Excessive Drowsiness among Truck Drivers in Benin in 2023: Associated Factors and Risk of Crashes Occurrence ()
1. Introduction
Drowsiness is difficulty staying awake and an increased propensity to fall asleep at an inappropriate or even dangerous time [1] . It’s an intermediate physiological state between wakefulness and sleep [2] . It can however become pathological (excessive drowsiness) if it occurs at a time when the person had not expected to sleep, during waking hours; when there is an unwanted and sometimes uncontrollable urge to sleep throughout the day [3] [4] . This condition affects many people around the world, and can be the root of accidents in everyday life, at work or on the public highway. In fact, excessive daytime drowsiness is responsible for around 10% to 30% of road accidents worldwide [5] .
Excessive drowsiness is common, with a prevalence of 33.32% worldwide [6] . Among people in the United States, 27.8% are affected [7] . In Africa, daytime sleepiness was 31.07% in 2020 among Ethiopian students at the University of Gondar [3] .
The prevalence of excessive drowsiness among truck drivers was 23.4% in the city of Parakou, Benin, in 2014 [8] .
Road crashes are frequent in Benin; in 2021, the incidence of traffic accidents in the country stood at 85,298, or 6.8 per 1000 inhabitants [9] . And the number of people killed in 2019 varied between 8% and 20% of those injured [10] .
Despite the numerous causes of crashes, those related to drowsiness among drivers of large trucks in Cotonou are poorly documented. Consequently, the present study was initiated to investigate the factors associated with drowsiness among drivers of large trucks in Cotonou in 2023, and the risk of crash occurrence.
2. Materials and Methods
2.1. Study Framework
The study took place in the city of Cotonou, the economic capital of Benin, which makes up the Littoral County, one of the country’s twelve counties. In addition to the tertiary sector (trade and services) supported by a number of manufacturing industries, the town also benefits from the activities of the port sector (Port Autonome de Cotonou), the most important of which is that of second-hand vehicles. It is also the point of departure for large trucks carrying goods inland or to countries bordering Benin, such as Burkina-Faso and Niger [9] . A number of parking lots have been created for them in and around Cotonou. These include parking lots in Cotonou, Allada, Godonmè-gare and Sèmè Podji.
2.2. Study Type
This was a cross-sectional, descriptive and analytical study held in March 2023.
2.3. Participants
The study focused on drivers of large trucks in the parking lots of Cotonou, Allada, Godonmè-gare and Sèmè Podji, whether employed by transport companies or working on their own account.
Drivers aged 18 and over who were present in the above-mentioned parking lots during the data collection period and who had given their free and informed consent were included in the study.
2.4. Sampling
The sample size was calculated using the Schwartz formula, based on the prevalence of excessive drowsiness in Parakou, Benin, of 23.4% [8] and a precision of 0.05, then increased by 10%, giving a sample size of 304 drivers.
Drivers included in the study were selected for convenience. Parking lots were visited one after the other, in descending order of their capacity in terms of surface area. When all eligible and available drivers in a parking lot had been found, we moved on to the next parking lot.
2.5. Variables
The dependent variable was excessive drowsiness among large truck drivers, measured by the Epworth Sleepiness Score (ESS), developed and published by John. Drivers with Epworth scores above 10 were classified as excessive drowsiness [11] .
The independent variables are five groups of factors, namely:
Socio-demographic factors: age, marital status and level of education;
Health factors: history of diabetes, hypertension, musculoskeletal pathologies, vision disorders and epilepsy;
Sleeping habits: daily sleep duration, number of naps per week and snoring or nocturnal respiratory discomfort;
Behavioral factors: daily practice of sport, consumption of coffee, tea, tobacco, alcohol, cannabis and medication;
Working conditions: working hours, average driving time before a rest period, average daily driving time, seniority as a truck driver, average distance covered per day, number of substantial meals per day, habitual overweight, remuneration and night shifts.
2.6. Data Collection
Data were collected by administering a questionnaire designed to incorporate the Epworth Drowsiness Scale. This consists of ten questions, each with four modalities, scored from 0 to 3 points (0 = never dozing, 1 = little chance of dozing, 2 = good chance of dozing and 3 = very high chance of dozing).
2.7. Data Analysis
Collected data were recorded on an Excel 2019 sheet, processed and analyzed using Stata 15.0 software. Analysis was performed in two phases, one descriptive and the other analytical.
