Crash risk associated with the use of medicinal drugs: a ...lib.ugent.be/fulltxt/RUG01/002/272/963/RUG01... · Apart from driving under influence of a certain medicinal drug, other

Academic Year 2015 - 2016

Crash risk associated with

the use of medicinal drugs:

a meta-analysis

Elise Hente

Promotor: Prof. Dr. Prof. Dr. Alain Verstraete

Dissertation presented in the 2nd Master year in the programme of

Master of Medicine in Medicine

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“The author and the promotor give the permission to use this thesis for consultation and to

copy parts of it for personal use. Every other use is subject to the copyright laws, more

specifically the source must be extensively specified when using results from this thesis.”

Date

(handtekening)

Name (student) (promotor)

3

Acknowledgements A number of people deserve to be mentioned here, as this whole process would not have been

the same without them. First of all, I’d like to thank my thesis promoter, Professor Alain

Verstraete. He has guided me throughout this whole process, providing me of clear guidelines

and information while being available for feedback more than the average promoter would

have been. Furthermore, I’d also like to thank him for his patience, since I’m not exactly the

Usain Bolt amongst students considering rapid progress. Secondly, I’d like to thank my

eternal radiant mother. Whereas Professor Verstraete had to be patient with me for over three

years, she’s been patient with me for the last 24 years. I couldn’t imagine a world without her

optimism and kind heart, accepting me for the rascal that I am and embracing our common

features like no other ever could. You’ve always managed to keep my head above water and

my feet on the ground, and helped me become the life enjoying person I am today. For this,

no words could ever thank you enough. Thirdly, I’d also like to mention and thank my little

brother, knowing me better than I probably know myself. Words were never our strongest

point, which you solved by joining me on stressful days with your guitar, playing the most

beautiful songs anyone could ever imagine. I will never forget the first time you played Ed

Sheeran to calm me down, since I cried like a four year old. Never throw away this talent, for

it can change lives. Furthermore, I also have to thank you for participating in my silly kitchen

dances, since they often make me laugh so hard I can skip ab-training for a week. Next, I

would like to thank my dad. Your rational point of view on things and eloquent silence mean

more to this family than you could ever imagine, yet every motivational word spoken by you

is more convincing to me than all others combined. We are so much alike, you and I,

therefore words are often irrelevant and judgment is rare. Fifth, I would like to thank my best

friend, the ever-enthusiastic Magalie Van Loo. You are a role model to me both as a friend

and as a fellow student, motivating me through every chapter of my life. Never have you ever

been judgmental, therefore allowing me to be the purest form of myself in every way. For

that, I could never thank you enough. Sixth, I’d like to mention and thank the Department of

Biostatistics of the University of Ghent, for both providing guidelines and information

concerning statistics. I’d also like to thank my fellow student Matthias Soens for scoring all

articles a second time and guiding me through some difficulties of this meta-analysis. You

had no obligation to do so, since your own work on drugs and driving finished last year, yet

you took the time to refresh your memory and explain some necessary things for which I am

very grateful.

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Index

Acknowledgements .................................................................................................................... 3

Abstract ...................................................................................................................................... 6

1. Introduction ............................................................................................................................ 8

2. Considerations and difficulties ............................................................................................... 9

2.1. Confounding factors ........................................................................................................ 9

2.1.1. Drug dose ................................................................................................................. 9

2.1.2. Individual factors ...................................................................................................... 9

2.1.3. Environmental factors .............................................................................................. 9

2.1.4. Use of other substances .......................................................................................... 10

2.1.5. Drug activity ........................................................................................................... 10

2.1.6. Obtaining samples .................................................................................................. 10

2.1.7. Patient’s adherence to therapy ................................................................................ 10

2.1.8. Time period examined ............................................................................................ 10

2.1.9. Single vs. multiple vehicle crashes ........................................................................ 11

2.2. Types of research .......................................................................................................... 11

2.2.1. Pharmacoepidemiological studies .......................................................................... 11

2.2.2. Case-control studies ............................................................................................... 12

2.2.3. Culpability studies .................................................................................................. 12

2.2.4. Cohort studies ......................................................................................................... 12

2.3. Comparing studies ......................................................................................................... 13

2.3.1. Measuring methods ................................................................................................ 13

2.3.2. Case and control definition .................................................................................... 14

2.4. Types of drugs ............................................................................................................... 14

2.4.1. Depressants ............................................................................................................. 15

2.4.2. Narcotics ................................................................................................................. 15

2.4.3. Antidepressants ...................................................................................................... 15

2.4.4. Stimulants ............................................................................................................... 16

2.4.5. Minor analgesics .................................................................................................... 16

2.4.6. Anti-histamines ...................................................................................................... 17

2.4.7. Respiratory agents .................................................................................................. 17

2.4.8. Cardiovascular medication ..................................................................................... 18

3. Methods ................................................................................................................................ 19

3.1. Formulation of the research question ............................................................................ 19

3.2. Setting inclusion and exclusion criteria......................................................................... 19

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3.3. Searching and selecting the literature ............................................................................ 19

3.4. Quality appraisal of the literature .................................................................................. 22

3.5. Statistical analysis ......................................................................................................... 24

4. Results .................................................................................................................................. 25

4.1. Study characteristics ...................................................................................................... 25

4.2. Meta analysis on crash risk ........................................................................................... 25

4.2.1. Depressants ............................................................................................................. 25

4.2.2. Narcotics ................................................................................................................. 29


4.2.4. Stimulants ............................................................................................................... 32

4.2.5. Minor analgesics: NSAIDs, paracetamol, others ................................................... 33

4.2.6. Antihistamines ........................................................................................................ 34

4.2.7. Respiratory agents .................................................................................................. 35

4.2.8. Cardiovascular medication ..................................................................................... 36

4.2.9. Other medication .................................................................................................... 38

4.2.10. Summarizing table of all results ........................................................................... 38

4.3. Culpability meta-analysis .............................................................................................. 40

4.3.1. Depressants ............................................................................................................. 40


4.3.3. Other groups of medicinal drugs ............................................................................ 41

4.3.4. Summarizing table of all culpability study results ................................................. 42

5. Discussion ............................................................................................................................ 42

5.1. General considerations .................................................................................................. 42

5.2. Depressants .................................................................................................................... 43

5.3. Narcotics ........................................................................................................................ 44

5.4. Antidepressants ............................................................................................................. 44

5.5. Stimulants ...................................................................................................................... 45

5.6. Minor analgesics ........................................................................................................... 45

5.7. Antihistamines ............................................................................................................... 46

5.8. Respiratory agents ......................................................................................................... 46

5.9. Cardiovascular drugs ..................................................................................................... 47

5.10. Limitations .................................................................................................................. 47

5.11. Final conclusions ......................................................................................................... 47

6. References ............................................................................................................................ 49

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Abstract Objective

To perform a meta-analysis in order to determine whether the intake of certain types of

medicinal drugs by car drivers increases the risk on having a motor vehicle accident .

Methods

After an extensive literature search, a quality assessment was conducted on all retrieved

articles. A scoring system was created in order to distinguish low, average and high quality

studies.

Eight groups of medicinal drugs were included: depressants, narcotics, antidepressants,

stimulants, minor analgesics, antihistamines, respiratory agents and cardiovascular

medication. We tried to further subdivide each category as much as possible.

All obtained results were analyzed using Review Manager 5.3., and both odds ratios and study

heterogeneity were calculated for all medicinal drug groups.

Results

A total of 42 studies were included in this meta-analysis, consisting of eight culpability

studies, 21 case-control studies, seven cohort studies, and two articles including both a case-

control and a culpability study.

For anxiolytic and tranquillizing depressants and hypnotic depressants we calculated an OR of

1.98 [1.68, 2.32] and 1.89 [1.57, 2.29] respectively. Culpability analysis showed no

significant correlation for driving under influence of depressants and causing an accident.

For narcotics we became an OR of 2.95 [2.06, 4.21], the analysis of opiates separately

resulted in an OR of 3.16 [2.09, 4.79].

For antidepressants we became an OR of 1.57 [1.08, 2.29], for tricyclic antidepressants and

other non-sedating antidepressants we calculated an OR of 1.93 [1.26, 2.96] and 1.45 [1.04,

2.01] respectively. Culpability analysis also showed no significant correlation for driving

under influence of antidepressants and causing an accident.

We became a significantly increased OR for having a fatal crash when driving under

influence of either benzodiazepines or narcotics.

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Not enough studies on medicinal stimulants were included to become a reliable result.

For minor analgesics we calculated an increased OR of 1.32 [1.09, 1.59]. The use of NSAID’s

while driving was assessed separately, but did not show any significant association with

having a car accident.

Antihistamines, respiratory agents and cardiovascular drugs were also assessed separately, but

they also did not show any significant increase in OR.

Conclusions

There’s a significant correlation between driving under the influence of anxiolytic

benzodiazepines and tranquillizers, hypnotics such as Z-drugs, narcotic analgesics such as

opiates, tricyclic and newer, non-sedating antidepressants and the occurrence of a car

accident. Short half-life benzodiazepines tend to form less of a risk than long half-life

benzodiazepines.

The inclusion of more studies is necessary in some drug categories in order to become a

significant result. Most results, however, matched our expectations.

Apart from driving under influence of a certain medicinal drug, other factors such as the

severity of the underlying disease and a patient’s quality of life should also be taken into

consideration, and the importance of the correct use of medicinal drugs is emphasized once

again.

