Sleepiness and Driving Accidents

8/4/2019 Sleepiness and Driving Accidents

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doi: 10.1111/j.1365-2869.2009.00818.x

Sleepiness, near-misses and driving accidents among a

representative population of French drivers

P A T R I C I A S A G A S P E 1 , 2 , J A C Q U E S T A I L L A R D 2 , 3 , V I R G I N I E B A Y O N 4 ,

E M M A N U E L L A G A R D E 5 , N I C H O L A S M O O R E 6 , J A C Q U E S B O U S S U G E 7 ,G U I L L A U M E C H A U M E T 2 , B E R N A R D B I O U L A C 3 and P I E R R E P H I L I P 2 , 3

1INRETS-LCPC, LEPSIS, Paris, 2GENPPHASS, CHU Bordeaux, Bordeaux, 3CNRS UMR-5227, Universite Bordeaux 2, Bordeaux, 4Hopital

Hotel-Dieu, Paris, 5INSERM U897, ISPED, Universite Bordeaux 2, Bordeaux, 6INSERM U657, Universite Bordeaux 2, Bordeaux and 7ASFA,

Paris, France

Accepted in revised form 28 October 2009; received 12 August 2009

S U M M A R Y Study objectives were to determine the prevalence of sleepy driving accidents and to

explore the factors associated with near-miss driving accidents and actual drivingaccidents in France. An epidemiological survey based on telephone interviews was

conducted on a representative sample of French drivers. The questionnaire included

sociodemographics, driving and sleep disorder items, and the Epworth sleepiness scale.

Of 4774 drivers (response rate: 86%), 28% experienced at least one episode of severe

sleepiness at the wheel (i.e. requiring to stop driving) in the previous year; 11% of

drivers reported at least one near-miss accident in the previous year (46% sleep-related);

5.8% of drivers reported at least one accident, 5.2% of these being sleep related (an

estimate of 90 000 sleep-related accidents per year in France). Sleepy driving accidents

occurred more often in the city (53.8%), during short trips (84.6%) and during the day

(84.6%). Using logistic regression, the best predictive factor for near-misses was the

occurrence of at least one episode of severe sleepiness at the wheel in the past year [odds

ratio (OR) 6.50, 95% confidence interval (CI), 5.208.12, P < 0.001]. The bestpredictive factors for accidents were being young (1830 years; OR 2.13, 95% CI, 1.51

3.00, P < 0.001) and experiencing at least one episode of severe sleepiness at the wheel

(OR 2.03, 95% CI, 1.572.64, P < 0.001). Sleepiness at the wheel is a risk factor as

important as age for traffic accidents. Near-misses are highly correlated to sleepiness at

the wheel and should be considered as strong warning signals for future accidents.

keyword s accidents, age, epidemiology, french drivers, near-misses, sleepiness

I N T R O D U C T I O N

Daytime sleepiness is widespread and has a negative impact on

everyday life (Ohayon et al., 1997). Sleepiness can be defined

as difficulty in remaining awake even while carrying out

activities (Dement and Carskadon, 1982).

Epidemiological studies from the 1990s showed that fatigue

and sleep-related accidents represent up to 20% of all traffic

accidents in industrial societies (Connor et al., 2002; Horne

and Reyner, 1995; Philip et al., 2001). Traffic accidents at work

or during the trip from work to home are a major cause of

injury and death among workers (Barger et al., 2005; Harrison

et al., 1993; Personick and Mushinski, 1997). Though drows-

iness has been identified as the reason behind fatal road

crashes and many industrial accidents (Connor et al., 2001b;

Hakkanen and Summala, 2000, 2001; Mitler et al., 1988,

1997), many people drive when their alertness is at its lowest

level and seem not to be concerned by public health campaigns

alerting drivers about the risk of sleepiness at the wheel

(Fletcher et al., 2005; Nabi et al., 2006).

