FEDERAL MOTOR CARRIER SAFETY ADMINISTRATION

FMCSA Contact: Albert Alvarez, MC-RTR, 202-385-2387

Sleep Apnea Crash Risk Study

Background

Sleep apnea is a condition in which a narrowing or closure of the upper airway during

sleep causes repeated sleep disturbances, and possible complete awakenings, leading to

poor sleep quality and excessive daytime sleepiness. This study was completed to assess

the risks of commercial motor vehicle (CMV) crashes due to the presence of sleep apnea

among truck drivers. 

The primary objectives of the study were to: 

(1) Obtain additional and more meaningful crash data by linking the University of

Pennsylvania (UPenn) sleep apnea database to the FMCSA Motor Carrier Management

Information System (MCMIS) crash database, 

(2) Understand the impact of sleep apnea and driver impairment on crash involvement,

the number of crashes, and the severity of crashes, and 

(3) Gain insight into how crash rates are impacted before and after drivers are diagnosed

with sleep apnea. 

The study used a modeling approach to analyze specific data regarding truck drivers and

sleep apnea. The hypothesis tested was that "A driver diagnosed with sleep apnea is more

likely to be involved in a motor vehicle crash than a driver with no history or symptoms of

sleep apnea, after controlling for differences in the other predictor variables included in

the model." The data gained in this study did not support this hypothesis.

Study Design and Methodology

The overall goal of the study was to analyze crash risk due to sleep apnea in CMV drivers.

The CMV drivers with sleep apnea were compared to CMV drivers who did not have sleep

apnea, looking at the relative crash risks as a function of both the presence and the

severity of sleep apnea. To accomplish these analyses, relevant variables from the UPenn

sleep apnea database were linked with crash data from the MCMIS crash file, and a specific

database was created. The data were analyzed using contingency tables, logistic

regression, and a Poisson regression model. 

University of Pennsylvania Sleep Apnea Study

From 1996 to 1998, the University of Pennsylvania Center for Sleep and Respiratory

Neurobiology collected data for a study on the prevalence and consequences of obstructive

sleep apnea among commercial vehicle drivers, and involved the overnight laboratory

testing of 406 subjects. The three objectives of the study were to: 

(1) Estimate the prevalence of sleep apnea among a sample of commercial truck drivers

living in Pennsylvania within 50 miles of the University of Pennsylvania, 

(2) Examine the relationship in commercial truck drivers between severity of sleep apnea

and decreased function related to driving performance, and 

(3) Develop a profile of an overall sample of commercial truck drivers with regard to their

sleep apnea-related characteristics and risks. 

The subjects were chosen after UPenn sent a multivariable apnea prediction questionnaire

to a random sample of 4,826 commercial driver's license (CDL) holders residing in

Pennsylvania. From the 1,391 responses that were returned, 406 drivers were selected to

participate in the overnight laboratory study. The overall findings from this study revealed

that 28 (6.9 percent) of the 406 participants were diagnosed with severe sleep apnea, 32

(7.9 percent) had moderate sleep apnea, 86 (21.2 percent) had mild sleep apnea, and 260

(64 percent) had none.

The goal of the Federal Motor

Carrier Safety Administration

(FMCSA) is to reduce the large

truck fatality rate by 41 percent

from 1996 to 2008. This reduction

translates into a rate of 1.65

fatalities in truck crashes per 100

million miles of truck travel.

FMCSA's Research and Technology

programs encompass a range of

issues and disciplines, all related to

motor carrier and bus safety and

security. FMCSA defines a

"research program" as any

systematic study directed toward

fuller scientific discovery,

knowledge, or understanding that

will improve safety, and reduce the

number and severity of commercial

motor vehicle crashes. Similarly, a

"technology program" defines

those programs that adopt,

develop, test, and/or deploy

innovative driver and/or vehicle

best practices, and technologies

that will improve safety and

reduce the number and severity of

commercial motor vehicle crashes. 

Currently, FMCSA's Office of

Research and Technology is

conducting programs in order to

produce safer drivers, improve

safety of commercial motor

vehicles, produce safer carriers,

advance safety through

information-based initiatives, and

improve security through safety

initiatives. The study described in

this Tech Brief was designed and

developed to support the FMCSA

Research and Technology strategic

objective to produce safer drivers.