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1. Introduction Traffic accidents are one of the main causes of traumatic injuries worldwide, causing on

human suffering and a burden on the health care system, and every attempt of preventing

these accidents is therefore important to society (47). The role of alcohol in increased crash

risks has been established beyond doubt, but the role of medicinal drugs remained uncertain

for a longer period of time (16). Since a lot of effects - and side effects- of several types of

medicinal drugs disturb sensory functions, perception, cognitive and motor skills, the need for

studies on driving performance under the influence of these drugs does not seem irrational

(60). This has also been concluded in a Consensus Development Panel in the United States in

1985: “most drugs that affect the central nervous system have the potential to impair driving

ability” (39). The use of lithium as a mood stabilizer, for instance, has been linked to impaired

memory and slow reaction times (22). Other commonly detected drugs with such effects

include barbiturates, benzodiazepines and several sedative-hypnotic drugs. These drugs can

impair human functions, either alone or when combined with alcohol (41).

Initially, experimental studies were conducted using instrumented cars. These simulation

studies have provided much useful information on the role of certain drugs on driving

performance, but were, unfortunately, not able to accurately predict the effects of these drugs

under actual driving conditions since necessary resources for this kind of research were not

available (16, 60, 61).

Besides alcohol consumption, medicinal drug use in Western countries has increased

considerably over the past decades. Since many drugs in crash victims are liable to impair

driving skills this has led to the assumption that these drug effects also increasingly endanger

road traffic. This comes with a rising concern about the use of medicinal drugs for those who

participate in traffic. Nevertheless, after years of research, there is still uncertainty as to

whether this translates into an increased crash risk. (16, 31). On the other hand, people need

these drugs to get better, and they should be prevented from driving only if the crash risk is

significantly increased. It is not desirable that patients stop taking necessary drugs just to be

allowed to drive. Meta-analyses can provide an answer to this uncertainty by comparing

estimates of risk from different studies, which is exactly our main goal in this article.

Apart from an increased consumption of numerous medicinal drugs, there are several other

factors pleading for an increased risk. The aging population, for example, is even more likely

to use several medicinal drugs, such as anticoagulants and psychotropic agents, but they are

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also more likely to be affected by these drugs due to age-related changes in pharmacokinetics

and -dynamics which could make driving even more difficult. (13, 48). Elderly patients with,

for instance, diabetes may also have more frequent motor vehicle crashes due to

complications associated with advanced disease, such as retinopathy and neuropathy, or from

hypoglycaemia, a common side effect of some anti-diabetic drugs (30). Increased

multimorbidity and the resulting use of different types of prescription drugs (polypharmacy)

is also more likely in the aging population, which leads to a greater risk of interactions and

adverse drug effects (44). These and numerous other considerations make it interesting

comparing younger and elder populations, both driving under influence of the same drugs.

2. Considerations and difficulties A positive test result indicates that the driver has used the detected drug, but does not

necessarily mean that the driver was impaired by the drug at the time of the accident.

Variations in numerous factors of both driver and ingested drug make it difficult to determine

driver impairment (38).

2.1. Confounding factors

2.1.1. Drug dose

Despite the knowledge that certain drugs impair performance the link with crash risk remains

weak and experimental research, so far, does not specify the magnitude of the effect. This is

mainly because all studies depend on a range of factors that need to be taken into account, for

example the doses of the drugs used by drivers causing a crash (39).

2.1.2. Individual factors

Subsequently there are additional, individual factors that make it even more difficult to relate

drug concentration levels to driving impairment. Pharmacological and physiological factors

such as individual tolerance, health state including visual function and cognitive status,

metabolism, and interactions with other drugs and/or alcohol should be taken into account,

supplemented by acquired factors such as driving experience and risk taking behavior. The

treated disease could also influence a person’s driving patterns. Patients treated for anxiety,

for instance, might be less likely to drive, creating bias in the opposite direction (59, 60).

2.1.3. Environmental factors

It is not surprising that several car accidents occur under different circumstances. These

circumstances may -to some extent- contribute to an accident, and should therefore not be

overlooked. Weather conditions could impair the vision of a driver with an otherwise perfect

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eyesight, and poorly maintained roads or cars with defects could also be causal factors of an

accident, even with most experienced, drug-free drivers.

2.1.4. Use of other substances

Some of the drivers included in a study are not only under the influence of medicinal drugs,

but have also consumed alcohol, recreative drugs, or both. Combinations and drug

interactions could lead to misinterpretations of driving under the effects of a certain medicinal

drug, since the occurring crash might have been avoided if alcohol or another prescription

drug was not present.

2.1.5. Drug activity

Drug kinetics are responsible for the duration of the sedative effect as well. Benzodiazepines

with long half-lives, for example, have been shown to be associated with an increased risk of

motor vehicle accidents, whereas benzodiazepines with a much shorter half-life (such as

triazolam) do not show this association (5, 29).

2.1.6. Obtaining samples

Another difficulty arises from the fact that the actual presence of medicinal drugs in the

human body can’t be measured unless blood-, saliva- or urine samples are taken. In many

countries laws make it impossible to take blood samples from uninjured drivers, which are

often required for establishing a control group, resulting in unacceptable high refusal rates for

collecting blood samples from random drivers. This creates a very different situation from the

one obtained for driving under the influence of alcohol, which can be measured using a

simple breath analyzer. There are alternative measuring methods available in these situations,

but they come with certain limitations, and the use of non-equivalent biological samples for

cases and controls could also lead to a comparison bias (5, 24). Furthermore, most analytical

methods detect only a limited number of drugs, missing other impairing substances.

2.1.7. Patient’s adherence to therapy

When drug exposure is measured by self report or records of prescriptions dispensed on an

outpatient basis, over- and underreporting or non-compliance could lead to an over- or

underestimation of the medicinal drug intake (12).

2.1.8. Time period examined

Some studies include any drug intake in years prior to a traffic accident, others only take

recent drug use into consideration and some do both. The results included in this meta

analysis are those of cases still in therapy, and therefore patients who were recently exposed

to the studied drug before having an accident. Unfortunately, not all studies apply the same

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time span, resulting in different conceptions of the term “recently” and slight differences in

examined time periods.

2.1.9. Single vs. multiple vehicle crashes

Accidents can occur between multiple vehicles, the so called multiple vehicle crashes (MVC),

but also amongst cars and stationary objects. In these cases of single vehicle crashes (SVC)

one can assume that the driver itself is at fault, which makes the role of a possible

accompanied drug intake as a causal factor more reliable compared to cases where both

parties could have caused the accident, regardless of whoever drove under influence of drugs.

Furthermore, not being able to avoid an MVC, even as a non culpable driver, leads to multiple

considerations if prescription drugs are involved. These are the basic considerations in

culpability studies, and will be discussed separately.

2.2. Types of research Several types of studies try to determine a relationship between medicinal drugs and car

crashes. These can be divided in experimental and epidemiological studies, each consisting of

a different methodology with its own strengths and limitations. Since all of these studies are

conducted differently, it is important to take a difference in results due to a different

methodology into consideration.

In most of these studies, accident risk associated with the use of medicinal drugs can be

assessed by comparing their prevalence among the general driving population (controls) with

the prevalence among drivers who were injured, killed or involved in a traffic accident

(cases).

The studies used in this meta-analysis are sample surveys, case-control studies, culpability

studies, case-crossover studies and cohort studies. Pharmacoepidemiological studies were not

included, since they do not include both case- and control group. (20)

2.2.1. Pharmacoepidemiological studies

Epidemiological studies on drugs examine the prevalence of drug use in various driving

populations. The lack of a reference group, in this case for instance persons admitted to an

emergency department, however, results in the inability to calculate estimates of risk. Only

when comparing the prevalence of a certain drug among the general driving population with

the prevalence of the same drug in crashed drivers, these studies become useful for our meta-

analysis. This seems like a simple problem to solve, but in reality several kinds of difference

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among these studies make it a rather difficult task. These difference are discussed in chapter

2.3. (20).

2.2.2. Case-control studies

Case-control studies are a type of epidemiological study where, in this case, crashed drivers

are compared to a suitable control group of drivers not having a crash. In both groups the

percentage of drivers testing positive for a certain medicinal drug is determined. Cases could

be, for instance, crashed drivers admitted to an emergency department, with drivers randomly

stopped at gas stations serving as their control group. Matching these cases and controls by

age, gender, time of day, day of the week and so on, results in a reliable methodology for

examining the relationship between drugs and driving. In reality, however, this type of study

is very time consuming and expensive depending on the applied research method (20).

2.2.3. Culpability studies

Culpability studies are case-control studies where the population also consists of drivers

involved in an accident. They investigate whether there is an association between driving

under the influence of medicinal drugs and the responsibility for a traffic accident. The cases

in these studies are usually drivers who are partially or completely responsible for the car

crash, while the control group consists of drivers who are not responsible for the crash.

Responsibility is often assessed using so called culpability scores by evaluators who are not

aware of the toxicology results.

Culpability studies, however, also come with certain limitations. The true source of

responsibility, however, can be misjudged and might cause a misclassification bias, leading to

an underestimation of the relative risk. Furthermore, control subjects may have borne some

responsibility, as they failed to avoid a crash. Finally, culpability studies come with a high

percentage of responsible drivers when fatally injured drivers are included, leading to

difficulties in finding statistical significant differences between drug-free and drug-positive

drivers (20).