Experimental studies have been conducted to assess the

impact of sleepiness at the wheel on driving performance. It has

Correspondence: Pierre Philip, MD, PhD, Universite Bordeaux 2,

CNRS UMR-5227, GENPPHASS et Clinique du Sommeil (CHU

Pellegrin), Place Ame lie Raba-Le on, 33076 Bordeaux Cedex, France.

Tel.: 33 5 57 82 01 73; fax: 33 5 57 82 00 38; e-mail: [email protected]

J. Sleep Res. (2010) 19, 578584 Sleep and driving

578 2010 European Sleep Research Society


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been demonstratedthat healthy subjects driving in themiddle of

the night present major driving impairment related to circadian

and homeostatic pressure (Philip et al., 2006),and that extended

driving impairs nocturnal driving performances (Sagaspe et al.,

2008). Many drivers are also patients affected by sleep disorders

and have to manage deteriorated driving skills (Sagaspe et al.,

2007). A recent 100-car naturalistic driving study using ques-

tionnaires about driver sleep habits and instrumented vehicles

with video-recording for 1 year found that 12% of crashes (and10% of near-crashes) were related to drowsiness (Dingus et al.,

2006). Drowsy driving crashes tend to occur late at night when

thecircadian physiological sleep pressure is at its highest (Mitler

et al., 1997). They also tend to occur on major highways at

higher speeds leading to greater morbidity and they dispropor-

tionately involve young men (as do motor vehicle crashes in

general; Horne and Reyner, 1995; Pack et al., 1995).

In France, fatigue has been studied as a road traffic crash

risk factor (Philip et al., 2001), but data on the role of

sleepiness in the burden of road accidents in this country are

missing.

France has decreased drastically its accidental rate from

8000 deaths per year in 2000 to 4800 in 2008 (Observatoire

National Interministe riel de Se curite Routie` re (ONISR),

2008). Behavioral factors like sleepiness start being considered

as future targets to reduce death and injuries on French roads.

Following a national campaign directed by the French

Ministry of Transport on the risk of sleep-related accidents,

the French Highway Association (Association des Socie te s

Francaise dAutoroute, ASFA), a not-for-profit foundation

whose mission is to provide information about highway

accidents and prevent them, commissioned our team to

conduct a study on sleepiness at the wheel in order to inform

and educate drivers via public campaigns.

We investigated driving accidents and near-miss accidentsusing data extracted from a self-reported telephone-based

survey of a representative sample of French drivers (n = 5000)

in order to: (1) evaluate the prevalence of daytime sleepiness

and prevalence of self-reported sleepy driving accidents; and

(2) test the associations of demographic, driving, behaviors

and sleep disorders variables with both self-reported near-miss

accidents and actual driving accidents.

M A T E R I A L S A N D M E T H O D S

Sample

Association des Socie te s Francaise dAutoroute provided our

research team with access to a telephone database of citizens

representative of the population of French drivers.

Out of 18 million telephone numbers (French homes base)

of the society Pages Jaunes Marketing Service, 20 846

telephone numbers were randomly selected according to some

defined quotas representative of the national statistics on

French drivers of personal vehicles: department, age, gender,

type of home and socio-professional status. From October to

November 2007, 5000 interviews were obtained by telephone

from this selected base (5555 drivers were contacted and 5000

accepted to be interviewed). We eliminated an extra 226

questionnaires with missing or inappropriate responses, and

we ran our analyses on a sample of 4774 drivers (response rate:

86%).

Our team prepared a specific telephone questionnaire

designed to collect information from this large cohort of

French drivers about the prevalence of daytime sleepiness and

prevalence of self-reported sleepy driving accidents, andcontribution of demographic, driving, behaviors and sleep

disorders variables in near-misses and accidents. A near-miss

driving accident was defined as an event that had not caused

any harm and therefore had limited immediate impact (e.g.

inappropriate line crossing). A driving accident was defined

only if material damage or physical injury occurred.

A questionnaire took a mean of 10 min to be completed.

