, as well as how poly-substance use further impairs trafficrelated risks.

*This study was not supported by grant funding. 183 Ó 2013, Baywood Publishing Co., Inc. doi: http://dx.doi.org/10.2190/DE.43.2.f http://baywood.com

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INTRODUCTION Impaired drivers risk injuring themselves and others, often without realizing that ingesting a combination of alcohol, illicit drugs, prescription medications, or over-the-counter medications can lead to greater impairment. In 2010, an estimated 1.5 million people were arrested for impaired driving, and over 10,000 fatalities occurred as a result of impaired driving crashes (National Highway Traffic Safety Administration [NHTSA], 2012). Despite evidence of decrease in alcohol-impaired driving (Compton & Berning, 2009), driving under the influence of other drugs seems to be increasing (Kelly, Darke, & Ross, 2004). Eighteen percent of fatally-injured drivers tested positive for drugs in 2009 (NHTSA, 2010). The problem of drugged driving is escalating; this is particularly true among older drivers who are unaware of the interaction of alcohol in combination with other drugs (LeRoy & Morse, 2008). Drivers are culpable for the risks they take, and prosecution can occur as a consequence of being affected by a legally-prescribed medication or over the counter medication (DuPont, Logan, Shea, Talpins, & Voas, 2011). A wealth of articles has been published on driving under the influence of alcohol. However, there has been a relative dearth of studies conducted to illuminate the problem of drugged driving. One possible explanation for this is the lack of a standardized or common approach to the field-testing of drugged drivers. In contrast, standard approaches to assess alcohol intoxication have been available for many years and are widely used (DuPont, Voas, Walsh, Shea, Talpins, & Neil, 2012; Maxwell, 2012; Reisfield, Goldberger, Gold, & DuPont, 2012). While a field officer may observe drugged-driving impairment, quantifying the amount or influence of a substance other than alcohol, or a substance in combination with alcohol, is a difficult task (Brookoff, Cook, Williams, & Mann, 1994; Reisfield et al., 2012). To address the issue of identifying and prosecuting impaired drivers, 47 states have programs to certify law enforcement officers as Drug Recognition Experts (DRE), who can identify drug-impaired drivers and present evidence in the courtroom setting (National Association of State Alcohol and Drug Abuse Directors, 2011; Walsh, 2009). Although a drug use detection protocol was proposed to policymakers, an agreement regarding an adopted protocol remains to be resolved on a national level and, at the present time, remains within the states’ discretion (DuPont et al., 2011; National Association of State Alcohol and Drug Abuse Directors, 2011). Numerous drugs can impair one's ability to drive safely. Research has focused on the most commonly used illegal drugs and prescription drugs with high abuse potential. The most prevalent drugs of abuse are cocaine, opiates, amphetamine/ methamphetamine, cannabis, and benzodiazepines (Lacey, Kelly-Baker, FurrHolden, Boas, Romano, Torres, et al., 2009). After alcohol, cannabis was the most frequently used substance in the majority of impaired driving studies (Johnson, Kelley-Baker, Voas, & Lacey, 2012; Longo, Hunter, Lokan, White, &