2.2.4. Cohort studies

A cohort or panel study consists of a passive follow-up of a population and an observation of

certain characteristics or events related to this group of people. In our case, people using a

certain type of medicinal drug are followed over a certain period of time, and afterwards the

total number of accidents in this population are documented. These results can be compared to

another population, in this case, logically, persons not receiving the same medicinal drug

during that same period of time. Databases are often used for collecting all necessary

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information, such as police records, hospital records and prescription records, which can

matched for each case. Cohort studies are therefore often able to include large populations in

a cost-effective way, since all examinations have already been executed by other parties.

2.3. Comparing studies The aim of many research groups is to extend the work of previous studies, and it is therefore

important to work thoroughly and critically on one’s own research and the studies they are

founded on. A proper insight of these methodologies is therefore definitely a necessity to

deliver a qualitative reading.

First of all, there is a difference in drug measuring methods used by the different types of

studies. We made a subdivision according to the most common used methods and discussed

their strengths and limitations in a corresponding table. We also applied this subdivision in the

scoring system used for the quality assessment of the articles included in our meta-analysis

(see 3. Methods).

2.3.1. Measuring methods

Table 2.3.1. Strengths and limitations of different measuring methods used in studies

investigating crash risk and medicinal drug use

Measuring

Method

Strengths Limitations

Self report of drug

use

• Easy to obtain

• Cost-effective

• Results depend on the subject’s cooperation and honesty,

and are therefore not always accurate

• Possibility for over/underreporting

Drug prescriptions • Low to non-existent non-

response rate

• Easy to obtain by databases

• One does not know whether patients who filled

prescriptions for certain drugs actually took these

medications and, if so, for how long and in what quantity

(non-compliance to therapy)

Urine sample or

mix urine + other

samples

• Urine: easy to obtain

• Possible time-delay between the crash and the actual

sample collection

• Some metabolites may not indicate recent use of a drug,

and for that reason may not reflect impairment at the time

of the crash

Saliva sample or

mix saliva + blood

• Saliva: easy to obtain • Possible time-delay between the crash and the actual

sample collection



of the crash

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Measuring

Method

Strengths Limitations

Blood sample • Drug concentrations can be

determined precisely

• More difficult to obtain in certain situations

• Possible time-delay between the crash and the actual

sample collection



of the crash

• Expensive

• Depending on analytical techniques applied.

These measuring methods differ in reliability, with blood samples as most- and self report as

least reliable method, with the highest probability of sampling bias. This consideration

becomes even more important in the comparison of different studies with each other, since

they might not be comparable at all.

As mentioned in table 2.3.1., the presence of certain metabolites in a biological sample does

not necessarily mean that the driver was under influence of the drug at the time of sampling

nor the crash.

Furthermore, different analytical techniques are used to analyse the samples, with different

limits of detection and quantification. Different cut-off levels to define a positive sample are

also applied (20).

2.3.2. Case and control definition

Depending on the study, both case and control groups do not always accurately represent the

general population, and results may therefore under- or over-estimate the prevalence of drugs

in these groups. Sample populations can differ in several sociodemographic factors, such as

age and gender. Including only drivers under a certain age, for instance, could lead to a much

higher proportion of drug-positive samples than other similar studies (46). The wide variety of

confounding factors mentioned in 2.1. are all examples of factors that should be taken into

consideration when comparing cases and their control-population. Since they have already

been discussed in detail elsewhere, we will not repeat them in this paragraph.

2.4. Types of drugs

Drugs can be subdivided in many ways. A common used classification is one according to

purpose, distinguishing recreational from medicinal drugs. In this meta-analysis we only focus

on the use of medicinal drugs, which therefore excludes common used illicit drugs such as

cannabis, cocaine, LSD, psilocybin, et cetera. Medicinal use of these drugs was not taken into

consideration, since they are rarely prescribed and even illegal in most countries.

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2.4.1. Depressants

Under the term “depressants” we consider any drug that reduces central nervous system

function or functions in any other part of the body. Both short and long term effects could be

considered dangerous when used while driving, since they often include coordination and

vision impairment, alterations in time perception and reaction time, reduced concentration and

slowed brain function. A lot of them also come with drowsiness as a hangover effect, which

in itself could also lead to impaired driving functions. Tolerance for many of these

depressants, on the other hand, could lead to a distorted interpretation of depressant use on a

longer term. Drugs included in this group are benzodiazepines, both anxiolytic and hypnotic,

Z-hypnotics, such as zolpidem and zopiclone, and other tranquillizers.

Pharmacokinetic properties of benzodiazepines, such as their half-life and whether or not

active metabolites are formed, may affect the duration of the effects. There is a classic

distinction of benzodiazepines in long half-life BZD, intermediate half-life BZD and short

half-life BZD. They are indicated in cases of insomnia, anxiety, spasticity, dystonia,

myoclonus and epilepsy, and come with a range of side-effects leaning close to their

therapeutic effects, as mentioned in previous paragraph.

Z-hypnotics differ in chemical structure, but their working mechanism and side effects are

similar to those of the benzodiazepines (4).

2.4.2. Narcotics

Narcotics include all types of opioid analgesics, and are mostly subdivided according to their

analgesic ability. Narcotics with low analgesic potency include codeine and tramadol,

followed by the stronger narcotic agent pethidine and very powerful narcotics such as

morphine, methadone and oxycodone.

They are indicated when non-narcotic analgesics do not suffice in the treatment of moderate

to severe pain, and come with a wide variety of side-effects such as constipation, sedation –

especially in the first days of therapy-, euphoria, orthostatic hypotension, sweating and

pylorus spasms. Tolerance occurs, depending on drug dose and duration of therapy, as well as

physical and psychological dependence. Almost all narcotic analgesics show interactions with

other medicinal drugs (4).

2.4.3. Antidepressants

Antidepressants are subdivided according to their chemical structure and/or their working

mechanism. Most commonly used agents include the reuptake inhibitors, selective such as the

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SSRI’s or non-selective such as de tricyclic antidepressants. Prescription of other agents such

as MAO-inhibitors is less common, and most studies on driving under the influence of

antidepressants focus on the first two categories mentioned.

They are best known for their therapeutic efficacy in depression, but are also used for the

treatment of panic- and generalized anxiety disorders, post-traumatic stress disorder (PTSD),

obsessive compulsive disorder (OCD), and some other, less common, specific indications.

Side effects include sexual dysfunction, tremor and sweating, withdrawal symptoms and

anticholinergic effects, lowering of the seizure threshold and triggering the manic phase in

patients with a bipolar disorder. There’s also an increased risk of aggressive behavior and

suicidal thoughts, especially at the start of treatment and mostly with SSRI therapy.

Hyponatremia also occurs as a side effect in several cases, accompanied by an increased risk

of agitation and confusion, especially in the elderly and, again, mostly with SSRI therapy.

Lastly, every antidepressant comes with a range of drug interactions, making the occurrence

of unwanted effects even more likely when not used properly (4).

2.4.4. Stimulants

Prescription stimulants are amphetamine-like drugs with, as the name says, stimulating effects

on the body. They are indicated in cases of ADHD or narcolepsy. One could say these effects,

such as an improved concentration and alertness, could have a positive effect on one’s driving

skills. Side-effects of these stimulants on the other hand, such as hyperexcitability, irritability

and panic attacks could lead to the opposite result. Insomnia also often accompanies

medicinal stimulant intake, leading to fatigue during the next day and therefore an opposite

effect as initially intended.

Examples of medicinal drugs included in this group are methylphenidate and

dextroamphetamine (15).

2.4.5. Minor analgesics

Two widely used examples of this subgroup are paracetamol and NSAIDs. All opiate and

opioid analgesics were included in the “narcotics” group (see 2.4.2.).

Paracetamol has well known analgesic and antipyretic, but no anti-inflammatory properties.

Because of the favorable tolerability and safety profile, paracetamol is considered to be the

first choice in symptomatic treatment of pain and fever. Adverse effects are limited, namely a

17

limited irritation of the gastro-intestinal tract and, more dangerously, hepatotoxicity when

being overdosed. This can be prevented by adhering to the daily recommended dose.

NSAIDs have analgesic, antipyretic and anti-inflammatory properties. They have their

indication in a variety of inflammatory diseases, and in the treatment of pain of various

causes. Side effects of these substances are extensive, including gastrointestinal injuries,

hypertension, acute renal failure, hepatotoxicity, interactions with other drugs and

hypersensitivity reactions. Immediate side effects on the nervous system, however, such as

dizziness or drowsiness, do not occur. Given their wide range of side effects, these drugs

should be taken with caution. Patients at risk should receive the most appropriate NSAID for

their profile, based on drug selectivity, interactions and dose, to minimize the occurrence of

undesirable effects as much as possible (4).

2.4.6. Anti-histamines

The H1-antihistamines are widely prescribed in treatment and prevention of allergies. They

are indicated in a wide range of allergic reactions such as hay fever, urticaria, allergic

reactions to food or medicines, et cetera. Different generations of antihistamines have been

developed, all of them containing a specific range of indications and side-effects. The most

common side effects are sedation, varying according to product, individual and age, and

anticholinergic effects. Interactions with other drugs are also possible.

When taking an antihistamine causing drowsiness, it is often advised to take the drug before

going to bed rather than taking it in the morning. Therefore, some of the side-effects can be

partially avoided, possibly decreasing traffic crash risk. Current generation antihistamines are

also much less impairing then first-generation antihistamines.