The questionnaires were completed by 30 interviewers operat-

ing for an independent company specializing in phone-based

medical studies. To minimize potential bias, these interviewers

were overseen by supervisors and a psychologist specially

trained by a sleep specialist of our team working on sleepiness

at the wheel. We, for instance, clearly explained to the

interviewers the difference between fatigue and sleepiness at

the wheel. The rationale of the study and the proper strategy

were particularly emphasized.

Volunteers had to respond to the following.

1. Thirty-one questions covering information on gender, age,

body mass index (BMI), marital status, profession and

driving habits.

2. Two questions exploring the occurrence of near-misses and

driving accidents.

3. A set of 10 questions previously used in an epidemiological

survey (Philip et al., 1999) exploring frequency and condi-

tions where sleepiness at the wheel affected drivers. Sixquestions explored the consumption of alcohol, coffee and

caffeinated beverages. The potential effects on alertness of

coffee and taking a nap were also investigated to explore

countermeasures to sleepiness.

4. Specific questions from the Basic Nordic Sleep Question-

naire (Partinen and Gislason, 1995) to explore the snoring

frequency.

5. The Epworth Sleepiness Scale (ESS; Johns, 1991) to explore

excessive daytime somnolence. The ESS is an eight-item

questionnaire designed to evaluate the subjects likelihood of

falling asleep in common situations.

6. Patients were also asked if they had ever been diagnosed and

treated for sleep disorders [obstructive sleep apnoea syn-

drome (OSAS), restless legs syndrome (RLS), insomnia,

narcolepsy hypersomnia] or psychiatric disorders (anxiety

or depression).

Statistical analyses

Quantitative variables were expressed as mean and standard

deviation (SD), and qualitative variables were expressed as

relative frequency.

Sleepiness, near-misses and driving accidents in France 579

2010 European Sleep Research Society, J. Sleep Res., 19, 578584


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For regression analyses, all variables associated with driving

accidents and near-miss driving accidents were initially exam-

ined separately using univariate models.

Time since drivers licence (years), gender, age, BMI

(kg m)2), marital status, professional driver, ESS scores,

pathologies, stimulant effect of coffee, tea or cola, stimulant

effect of nap, severe sleepiness at the wheel needing to stop and

near-miss accidents during the previous year were submitted as

independent variables. Years of detention of a driving licencewere categorized by period of 10 years of driving; age was

classified into 1830, 3150, 5165 and >65 years; BMI was

analysed as a continuous variable; marital status was catego-

rized as single, married, separated and divorced or widowed;

professional driver was dichotomized into yes or no; ESS

scores were classified into 010 or >11; pathologies were

classified into OSAS, RLS, insomnia, anxietydepression,

narcolepsy hypersomnia, multi-pathologies and without sleep

pathology; stimulant effect of coffee, tea or cola was dichot-

omized into yes or no; stimulant effect of nap was dichoto-

mized into yes or no; severe sleepiness at the wheel requiring to

stop was dichotomized into yes or no; and near-miss accident

during the previous year was dichotomized into yes or no.

Multivariate logistic regression analyses were performed for

all variables that showed a significant association in univariate

models (P < 0.05) to control confounding factors and to

determine the main correlates. The referent group for each

factor was selected as drivers supposed to be at the lowest risk

for near-misses or accidents.

A significant association was found between driving acci-

dents and the following variables: licence, age, marital status,

professional driver, ESS scores, pathologies, stimulant effect of

coffee, tea or cola, and severe sleepiness at the wheel needing to

stop. As near-miss driving accidents is a well-known predictive

variable for driving accidents (Powell et al., 2007) and in orderto reduce co-linearity with severe sleepiness episodes at the

wheel, we decided to not include in the model this specific

variable.

A significant association was found between near-miss

driving accidents and the following variables: licence, gender,

age, BMI, marital status, professional driver, ESS scores,

pathologies, stimulant effect of coffee, tea or cola, stimulant

effect of nap and severe sleepiness at the wheel needing to stop.

Statistical tests of the regression estimates odds ratio (ORs)

were based on Wald statistics. Odds ratio and their 95%

confidence intervals (CIs) are presented to show the associa-

tion.

