Alcohol Consumption, Smoking, Job Stress and Road Safety in Professional Drivers

DATS (Development and Advising in Traffic Safety) Research Group, INTRAS (University Research Institute on Traffic and Road Safety), University of Valencia, Valencia, Spain

*Corresponding Author:

Sergio A. Useche
DATS (Development and Advising in Traffic Safety) Research Group
INTRAS (University Research Institute on Traffic and Road Safety)
University of Valencia, Valencia, Spain
Tel: (+34) 963 393 880
E-mail: sergio.useche@uv.es

Received date: April 06, 2017; Accepted date: April 24, 2017; Published date: April 30, 2017

Citation: Useche SA, Serge A, Alonso F, Esteban C (2017) Alcohol Consumption, Smoking, Job Stress and Road Safety in Professional Drivers. J Addict Res Ther 8:321. doi:10.4172/2155-6105.1000321

Copyright: ©2017 Useche SA, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use. Distribution, and reproduction in any medium, provided the original author and source are credited.

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Abstract

Objective: The aim of this study was to describe the prevalence of two addictive behaviours (regular alcohol consumption and smoking) among professional drivers and its relationship to Job Strain (job stress indicator of the Demand-Control model) and self-reported road safety outcomes.

Methods: The study sample was composed by 2445 Colombian professional drivers with an average of 38.01 years of age, a mean of driving experience of 15.81 years and, in average, 7.35 hours of daily driving. Participants of this study had a mean of 0.41 road accidents and 0.50 traffic fines registered in the last two years. It was designed a questionnaire composed by three sections: a) socio-demographic data, including items such as gender, age, and driving habits, b) Job Strain (JCQ), and c) self-reported habits related to addictive behaviors, in particular smoking and alcohol consumption.

Results: It was found that 20.3% of professional drivers have the habit of actively consuming tobacco, and 27.9% of drinking alcohol regularly. Furthermore, 28% of the sample presents Job Strain. Further, significant trends between smoking and: a) gender (i.e., being a male driver), and b) the fact of having Job Strain were found. Regarding alcohol consumption, two-step cluster analysis allowed to establish profiles of drivers when combining the fact of drinking alcohol regularly (or not) and the self-reported rates of fines and traffic accidents registered for the last two years. Finally, significant differences in Job Strain were established between drivers in both clusters, being the mean score higher for drivers reporting regular alcohol consumption and higher rates of fines and crashes.

Conclusion: This research suggests the need for the development of comprehensive interventions on psychosocial factors at work and lifestyle issues among professional drivers, based on the reported rates of the two addressed addictive behaviors and its relationship to adverse health, occupational and safety outcomes.

Keywords

Addictive behaviors; Alcohol consumption; Smoking; Job stress; Job strain; Road safety; Professional drivers

Introduction

Statistically, some habits related to addictive substances, such as regular alcohol consumption, increase not only the probability of suffering a traffic crash, but also the probability of increasing the severity of injuries caused by accidents when impaired by alcohol [1]. In fact, alcohol and psychotropic substances constitute some of the factors better significantly differentiating vehicle collision victims regarding demographic, injury type and/or severity and crash characteristics related to pre-crashes [2].

According to Seppala et al. [3], the association of a growing crash risk linked to the increasing of blood alcohol levels is enough documented, i.e., even at low blood alcohol levels (<0.05 g/dL), an increased crash risk is to be expected [4,5], mostly when there are other risk factors present, such as sleep decreasing or sleep systematic disorders [6,7], fatigue [8-10], stress [11-14], mood disorders [15,16] and/or different types of drugs [17], which tend to increase the risk even more.

Furthermore, psychotropic drugs apart from alcohol have been found to be related very less frequently to the occurrence of traffic crashes. Nevertheless, at glance of existing statistics, there is a nonunder estimable potential risk related to drug use [3]. While other substances by itself are not as important as alcohol as a direct crash predictor, it should be kept in mind that the prevalence of drug consumption is very lower to alcohol and tobacco consumption in global terms, so that significant bias can occur in the causal analysis of the occurrence of traffic accidents related to these behaviors [18].