2.4.7. Respiratory agents

Respiratory agents consist of medication for the treatment of asthma and COPD, such as β2-

agonists, anticholinergic agents and corticosteroïds, as well as other medicinal drugs such as

antitussives, mucolytics and expectorants. There is a distinction between agents used in an

asthma-attack, which are short-acting, and agents used for the maintenance therapy of asthma,

which are long-acting. The same applies to the treatment of COPD exacerbations and the

maintenance therapy of COPD, and the majority of these substances is administered by

inhalation.

Side effects of medicinal drugs used for the treatment of asthma and COPD include

nervousness, insomnia, headaches, tremors, tachycardia and hypokalaemia, as well as certain

18

interactions with other drugs. Codeïne as an antitussive sometimes causes central side effects

such as dizziness, somnolence and sedation, as well as a few unwanted gastro-intestinal side

effects and an increased risk of triggering an asthma-attack. Codeïne, however, was included

in the narcotic drug group in our analysis. Other antitussives cause respiratory depression,

confusion and anticholinergic symptoms as side effects. Side effects of some mucolytic agents

include dizziness, somnolence and headaches (4).

2.4.8. Cardiovascular medication

Three cardiovascular agents were included in our study, namely diuretics, calcium channel

blockers and anticoagulants, although the last mentioned does not really belong to this

category.

Diuretics lower both morbidity and mortality in hypertension. Different types of diuretics are

indicated based on a patient’s profile. Side effects include ion shortages, weakness and muscle

spasms, photosensitivity, central effects such as depression and agitation, and a wide range of

interactions with other medicinal drugs.

Calcium channel blockers are indicated in several cardiovascular diseases, such as arrhythmia,

atrial fibrillation, Raynaud's syndrome, stable and vasospastic angina and supraventricular

tachycardia. Important side effects include peripheral vasodilatation with headaches, edema,

heath waves, hypotension and compensatory tachycardia. Interactions with other medicinal

drugs are also possible.

Anticoagulants are mainly subdivided in short acting heparins and longer acting coumarins or

vitamin K antagonists. Heparins are indicated after a pulmonary embolism, DVT, myocardial

infarction and unstable angina. Their side effects include hemorrhages, thrombocytopenia,

hyperkalaemia, allergic reactions and osteoporosis in long-term use. Vitamin K antagonists,

such as warfarin, have a narrow therapeutic-toxic margin. They are indicated in treatment and

prevention of thromboembolic processes, in patients having received a heart valve prosthesis

and in patients with atrial fibrillation. Their side effects also include hemorrhages, as well as

allergic reactions and skin necrosis (4).

19

3. Methods

3.1. Formulation of the research question The formulation of the research question was mainly based on the meta-analysis of Rune

Elvik (Risk of road accident associated with the use of drugs: A systematic review and meta-

analysis of evidence from epidemiological studies) (19). This article also shows evidence that

there is plenty of literature available on the topic of drugs and driving. Our primary aim was

to perform a similar research, also including relevant articles and studies dating from after

Elvik’s meta-analysis was published, where finding a relationship between the use of different

types of medicinal drugs and the risk of having a car crash when driving under influence of

these drugs is set as our main goal. The eventual title became “Crash risk associated with the

use of medicinal drugs: a meta-analysis”.

3.2. Setting inclusion and exclusion criteria Aiming at carrying out a good meta-analysis, searching for relevant articles forms a

fundamental base. An important requirement to be included in our analysis was the ability to

determine estimates of risk. Therefore we had to look for studies containing both cases and a

corresponding control group, as carried out in case-control and cohort studies.

Epidemiological studies not defining a reference population were therefore excluded.

Studies containing a suitable methodology also had to include the right research questions to

fit our analysis. This means the study had to be based on medicinal drugs of any kind, and

determining the risk of a car crash. Studies researching accidents with pedestrians, bicycles

and only motorcycles or non-vehicle related accidents were therefore also excluded. We

considered subdividing general users of a medicine and users under influence of this medicine

at the time of the accident. Since users of medicinal drugs relevant for this study (such as

antidepressants) often take these drugs on a chronic basis, and since one is considered a non-

user once their therapy has ended, we included both groups in our analysis to prevent a

selection bias.

Given that our main goal was to extend the meta-analysis carried out by Elvik, we started

including studies published after Smart and Fejer’s sample survey from 1975, which is also

Elvik’s first included article (19, 61).

3.3. Searching and selecting the literature We systematically searched for literature relevant to our meta-analysis, using PubMed, Web

of Science and Science Direct as databases. The comprehensive collection of scientific

20

articles offered by PubMed are accessible by using specific search terms. For our study, terms

such as “crash risk” and “medicinal drugs” were used to become a first selection of articles.

Because we only want to include studies published after Smart and Fejer’s sample survey

from 1975 (see 3.2.) older studies were not selected, and we eventually ended up with a first

selection of 8 articles. The same procedure, therefore applying the same search terms, was

used by databases Web of Science and Science Direct, leading to respectively 28 and 27

selected articles. Immediately after finding an article, abstract and title were assessed and a

first selection was made, leaving out 23 of the search results.

Out of the 40 articles retrieved, we started excluding articles not meeting our inclusion criteria

after reading the full text. These inclusion criteria are discussed elsewhere (see 3.2.), and

applying them left us with 13 articles.

A last number of articles was found by reference screening during the literature study,

eventually leading to a final number of 41 articles.

21

Figure 3.3.: Flowchart for the literature search

22

3.4. Quality appraisal of the literature Before we could start our meta-analysis of existing studies on the relationship between crash

risk and medicinal drug use, we had to develop a scoring system that would serve as a

measure of quality of an article included in our statistical assay. This scoring system was

based on the scoring system applied by Rune Elvik (19) and the Newcastle Ottawa Scale (49).

We scored studies based on their measuring method, accident severity, confounding factors

and, if mentioned or measured, dose-related crash risk. Since each study has a different

methodology, some scoring topics slightly differ from one another. These similarities and

differences are shown in the corresponding table. In order to minimize errors in our scoring,

all studies were scored by two different persons. The final results were then compared and

revised where necessary.

A distinction in measuring methods was made between self report, drug prescriptions

received, urine samples or a combination of urine and other samples, oral fluid or a

combination of saliva and other samples, and blood samples for both cases and controls.

These five measuring methods respectively resulted in a score from 1 to 5.

Accident severity was scored based on specificity of the sustained injuries. Studies describing

a crash as simple as “accident” were scored 0 points compared to studies having one or more

specific levels of injury, such as “slightly injured”, “severely injured” or “fatally injured”,

which received respectively 1 and 2 points.

The extent into which variables were adjusted for confounding factors formed a third scoring

topic. “Age”, “gender”, “driving experience” - often referred to as “mileage driven”-, “drug

dose”; “use of alcohol” and “health status” made six confounders, each counting for 0.5

points. Every extra confounding factor received an additional 0.5 point, but only two extra

confounders were allowed in the final rating. We decided to bisect the points to minimize the

impact on the final score of studies not assessing for confounding factors.

Studies investigating dose dependence received an additional point if the research was made,

even without showing any relationship, and another additional point if it did.

A total was calculated for each study and converted to a percentage. Studies scoring 0 to 39%

were considered to be low quality studies, studies scoring 40-59% were considered to be

average quality studies, and studies scoring 60-100% were considered to be high quality

studies.

23

Table 3.4.: Scoring system used in our quality assessment

Type of study Scoring topic

All studies Measuring method of drug use (Total of 5 points)

• Self report = 1

• Prescriptions = 2

• Urine sample or mix urine + others = 3

• Saliva sample or mix saliva + blood = 4

• Blood sample = 5

Severity of the crash (Total of 2 points)

• Mix of injuries and property damage = 0

• Specific level of injury (fatal/injury/property damage) = 1

• At least 2 levels of accident severity included in the same study = 2

Confounding factors (risk adjusted for…) (Total of 5 points)

• Age = 0.5

• Gender = 0.5

• Driving experience = 0.5

• Dose of drugs used = 0.5

• Other drug use = 0.5

• Alcohol consumption = 0.5

• Health status, comorbidity = 0.5

• 1 other confounding factor: +0.5 extra point

• >1 other confounding factors: +1 extra point

Dose dependency (Total of 2 points)

• Not tested = 0

• Tested, but not found = 1

• Tested and found = 2

Case-control study

Culpability study

Selection of cases, case definition (Total of 2 points)

• Not described = 0

• Database or selfreport = 1

• Independent confirmation = 2

Case representativeness (Total of 1 point)

• Possible selection bias or not described = 0

• Representative cases = 1

Selection of controls

• Not described = 0

• Hospital controls = 1

• Community controls (roadside study) = 2

Control representativeness (Total of 1 point)

• Not representative to cases = 0

• Representative to cases = 1

Same measuring method used for cases and controls (Total of 1 point)

• No = 0

• Yes = 1

24

Non-response rate (Total of 2 points)

• Not described or taken into account = 0

• Described but with a large difference between cases and controls = 1

• Comparable between cases and controls

OR 5% absolute difference

OR 50% relative difference = 2

Cohort study Cohort representativeness (Total of 1 point)

• Possible selection bias or not described = 0

• Representative cohort = 1

3.5. Statistical analysis Based on the meta-analysis conducted by Abridge et al (1), we decided to use the same

statistical software, namely Review Manager 5.3. Two different analyses were made: one

investigating drug use and concomitant crash risk, and one investigating responsibility of a

crash and concomitant drug use.