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White, 2000; Sewell, Poling, & Sofuoglu, 2009). In combination with alcohol, cannabis produces acute impairment in defensive driving strategies, such as a slower rate of driving speed (Hartman & Huestis, 2013; Sewell et al., 2009). Despite the fact that cannabis use is associated with significant risk of motor vehicle crashes (Asbridge, Hayden, & Cartwright, 2012; Li, Brady, DiMaggio, Lusardi, Tzong, & Li, 2012), more than half of those who used cannabis and drove did not believe cannabis use increased safety risks (Swift, Jones, & Donnelly, 2010). In fact, cannabis is often viewed as the "least risky illicit drug" (Arterberry, Treloar, Smith, Martens, Pedersen, & McCarthy, 2012) and fewer consequences are expected by cannabis users (Gaher & Simons, 2007). Although the highest percentages of drivers testing drug-positive across studies are cannabis users (Compton & Berning, 2009; Jones, Shinar, & Walsh, 2003; Kelly et al., 2004), prescription drug misuse is a growing problem, with serious health and public safety consequences (DuPont, 2010). The prevalence of non-medical use of prescription sedatives, tranquilizers, stimulants, and opiates has significantly increased from 1991 to 2007 (McCabe, Cranford, & West, 2008). As prescription drug abuse has increased, so have hospitalizations for drug poisonings (Coben, Davis, Furbee, Sikora, Tillotson, & Bossarte, 2010), overdose deaths (Paulozzi, Budnitz, & Xi, 2006; Warner, Chen, & Makuc, 2009), and drugged driving. Studies of accident-involved drivers indicate that detection rates varied by community and that the most commonly-detected prescription medication were benzodiazepines (2-15%), amphetamines (2-6%), and opioids (3-5%) (Kelly et al., 2004). A national roadside survey of weekend nighttime drivers conducted in the United States found that 4.0% tested positive for a prescription or overthe-counter drug (Lacey et al., 2009). Analysis of data from the Fatality Analysis Reporting System (FARS) for 2005-2009 found stimulants, including cocaine, were present in 9% of fatally injured drivers, followed by narcotics (5.7%), and depressants (4%) (Brady & Li, 2013). While it is unknown whether drivers who tested positive for prescription drugs had valid prescriptions and were taking them appropriately, there is evidence that benzodiazepines, other sedative/ hypnotic drugs, and opioids impair driving and increase risks for motor vehicle crashes (Dassanayake, Michie, Carter, & Jones, 2011). Much of what is known about drug impaired driving has relied upon testing of oral, urine, or blood samples from drivers involved in motor vehicle accidents and drivers stopped for roadside surveys (Kelly et al., 2004). Both methods have serious limitations. The collection of information by law enforcement about drug involvement in fatal crashes, the availability of toxicology results for fatal accident reporting, and rates of drug testing of fatally injured drivers vary widely by states and jurisdictions within states (NHTSA, 2010). In 2009, Maine did not report any drug tests to FARS and only 2% of fatally injured drivers were drug tested in Mississippi. Thus, findings based on FARS data must be interpreted with caution. One cannot assume that national rates are applicable

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to a particular state. Participation in the 2007 National Roadside Survey was voluntary, and 14% of participants refused the breath-alcohol measure, 29% refused to provide oral fluid samples, and 61% refused to provide blood samples for analysis (Compton & Berning, 2009). Detection of drug use among drivers using roadside survey methodology is hampered by such high refusal rates. Another approach to assessing the prevalence of types of drugs used by drivers is to collect self-report data from drivers arrested for impaired driving. While this approach has its limitations as well, this method can supplement other sources of data on drugged driving needed by highway safety policymakers. THE CURRENT STUDY The objectives of this study were to determine: 1. the types of drugs used by convicted DUI offenders on the day of their arrest; 2. the prevalence of poly-substance use (i.e., the use of two or more drugs concurrently) on the day of their arrest, in particular the combining alcohol with one or more other drugs; and 3. the offender characteristics associated with different drug use patterns. Data were collected from individuals enrolled in the Mississippi Alcohol Safety Education Program (MASEP). MASEP is a statewide, court-mandated intervention for persons convicted of first offense driving under the influence (DUI) of alcohol or another drug. The program is one of only a few statewide programs in the United States operated by a single agency, Mississippi State University, and, to our knowledge, is the only DUI intervention in the nation operating under the auspices of a university. MASEP consists of two interrelated units. The MASEP Operation Unit interfaces with the judicial system and manages programs in 41 locations throughout the state. The MASEP Research & Development Unit conducts research on DUI recidivism, as well as the characteristics, risk behaviors, and service needs of DUI offenders. Research findings are used to revise the curriculum to improve program effectiveness and to inform public safety policy. METHOD Participants and Procedures All individuals enrolled in MASEP are assessed during the first session of the four-session program. The assessment includes measures of alcohol and drug use, substance use problems, history of other criminal history and traffic citations, as these factors have consistently been found to be predictors of DUI recidivism (Beerman, Smith, & Hall, 1988; C’de Baca, Miller, & Lapham, 2001;

