Medication use and the risk of motor vehicle collisions among licensed drivers: A systematic review
Introduction
While the number of motor vehicle collisions (MVC) and subsequent fatalities has steadily declined over the past decade among many high-income countries, MVC still remains one of the leading causes of mortality not just globally, but also within the United States (U.S.) (Sise et al., 2014, Rockett et al., 2012, Oster and Strong, 2013). In 2010, this equated to approximately one death per collision every fifteen minutes in the U.S. (Oster and Strong, 2013). Besides the inherent risks to morbidity and mortality, MVCs are estimated to cost the U.S. over $300 billion dollars per year (Oster and Strong, 2013).
While driving under the influence of alcohol is a well-documented area of study, driving under the influence of drugs (DUID) is also an emerging public health and traffic safety concern (Hayman and Crandall, 2009, Morland, 2000, Movig et al., 2004, Walsh et al., 2004). Driving under the influence of drugs entails the use of illicit drugs, i.e. drugs that are obtained illegally and with no real medical benefit, such as cocaine and methamphetamine. Driving under the influence of drugs can also entail the use of licit substances, such as common prescription or over-the-counter medications, whose effects impair the driver’s ability to safely operate a motor vehicle from one destination to another. However, it’s important to realize that while licit drugs can be obtained illegally, abused, or misused, the intent of use by the driver is often difficult to determine. In 2009, approximately 28% of all fatally injured U.S. drivers that were tested for either illicit or licit drugs tested positive for one or more of these substances (National Highway Traffic Safety Administration, 2010). In addition, recent research suggests that DUID is increasing nationally (Wilson et al., 2014).
Due to the complexity of DUID, the primary focus of this paper pertains to the association between licit drug use and MVC. However, one of the fundamental challenges to studying the effects of licit drugs on driving ability is that the relationship is not always as apparent when compared to alcohol (National Highway Traffic Safety Administration, 2010). For example, some drugs may not noticeably impair the skills (cognition, psychomotor function, physical ability) necessary to operate a motor vehicle (Coopersmith et al., 1989, Carr, 2000, Carr et al., 2006, Cheung and McCartt, 2011). Drugs that are perceived to affect the central nervous system may exhibit different effects among individuals; this may be attributed to the pharmacokinetic or pharmacodynamic properties of the drug (Jusko, 2013), the drug’s half-life (Brown et al., 2013), interactions with other consumed drugs (Bushardt et al., 2008), tolerance (Stein and Baerwald, 2014), drug elimination rate (Bushardt et al., 2008), dosage (Brown et al., 2013), route of administration (Bushardt et al., 2008), solubility (Augustijns et al., 2014), intestinal pH (Augustijns et al., 2014), current health status of the individual (Bushardt et al., 2008), genetics (Daly, 2014), etc. It may also be difficult to partition out the effects of the licit drug and the medical condition for which it was taken to remedy (Bushardt et al., 2008). For example, several medical conditions have been associated with an increased risk of MVC. These include, but are not necessarily limited to, sleep apnea (Ellen et al., 2006), dementia (Brown and Ott, 2004), arthritis (Cross et al., 2009), diabetes (Hansotia and Broste, 1991), epilepsy (Hansotia and Broste, 1991), anxiety (Sagberg, 2006), depression (Sagberg, 2006), and Parkinson’s disease (Uc et al., 2006).
Numerous reviews and meta-analyses have investigated the association between licit drug use and MVC and/or driving ability. These reviews have focused predominately on opioids (Borgeat, 2010, Fishbain et al., 2002, Fishbain et al., 2003, Jones et al., 2012, Kress and Kraft, 2005, Leung, 2011, Mailis-Gagnon et al., 2012, Soyka, 2014, Strand et al., 2013), benzodiazepines (Jones et al., 2012, Leung, 2011, van Laar and Volkerts, 1998, Dassanayake et al., 2011, Rapoport et al., 2009, Smink et al., 2010), antihistamines (Popescu, 2008, Roberts, 2005), psychoactive drugs (Cooper et al., 2011, Joris and Monique Anna Johanna, 2009, Krueger, 2010, Rapoport and Baniña, 2007, Verster and Mets, 2009), antidepressants (Dassanayake et al., 2011, Brunnauer and Laux, 2013, Ramaekers, 2003, Ravera et al., 2012, Verster and Ramaekers, 2009), hypnotics (Krueger, 2010, Verster et al., 2006), anxiolytics (Vermeeren et al., 2009, Verster et al., 2005), and sleep medications (Gunja, 2013, Leufkens and Vermeeren, 2014, Verster et al., 2007a, Verster et al., 2007b). Some reviews have also examined multiple drug categories (Hetland and Carr, 2014, Elvik, 2013, Kelly et al., 2004, Orriols et al., 2009). However, the majority of these studies have reviewed or analyzed licit drugs in broad groups (Fishbain et al., 2002, Jones et al., 2012, Kress and Kraft, 2005, Leung, 2011, Dassanayake et al., 2011, Rapoport et al., 2009, Joris and Monique Anna Johanna, 2009, Ravera et al., 2012). There is the potential that if the drugs within these groups were reviewed individually, the outcome measures of interest may be varied as some drugs may be more or less driver-impairing than others. Therefore, the purpose of this study was to perform a systematic review of the literature to investigate which specific medications, including typical prescription or over-the-counter drugs, may be associated with an increased risk or odds of MVC and/or driving ability among licensed drivers 15 years of age and older.
Section snippets
Study eligibility
The inclusion criteria for studies, which was defined a priori, were as follows: (1) English-language studies published on or after January 1, 1960, (2) licensed drivers 15 years of age and older, (3) studies published in a peer-reviewed journal or non-published (i.e. “grey literature”), which included master’s theses, doctoral dissertations, and conference papers, (4) studies limited to randomized control trials, cohort studies, case-control studies, or case-control types of studies, i.e. case
Study characteristics
The search processes for the selection of studies, as well as reasons for excluding studies, are presented in Fig. 1. Of the 6516 records obtained, 208 studies met the original study question. Of these 208 studies, 27 pertained to the association of specific medications and the odds or risk of motor vehicle collision, while the others pertained to the association of specific medications and affected driving ability determined through the use of driving simulators (n = 90) or actual driving
Findings
The principal finding of this study is that among the 53 specific medications investigated by the 27 studies included in this review, 15 medications (28.3%) were associated with an increased risk of motor vehicle collision. The medications that were associated with an increased risk of collision were: Buprenorphine, Codeine, Dihydrocodeine, Methadone, Tramadol, Levocitirizine, Diazepam, Flunitrazepam, Flurazepam, Lorazepam, Temazepam, Triazolam, Carisoprodol, Zolpidem, and Zopiclone. Two (3.8%)
Acknowledgements
The authors would like to thank Jean Siebert of the West Virginia University Health Sciences Library for her assistance with the search process. MZ and TMR received support from grants (R01HD074594 from the National Institutes of Health, National Institute of Child Health & Human Development; R21CE001820 from the Centers for Disease Control and Prevention, National Center for Injury Prevention and Control). GAK was partially funded by the National Institute of General Medical Sciences (NIGMS)
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