All analyses were performed using spss statistical software

package (SPSS, version12.0, Chicago, IL, USA).

R E S U L T S

Sample

Table 1 summarizes the demographic characteristics of the

sample. Half of the responders were females (54.3%). The

mean age was 48.5 15.5 years. The BMI ranged from 14 to

61 kg m)2. Most of the subjects were married (72.1%), 59.5%

of the subjects declared to have a job and 11.5 self-considered

themselves as a professional driver.

Table 1 Demographic characteristics of the 4774 respondents

Characteristics

Percent or

Mean SD

Age (years) (%)

1830 12.7

3150 43.8

5165 27.7

>65 15.8

Female (%) 54.3BMI (kg m)2) 24.4 4.2

Marital status (%)

Married 72.1

Single 14.6

Separated or divorced 11.9

Missing 1.4

Number of children 1.8 1.3

Professional status (%)

Farmers 1.9

Artisans 3.1

Senior executive 8.5

Middle management 15.0

Employees 19.5

Working class 16.4

Retired 26.0

Non-worker 9.6

With a job (%) 59.5

Work (%)

Diurnal work 87.4

Nocturnal work 2.8

Shift work 9.8

Missing 0

Time of work per week (h) 37.5 10.6

Main reasons for driving on road (%)

Homework 52.3

Professional reason 7.4

Hobbies 30.6

Shopping 7.9

Weekend 0.6

Holidays 0.4

None 0.8

Licence (years) 26.2 14.0

Frequency of driving a car (%)

Once or more per day 70.0

Once or more per week 27.9

Once or more per month 1.2

Once or more per year 0.3

Missing 0.6

Kilometers driven per year (%)

05000 15.1

500010 000 20.5

10 00015 000 21.7

15 00025 000 24.5

25 00040 000 11.8

>40 000 5.6

Missing 0.8

Percentage of driving between 00:00

and 06:00 hours

11.8 7.6

Self-considered as professional drivers (%) 11.5

BMI, body mass index.

580 P. Sagaspe et al.



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Prevalence of sleep complaints

The mean score for the ESS was 5.7 3.8. The prevalence of

excessive daytime sleepiness in our population was 11.8% (ESS

score 11), 1% reported an ESS score >16.

Sleepiness at the wheel

Over one-quarter of the respondents (28.2%) declared they

experienced at least one episode of severe sleepiness at the

wheel (i.e. requiring to stop driving) in the previous year

(Table 2).

Near-miss driving accidents

One-tenth of the drivers (10.7%; 510 of 4774) reported at least

one near-miss accident during the previous year, 46% of these

being reportedly sleep-related (4.9% of drivers; 233 of 4774).

Near-miss sleepy accidents principally occurred on a highway

(77.6%), during a long trip (63.8%), and occurred more often

during the night (40.9%) than the day (29.4%). Young drivers

(1830 years) were the most exposed group to both all near-misses (1830 years: 19.6%; 117 of 597; 3150 years old:

11.8%; 2502115; 5165 years old: 7.8%; 102 of 1312; and

>65 years old: 5.5%; 41750) and to sleep-related near-misses

(1830 years: 10.1%; 60 of 597; 3150 years old: 5.7%;

1202115; 5165 years old: 3.4%; 45 of 1312; and >65 years

old: 1.1%; 8 750).

Driving accidents

In the past year, 5.8% of drivers (278 of 4774) reported at least

one driving accident, and 5.2% of these were sleep related (13

of 4774). Sleepy driving accidents occurred more often in the

city (53.8%). They concerned more often short trips (84.6%),

and most of them occurred during the day (84.6%).

Young drivers were the most exposed group to accidents

(1830 years: 12.9%; 77 of 597; 3150 years old: 6.1%;

1302115; 5165 years old: 3.4%; 45 of 1312; and >65 years

old: 3.5%; 26750).

Factors associated with near-miss driving accidents and actual

driving accidents

Table 3 describes the risk factors associated with near-miss

accidents.