Alcohol consumption, smoking and its relationship to driving performance.

vAlthough variable conclusions have been described in the literature in this field, many psychotropic drugs have been systematically and constantly associated with different negative outcomes at work, such as health complains, interpersonal problems [19], absenteeism [20] and, in the field of transportation, to the decreasing of driving performance [21,22]. This is the case of alcohol consumption and the behavior of smoking while driving [23,24].

The evidence shows that poor outcomes in driving performance are related, among other variables, to alcohol, even in low dosage, and too many other drugs are also linked to impairment [1]. These behaviors, all linked to addictions by years, have been demonstrated to be impairers of different processes needed to perform accurately the driving task [25], such as data processing, attention and rapid response to environmental demands, judgment, fatigue management, and the avoiding of aggression and risky behaviors [1,26].

In brief, recent studies have found that, in some countries, almost 10% of professional drivers use alcohol while driving [27] and several research experiences have documented that a wide amount of drivers use to drive after consuming alcohol and other psychoactive substances [28,29].

The alcohol consumption, even when takes place several hours previous to the driving task, is also related to losses regarding sleep and resting periods, and prolonged effects that use to interact with fatigue and driving demands to facilitate the commission of driving errors or violations [30], essentially when the operator uses to drive for long hours and/or under pressure, as normally happens in the field of professional driving [31]. Furthermore, it can continue affecting the driver when the breath alcohol level has fallen to near-zero [32]. In short, Barrett et al. [7] have concluded that there is no 'safe' level of alcohol intake for otherwise sleepy drivers, at any time of the day.

Finally, Drummer et al. [21] have found relevant evidence revealing that drivers dead in vehicle crashes and taking psychoactive drugs or combining two or more, were more likely to be responsible for the suffered accidents than those drivers taking neither drugs nor alcohol.

Associations between job stress and alcohol/tobacco consumption

Psychosocial factors at work and specially job stress have been demonstrated to have a predictive effect on two very relevant health risk-behaviors: smoking and alcohol consumption [33,34]. Nevertheless, the predictive value of job stress on smoking and alcohol consumption is relatively limited, and may largely depend on subjective characteristics such as coping mechanisms [33], the quantity and quality of objective stress factors at work [35], the workplace culture [36] and the perception of supportive factors in the work and/or social environment [35,37-39].

According to Frone [40], the alcohol consumption uses to represent a strategy for regulating negative emotions or thoughts derived from adverse working conditions. Furthermore, the accumulated evidence in the field implies that the relationship between psychosocial factors at work and alcohol consumption could be mediated by individual differences, essentially regarding the fact of using alcohol to cope with work stressors [39,40], In the specific case of Demand-Control model, that uses the concept of Job Strain as stress indicator [41,42], it has been demonstrated in some groups of workers that the presence of Job Strain (i.e., a high amount of demands combined with a low level of control/decision latitude at work) is associated with hazardous drinking habits [43] and frequent alcohol consumption [44]. For the case of the relationship between Job Strain and smoking among workers, the evidence has shown that smoker workers are more likely than non-smokers to report work-related indicators, such as Job Strain in the case of Demand-Control model [45] and the Effort-Reward Imbalance in the ERI model [46] in terms of prevalence and intensity of tobacco consumption. This phenomenon seems to be associated to other factors present in the work environment such as social support and job overload [47].

In the same way than adverse physical [48,49] and mental [50] health conditions, fatigue [8,10], smoking and alcohol consumption [51], job stress, closely linked to typically adverse working conditions, has been systematically associated to poor safety outcomes in the field of public transportation [52,53]. However, several studies dealing with alcohol consumption and job stress have suggested the need of further to clarify the causal nature of the association between stressors at work and addictive habits related to alcohol and other substances [54].

Objective

The aim of this study was to describe the prevalence of two addictive behaviours (regular alcohol consumption and smoking) among professional drivers and its relationship to: a) Job Strain (job stress indicator of the Demand-Control model), and b) self-reported road safety outcomes along the last two years.

Methods

Sampling

For this study, it was used a sample of n=2445 professional Colombian drivers (93.9% men and 6.1% women) between 18 to 77 years of age, with a mean of x̄=38.01 (SD=10.22) years. The average driving experience of these professional drivers was x̄=15.81 (SD=9.56) years. The mean of week days driving was x̄=4.46/5 (SD=1.17). Furthermore, over the last two years, participants of this study had x̄=0.41 (SD=0.91) occupational accidents at the wheel and x̄=0.50 (SD=1.26) traffic fines or penalties.