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Nochajski, Miller, Wieczorek, & Whitney, 1993; Peck, Arstein-Kerslake, & Helander, 1994; Robertson, Gardner, Xu, & Costello, 2009). Assessment data is then used in the third session to provide personalized feedback to participants geared toward motivating change of alcohol and drug misuse, cessation of impaired driving, and, when appropriate, engagement in substance abuse treatment. A supplemental drug questionnaire was added to the assessment measures from March through November 2012. MASEP participants were informed that completion of the supplemental drug questionnaire was voluntary and that their answers would be kept confidential. No incentive or other inducement to participate in the study was offered. Study participants were instructed to record the MASEP registration/assessment form number on the drug questionnaire. They were told not to write their names or other identifying information on the drug questionnaire. Study participants’ answers to the supplemental drug questionnaire were linked to their assessment measures using the registration/assessment form number. Researchers received a dataset for analyses that had been stripped of identifiers (i.e., names). The Mississippi State University Institutional Review Board provided human subjects research approval for the study. During the time that the supplemental drug questionnaire was collected, a total of 8,374 DUI offenders enrolled in MASEP. Of those, 7,422 or 88.6% of MASEP participants completed the drug questionnaire and provided the information necessary to link the questionnaire to their assessment measures. Some participants (n = 570) denied using any substance on the day of their arrest. We refer to this group as the denial group because they denied the use of alcohol or another drug on the day of their arrest even though they were convicted of DUI. We compared the denial group to those who admitted to alcohol or drug use on the day of their arrest on the following variables: gender, race/ ethnicity, age, education, and inconsistent responding. Inconsistent responding was measured with a series of six logic tests to identify responding to the assessment in an inconsistent or unreliable manner. For example, one assessment question asked how many days per week the respondent felt happy while another later asked how many days per week the respondent felt sad, and both questions were given the same time scale for possible responses. If the respondent answered the maximum or minimum both times, then this was considered an inconsistent response. A total score was calculated for the number of inconsistent responses, with scores ranging from 0 to 6. We found that those in the denial group were more likely to be Hispanic or African American than Caucasian, and to be less educated. In addition, there was a statistically significant difference in mean inconsistency response score between deniers and non-deniers (mean difference = –0.159; t = –5.463; p < .001). There were no significant differences between deniers and admitters on the basis of gender or age. Since their denial of any substance use may have been an issue of low literacy, we excluded cases from analyses that denied use of alcohol or another drug on the day of their arrest,

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as well as cases missing demographic information (n = 513), leaving us with an analytic sample of 6,339. The characteristics of the analytic sample of DUI offenders are presented in Table 1. Measures Demographic variables of sex, race/ethnicity, age, education, and employment status were obtained from the MASEP registration/assessment form. The Highest Level of Education was measured on a 10-point scale ranging from some grade school (coded as 0) to advanced degree (coded as 9). Employment status was categorized as full- or part-time employed, unemployed, or disabled/retired/ homemaker. History of other criminal involvement was measured by one item that asked about arrests for crimes other than drinking and driving (yes = 1, no = 0). Participants were also asked to report the number of traffic tickets for moving violations (e.g., speeding, running a red light or a stop sign) with response options ranging from none (coded 0) to 8 or more (coded 8). Substance use problem severity was assessed by the Alcohol Use Disorders Identification Test (AUDIT) (Babor, Higgins-Biddle, Saunders, & Monteiro, 2001) and the Short Inventory of Drug Consequences (Miller, Tonigan, & Longabaugh, 1995). The AUDIT was developed by the World Health Organization and has proved to be both a highly reliable and valid measure of alcohol problems (Babor et al., 2001). Additionally, Sinclair, McRee, and Babor (1992) found the AUDIT to have high test-retest reliability (r = .86) in a sample of individuals identified as problem drinkers, non-problem drinkers, and cocaine abusers. The internal consistency reliability of the AUDIT among our sample was very good (Cronbach's alpha = .82). AUDIT scores below 7 were considered low risk for an alcohol problem, scores 8 to 15 suggest harmful involvement with alcohol and warrant further assessment, scores 16 to 19 indicate high risk for an alcohol problem, and scores 20 and higher denote a serious alcohol problem. The Short Inventory of Drug Consequences (SIP) is a short version of the Drinker Inventory of Consequences (DrInC) (Miller et al., 1995). The wording of the DrInC was modified to produce a parallel form to ask about adverse consequences of both alcohol and other drug use. The 15-item version was derived by selecting three items from each subscale with the strongest relationship to overall subscale scores. There are five types of consequences: physical (e.g., My physical health has been harmed by my drinking or drug use), intrapersonal (e.g., I have felt guilty or ashamed because of my drinking or drug use), social responsibility (e.g., I have spent too much money or lost a lot of money because of my drinking or drug use), interpersonal (e.g., My family has been hurt by my drinking or drug use), and impulse control (e.g., I have had an accident while drinking or using drugs). Affirmative responses to each item are coded one and summed, with higher scores indicating more alcohol or drug related problems. The SIP has been shown to be reliable, valid, and clinically useful (Alterman, Cacciola,