Males had 1.51 (95% CI, 1.221.87, P < 0.001) morechances to be involved in a near-miss accident than females.

Compared with our reference group (3150-year-old drivers),

5165-year-old drivers near-miss accidental risk was associ-

ated with a reduction to 0.75 (95% CI, 0.570.98, P < 0.05),

whereas 1830-year-old drivers near-miss accidental risk was

associated with an increase to 1.86 (95% CI, 1.412.44,

P < 0.001). Being sleepy (ESS score 11) was associated with

a risk of near-miss driving accidents (OR 1.67, 95% CI, 1.29

2.15, P < 0.001). Interestingly, we found that

Table 2 Sleepiness at the wheel, near-misses and traffic accidents

Characteristics

Percent or

Mean SD

ESS 5.7 3.8

Severe sleepiness at the wheel needing to stop (%)

Never 71.8

Less than once per month 24.4

At least once per month 3.1

At least once per week 0.7Sleepy during (%)

Nocturnal driving 46.8

Diurnal driving 39.4

Both 13.8

Near-miss driving accidents during

previous year (at least once) (%)

10.7

Near-miss sleepy driving accidents

during previous year (at least once) (%)

4.9

Number of near-miss sleepy

driving accidents during

previous year

4.4 14.5

Location of near-miss sleepy

driving accidents (%)

City 2.2

Road 35.1

Highway 59.3

All types of roads 3.4

Type of trip for near-miss sleepy


Short trip 40.3

Long trip 57.6

All type of trips 2.1

Time of day for near-miss sleepy


Day 44.6

Night 45

Day and night 10.4

Driving accidents during previous

year (at least once) (%)

5.8

Sleepy driving accidents during previous

year (at least once) (%)

0.3

Number of sleepy driving accidents

during previous year

1.1 0.3

Location of sleepy driving

accidents (%)

City 53.8

Road 38.5

Highway 7.7

Type of trip for sleepy driving

accidents (%)

Short trip 84.6

Long trip 15.4

Time of day for sleepy driving accidents (%)

Day 84.6

Night 15.4

Anticipatory measures before departure (%) 55.6

Type of preparation (%)

Normal sleep duration 68.6

Earlier departure in order to plan breaks 12.7

Plan to sleep during breaks 5.5

Take stimulant drinks 3.7

Second rested driver 6.8

Nothing 2.7

ESS, Epworth Sleepiness Scale.




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anxietydepression was also predictive for accidental risk (OR

1.82, 95% CI, 1.272.62, P < 0.001). Subjectively, caffeine-

sensitive drivers accidental risk was 1.20 (95% CI, 0.971.47,P = 0.09) compared with insensitive drivers. Finally, experi-

encing at least one episode of severe sleepiness at the wheel in

the previous year was associated with a 6.50-fold increase

(95% CI, 5.208.12, P < 0.001) in the near-miss accident

rate.

Table 4 contains information about the risk factors associ-

ated with driving accidents.

Unmarried drivers had 1.40 (95% CI, 0.991.96, P < 0.05)

more chances to be involved in an accident than married

drivers. Compared with our reference group (3150-year-old

drivers), 5165-year-old drivers accidental risk was associated

with a reduction to 0.63 (95% CI, 0.440.90, P < 0.01),

whereas 1830-year-old drivers accidental risk was associated

with an increase to 2.13 (95% CI, 1.513.00, P < 0.001).

Professional drivers were at higher risk of driving accidents

than non-professionals (OR 1.52, 95% CI, 1.082.13,

P < 0.05). Subjectively, caffeine-sensitive drivers accidental

risk was 1.43 (95% CI, 1.111.85, P < 0.01) compared with

insensitive drivers. Finally, experiencing at least one episode of

severe sleepiness at the wheel in the previous year was

associated with a 2.03-fold increase (95% CI, 1.572.64,

P < 0.001) in the accident rate.