The primary goals of this initiative

are to ensure that commercial

drivers are physically qualified,

trained to perform safely, and

mentally alert. 

Office of Research and Technology

400 Virginia Ave, SW

Suite 600

Washington, DC 20024

Tech Brief
The results of the study showed that the prevalence rates of sleep apnea among commercial truck drivers are

similar to sleep apnea rates found in other general populations. The study also revealed that the prevalence of

sleep apnea depends on the relationship between two major factors—age and degree of obesity as measured by

body mass index (BMI)—with the prevalence of sleep apnea increasing with increasing age and BMI. Another

meaningful study finding showed that the prevalence of sleep apnea depends on the average duration of sleep

over consecutive nights at home. Short sleep duration, six hours or less per night, results in an increase in the

prevalence of sleep apnea. 

Motor Carrier Management Information System (MCMIS) Crash Database

In order to analyze the relationship between sleep apnea and crash risk, researchers compared the sleep apnea

information gained from the UPenn study with the crash risk history of each of the 406 commercial drivers who

participated in that study. Crash histories for these drivers were obtained from state motor vehicle records for a

period of seven years prior to diagnosis in the UPenn sleep study (1989 - 1996) and from the Motor Carrier

Management Information System (MCMIS) crash database for a seven year period following diagnosis (1996 -

2003). The MCMIS database was selected as the source of crash data primarily because it is the only database

which contains CDL information for each driver involved in a crash. This allowed researchers to link the crash

data to the patient data for each of the 406 subjects who underwent in-laboratory testing during the UPenn

sleep apnea study. 

The MCMIS crash database contains a census of trucks and buses involved in fatal, injury, and tow-away crashes.

It is maintained by FMCSA and contains data on trucks and buses in crashes that meet the uniform crash data

standards developed through the National Governors Association (NGA). An NGA-reportable crash involves a

truck (a vehicle that is designed, used, or maintained primarily for carrying property and has at least two axles

and six tires) or a bus (a vehicle with a seating capacity of at least nine people, including the driver). The crash

must result in either at least one fatality, at least one injury for which the injured person was taken to a medical

facility for immediate medical attention, or at least one vehicle that was towed from the crash scene as a result of

disabling crash damage. The crashes are reported by the individual states and transmitted to FMCSA through a

microcomputer-based software system called SAFETYNET. While some states do not report all NGA-eligible

crashes and the MCMIS crash file captures only approximately 60 percent of all NGA-reportable crashes per year, it

was confirmed that Pennsylvania, the state in which the majority of crashes occurred involving the drivers used in

this study, commonly reports about 96 percent of all truck crashes to MCMIS. 

Study Database

After confirmation of databases to combine, data requirements were defined and a new database was created to

support the analysis of crash risk due to sleep apnea in commercial drivers. This involved merging selected data

elements collected by UPenn during the sleep apnea study with crash information contained in the MCMIS crash

file. Some of the data elements collected using the UPenn study survey questionnaires included driver

demographic information, medical histories, information about sleeping habits, and job-related driving data,

including monthly/yearly miles driven, years of commercial driving experience, driving schedule, and types of

vehicles driven. Data elements from the MCMIS crash file included the date and time of the crash, number of

injuries and fatalities, and the state in which the crash occurred. These were just a few of the data elements

combined to create the database to be analyzed regarding sleep apnea and crash risk.

Methods of Analysis

After choosing specific data elements to include in the database created for this study, the data were analyzed

using three different methods. The first method was Contingency Table Analysis, a statistical method used 

for exploring multivariate but discrete data distributed into tables. In general, the rows of a contingency 

table represent alternate outcomes, and the columns denote exposure (or lack of exposure) to a treatment or 

risk factor. 

The data were also analyzed using logistic regression, which is a mathematical modeling approach that can be

used to describe the relationship of several independent or predictor variables to a binary or dichotomous

dependent variable, such as crash or no crash. Logistic regression models identify factors that affect the

likelihood of an outcome and can be used to predict the outcome of an event. Because the outcome of interest

for this study is a heavy truck-related crash, logistic regression models were used to predict the occurrence of a

crash event based on a set of predictor variables, which included such driver characteristics as the presence and

severity of sleep apnea, age, gender, alcohol and drug use, and annual miles driven.