Some study results consisted of percentages, for example Li et al, McGwin et al, Brault et al

and Matthijssen et al (11, 38, 42, 43). These pecentages were converted to absolute numbers

and rounded where necessary, which could lead to an over- or underestimation.

An important limitation of Review Manager is the inability to include existing estimates of

risk. Therefore we were unable to include the studies by Dischinger et al (14), Reguly et al

(57), Corsenac et al (12), Orriols et al (50), Gibson et al (23), Meuleners et al (44),

Wadsworth et al (63), Hebert et al (27), Rapoport et al (54), Gustavsen et al (26), Bachs et al

(3), and Bramness et al (9, 10), which would have made an important asset to our meta-

analysis.

Another existing problem lies with the primary studies, which often only publish the striking

results rather than those that do not match the expectations. Due to this publication bias in

primary studies, it is possible that we also obtain a certain bias in our meta-analysis.

We applied the Mantel-Haenszel method and the "random effects model" for pooling our

results. To become an estimate of the increased risk, odds ratios were calculated for all

results, as well as the heterogeneity (I²) of the study results.

Odds ratios (OR) depict the relationship between two odds. An odds ratio is the ratio

between the probability that a particular event will occur, and the probability that this same

event will not occur. If both events are equally possible to occur, the OR will be equal to 1. If

25

a particular outcome is more or less likely to occur, then the odds ratio will respectively be

larger or smaller than 1.

The heterogeneity (I²) in a meta-analysis refers to the variation in study outcomes between

studies. Ideally, all results incurred following the same methodology, yet still there will

always be some variation in results by chance. The question, however, is whether the

variation is greater than would be expected by this chance alone. When it is, it is called

heterogeneity. In other words, heterogeneity is a measure of consistency between trials in a

meta-analysis. We managed to circumvent some of the heterogeneity by discussing

culpability studies separately (64).

4. Results

4.1. Study characteristics

After our literature search we were able to include 42 studies, of which eight culpability

studies, 21 case-control studies, seven cohort studies, and two including both a case-control

and a culpability study. Seven studies were of low quality (0-39%), 27 of average quality (40-

59%) and eight of high quality (60-100%). A table containing all studies and their

characteristics can be found in the addendum.

4.2. Meta-analysis on crash risk

4.2.1. Depressants

Obtaining a complete homogenous collection of results regarding an exact type of depressant

was rather difficult. Therefore we have made subdivisions based on descriptions retrieved

from all studies. Some combined all types of benzodiazepines under one term, such as Oster

et al and Leveille et al (37, 52), while others made a subdivision according to half life, for

example Hemmelgarn et al (29), or in hypnotic or anxiolytic characteristics like Neutel et al

(47, 48), or combined both drug characteristics like Ravera et al (55). These reflections result

in a duplicate occurrence of some study outcomes in different comparisons.

26

4.2.1.1. Benzodiazepines, anxiolytics, tranquillizers

Table 4.2.1.1: Characteristics of studies including benzodiazepines, anxiolytics or other

tranquillizers: drug description of the studied drug, quality of the study, fatal crashes

or severe injured drivers included in the study

Author Description Assum et al Benzodiazepines

Smart et al Tranquillizers Matthijssen et al Benzodiazepines

Skegg et al Minor tranquillizers Bramness et al Diazepam

Oster et al Benzodiazepines Engeland et al BZD tranquillizers

Ray et al Benzodiazepines Dubois et al Benzodiazepines

Leveille et al Benzodiazepines Vingilis et al Tranquillizers

Neutel et al Anxiolytics Ravera et al Anxiolytics

Hemmelgarn et al Long half life BZD Yang et al Long half life BZD

Neutel et al Benzodiazepines Hou et al Benzodiazepines

McGwin et al Benzodiazepines Kuypers et al Benzodiazepines

Movig et al Benzodiazepines Li et al Depressants

Mura et al Benzodiazepines Gjerde et al Benzodiazepines

Brault et al Benzodiazepines Hels et al Benzodiazepines + Z-products

Inclusion of fatal/severe injuries in underlined studies

High quality studies are marked green

Average quality studies are marked blue

Low quality studies are marked orange

Figure 4.2.1.1.a.: Review Manager summary of all studies including benzodiazepines,

anxiolytics or other tranquillizers

27

A total of 25 studies were included in this first subgroup, all resulting in an increased OR

when driving under influence of any type of depressant, with 18 studies showing a

significantly increased OR. There is an overall elevated significant OR increase of 1.98 and a

heterogeneity of 87%. This indicates an almost twice as high probability of crash occurrence

when driving under influence of aforementioned. The OR for having a fatal crash or an

accident causing severe personal injuries was three times higher, showing a significantly

increased OR of 3.00 (OR: 3.00 [1.66, 5.40]) and a heterogeneity of 84%.

When excluding all low quality studies, we became a significantly increased OR of 2.04 (OR:

2.04 [1.68, 2.48]) and a heterogeneity of 84%. Including only high quality studies, the OR and

heterogeneity increased to respectively 2.40 (OR: 2.40 [1.36, 4.22]) and 92%.

Studies who made a subdivision in long acting and short acting benzodiazepines (18, 29, 55,

66) were also assessed separately, showing an OR of 1.00 with a heterogeneity of 91% for

short-acting benzodiazepines, compared to a significantly increased OR of 1.39 with a

heterogeneity of 25% for long acting benzodiazepines. Only four studies, however, were

included.

Figure 4.2.1.1.b.: Review Manager summary of all studies including short acting

benzodiazepines

Figure 4.2.1.1.c.: Review Manager summary of all studies including long acting

benzodiazepines

28

4.2.1.2. BZD Hypnotics, Z-products, barbiturates

Table 4.2.1.2.: Characteristics of studies including BZD Hypnotics, Z-drugs or

barbiturates: description of the studied drug, quality of the study, fatal crashes or

severe injured drivers included in the study

Author Description Vingilis et al Sleeping pills

Smart et al Barbiturates Ravera et al Hypnotics + sedatives

Neutel et al Hypnotics Yang et al Zolpidem

Movig et al Barbiturates Hou et al Barbiturates

Mura et al Hypnotics Kuypers et al Z-drugs

Brault et al Barbiturates Gjerde et al Zopiclone

Engeland et al BZD hypnotics Hels et al BZD + Z-drugs





Figure 4.2.1.2.: Review Manager summary of all studies including BZD Hypnotics, Z-

drugs or barbiturates

This second subcategory consists of 13 studies, all including medicinal drugs mainly indicated

for sleeping disorders. Eight of them showed a significantly increased OR. Only a single

study showed a non-significantly decreased OR for having a car crash under influence of, in

this case, barbiturates, against all others showing an increase. The overall OR for having a car

accident whilst driving under influence of different types of hypnotics is significantly

increased to 1.89, with a study heterogeneity of 59%. Chances of sustaining severe injuries or

dying in a car crash were 2.17 times higher (significantly increased OR: 2.17 [1.54, 3.05]),

with a heterogeneity of 0%.

29

Barbiturates separately lead to a non-significantly increased OR of 2.03 (OR: 2.03 [0.77,

5.33]) and a heterogeneity of 48%, and Z-products to a significantly increased OR of 2.00

(OR: 2.00 [1.40, 2.88]) and a heterogeneity of also 48%. Only a limited number of studies

were included in this subgroup. The inclusion of more studies would be necessary in order to

obtain a significant result, but since barbiturates are not used as a common medicinal drug

anymore, further assessment of driving under influence of these drugs is unnecessary.

When excluding all low quality studies we became a significantly increased OR of 2.03 (OR:

2.03 [1.50, 2.75]) and a heterogeneity of 57%. Including only high quality studies resulted in

a significantly increased OR of 3.04 (OR: 3.04 [1.70, 5.44]) and a heterogeneity of 4%.

4.2.2. Narcotics

Table 4.2.2.: Characteristics of studies including narcotics: description of the studied

drug, quality of the study, fatal crashes or severe injured drivers included in the study

Author Description Matthijssen et al Codeine

Ray et al Antihistamines or opioids Engeland et al Natural opium alkaloids

Leveille et al Opioids Vingilis et al Codeine, Demerol, Morphine

Movig et al Opiates Woratanarat et al Morphine

Mura et al Morphine Kuypers et al Opiates

Brault et al Opiates Hels et al Narcotics

Assum et al Opiates Li et al Medicinal opioids





Figure 4.2.2.: Review Manager summary of all studies including any kind of narcotic as

a studied drug

30

Narcotics include opiates such as morphine and codeine, or other opioids such as oxycodone.

Two exceptions in this category are Ray et al (56), who also included antihistamines, and

Vingilis et al (62), who also included Demerol (pethidin). A total of 13 studies were enrolled

in this subcategory, with 8 showing a significant OR increase. Figure 4.2.2. shows a

heterogeneity of 91% and a significantly increased OR of 2.95, making it almost three times

as likely having a car crash when driving under influence of narcotics. Not surviving the crash

or sustaining serious injuries was 3.52 times as likely (significantly increased OR: 3.52 [1.50,

8.25]), with a heterogeneity of 87%. Opiates separately lead to a significantly increased OR of

3.16 (OR: 3.16 [2.09, 4.79]) and a heterogeneity of 87%, compared to an OR of 1.61 (OR:

1.61 [0.81, 3.22]) and, again, a heterogeneity of 87% when driving under influence of opioids.