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Table 1. Characteristics of DUI Offenders (N = 6,339) Variables

% or Mean (SD)

% Male

77.7

Race % Black % White % Othera

35.5 58.3 6.1

Education Less than high school High school graduate/GED Some college College degree or more

18.1 32.6 38.1 11.1

Employment status Employed Unemployed Disabled/retired/homemaker

69.9 20.5 9.6

Average age Median age

35.9 (13.1) 33.0

Substance use problems AUDIT score % Low risk (0 to 7) % Medium risk (8 to 15) % High risk (16 to 40)

9.3 (6.9) 46.7 36.4 16.9

AOD Consequences score % Reporting no AOD-related consequences

4.4 (4.4) 22.8

% Arrested for crime other than DUI

33.0

Traffic tickets for moving violations % None % 1 or 2 % 3 or 4 % 5 or more

19.3 34.3 23.7 22.7

aThe Other racial/ethnic category includes 0.6% Asian, 1.8% Hispanics, 2.1% Native

Americans, and 1.7% of mixed or other races. Note: SD = standard deviation.

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Ivey, Habing, & Lynch, 2009; Forcehimes, Tonigan, Miller, Kenna, & Baer, 2007). The internal consistency reliability for the total score of the Short Inventory of Drug Consequences was .89 among individuals seeking treatment for alcohol problems (Miller et al., 1995) and was .91 among our sample of DUI offenders. The supplemental drug questionnaire surveyed the use of alcohol, marijuana, other illicit drugs, and a variety of prescription and over-the-counter (OTC) medications on the day of the DUI arrest. Since there are numerous medications that may impair driving, participants were asked about prescription and OTC drugs that cause drowsiness and dizziness and that, when combined with alcohol, intensified the effects or increased risk for overdose (National Institute on Alcohol Abuse and Alcoholism [NIAAA], 2007). Participants were asked if they took medication for treating severe pain from injury, post-surgery, or migraines, anxiety, sleep problems, depression, high blood pressure, nausea, seizures, allergies, cold or flu, cough, and motion sickness. For each symptom/ disorder, several drugs commonly used to treat each condition were given as examples. Prescription pain medications listed included muscle relaxants (e.g., Flexeril), opioids, and other narcotic analgesics (i.e., Demerol, Percocet, and Oxycodone) for the treatment of severe pain from injury, post-surgery, or migraines. Benzodiazepines (e.g., Ativan and Xanax) were listed as medications for the treatment of anxiety. Selective serotonin reuptake inhibitors (SSRIs, Prozac, and Paxil) and other anti-depressant medication (e.g., Wellbutrin) were given as examples of medications for the treatment of depression. Ambien and other sedative hypnotics were noted as medications for the treatment of sleep problems. Participants were also asked if they had taken any other medication with a warning label that said “May cause drowsiness. Alcohol may intensify this effect. Use care when operating a car or dangerous machinery.” Examples of OTC medications that cause drowsiness and may impair driving included Benadryl, Sudafed, and Dimetapp for allergies, colds, or flu, and cough medicine such as Robitussin. Additionally, participants were asked if they had used illegal drugs on the day of their arrest, including marijuana, cocaine, methamphetamine, or another illegal drug. Data Analysis Data analyses were conducted using IBM SPSS version 21 to generate descriptive statistics for all measures and to run binary logistic regression models. Multivariate logistic regression models were used to determine the offender characteristics that were associated with use of cannabis, prescription pain medication, prescription medication for the treatment of anxiety disorders, or alcohol only on the day of arrest. A fifth logistic regression model examined the offender characteristics that were associated with combining alcohol with another drug. All logistic regression models included the following set of variables: gender, race/ethnicity, age in years, education, employment status, AUDIT score,