Procedure: Design and ethics

For this cross-sectional study, participants have been selected through a convenience sampling in six main cities of Colombia. Both transport companies (public, freight and private transport) as individuals, invited through cooperation of various institutions, were contacted during a period of approximately eight months for data collection purposes. Professional drivers have completed the questionnaire, designed in a paper version, guaranteeing the anonymity of the participants, and emphasizing on the fact that the data would only be used for research purposes. It was used an informed consent statement, signed by both parties before the participants answered the questionnaire. The global response rate was approximately 85%.

Description of the Instrument

The first part of the questionnaire asked for socio-demographic data, including items such as gender, age, and driving habits, i.e., driving experience, daily intensity of driving and days driving per week.

Secondly, job stress conditions were assessed according to the Karasek's Job Strain model [55]. For this purpose, it was used the 27 item-scale of the Colombian Job Content Questionnaire (JCQ) [56]. The JCQ has been widely used to assess psychosocial factors in the workplace and their effects on health. The response scale consists of a 4-point likert scale (1=“totally disagree” and 4=“totally agree”). The 27 items of the JCQ are grouped in six sub-scales: support from supervisors (4 items. α=0.87), peer support (4 items. α=0.79), skill discretion (6 items. α=0.75), decision authority (3 items. α=0.69), psychological demands (6 items. α=0.66) and job insecurity (4 items. α=0.53). Decision latitude was calculated as de sum of skills discretion and decision-making. Job strain was computed as the ratio between psychological demands and decision latitude (demands/ decision latitude). A value of 1.0 or higher in Job Strain indicates the presence of Job Stress in the participant.

Finally, the third part of the survey consisted of questions about a) self-reported habits related to addictive behaviors: Do you smoke (Yes/ No)? Do you Drink Alcohol Regularly? (Yes/No), and b) traffic accident and penalty rates along the last two years.

Statistical analysis

Descriptive statistics (e.g. mean, standard deviation) and Pearson’ (bivariate) correlational analysis were performed to present the global results and associations between the study variables of the general sample of professional drivers. Frequencies were used to obtain the reported prevalence of key addiction-related indicators. For comparing specific trends in alcohol and tobacco consumption (according to gender and other categorical variables), Chi-Square tests were conducted. Finally, two-step cluster analysis and One Way ANOVA were performed to determine the adjustment of the combination of central study variables and comparing these groups in terms of Job Strain, respectively. All statistical analyses were performed using ©IBM SPSS (Statistical Package for Social Sciences), version 23.0.

Results

Descriptive data and bivariate correlations

Table 1 presents the descriptive statistics of the study and the bivariate correlations between them. Regarding Job Stress, it was found that 28.0% of professional drivers present Job Strain. Although it was found that this sample of professional drivers had an average of job strain below the risk score (x̄=0.88; SD=0.28), keeping in mind that values greater than 1.0 represent an unfavorable inequality between demands and control at work, the average self-reports on accidents and traffic fines were relatively high. In other words, in average, four of each ten drivers have reported at least one traffic accident along the last two years, and the average of received sanctions is approximately of 0.5 per driver.

Table 1: Descriptive statistics and Pearson correlations between the study variables; **correlation is significant at the 0.01 level (2-tailed); *correlation is significant at the 0.05 level (2-tailed).

Regarding adverse lifestyle or addictive behaviors (i.e., smoking and alcohol consumption), it was found that 20.3% of the sample of professional drivers has the habit of actively consuming tobacco and 27.9% of drinking alcohol regularly.

Correlational analysis allowed establishing significant measures of association between components of the Demand-Control model (measured by JCQ) and alcohol and tobacco consumption among professional drivers. For instance, tobacco consumption was negatively associated with skill discretion and perceived levels of control at work. On the other hand, regular alcohol consumption was found positively associated with decision authority, perceived control at work and psychological demands present in the work environment. It was also observed that alcohol consumption results negatively and significantly associated with the number of hours driving per week, the age of participants, and their years of experience as professional drivers. Further, it was found a significant and positive association between tobacco consumption and driving hours, and a negative correlation between this behavior and the age and driving experience of participants.