D I S C U S S I O N

Our study is the first one to focus on sleepiness, near-missesand accidental risk among a population of regular French

drivers. Our sample matches the general population of drivers

in France, and therefore our results should be applicable to the

French population.

Our data show that one-third of French drivers experienced

at least one episode of severe sleepiness at the wheel (i.e.

requiring to stop driving) in the previous year. This ratio

matches with that of a French study (Nabi et al., 2006)

investigating workers of the GAZEL cohort. This episode of

sleepiness at the wheel is a major contributing factor to the

occurrence of near-miss accidents or actual accidents. This

result corroborates Connor et al.s study (Connor et al., 2002)

showing that acute sleepiness in car drivers significantly

increases the risk of a crash.

In our sample, 11.8% complained of excessive chronic

daytime somnolence (ESS 11). We have shown an association

between pathological ESS scores and near-miss driving acci-

dents but, contrary to the Powell study (Powell et al., 2007),

we did not found such an association between ESS scores and

accidents. In the Powell study, near-miss accidents and

accidents were reported over a 3-year period, increasing the

statistical power and not only in the previous year like we did

Table 3 Multivariate logistic regression results for prediction of near-miss driving accidents

Total (n)

Near-miss driving accident

OR (95% CI) P-valueYes (n) %

Gender

Male 2178 288 13.2 1.51 (1.221.87) 0.001

Female 2596 222 8.6 Referent

Age (years)

3150 2115 250 11.8 Referent1830 597 117 19.6 1.86 (1.412.44) 0.001

5165 1312 102 7.8 0.75 (0.570.98) 0.05

>65 750 41 5.5 NS

ESS

010 4134 384 9.3 Referent 0.001

11 565 117 20.7 1.67 (1.292.15)

Pathologies

Controls 3310 316 9.5 Referent

OSAS 105 13 12.4 NS

RLS 93 14 15.1 NS

Insomnia 424 46 10.8 NS

Anxietydepression 348 54 15.5 1.82 (1.272.62) 0.001

Narcolepsy hypersomnia 7 1 14.3 NS

Multi-pathologies 487 66 13.6 NS

Stimulant effect of coffee, tea or colaNo 2964 277 9.3 Referent

Yes 1750 230 13.1 1.20 (0.971.47) 0.090

Severe sleepiness at the wheel needing to stop

No 3363 147 4.4 Referent

Yes 1411 363 25.7 6.50 (5.208.12) 0.001

CI, confidence intervals; ESS, Epworth Sleepiness Scale; OR, odds ratio; OSAS, obstructive sleep apnoea syndrome; RLS, restless legs

syndrome.




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in our study. Moreover, only 1% of our sample reports an ESS

score over 16. This relatively low frequency (Baldwin et al.,

2004) could be explained by the fact that our sample of drivers

is active and healthy (Connor et al., 2001a). However, our

result is in line with the one of Connor et al. (2002), indicating

no increase of severe accidental risk (i.e. injured drivers)

associated with measures of chronic sleepiness. Possibly falling

asleep in common situations (up to a certain degree of severity,

i.e. 11 < ESS score < 15) does not necessarily accurately

predict an accident risk. Another possible explanation could

be that sleepiness while driving has a major statistical weightcompared with chronic daytime sleepiness (ESS score), and

thereforewe removed thisvariablefrom our model. We observed

no relationship between snoring and sleep-related accidents.

Our survey shows that sleepiness at the wheel is a risk factor

as important as age for traffic accidents. Young drivers (18

30 years old) are at particular risk for accidents because they

present a high propensity to risk taking, alcohol or substance

consumption (Calafat et al., 2009; Zakletskaia et al., 2009).

We show that being subjectively sensitive to caffeinated

products (coffee, tea or cola) is a risk factor for near-misses

and traffic accidents. Our results go in line with a study (Retey

et al., 2006) demonstrating that subjects with the largest

impairment from one night of sleep deprivation (i.e. vulnerable

subjects) show the largest benefit from caffeine (i.e. caffeine-

sensitive subjects). Caffeine is an adenosine receptor antago-

nist, and adenosinergic mechanisms could therefore be a good

candidate to explain inter-individual differences to vulnerabil-

ity to sleep loss.