Parameters for central tendency and dispersion were used in the descriptive phase. The following thresholds were used for variable transformation:
Average daily sleep duration: less than 6 hours of sleep per day puts you at risk of excessive drowsiness [12] [13] [14] ;
Number of naps: less than 2 per week = risk of excessive drowsiness [15] ;
Alcohol consumption: more than 2 standard glasses per day plus = excessive consumption [9] [10] ;
Average driving time before taking a rest: less than 5 hours (4 h 30 minutes), in Benin [16] ;
Average daily driving time: 10 hours [17] [18] ;
Average distance covered daily: less than 500 kilometers (464 km) [3] [18] [19] .
The analytical phase consisted of a bivariate and then a multivariate analysis. For the bivariate analysis, simple logistic regression was used. The multivariate analysis was a top-down stepwise multiple logistic regression, at a significance level of 5%. Variables with a p-value less than or equal to 20% at the end of the bivariate analysis were included in the initial logistic regression model. Baseline modalities were those with the lowest risk.
The fit of the final model was tested, and we concluded that there was a "good fit" because the p-value of the Hosmer-Lemshow test was greater than 5%.
Once the factors associated with drowsiness had been identified, we investigated the link between drowsiness as an exposure and accident history (occurrence of accidents in drivers’ past) as an event. To this end, a comparison of proportions was made, using Pearson’s test, with a threshold of 5%.
2.8. Ethical Concerns
Authorization was granted by the administrative authorities in charge of transport. An information note was presented to drivers prior to the abstention of their consent. Data were collected and processed with respect for anonymity and confidentiality.
3. Results
3.1. Sample Description
Altogether 304 truck drivers were surveyed, all male, aged 35.98 ± 8.42 years. Average daily sleep duration on the working day was 4.28 ± 1.79 hours. There was one driver with epilepsy and one with impaired vision. The socio-demographic features of the drivers are presented in Table 1.
3.2. Prevalence of Drowsiness among Large Truck Drivers in Cotonou in 2023
The mean Epworth drowsiness test score was 9.89 ± 2.83, with a range of 2 to 21. Of the 304 large truck drivers surveyed, 89 (29.2%) had an Epworth drowsiness scale score of over 10. The prevalence of excessive drowsiness among long-haul truck drivers in Cotonou in 2023 was therefore 29.2%.
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Table 1. Socio-demographic features of truck drivers, Cotonou, 2023 (n = 304).
3.3. Identification of Associated Factor with Excessive Drowsiness among Large Truck Drivers through Bivariate Analysis
These results are shown in Table 2 and Table 3, which demonstrate that factors associated with drowsiness were: daily sleep duration on the working day, high blood pressure, lack of daily exercise and average distance travelled per day.
3.4. Multivariate Analysis Results
The following Table 4 presents the final model of the multivariate analysis.
The final model revealed three variables associated with drowsiness among Benin’s large truck drivers in 2023: absence of sport, distance traveled per day by drivers and daily sleep duration on the working day.
Thus, knowing the fact of sleeping less than 6 hours on the working day and the distance traveled per day, drivers who did not practice sport were twice as likely to experience sleepiness as those who practiced it. Those who slept less than 6 hours per day were 3 times more likely to be sleepy than those who slept more than 6 hours per day, taking into account sports practice and distance traveled per day. Drivers who traveled more than 500 kilometers per day were 1.82 times more likely to have excessive sleepiness than those who traveled less than 500 kilometers, taking into account sleeping less than 6 hours per day and playing sports.
The Hosmer-Lemshow test had a p-value of 0.60, greater than 0.05, indicating that the model was adequate.
3.5. Link between Drowsiness and Accidents
The findings are given in the contingency Table 5.
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Table 2. Association between drowsiness, sleeping habits factors and behavioral factors of large truck drivers, Cotonou, 2023; results of bivariate analysis (n = 304).
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Table 3. Association between drowsiness, socio-demographic factors, health and working conditions of large truck drivers, Cotonou, 2023; results of bivariate analysis (n = 304).
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Table 4. Factors associated with drowsiness among truck drivers, Cotonou, 2023; multivariate analysis (n = 304).
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Table 5. Relationship between drowsiness and prior crashes among truck drivers, Cotonou, 2023 (n = 304).