Finally, crash rates were normalized by the number of miles driven annually for each driver, and a Poisson

regression model was estimated. Poisson regression analysis is a regression technique used for modeling

dependent variables that describe discrete data. The methodology of Poisson regression analysis assumes that 
the underlying distribution of the dependent variable under consideration is Poisson. The Poisson distribution is

often used to model the occurrence of rare events, such as the number of motor vehicle crashes occurring either

over time or as a function of the number of miles driven. Thus, Poisson regression is an appropriate statistical

technique for analyzing crash rates. 

Results

As mentioned previously, age and body mass index were the two major factors that are linked to the prevalence

of sleep apnea. However, only age was found to be associated with both sleep apnea and the occurrence of

crashes during the pre-diagnosis period. The mean age of subjects with some degree of sleep apnea was 48.7

years compared to an average age of 45.5 years for those who were not diagnosed with sleep apnea. On the

other hand, while older drivers are at higher risk for sleep apnea, younger drivers with fewer years of

commercial driving experience have an increased likelihood of motor vehicle crashes. 

Looking at the seven year period following participation in the UPenn sleep study (1996 - 2003) in Table 1, crash

involvement among sleep apnea patients was shown to be higher than in the comparison group—14.4 percent

vs. 11.2 percent. But the chi-square statistic, used in this case to test an association between crashes and sleep

apnea, was not significant (p = 0.3). The results for all crashes over the entire 14 year period can be similarly

interpreted. Therefore, there is no statistical evidence in these data to suggest that the presence of sleep apnea

significantly increases the likelihood or the risk of motor vehicle crashes. 

Looking at the results in a logistic regression model, with odds ratios adjusted for potential confounding effects

of various factors by including them as independent variables, the same results are revealed. Various factors

included age, gender, alcohol use, body mass index, self-reported daytime sleepiness, presence or absence of

high blood pressure, use or nonuse of

medications causing drowsiness, unusual

work/sleep schedule, and miles driven per

year. The odds ratios included 95 percent

confidence intervals, which indicated that

the relationship between sleep apnea

severity and motor vehicle crashes is not

statistically significant. The null

hypothesis being tested in the logistic

regression analysis is that the two

possible outcomes, crash or no crash, are

equally likely (odds ratio = 1). Since the

value of 1 is contained in every

confidence interval at a 95 percent level

of significance in this model, the null

hypothesis of no association cannot be

rejected, and, therefore, it was concluded

that there is no statistical evidence in the

data to suggest that drivers with sleep

apnea are more likely than drivers

without sleep apnea to have a commercial vehicle crash.

If the presence of sleep apnea in commercial drivers does not significantly influence the crash risk, the question

arises as to whether the severity of sleep disturbances contributes to crashes. However, once again, the data

lacked statistical evidence of a positive association between sleep apnea severity and post-diagnosis crash

involvement, as indicated by an insignificant chi-square test (p = 0.7). Similar findings of no association of sleep

apnea severity were obtained for pre-diagnosis crashes and all crashes, as well.

Next, the hypothesis that drivers with various degrees of sleep apnea had a greater likelihood of being involved

in multiple crashes over the 14 year period was investigated. No association was found between sleep apnea

presence or severity and multiple crashes. This suggests that the commercial drivers in this study who were

diagnosed with sleep apnea were not at increased risk for having more than one crash over the 14 year period

prior to and following diagnosis.

In the final logistic regression analysis, the relationship between sleep apnea severity and the severity of motor

vehicle crashes was assessed. In this analysis, a significant relationship was found between severe sleep apnea

and severe crashes (those which include multiple injuries and vehicle towed from accident scene). Drivers with

severe sleep apnea were 4.6 times more likely to be involved in a severe crash in the seven year period than

drivers with no sleep apnea. However, it was also found that drivers with severe sleep apnea were no more

likely to involved in moderate/minor crashes than those without sleep apnea. Moreover, no statistically

significant relationship was found between either moderate or mild sleep apnea and crash severity.

