Gender Differences in Prevalence of Sleepy Driving Among Young Drivers in Saudi Arabia

Yousef D Alqurashi,1 Abdullah S Alqarni,1 Faisal Maher Albukhamsin,1 Abdullah Abdulaziz Alfaris,1 Bader Ibrahim Alhassan,1 Waleed Khalid Ghazwani,1 Abdulaziz Abdulrahman Altammar,1 Mutlaq Eid Aleid,1 Hayfa Almutary,2 Abdulelah M Aldhahir,3 Saleh A Alessy,4 Rayyan Almusally,5 Abir Alsaid,5 Mahmoud Ibrahim Mahmoud,5 Hatem Othman Qutub,5 Tunny Sebastian,6 Suliman Alghnam,7 Michael I Polkey8 1Respiratory Care Department, College of Applied Medical Sciences, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia; 2Medical Surgical Nursing Department, King Abdulaziz University, Jeddah, Saudi Arabia; 3Respiratory Therapy Department, Faculty of Applied Medical Sciences, Jazan University, Jazan, Saudi Arabia; 4Department of Public Health, College of Health Sciences, Saudi Electronic University, Jeddah, Saudi Arabia; 5Internal Medicine Department, King Fahd Hospital of the University, Imam Abdulrahman Bin Faisal University, Khobar, Saudi Arabia; 6Clinical Nutrition Department, College of Applied Medical Sciences, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia; 7Population Health Section-King Abdullah International Medical Research Center (KAIMRC), King Saud Bin Abdulaziz University for Health Sciences (KSAU-HS), Riyadh, Saudi Arabia; 8Royal Brompton Hospital, Guy’s and St Thomas’ NHS Foundation Trust, London, UKCorrespondence: Yousef D Alqurashi, Respiratory Care Department, College of Applied Medical Sciences, Imam Abdulrahman Bin Faisal University, Dammam, 34221, Saudi Arabia, Email [email protected]: Sleepy driving is associated with Motor Vehicles Accidents (MVAs). In Saudi Arabia, previous studies have addressed this association among men only. Therefore, the aim of this study was to compare the prevalence of sleepy driving and associated factors between genders.Methods: In a cross-sectional study design, we offered a self-administered online questionnaire to 3272 participants from different regions of Saudi Arabia. The questionnaire included 46 questions covering sociodemographics, driving habits, sleeping habits, Epworth Sleepiness Scale, and Berlin questionnaire to assess the risk of sleep apnea. Univariable and multivariable logistic regression analyses were used to determine the significant factors associated with self-reported sleepy driving, defined as operating a motor vehicle while feeling sleepy in the preceding six months.Results: Of the 3272 invitees, 2958 (90%) completed the questionnaire, of which 1414 (48%) were women. The prevalence of sleepy driving in the preceding six months was 42% (men: 50% and women 32%, p< 0.001). Specifically, participants reported the following: 12% had had to stop their vehicle due to sleepiness (men: 16.2% and women 7%, p< 0.001), 12.4% reported near-miss accidents (men: 16.2% and women: 8.2%, p< 0.001) and 4.2% reported an accident due to sleepiness (men: 4.3% and women: 4%, p=0.645). In multivariable analysis, being male, younger age, use of any type of medications, shift working, working more than 12 hours per day, driving duration of 3– 5 hours per day, driving experience of more than 2 years, excessive daytime sleepiness and risk of having obstructive sleep apnea were all associated with increased likelihood of falling asleep while driving in the preceding 6 months.Conclusion: Sleepy driving and MVA are prevalent in both gender but was higher in men. Future public health initiatives should particularly focus on men, since men reported a greater likelihood of both sleep-related MVA and “near miss” events.Keywords: sleepiness, driving, obstructive sleep apnea, OSA, gender difference

Global carbon intensity of crude oil production

Producing, transporting, and refining crude oil into fuels such as gasoline and diesel accounts for ∼15 to 40% of the “well-to-wheels” life-cycle greenhouse gas (GHG) emissions of transport fuels (1). Reducing emissions from petroleum production is of particular importance, as current transport fleets are almost entirely dependent on liquid petroleum products, and many uses of petroleum have limited prospects for near-term substitution (e.g., air travel). Better understanding of crude oil GHG emissions can help to quantify the benefits of alternative fuels and identify the most cost-effective opportunities for oil-sector emissions reductions (2). Yet, while regulations are beginning to address petroleum sector GHG emissions (3–5), and private investors are beginning to consider climate-related risk in oil investments (6), such efforts have generally struggled with methodological and data challenges. First, no single method exists for measuring the carbon intensity (CI) of oils. Second, there is a lack of comprehensive geographically rich datasets that would allow evaluation and monitoring of life-cycle emissions from oils. We have previously worked to address the first challenge by developing open-source oil-sector CI modeling tools [OPGEE (7, 8), supplementary materials (SM) 1.1]. Here, we address the second challenge by using these tools to model well-to-refinery CI of all major active oil fields globally—and to identify major drivers of these emissions