Even though only three studies included medicinal opioids, all three were of high quality.

After excluding all low quality studies, we became an overall significantly increased OR of

2.78 (OR: 2.78 [1.48, 5.23].) and a heterogeneity of 89%. Including only high quality studies

resulted in an overall increase of 2.27 (significantly increased OR: 2.27 [1.33, 3.88]) and a

heterogeneity of 78%, keeping in mind that half of these high quality studies studied opioids

and probably not opiates. Excluding these studying opioids left us with a significantly

increased OR of 3.76 (OR: 3.76 [1.97, 7.19]) and a heterogeneity decrease to 22%.


Table 4.2.3.: Characteristics of studies including antidepressants: description of the

studied drug, quality of the study, fatal crashes or severe injured drivers included in

the study

Author Description Matthijssen et al TCA

Ray et al TCA Bramness et al Non-sedating AD

Leveille et al Antidepressants Vingilis et al Antidepressants

McGwin et al Antidepressants Woratanarat et al Antidepressants

Movig et al TCA Ravera et al SSRI

Mura et al Antidepressants Hou et al TCA

Lam et al Antidepressants Orriols et al Antidepressants





31

Figure 4.2.3.: Review Manager summary of all studies including any kind of

antidepressant as a studied drug

A total of 13 studies were included in the antidepressants category, four of them showing a

significant OR increase, and three studies showing a decreased OR for having a car crash

under influence of antidepressants. The OR for all studies combined was generally increased

to 1.57, which is statistically significant, but with a large heterogeneity of 99%.

Four of these studies, three high-quality and one average quality study, researched the effect

of tricyclic antidepressants as a separate group. Including only these studies resulted in a

significantly increased OR of 1.93 (OR: 1.93 [1.26, 2.96]) and a heterogeneity of 0%.

Only two studies studied newer, less sedating, antidepressants such as selective serotonin

reuptake inhibitors (SSRI’s). Including only these two studies, both of average quality, we

became a significantly increased OR of 1.45 (OR: 1.45 [1.04, 2.01]) and a heterogeneity of

86%.

Excluding all low quality studies yielded a significant OR increase of 1.34 (OR: 1.34 [1.11,

1.63]) and a heterogeneity of 82%. Including only high-quality studies resulted in a

significantly increased OR of 1.68 (OR: 1.68 [1.22, 2.32]) and a heterogeneity of 0%.

32

4.2.4. Stimulants

The term “psychoactive drugs” contains many different types of drugs. This term was one of

the most difficult to subdivide, since our interpretation did not always match other definitions.

We mainly considered psychoactive drugs to be stimulating drugs, such as methylphenidate,

but not every interpretation matched ours. For this reason, other drugs such as

benzodiazepines, tranquillizers, hypnotics and antidepressants are often also included. Three

studies were excluded (24, 56, 65) since no stimulants were included here, leaving us with

only two studies including stimulants. In addition, the term stimulants could also refer to

illicit drugs, which could lead to an interpretation bias.

Table 4.2.4.: Characteristics of studies including stimulants: description of the studied


Author Description

Honkanen et al Psychotropics: benzodiazepines, TCA, neuroleptics, stimulants, barbiturates,

and some newer antidepressants

Li et al Stimulants





Figure 4.2.4.: Review Manager summary of all studies including stimulants as studied

drugs

Including the two studies discussed in previous paragraph, we became an OR of 2.45 and a

heterogeneity of 65%. Again, these results could lead to a misinterpretation due to the broad

definition of the term “stimulants”, possible inclusion of illegal stimulants and the limited

number of studies included. Li et al has studied the relationship between driving under

influence stimulants and having a fatal accident, leading to an individual significantly

increased OR of 3.59.

33

4.2.5. Minor analgesics: NSAIDs, paracetamol, others

Table 4.2.5.: Characteristics of studies including minor analgesics: description of the


the study

Author Description





Skegg et al Minor analgesics

Honkanen et al Analgesics

McGwin et al NSAID

Mura et al NSAID

Engeland et al NSAID

Vingilis et al Pain relievers

Figure 4.2.5.: Review Manager summary of all studies including minor analgesics such

as NSAIDs or paracetamol as studied drugs

Six studies researched the effect of minor analgesics, mostly NSAIDs or paracetamol, on

crash risk. Four of them showed an individual significant OR increase, while two showed an

OR decrease. All studies combined resulted in a significantly increased OR of 1.32 and a

heterogeneity of 54%. Skegg et al performed the only study researching for fatal crash risk

and minor analgesic association, giving us an individual OR of 2.37.

Including only these three studies mentioning NSAIDs as a separate group, we became an OR

of 1.19 (OR: 1.19 [0.88, 1.61]) and a heterogeneity of 46%.

Excluding all low quality studies left us with an even smaller OR of 1.08 (OR: 1.08 [0.65,

1.80]) and a heterogeneity of 52%. The only high quality study included here was the one by

Mura et al, researching for NSAIDs and showing an OR of 0.64.

34

4.2.6. Antihistamines

Table 4.2.6.: Characteristics of studies including antihistamines: description of the


the study

Author Description





Skegg et al Antihistamines

Jick et al Antihistamines

Ray et al Antihistamines or opioid

analgesics

Leveille et al Antihistamines

Woratanarat et al Antihistamines

Figure 4.2.6.: Review Manager summary of all studies including antihistamines as

studied drugs

Five studies included antihistamines in their research. Two of them showed a decreased OR,

all of them combined resulted in an OR of 1.07 and a heterogeneity of 0%. Skegg et al is the

only study who examined fatalities under influence of antihistamines, showing an OR of 1.79.

Excluding al low quality study left us with an OR of 1.15 (OR: 1.15 [0.81, 1.64]) and the

same heterogeneity of 0%. Including only the two high quality studies lead to an OR of 1.10

(OR: 1.10 [0.76, 1.60]) and, again, a heterogeneity of 0%.

It is, however, important to take the evolution of these drugs into consideration when

comparing these studies. Older generation antihistamines show more sedating side effects

than newer generations, which may have a significant impact on driving behavior and crash

risk.

35

4.2.7. Respiratory agents

Table 4.2.7.: Characteristics of studies including respiratory agents: description of the


the study

Author Description





Skegg et al Asthma preparations

Honkanen et al Respiratory agents

Mura et al Antitussives

Bramness et al Salbutamol

Engeland et al Selective β2 receptor agonist

Woratanarat et al Cough suppressants

Figure 4.2.7.: Review Manager summary of all studies including respiratory agents as

studied drugs

The group of respiratory agents contains a wide variety of medicinal preparations. Two

studies showed an OR decrease, the four other studies showed an increase. All together, they

resulted in an OR of 1.14. Only one study researched for driver fatalities under influence of

respiratory agents, showing an OR of 3.01.

Excluding all low quality studies left us with three studies, showing an OR of 1.26 (OR: 1.26

[0.56, 2.83]). There is only one high quality study to be included, with an OR of 0.75.

36

4.2.8. Cardiovascular medication

4.2.8.1. Diuretics

Table 4.2.8.1.: Characteristics of studies including diuretics: description of the studied


Author Description Inclusion of fatal/severe injuries in underlined studies




Skegg et al Diuretics

Honkanen et al Cardiovascular drugs

McGwin et al Diuretics

Figure 4.2.8.1.: Review Manager summary of all studies including diuretics

As the figure shows, three average quality studies were included in this subcategory.

Honkanen et al researched for “cardiovascular drugs” in general, and were therefore included

in all three subgroups. None of the studies showed a significant result. One of three studies

showed decreased OR. Skegg et al were the only study researching for crash fatalities,

showing an increased OR of 2.84.

All together we became an OR of 1.17, with a heterogeneity of 34%. Including only the two

studies researching for diuretics as a separate group, we became an OR of 1.26 (OR: 1.26

[0.41, 3.88]) and a heterogeneity of 57%.

4.2.8.2. Anticoagulants

Table 4.2.8.2.: Characteristics of studies including anticoagulants: description of the


the study






McGwin Anticoagulant

Delaney et al Warfarin

37

Figure 4.2.8.2.: Review Manager summary of all studies including anticoagulants

Three average quality studies studied the effect of anticoagulants on crash risk. None of them

showed a significant OR. One of them, studying warfarin in particular, showed a decreased

OR of 0.65. All together we became a slight OR increase of 1.03. None of these studies

investigated fatalities or severe injuries when driving under influence of anticoagulants.

Including only those studies who research for anticoagulants in particular, we became an OR

decrease of 0.92 (OR: 0.92 [0.39, 2.21]) and a heterogeneity of 65%. None of the calculated

OR’s were statistically significant.

4.2.8.3. Calcium channel blockers

Table 4.2.8.3.: Characteristics of studies including calcium channel blockers:

description of the studied drug, quality of the study, fatal crashes or severe injured

drivers included in the study






McGwin Ca channel blocker

Engeland et al Ca receptor antagonist

Figure 4.2.8.3.: Review Manager summary of all studies including Ca channel blockers

One low quality and two average quality studies examined the effects of calcium channel

blockers on crash risk. None of them showed a significant result. Two of them showed an OR

decrease, and together they also lead to an overall decrease in OR of 0.75 and a heterogeneity

of 21%. Including only those studies who research for calcium channel blockers in particular,

we became an OR decrease of 0.67 (OR: 0.67 [0.48, 0.94]) and a heterogeneity 0%. None of

the calculated OR’s were statistically significant.