Categorical comparisons

Differential rates on gender regarding the prevalence of alcohol (28.1% men and 25% women) and tobacco (21.0% men and 10.1% women) consumption were found. In general, the obtained rates regarding both issues were higher in terms of relative percentages for men. Trough Chi-Square (X²) analysis it was possible to establish a significant trend between gender, i.e., the fact of being a male professional driver, and the fact of having the habit of tobacco consumption (X²=9.441; p<0.001). It was also established that there is an existing association between the fact of presenting Job Strain (Job Stress indicator of DC model) and tobacco consumption (X²=4.848, p=0.017).

Cluster analysis and mean comparisons

Through a two-step cluster analysis, it was determined the optimum number of clusters for the combination of two continuous variables (i.e., accidents and traffic fines) and a categorical variable (i.e., regular alcohol consumption). Starting from fifteen possible clusters, it was found an optimal combination of the variables for two clusters (e.g. Silhouette measure of cohesion and separation near 0.7), according to the values registered for the aforementioned variables:

Cluster 1: Professional drivers with a higher rate of accidents (x̄=0.47; SD=0.92) and traffic fines (x̄=0.46; SD=0.86) reported in the last two years, presenting the habit of regular alcohol consumption (99.8%) containing 34.2% of the valid sample.

Cluster 2: Professional drivers with a lower rate of accidents (x̄=0.34; SD=0.95) and traffic fines (x̄=0.41; SD=0.87) reported in the last two years not presenting the habit of regular alcohol consumption (0.02%) containing 65.8% of the valid sample.

The summary of the two-stage two-cluster model can be seen in the Figure 1 below:

Figure 1: Information of two-cluster model obtained and cluster quality measurement.

It is observed that the number of records of each cluster is representative. So that the two conglomerates are maintained, it has not been considered necessary to apply another method of nonhierarchical analysis, to obtain consistent, representative and epidemiological-oriented results.

Furthermore, when comparing mean scores of Job Strain between groups through Analysis of Variance (ANOVA), it was found that professional drivers in Cluster 1 (x̄=0.885; SD=0.27) have a significantly lesser mean for Job Strain than those included within Cluster 2 (x̄=0.856; SD=0.26), with F(1.1487)=3.942; p=0.048, as graphically shown in Figure 2.

Discussion and Conclusion

Different studies have described in general the negative influence of alcohol [23,57] and tobacco [58,59] on driving and road safety. However, in the case of developing countries (as the case of Colombia), a considerably minor amount of evidence has been accumulated regarding the impact of substance consumption upon subsequent health and safety outcomes among professional drivers [60,61] and its relationship with working conditions and job stress, even considering the typical adverse working conditions and high prevalence of addictive behaviors and Job Strain registered among professional drivers [8,53,62]. On the other hand, the existing empirical evidence about alcohol and tobacco consumption has addressed different factors modulating the impact of psychosocial factors at work, almost always in the form of increased health and accident risk, as well as with a higher prevalence of risky behaviors at the wheel. Although risky behaviors are not equivalent to accidents by themselves, do predict an important part of them [26,63]. For the specific case of Colombia, which rate of deaths in traffic accidents have been between 5000 and 6000 persons/year along the last years [64], the registered data for substance consumption among drivers results adverse by itself. The third cause of death and sixth cause of injuries derived from traffic accidents in 2012 were related to the consumption of alcohol and/or other substances considered as psychotropic [63]. Specifically, in the occupational field, Sánchez and Forero [65] have found that injuries related to driving task constitute 25% of all industrial accidents registered. Other studies have found that alcohol and/or substance consumption among Colombian drivers oscillate between 18% and 24% [66] compared to the 27.9% found in this research.

Figure 2: Comparative job strain means between clusters 1 and 2.

In the present study it has been found, in brief, a set of relevant associations between self-reported health habits widely related to addictive behaviors, demographic and work-related variables. In this sense, it is worth mentioning that other empirical studies dealing with professional drivers have also targeted smoking as a classical risk factor, whose interaction with specific occupational stressors. Job Strain and social support could facilitate the development and maintenance of maladaptive habits and behaviors among them (in fact. smoking intensity has demonstrated to be predicted by occupational stress) [67], contributing to show different adverse health symptoms [68,69] and negative occupational outcomes in the mid and long term, both in professional drivers as in a very long list of other groups of workers [70]. On the other hand, alcohol consumption has been largely related to several health complains [71,72] and directly linked to the increasing of incidentallity and accidentality rates in the field of professional driving [73,74]. In this sense, it is striking that approximately one in five Colombian professional drivers currently has the habit of smoking (20.3%) and one in four (27.9%) of them regularly consume alcohol.