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total Drug Consequences score, criminal history other than DUI, and number of traffic tickets. Odds ratios (OR) and 95% confidence intervals were calculated for independent variables in the models. RESULTS Drug Use on the Day of DUI Arrest The percentage of DUI offenders reporting different types of drugs used on the day of arrest are displayed in Table 2. Alcohol was by far the most commonly used drug, with 83.1% of the DUI offenders reporting that they had been drinking on the day of their arrest. Although cannabis was the second most prevalently used drug at 18.8%, a substantial proportion of DUI offenders used prescription medications that can impair driving, such as sedatives, pain medication, and other medications with a warning label. Only about 2% of participants reported using other illegal drugs, such as cocaine or methamphetamine. Poly-Substance Use on the Day of DUI Arrest Table 2 also displays the number of drugs taken on the day of arrest. Most participants reported using only one drug on the day of arrest. Of those reporting using only one drug, 82.4% reported alcohol use, and the rest reported using cannabis. Over one-third of the participants reported taking two or more drugs. Among the poly-substance users, the majority (78.4%) combined alcohol with another drug. Offenders Characteristics Associated with Type of Drug(s) Used on Day of Arrest Table 3 displays the demographic characteristics, substance use problems, and other factors associated with different types of drugs and the combining of alcohol with another drug. The first logistic regression model examines individuals reporting use of cannabis on the day of their arrest. Cannabis users tended to be male, younger, less educated, more likely to be African American, and more likely to be unemployed than those who did not use cannabis. Cannabis users had significantly lower scores on the AUDIT, which only assesses alcohol use problems, but reported significantly more substance use-related consequences than DUI offenders who did not report using cannabis on the day of their arrest. In addition, cannabis users were twice (OR = 2.18) as likely as those who did not report cannabis use to have been arrested for crimes other than DUI. Finally, cannabis users were the only group with a significantly higher number of traffic citations for moving violations, such as speeding or running a stop light, than non-cannabis using DUI offenders.

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Table 2. Percentage of DUI Offenders Reporting Drug(s) Used on the Day of Their Arrest Substance

% Day of arrest (N = 6,339)

Alcohol

83.1

Prescription pain medication Opioids and other narcotic analgesics Muscle relaxant

11.1 8.4 5.4

Prescription sedatives Medications for anxiety disorders Antidepressant medications Insomnia medications

16.9 9.9 8.6 5.3

OTC allergy, cold, and cough medications

9.3

Other prescription medications Blood pressure Seizures Nausea

17.4 11.7 2.6 1.9

Cannabis

18.8

Other illegal drugs Cocaine Methamphetamine Other illegal drug

2.4 1.2 0.6 1.2

Total number of drugs One Two Three or more

63.5 23.4 13.1

The second and third logistic regression models focus on prescription pain and anti-anxiety medication users respectively. Unlike cannabis users, individuals who used these drugs on the day of their arrest were more likely to be female and White. Male DUI offenders were 31.2% less likely than females to report use of pain medication and 58.3% less likely to report use of anti-anxiety medications as female DUI offenders. DUI offenders reporting use of anti-anxiety medications were 3.65 times as likely to be White instead of African American, and almost twice (OR = 1.93) as likely to be of another racial/ethnic group as African

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American. DUI offenders reporting use of pain medications were 1.42 times as likely to be White instead of African American, and there was not a significant difference African Americans and other racial/ethnic groups in use of pain medications. Users of prescription pain and anti-anxiety medications were more likely to be unemployed and were four to five times as likely to be disabled, retired, or a homemaker. Users of prescription pain and anti-anxiety medications were similar to cannabis users in their alcohol and drug problem severity scores and in having a criminal history of offences other than DUI. That is, users of these prescription medications had significantly lower AUDIT scores, higher substance use-related problems, and were more likely to have been arrested for a crime other than DUI than offenders who did not report using these drugs. Last, we examined individuals who reported only drinking alcohol on the day of their arrest (model 4) and individuals who combined alcohol with another drug (model 5). Those reporting alcohol use only were more likely to be male, younger, employed, have higher AUDIT scores, report fewer substance use-related consequences, and were less likely to have an arrest history for crimes other than DUI. There were no significant racial/ethnic or education differences among those reporting alcohol only compared to DUI offenders reporting drug use or poly-substance use. Those who combined alcohol with another drug that may impair driving were more likely to be White, older, more educated, disabled/ retired/homemaker, have higher AUDIT scores and more substance use-related consequences, and were more likely to have been arrested for other crimes. There were no gender differences among individuals who combined alcohol with other drugs. DISCUSSION This study examined self-reported use of alcohol and other drugs that impair driving among individuals convicted of first time DUI in Mississippi. Our findings are consistent with previous drugged driving research utilizing different methodology. Most of our participants drank alcohol on the day of their arrest. Alcohol is the most commonly detected drug by toxicology testing of drivers (Brady & Li, 2013; Callaghan, Gatley, Veldhuizen, Lev-Ran, Mann, & Asbridge, 2013; Hels, Lyckegaard, Simonsen, Steentoft, & Bernhoft, 2013; Longo et al., 2000; Romano & Pollini, 2013; Walsh, Flegel, Atkins, Cangianelli, Cooper, Welsh, et al., 2005). After alcohol, cannabis was the drug most frequently used by our participants. These findings are not surprising, as alcohol is the most commonly used drug in the United States and cannabis is the most commonly used illicit drug in the country (Substance Abuse and Mental Health Services Administration, 2011). Cannabis impaired driving is particularly problematic from a highway safety standpoint. Marijuana has been decriminalized in the District of Columbia and 20 states including Washington, Oregon, California, Nevada, Montana, Colorado,