38

4.2.9. Other medication

A lot of other medicinal drug groups have also been included in different studies. These

results, however, were only mentioned in one, sometimes two articles, and were therefore not

further discussed in our meta-analysis. The table below lists all articles and their matching

medicinal drug groups which were not enrolled in our study.

Medicinal drug Group Article(s) Lithium Etminam et al

Antacids Skegg et al Metformin Hemmelgarn et al

Antibiotics Skegg & Engeland et al Methadone Matthijssen et al

Arthritis medication McGwin et al Oral contraceptives Skegg et al

Chemotherapeutic agents Honkanen et al Other CV agents McGwin & Skegg et al

Glaucoma medication McGwin et al Skin preparations Skegg et al

Hormones Honkanen et al Spasmolytics Honkanen et al

Insulin Hemmelgarn & Honkanen et al Sulfonylurea Hemmelgarn et al

4.2.10. Summarizing table of all results

Studied drugs Number of

included

articles

Heterogeneity

(I²)

Odds Ratio (OR)

1. Benzodiazepines,

anxiolytics, (minor)

tranquillizers

25 87% 1.98 [1.68, 2.32]*

Fatal/severe injuries 6 84% 3.00 [1.66, 5.40]*

Average + high quality studies 21 84% 2.04 [1.68, 2.48]*

High quality studies 8 92% 2.40 [1.36, 4.22]*

Short half life BZD only 4 91% 1.00 [0.76, 1.31]

Long half life BZD only 4 25% 1.39 [1.26, 1.53]*

2. Benzodiazepine hypnotics,

Z-drugs, barbiturates

13 59% 1.89 [1.57, 2.29]*


Barbiturates only 4 48% 2.03 [0.77, 5.33]

Z-drugs only 4 48% 2.00 [1.40, 2.88]*



3. Narcotics 13 91% 2.95 [2.06, 4.21]*


Opiates only 9 87% 3.16 [2.09, 4.79]*

Opioids only 3 87% 1.61 [0.81, 3.22]



High quality studies opiates 3 22% 3.76 [1.97, 7.19]*

39

4. Antidepressants 13 99% 1.57 [1.08, 2.29]*

Fatal/severe injuries 4 87% 1.37 [0.83, 2.25]

TCA only 4 0% 1.93 [1.26, 2.96]*

Non-sedating AD only 2 86% 1.45 [1.04, 2.01]*



5. Stimulants 2 65% 2.45 [0.86, 6.97]

Fatal 1 - 3.59 [2.71, 4.76]*

6. Minor analgesics 6 54% 1.32 [1.09, 1.59]*

Fatal 1 - 2.37 [0.82, 6.84]

NSAIDs only 3 46% 1.19 [0.88, 1.61]

Average + high quality studies 4 52% 1.08 [0.65, 1.80]

High quality studies 1 - 0.64 [0.32, 1.30]

7. Antihistamines 5 0% 1.07 [0.78, 1.47]

Fatal 1 - 1.79 [0.54, 5.94]


High quality studies 2 0% 1.10 [0.76, 1.60]

8. Respiratory agents 6 0% 1.14 [0.88, 1.49]

Fatal 1 - 3.01 [0.68, 13.36]


High quality studies 1 - 0.75 [0.26, 2.17]

9. Diuretics 3 34% 1.17 [0.64, 2.14]

Fatal 1 - 2.84 [0.64, 12.56]

Diuretics only 2 57% 1.26 [0.41, 3.88]

10. Anticoagulants 3 55% 1.03 [0.54, 1.97]

Anticoagulants only 2 65% 0.92 [0.39, 2.21]

11. Calcium channel

blockers

3 21% 0.75 [0.52, 1.08]

Calcium channel blockers only 2 0% 0.67 [0.48, 0.94]

*Statistically significant (p<0.05)

40

4.3. Culpability meta-analysis

As mentioned before, culpability studies are discussed separately since they differ in study

design and come with different considerations compared to the previous meta-analysis.

4.3.1. Depressants

Table 4.3.1.: Characteristics of culpability studies including depressants: description of

the studied drug, quality of the study, fatal crashes or severe injured drivers included

in the study

Author Description





Jick et al Hypnotics/tranquillizers

Benzo group Benzodiazepines

McGwin et al Benzodiazepines

Drummer et al Benzodiazepines

Hours et al Anxiolytics

Poulsen et al Sedatives

Drummer et al Benzodiazepines

Figure 4.3.1.: Review Manager summary of all culpability studies including depressants

A total of seven culpability studies included different types of depressants in their research.

We found an overall increased OR of 1.22. Four studies searched for fatalities and/or severe

injuries amongst these drivers, showing an OR of 1.53 (OR: 1.53 [0.92, 2.55]). Including only

these studies who searched for the effects of benzodiazepines, we became an OR of 1.19 (OR:

1.19 [0.90, 1.57]). All analyzes showed a heterogeneity of 0%.

None of the calculated OR’s were statistically significant.

41


Table 4.3.2.: Characteristics of culpability studies including antidepressants: description

of the studied drug, study quality, fatal crashes or severe injury included in the study

Author Description





McGwin et al Antidepressants

Sagberg Antidepressants

Hours et al Antidepressants

Orriols et al Antidepressants

Drummer et al Antidepressants

Figure 4.3.2.: Review Manager summary of all culpability studies including antidepressants

Five studies searched for the effects of antidepressants on driver culpability, leading to a

significantly increased OR of 1.39. Responsibility for fatal accidents or accidents leading to

severe injuries also resulted in a significantly increased OR of 1.39 (OR: 1.39 [1.29, 1.50]).

Excluding the low quality study left us, again, with a significantly increased OR of 1.39 (OR:

1.39 [1.29, 1.49]). All separate analyzes showed a heterogeneity of 0%.

4.3.3. Other groups of medicinal drugs

As mentioned in our first meta analysis on crash risk and drug intake (4.2.9.), a lot of studies

include other types of medicinal drugs but they remain unique in their research. Therefore, no

comparisons can be made for these types of medicinal drug groups. The table below lists all

articles and their matching medicinal drug groups which were not included in our study.

Medicinal drug Group Article(s) Hormones McGwin et al

Anti epileptic medication Hours et al Hypoglycemic agents McGwin et al

Anti psychotic medication Drummer et al (2015) NSAIDs McGwin et al

Antihistamines Drummer et al (2015) Opiate analgesics Hours et al

Arthritis medication McGwin et al Opioids Drummer et al (2015)

Cardiovascular agents McGwin & Hours et al Stimulants Drummer et al (2003)

Glaucoma medication McGwin et al Thyroid medication Hours et al

42

4.3.4. Summarizing table of all culpability study results

Studied drugs Number of

articles included

Heterogeneity

(I²)

Odds Ratio (OR)

1. Depressants 7 0% 1.22 [0.96, 1.55]

Fatal/severe injuries 4 0% 1.53 [0.92, 2.55]

Average quality studies 6 0% 1.24 [0.96, 1.60]

Benzodiazepines only 4 0% 1.19 [0.90, 1.57]

2. Antidepressants 5 0% 1.39 [1.29, 1.50]*


Average quality studies 4 0% 1.39 [1.29, 1.49]*

5. Discussion

5.1. General considerations As mentioned before, a lot of factors should be taken into consideration in comparing

different studies and interpreting their results. Ideally, all studies would consist of large

populations of cases and controls, matched perfectly according to age, gender, height, weight,

metabolic profile and health status. They would all be excellent drivers, driving the exact

same perfectly maintained car on the exact same perfectly maintained road, at the exact same

correct speed on the exact same rainless day and at the exact same time. None of the controls

would have ingested any kind of drug, while cases would have ingested only one single type

of medicinal drug to prevent drug-interactions, all in the exact same dose. This way, the

occurrence of a car crash would be attributed to the influence of the ingested drug with almost

complete certainty. Unfortunately, in reality this is not the case. Cases and controls are hardly

ever a perfect match, although some studies try to approach this as closely as possible.

Furthermore, some studies focus subpopulations that cannot be generalized to the general

population. Ray et al , Leveille et al, Hemmelgarn et al, McGwin et al, Etminam et al and

Delaney et al all studied medicinal drug intake by an elderly population, therefore creating a

population bias. Another example are Lam et al, who only included cases and controls with a

suicidal ideation.

One can never say for sure whether an accident is caused by a drug, since an accident is

nearly always multi-factorial, and other factors could have caused this accident as well, such

as other drugs ingested, or a poorly maintained car, a bad visibility due to a wrong spectacle

correction or bad weather circumstances and countless other factors. Luckily, statistics bring a

43

solution to this by calculating estimates of risk and determining the probability whether or not

an increased risk is based solely on chance, as well calculating a possible dissimilarity

between the results of these studies. Applying these estimates makes it possible to interpret

different study results, even in spite of the fact that they do not arise from an ideal setting,

resulting in reliable conclusions when qualitative studies are used.

5.2. Depressants An absolute strength in this category is the high number of included studies, while the

diversity in drug classification and the large heterogeneity are its main limitations.

For anxiolytic benzodiazepines and other tranquillizers, we calculated a significant increase in

OR of 1.98, which is not surprising taking both therapeutic and side-effects of these drugs into

consideration. Short-half life benzodiazepines, however, did not show an increase in OR, it is

not illogical that the effect of a medicinal drug with a short therapeutic duration taken before

going to bed could be worn off by the time this person steps into his car the next morning.