Regarding the reported differential trends between men and women in terms of tobacco consumption (21% versus 10.1%, respectively), sex differences in this respect have been documented by international comparative studies determining, for instance, according to Pampel [75], a 39.9% of prevalence among male America’s inhabitants and 18% for women, arguing that many biological, social and cultural factors tend to explain a substantial part of this disproportionality [76,77]. Although the found rates are not essentially equivalent to those documented in general population, the approximate ratio of 2:1 (men:women) is maintained almost steadily for the case of professional drivers.

The fact of presenting Job Strain, i.e., the indicator of Job Stress of the Demand-Control approach [41], has been also associated to the fact of being smoker. This suggests, indirectly (taking into account the results of the study and its limited inferential capacity) and directly (keeping in mind the preceding empirical evidence) the need of developing effective and systematic occupational interventions for reducing: a) occupational stress, as a measure for reducing, inter alia, the risk for tobacco consumption [33], and b) the habit of tobacco consumption inside and outside the occupational field, taking into account its implications for physical and mental health [68] and for occupational [70] and safety [59] outcomes of professional drivers.

Finally, it is worth mentioning the importance of addressing the relationship between regular consumption of alcohol and safe driving: two-step cluster analysis allowed establishing profiles of drivers when combining the fact of drinking alcohol regularly (or not) and the selfreported rates of fines and traffic accidents registered for the last two years. The adjustment and quality of the cluster model allow to support the clear difference in terms of these variables between professional drivers who have (or not) the habit of regularly drinking alcohol, in accordance to which has been suggested in several studies assessing the increased risk of misbehaviors and/or accidents at the wheel based on this behavior [1-7,43,77]. Furthermore, obtained differences in terms of Job Strain between drivers in both clusters (i.e., higher Job Strain rates for participants in cluster number 1) result consistent to other empirical research documenting the critical relationships existing between: a) job stress and both addressed addictive behaviors [34,78,79], b) job stress and traffic accidents [49,80,81], and c) addictive behaviors such as alcohol consumption, lifestyle factors and safety outcomes [81,82]. It also suggests the need for the development of comprehensive interventions on psychosocial factors at work, keeping in mind the recent growing evidence on the impact of working conditions and its related processes, such as fatigue [83], burnout [52] and social support [84], contributing to the emergence of new risks for health [85], and welfare of professional drivers [86].

Other factors related to work environments of professional drivers that must be kept in mind are attention/distractions at the wheel [87]. Some suggested interventions have integrated these issues in holistic programs addressing also the prevention of consumption of different substances such as tobacco and alcohol (during and before driving) as a manner to reducing objective risk of occupational accidents [87-89]. In brief, apart from merely confronting intra-organizational factors, this type of actions can play, also, a substantial role contributing for reducing the impact of the typical adverse occupational conditions of professional drivers [53] on public health, through the promotion of safety policies [90], healthier environments and potentially lower rates of injuries and deaths produced by traffic crashes.

Limitations of the Study

Although the questionnaires employed for this research process have a good reported reliability, remain vulnerable to self-report bias. The cross sectional design did not allow inferring causality from the association between job strain/addictive behaviors and traffic crashes in professional drivers, but only to describe associations and trends related to the study variables. Furthermore, the sampling strategy and geographical covering of the study limits the generalizability of the findings. Also, proportional rates of gender distribution present very differential values between the number of men and women participating in the study. It is mostly related to the predominance of male workers in this sector of the industry. However, for future research it is worth recommending performing convenience samples to address this potential biasing source. Finally, it is important to remark the need to study other aspects of alcohol and tobacco consumption (i.e., frequency, intensity, reasons) to obtain a greater explanatory character of the relation between these behaviors and: a) psychosocial factors at work, including different approaches to job stress, and 2) mid and long term outcomes in terms of occupational health and safety.