Count

Column %

Row %

Sleep 

Apnea

No Sleep

Apnea TOTAL

Crash

21

14.38

42.00

29

11.15

58.00

50

No Crash

125

85.62

35.11

231

88.85

64.89

356

TOTAL 146 260 406

Table 1. 2x2 Contingency Table: Presence of Sleep Apnea 

vs. Crash Involvement (Post-Diagnosis)
Researchers

This study was performed at the

Volpe National Transportation

Systems Center, Cambridge, MA

02142-1093, by Lawrence C. Barr,

along with Linda Boyle

(University of Iowa), and Greg

Maislin, (University of

Pennsylvania Biomedical

Statistical Consulting). PPA No.

SA1J/WPB No. AB136

Availability

The study final report FMCSART-04-008 will be made available

from USDOT's Information

Services Online Publishing at

http://isddc.dot.gov.

Key Words

Apnea, commercial motor

vehicle, crash, crash risk, 

sleep, sleep disorder, truck, 

truck driver.

Notice

This Tech Brief is disseminated

under the sponsorship of the

Department of Transportation in

the interest of information

exchange. The Tech Brief

provides a synopsis of the

study's final publication. The

Tech Brief does not establish

policies or regulations, nor does

it imply USDOT endorsement of

the conclusions or

recommendations. The U.S.

Government assumes no liability

for its contents or their use.

Web Site

FMCSA Tech and Analysis Briefs

may be accessed at:

http://www.fmcsa.dot.gov.

Technical Writer

Kirse Kelly

C2 Technologies, Inc.

September 2004

Publication No. FMCSA-RT-04-007

A Poisson regression model was estimated to explore the impact of sleep apnea on crash

rates, or crashes as a function of miles driven. The results indicated that none of the sleep

apnea severity categories had a significant impact on whether or not a driver had a crash.

Conclusions

In summary, the presence and severity of obstructive sleep apnea among a population of

406 commercial truck drivers was analyzed as a risk factor for CMV crashes. Contingency

tables and logistic regression models were developed to determine the likelihood of crash

involvement, crash severity, and multiple crashes among commercial drivers before and

after being diagnosed with sleep apnea. In addition, crashes were normalized by mileage

driven, and Poisson regression models were developed to determine the effect of sleep

apnea on crash rates. With the exception of a significant positive relationship between

severe sleep apnea and severe crashes, results from this study showed that the presence

and severity of sleep apnea in commercial truck drivers are not good predictors of motor

vehicle crash involvement. In other words, no compelling statistical evidence was found

that supports the hypothesis that sleep apnea increases crash risk among commercial

drivers. Furthermore, there was no evidence from the data used in this study to suggest

that crash risk is impacted before and after drivers are diagnosed with sleep apnea.

The results of this study contradict those found in several previous studies, which found a

strong positive relationship between sleep apnea and motor vehicle crashes. While it

appears that sleep apnea is not a risk factor for vehicular crashes among this sample of

CMV drivers, the limitations of the data must be acknowledged, and these results must be

interpreted with caution. For example, the data includes many confounding variables,

which were based on self-reported information, and many drivers may have misinterpreted

some survey questions. Incomplete crash and driving records add to the limitations of the

study, as well. Finally, the large majority of subjects analyzed in this study were short-haul

drivers who operated local routes, generally driving in dense urban environments

requiring a higher level of alertness than long-haul truckers, who drive long stretches on

interstate highways under monotonous driving conditions and who are more susceptible

to fatigue and daytime sleepiness. These are just a few of the limitations of the data

included this study.

Given the limitations in the data and the results of these analyses, some recommendations

for future research are suggested. These include: 

(1) Obtain complete and accurate crash records for every subject in the study population. 

(2) To the extent possible, obtain prospective information regarding treatments advised

and actually administered to the study subjects for the sleep disorders diagnosed

during clinical testing. These data should include the recommended treatment, if any,

and follow up on treatment receipt and effectiveness.

(3) Future studies should target long-haul commercial truck drivers who operate heavy

tractor-trailer vehicles. In over-the-road highway driving, the longer distances and

monotonous routine may contribute to drowsiness, and the higher speeds may

increase the danger to drivers. 

With these additions, a more reliable database can be created and a stronger case can be

made from the results of a future study.

References

Pack, A.I., Dinges, D.F., and Maislin, G. A Study of Prevalence of Sleep Apnea Among

Commercial Truck Drivers. Report No. DOT-RT-02-030. Federal Motor Carrier Safety

Administration, U.S. Department of Transportation, May 2002.