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Table 3. Characteristics of DUI Offenders by the Drugs Used on the Day of Arrest: Multivariate Models

Cannabis (N = 1,190) Variables

Prescription pain medicationsa (N = 706)

OR

95% CI

OR

95% CI

Gender (male)

1.296***

1.085-1.547

0.688***

0.569-0.833

Race (Black) White Otherc

0.526*** 0.401***

0.425-0.613 0.285-0.565

1.423*** 1.372

1.177-1.719 0.958-1.965

Age in years

0.921***

0.914-0.929

1.020***

1.013-1.028

Education

0.914**

0.867-0.965

0.951

0.898-1.006

1.258** 1.108

1.069-1.481 0.785-1.564

1.486*** 4.046

1.208-1.827 3.193-5.128

0.949*** 1.111***

0.938-0.961 1.090-1.132

0.961*** 1.091***

0.948-0.974 1.069-1.113

Arrested for crime other than DUI

2.177***

1.882-2.518

1.652***

1.380-1.978

Number of traffic tickets

1.044**

1.016-1.074

1.015

0.981-1.050

Employment status (employed) Unemployed Disabled/retired/ homemaker Substance use problems AUDIT score AOD consequences scale

Notes: OR = Odds Ratio; 95% CI = 95% Confidence Intervals. aPrescription pain medications include opioids, other narcotic analgesics, and muscle relaxants. bAntianxiety medications include benzodiazepines. cParticipants who reported using alcohol, but no other drugs on the day of arrest. dParticipants who combined alcohol with other drugs. eThe “other” racial/ethnic category includes Hispanics, Native Americans, and Asians. *p < .05; **p < .01; ***p < .001.

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Medication for anxiety disordersb (N = 629)

Alcohol onlyc (N = 3,315)

Alcohol and another drugd (N = 1,953)