However, no assurance is given here, and further elaboration of this matter requires the

inclusion of more studies. Hypnotic substances also result in a significantly increased OR of

1.89. Again, given the effects on the central nervous system this does not come as a surprise.

Apart from drug influence we should also take the treated disease into consideration in this

category. Underlying anxiety, fatigue because of sleepless nights or aggression could result in

a corresponding driving behavior, perhaps being the true cause of the accident.

The culpability analysis for driving under influence of depressants did not show any

significantly increased OR’s. Comparing this to the first analysis we can conclude that,

although driving under influence of depressants increases the risk on having a car crash, this

does not necessarily mean this accident is caused by this person.

Furthermore, we noticed a heterogeneity of 0% in all categories of our culpability analyses.

This could be explained by the similar methodology used by all studies, therefore showing no

important inconsistency between them.

Elvik also calculated a significantly increased OR for driving under influence of

benzodiazepines, comparable to our results. The slight difference in results could be explained

by our attempt to separate hypnotic benzodiazepines as much as possible from anxiolytic

benzodiazepines and tranquillizers.

44

5.3. Narcotics A reasonable number of studies were included in this category, being one of its strengths next

to the high share of high and average quality studies. The overall high heterogeneity is its

main limitation.

For narcotics in general we calculated a significantly increased OR of 2.95, next to a

significantly increased OR of 3.16 for opiates in particular. Again, taking both their

therapeutic and side-effects into consideration, this does not come as a surprise. Opioid drugs

separately, containing mainly non-opiate analgesics, also show an increase in OR of 1.61, but

it is not significant. This, however, is most likely due to the limited number of studies

included, considering the strong effects caused by these drugs. We can thus ultimately

conclude that the use of narcotic analgesics shows an overall increase in crash risk, especially

in the case of opiate analgesics.

Narcotic analgesics are indicated when minor analgesics do not suffice in managing cases of

moderate to severe pain, and considerations should be made about the users of these narcotic

drugs. Drivers using morphine, for instance, could be in a state of chronic pain underlying a

severe disease, which could be the true contribution to the accident rather than the influence

of morphine itself.

Elvik became a similar result in OR increase for driving under influence of opiates. Non-

opiate narcotic analgesics, however, were not discussed separately in his results.

5.4. Antidepressants A reasonable number of studies were included in this category, being one of its strengths next

to the high share of high and average quality studies. The overall high heterogeneity is its

main limitation.

We calculated an overall significant increase in OR 1.57 for crashes when driving under

influence of antidepressants. Tricyclic antidepressants showed a higher increase in OR than

the newer, less to non-sedating antidepressants, which matched our expectations.

Antidepressant use is often accompanied by certain sedating side effects, especially at the

start of therapy. The conditions treated with these drugs, however, such as depression and

anxiety, could equally endanger a patient’s safety in traffic, resulting in the question whether

or not receiving therapy leads to the smallest increase in crash risk. Once an antidepressant

becomes effective, on the other hand, its therapeutic effects are often phenomenal in severe

45

cases of depression or anxiety, possibly providing the answer to our question. We can

therefore conclude that, as well as in previous drug groups, both underlying condition and

avoiding its treatment cannot be underestimated concerning traffic safety.

The culpability analysis on driving under influence of antidepressants also showed a

significantly increased OR. We can therefore conclude that there is an observable connection

between driving under influence of antidepressants and causing an accident.

Elvik calculated a similar significantly increased OR for his antidepressant medicinal drugs,

but he did not discuss specific types of antidepressants as a separate category.

5.5. Stimulants As mentioned before, only two studies were included in this category, with a possible

inclusion of illicit drugs as well. Therefore, the conclusion can be made that there are not

enough studies available on driving under the influence of medicinal stimulants to perform a

good meta-analysis.

When reflecting on the underlying disorder treated with these drugs, such as ADHD, it does

not seem illogical that some cases could also benefit from this treatment when it comes to

traffic safety.

Elvik did include stimulants in his meta-analysis and calculated high OR’s, but these

stimulants consisted mainly of illicit agents such as most amphetamines, which we tried to

exclude as much as possible.

5.6. Minor analgesics Six studies in total were included in this category, which is not very much. This could be

considered a weakness of this analysis. There is also a moderate heterogeneity, indicating

some inconsistency between studies.

We calculated a slightly raised OR of 1.32, which was statistically significant. Although this

is not a particularly high increase, it shows a connection between driving under influence of

minor analgesics and having an accident.

Apart from the drug actually causing the accident, the treated condition could, again, also be

the cause of the accident.

46

Elvik became a non-significant OR of 1.06 (OR: 0.92, 1.21) for his analgesics category,

which included eight studies. Our subdivision into minor analgesic medication and narcotic

analgesics could be a possible explanation for this difference in results.

5.7. Antihistamines Our research on driving under the influence of antihistamines came with two limitations. First

of all, only five studies were included. Secondly, two of these studies were of low quality.

We became an OR of 1.07 with a heterogeneity of 0%, therefore not showing any important

inconsistency between these five studies. A higher number of studies should be enrolled in

order to become a significant result, which is not the case here.

These results, however, match our expectations since newer generations of antihistamines

show a lot less sedating side effects than older generations. If more studies had been included,

this consideration might have been observable when comparing older and newer studies.

Above all, as we mentioned earlier, it is often recommended to take antihistamines before

going to bed rather than taking them in the morning, in order to reduce the risk of possible

side-effects.

Elvik included seven studies and calculated a significantly increased OR of 1.12. As we

mentioned in 3.5., not being able to insert all results into Review Manager could be a possible

cause of missing some studies who included antihistamines.

5.8. Respiratory agents Six respiratory agents were included in this category, but half of them were of low quality.

The diversity among these respiratory agents included in our meta-analysis also might have

lead to bias.

We became an OR of 1.14 and a heterogeneity of 0%, again showing no important

inconsistency between these six studies.

Elvik calculated an increased OR of 1.33 (OR: (1.09, 1.62)) out of six included studies

including anti-asthmatics. By discussing these medicinal drugs as a separate group and by

including more studies he avoided having the same limitations as ours.

47

5.9. Cardiovascular drugs All three subcategories in this group only included three studies, being a major limitation in

this analysis.

Diuretics showed an OR of 1.17, calcium channel blockers an OR of 0.75 and anticoagulants

an OR of 1.03. Although more studies should be included to become a significant result, it

does not seem illogical that an effective treatment of cardiovascular diseases could eventually

lead to healthier, better drivers.

5.10. Limitations Most of the limitations of this meta-analysis have already been discussed elsewhere, but in the

context of any subsequent studies we briefly summarized them again in this chapter.

First of all, our choice of statistical software led to the exclusion of studies who only

published their calculated estimates of risk, since we were unable to enter these into Review

Manager. Secondly, despite all the considerations concerning confounding factors, we only

used unadjusted results in our meta-analysis. Using adjusted results would have been the

better option, but then all studies should have adjusted for the same confounding factors

which, unfortunately, is not the case. Third, not all included studies used the same

methodology and therefore differ in, for instance, study outcome or study population. Some of

them, for example, only took a drug test after testing positive for alcohol, therefore

constraining their population and causing a selection bias. Fourth, there are simply not enough

studies on certain types of medicinal drugs to be able to include them in our meta-analysis.

5.11. Final conclusions Overall, most OR’s for medicinal drugs are rather low, compared to those of alcohol and

illicit drugs. The DRUID project made divided drivers impaired by alcohol in different BAC-

level categories, indicating a continuous increase in the risk of injury and fatality, as shown in

table 5.11.. The OR for driving under influence of illicit stimulants is also increased to 110.26

according to the DRUID project, which is a pretty impressive result. We can therefore

conclude that the risk of driving under the influence of medicinal drugs is rather limited.

Indeed, Orriols found that the population attributable risk of impairing (class 2 & 3 in France)

medicinal drugs was 3.3%, meaning the number of crash victims would be reduced by only

3.3% if no one would drive after taking these drugs (15).

48

Table 5.11.: Alcohol related risks from all methodological approaches against a

reference of no other substance

BAC (g/l) OR BAC (g/l) OR BAC (g/l) OR

0.05 1.00 0.45 15.8 0.85 39.6

0.15 5.7 0.55 20.3 0.95 58.0

0.25 6.6 0.65 33.7 1.05 71.9

0.35 10.1 0.75 35.2 1.15 198.9

Of course, each victim is one too many, and we need to try and avoid driving under the

influence of medicinal drugs, by informing the patient of the risk when prescribing and

dispensing the medicines. When driving under the influence of medicinal drugs is inevitable,

the importance of a correct intake of these drugs should be emphasized. Furthermore, the

impact of some conditions is sometimes severe enough to endanger road traffic on its own,

meaning drug intake before driving could be a safer option.

On the other hand, it is important that the aging populations keeps its mobility and one should

avoid exaggerating the risks of driving under the influence of medicinal drugs, which could

prevent these elderly to drive after taking these drugs, thereby reducing their quality of life,

or, worse, stop taking their therapeutic drugs in order to drive. Common sense is needed in

order to allow people who take medication to stay mobile, while minimizing the risk to other

drivers.

49

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Crash risk associated with the use of medicinal drugs: a ...lib.ugent.be/fulltxt/RUG01/002/272/963/RUG01... · Apart from driving under influence of a certain medicinal drug, other