OR: 1.73; CI 95% [1.04; 2.87]; p = 0.03.
Drivers of large trucks suffering from excessive drowsiness were 1.73 times more likely to have a road accident, compared to those without drowsiness. A significant association exists between drowsiness and the occurrence of accidents among drivers of large trucks in Cotonou in 2023.
4. Discussion
Prevalence of drowsiness
The prevalence of excessive drowsiness in the present study (29.2%) seems higher than those obtained by Houehanou in Parakou, Benin in 2014 (23.4%) and by Akkoyunlu, in Zonguldak, Turkey in 2013 (26.6%) [8] [20] However, this is still below the 31.7% recorded in 2021 by Ahn in South Korea [21] . In the Democratic Republic of Congo, 42.7% of public transport bus drivers experienced daytime sleepiness in 2021 [22] .
Beyond the mere comparison of the prevalence of drowsiness, the quality of road infrastructure and vehicles, so as the living and working conditions of drivers must be taken into account [23] . Thus, comparison is easier with Houehanou’s results, as both studies were carried out in Benin, offering the same road infrastructure, living and working conditions. In addition, the town of Parakou is situated on the trajectory of the majority of truck drivers departing from Cotonou; it could be said that the two studies were carried out on the same population. With roughly the same sample size for both studies (304 and 316), we could explain the difference in prevalence by the time elapsed between the two studies. Indeed, from 2014 to 2023, nine years during which the whole world is going through an economic crisis, aggravated by the Covid-19 pandemic, and whose consequences spare no one and no sector of activity.
Associated factors to drowsiness
Furthermore, knowing the fact of sleeping less than six hours on the working day and the distance traveled per day, drivers who didn’t practice sports had twice the risk of excessive drowsiness than those who did. This finding was also made by Houehanou in Parakou in 2014 (p = 0.039) [8] . As De Mello found in Brazil in 2013, regular exercise increases alertness and vigilance while driving in drivers who spend several hours behind the wheel. Exercise even reduces drowsiness and promotes recuperative sleep at night [24] .
People sleeping less than six hours a day were three times more likely to be drowsy than those who slept more than six hours a day, taking into account the amount of sport and distance travelled per day. Querino Maia, found in 2013 in the USA that sleeping less than six hours was associated with excessive drowsiness, including at the wheel [25] . Failure to comply with the 24-hour time limit between delivery of the bill of lading and loading of the vehicle leads to the payment of penalties. It also depends on the time agreed to deliver the goods to the destination, which is often a long distance away [23] , or on the lack of or failure to respect the service schedule, sometimes linked to a modest remuneration, the payment of which is sometimes irregular or based on the number of journeys made.
Drivers who covered more than 500 kilometers a day were 1.82 times more likely to suffer from excessive drowsiness than those who covered less than 500 kilometers, taking into account the fact that they slept less than six hours a day and practiced sport. While living and working conditions are not the same, a similar result was also demonstrated by Rosso, in 2016 in Italy where , drivers who often travelled long distances and worked longer hours had excessive drowsiness [26] .
Furthermore, the association between drowsiness and the occurrence of accidents among drivers of large trucks is consistent with data in the literature, such as the study by Rosso in 2016 in Italy [26] and by Cahit Özer in Turkey in 2014 [27] .
Our study could be subject to information bias, as respondents might not give true answers to certain questions such as alcohol consumption, cannabis use, their health status in relation to a chronic illness, having experienced at least one accident and its possible causes, etc. To guard against such eventualities, we asked for and obtained the free and informed consent of the respondents, made it clear to them that the survey was anonymous, and reassured them that one of them would be present as an interviewer.
5. Conclusion
Based on this study, we identified average sleep time per working day, the average distance travelled per day and not practicing sports as factors associated with excessive drowsiness. These factors constitute behaviors which can only be modified by drivers themselves, but also by other transport stakeholders such as employers or truck owners, and customers whose goods are transported. A study of the latter two players will provide a clearer picture of their involvement, with a view to proposing the best communication strategy to contribute effectively to the prevention of road accidents.
Appendix
Epworth Sleepiness Scale
Circle the most appropriate number for each situation:
- 0 = no chance of dozing or falling asleep;
- 1 = low chance of falling asleep;
- 2 = average chance of falling asleep;
- 3 = high chance of falling asleep.
It is important that you answer each question as best you can
Total: …………………………
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