Near-miss accidents are more frequent than actual driving

accidents. A study (Powell et al., 2007) has shown that driving

near-miss sleepy accidents are dangerous precursors to actual

driving accidents. Near-misses are known to be highly corre-

lated to sleepiness at the wheel and should be considered as

strong warning signals for future accidents.

Interestingly, in our study, sleep-related near-miss accidents

occurred preferentially on the highway irrespective of duration

or timing of the trip. Sleep-related accidents occurred prefer-

entially in the city or on the open road, on a short trip and

during the day. Driving in a city or on an open road during the

day exposes the drivers to cross a higher number of vehicles or

obstacles, which could explain the occurrence of these accidents.

Our drivers report a higher rate of accidents (six accidents per

100 drivers) than the official police force statistics (threeaccidents per 1000 drivers; Observatoire National Interministe -

riel de Se curite Routie` re (ONISR), 2008). This high number is

probably due to the lower severity of our accidents. Many of

these probably did not result in traffic disruption or physical

injury, but simply in vehicle damage. Obviously we missed fatal

accidents, and probably the severely injured that could not

respond to a telephone survey. Even if the number of severely

injured drivers is probably low in our sample, the very high

number of total accidents represents a major cost for society.

Another interesting point is the fact that sleep-related

accidents represent a low proportion of total accidents in our

sample compared with national highway data (30% of total

accidents in the previous year; ASFA, 2008) or other epide-

miological studies conducted in developed countries (Connor

et al., 2002). Previous studies (National Transportation Safety

Board, 1990, 1995) showed that sleep-related accidents are

more severe than non-sleep-related accidents, and we suspect

that hospitalized victims of accidents were underrepresented in

our sample of responders. Even so, extrapolated to the French

general population (30 million drivers), sleepiness could

account for 90 000 accidents per year, a massive financial

burden for the French society.

Table 4 Multivariate logistic regression results for prediction of driving accidents

Total (n)

Driving accident

OR (95% CI) P-valueYes (n) %

Age (years)

3150 2115 130 6.1 Referent

1830 597 77 12.9 2.13 (1.513.00) 0.001

5165 1312 45 3.4 0.63 (0.440.90) 0.01

>65 750 26 3.5 NSMarital status

Married 3456 170 4.9 Referent

Single 687 69 10 1.40 (0.991.96) 0.05

Separated or divorced or widowed 562 38 6.8 1.78 (1.212.61) 0.01

Professional driver


Yes 535 48 9.0 1.52 (1.082.13) 0.05

Stimulant effect of coffee, tea or cola


Yes 1750 127 7.3 1.43 (1.111.85) 0.01

Severe sleepiness at the wheel needing to stop


Yes 1411 138 9.8 2.03 (1.572.64) 0.001

CI, confidence intervals; OR, odds ratio.




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Some methodological issues have to be considered. Near-

miss driving accidents and driving accidents were self-reported,

and we did not ask the nature, severity or circumstances of

material damage and injury. Still our findings are quite

consistent with other studies (Connor et al., 2002; Powell

et al., 2007) and we believe that they reflect the reality.

To conclude, our study shows that sleepiness plays a

significant role in urban and diurnal minor traffic accidents

in an active and healthy population (i.e. drivers). Thesefindings are quite new and extend significantly the field of

accidental risk for sleepiness at the wheel for long distance or

middle of the night travels to more classical and frequent urban

trips. Our next step should be to conduct a study similar to

Connor et al. (2002) in hospitalized French drivers to confirm

the prevalence of sleep-related accidents in severely injured

drivers. This new study should give us a full vision of accidental

risk on French roads in relation to sleepiness at the wheel.

A C K N O W L E D G E M E N T S

We thank Sandy Leproust for statistical support.

D I S C L O S U R E S T A T E M E N T

No financial conflict of interest.

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Sleepiness and Driving Accidents