OR

95% CI

OR

95% CI

0.417***

0.344-0.504

1.413***

1.242-1.608

0.838*

0.732-0.960

3.650*** 1.937**

2.828-4.711 1.210-3.100

0.932 1.205

0.831-1.045 0.956-1.519

1.305*** 0.909

1.152-1.478 0.702-1.179

0.993

0.985-1.001

0.995*

0.990-0.999

1.018***

1.013-1.022

1.075*

1.012-1.142

0.977

0.942-1.013

1.097***

1.056-1.140

1.419** 5.221***

1.145-1.759 3.942-6.915

0.734*** 0.260***

0.644-0.837 0.210-0.322

1.071 1.980***

0.928-1.235 1.627-2.410

0.966*** 1.133***

0.952-0.980 1.109-1.158

1.051*** 0.894***

1.041-1.061 0.881-0.908

1.041*** 1.048***

1.031-1.051 1.033-1.064

1.565***

1.289-1.900

0.602***

0.535-0.676

1.281***

1.131-1.450

1.033

0.996-1.071

1.000

0.978-1.022

1.001

0.978-1.024

OR

95% CI

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Arizona, New Mexico, Illinois, New Jersey, Delaware, Rhode Island, Vermont, Maine, Massachusetts, Michigan, New Hampshire, Connecticut, Alaska, Hawaii (Cerda, Wall, Keyes, Galea, & Hasin, 2012; Office of National Drug Control Policy, n.d.). Indications are that changes in marijuana policy reflect community opinion, thus marking changes in social standards about marijuana use and access to marijuana (Cerda et al., 2012; Johnson et al., 2012). Yet, results of this study underscore that cannabis users in particular, compared to users of other substances, engage in dangerous driving behaviors as indicated by the higher number of traffic citations, thus placing themselves and other drivers at risk. Although the relationship between cannabis use and accidents has not been established clearly, cannabis users are twice as likely to be involved in motor vehicle accidents (MVAs) as individuals who do not use cannabis (Hall, 2009). Additionally, from a prosecution perspective, there has not been an established level of tetrahydroncannabinol (THC) that indicates impaired driving, such as with BAC levels (Johnson et al., 2012). Per se drug laws (zero-tolerance) would result in the conviction of cannabis users in cases of DUI; however, a level of THC that indicates impairment continues to be unresolved in 5 of the 20 states that have legalized marijuana use (Johnson et al., 2012; Voas, Lacey, Jones, Scherer, & Compton, 2013). We also examined the use of other drugs that impair driving. Over half of the DUI offenders reported taking prescribed or OTC medications that may impair driving during the 24-hour period prior to the arrest. Also, in addition to taking drugs that are often abused, our respondents are taking non-narcotic medications for certain medical conditions (e.g., blood-pressure medications) that may cause drowsiness, particularly when mixed with alcohol. Our self-report data on prescription and OTC medication use is an important supplement to studies that rely on drug testing of drivers for several reasons. It is often the case that multiple factors affect the accuracy of current drug testing methods (Ramaekers, 2003) and existing testing procedures have not been able to address sufficiently the combined chemical effects of various substances on driving skills (Romano & Pollini, 2013). In addition, there are few tests that screen for OTC medicines or any of the synthetic substances that have been gaining popularity in recent years (Lessenger & Feinberg, 2008). Our findings emphasize the importance of developing different methods for drug testing, as well as different strategies to reduce impaired driving with relation to OTC and prescription medications (Romano & Pollini, 2013). Limitations The involuntary nature of the DUI intervention program introduces the possibility that some participants may not have been honest about their drug use. Although under-reporting of substance use is common among courtordered clients (Lapham, 2004; Lapham, C’de Baca, Chang, Hunt, & Berger,

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2002; Lapham, C’de Baca, McMillan, & Hunt, 2004), self-reports of alcohol consumption and drugs are generally valid and reliable (Del Boca & Darkes, 2003; Rosay, Najaka, & Herz, 2007). In addition, we employed several techniques for enhancing the accuracy of self-reporting, such as providing assurance of confidentiality, collecting the information when participants were sober, and encouraging respondents to provide complete and accurate information (Del Boca & Noll, 2000). Asking participants about the medications they were taking for various symptoms and conditions and giving examples of drugs commonly used to treat those conditions may have also resulted in greater reporting. Study findings are specific to Mississippi DUI offenders and may not be generalizable to drugged driving behaviors relative to other regions of the United States. Previous research on individuals arrested for impaired driving has shown that drug use prevalence levels vary across locations and time (Jones et al., 2003). State-specific data is needed to inform public education campaigns, law enforcement efforts, and highway safety policies, especially when other sources of impaired driving information are not available. This is certainly the case in Mississippi. Mississippi drivers did not participate in the 2007 National Roadside Survey. Additionally, there are only 42 law enforcement drug recognition experts (DREs) in the state, which severely limits the number of drug impaired driving cases that they can work. Furthermore, Mississippi is among the states that do not contribute sufficient drug test results to FARS to produce reliable estimates of drug-impaired driving. In conclusion, research on the characteristics of convicted DUI offenders can supplement knowledge of drugged driving obtained from biological testing of drivers in fatal accidents and roadside surveys. Self-reported information on drug use collected from convicted DUI offenders can be used to alert law enforcement officers to the types of drugs and drug and alcohol combinations that they may encounter and can be used to improve training of law enforcement officers as drug recognition experts. Findings can also be used to inform public education campaigns. One possible method for disseminating these findings could be through the utilization of public service announcements addressing the dangers of driving while under the influence of any medication that might cause drowsiness. Additionally, these findings could be used to better inform medical professionals, and may provide a useful prompt for medical professionals to inquire about all drugs their patients take, and to better explain the effects of those medications on driving ability, particularly when taken with alcohol. It is important to provide to the public information on how the use of prescription drugs and over-the-counter medications impair safe driving practices because if drivers fail to recognize the dangers of combining substances, whether licit (including alcohol) or illicit, or believe that medications legally prescribed for them will not affect their driving ability, then risks to the drivers, their families, and to the general public will remain.

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