Acknowledgement

The authors thank to the professional drivers that participated in the study and to the institutions that have facilitated this process. Further, we want to remark the work of Mireia Faus and Iris Lacuesta for their support in data collection and treatment. Also, thanks to Mayte Duce for their technical supporting the research process

References

1. Ogden EJ, Moskowitz H (2004) Effects of alcohol and other drugs on driver performance. Traffic Inj Prev 5: 185-198.
2. Stoduto G, Vingilis E, Kapur BM, Sheu WJ, McLellan BA, et al. (1993) Alcohol and Drug use among motor vehicle collision victims admitted to a regional trauma unit: Demographic injury and crash characteristics. Accid Anal Prev 25: 411-420.
3. Seppala T, Linnoila M, Mattila MJ (1979) Drugs, alcohol and driving. Drugs 17: 389-408.
4. Richter R, Hobi V (1975) The impairment of the ability to drive with blood alcohol concentrations of 0.5 per mile. A review of the literature. Schweiz Med Wochenschr 105: 884-890.
5. Banks S, Catcheside P, Lack L, Grunstein RR, McEvoy RD (2004) Low levels of alcohol impair driving simulator performance and reduce perception of crash risk in partially sleep deprived subjects. Sleep 27: 1063-1067.
6. Horne JA, Reyner LA, Barrett PR (2003) Driving impairment due to sleepiness is exacerbated by low alcohol intake. Occup Environ Med 60: 689-692.
7. Barrett PR, Horne JA, Reyner LA (2004) Alcohol continues to affect sleepiness related driving impairment, when breath alcohol levels have fallen to near-zero. Hum Psychopharmacol 19: 421-423.
8. Useche S, Cendales B, G�³mez V (2017) Measuring fatigue and its associations with job stress. Health and traffic accidents in professional drivers: The case of BRT operators. EC Neurology 4: 103-18.
9. Alonso F, Esteban C, Useche S, L�³pez de C�³zar E (2016) Prevalence of physical and mental fatigue symptoms on Spanish drivers and its incidence on driving safety. Adv Psychol Neurosci 1: 10-18.
10. Useche S, Alonso F (2017) The importance of fatigue-monitoring as a tool for the intelligent transport systems (ITS). EC Neurology 5: 71-73.
11. Cendales-Ayala B, Useche SA. G�³mez-Ortiz V. Bocarejo JP (2016) Bus operators' responses to job strain: An experimental test of the job demand-control model. J Occup Health Psychol.
12. Berraho M, Nejjari C, Elrhazi K, El Fakir S, Tessier JF, et al. (2006) Measuring levels of professionally-related stress in taxi drivers in Fes, Morocco. Sante Publique 18: 375-387.
13. Useche S, Cendales B, Alonso F, Serge A (2017) Comparing job stress, burnout, health and traffic crashes of urban bus and BRT Drivers. Am J Appl Psychol 5: 25-32.
14. Du C-L, Lin MC, Lu L, Tai JJ (2011) Correlation of occupational stress index with 24 h urine cortisol and serum DHEA sulfate among city bus drivers: A cross-sectional study. Saf Health at Work 2: 169-175.
15. Platek AE, Szymanski FM, Filipiak KJ, Ozieranski K, et al. (2016) Prevalence of depressive disorders in professional drivers� epidemiologic subanalysis of the RACER study. Psychiatr Pol 50: 859-871.
16. Alonso F, Esteban C, Useche S, Faus M (2016) Prevalence and perception of depressive symptomatology among Spanish drivers and its relation to driving safety. Int J Psychol Brain Sci 1: 54-61.
17. Alonso F, Esteban C, Montoro L, Tortosa F (2014) Psychotropic drugs and driving: Prevalence and types. Ann Gen Psychiatry 13: 14.
18. Katulanda P, Ranasinghe C, Rathnapala A, Karunaratne N, Sheriff R, et al. (2014) Prevalence. patterns and correlates of alcohol consumption and its' association with tobacco smoking among Sri Lankan adults: A cross-sectional study. BMC Public Health 14: 612.
19. Carrell SE, Hoekstra M, West JE (2011) Does drinking impair college performance? Evidence from a regression discontinuity approach. J Public Econ 95: 54-62
20. Bacharach SB, Bamberger P, Biron M (2010) Alcohol consumption and workplace absenteeism: The moderating effect of social support. J Appl Psychol 95: 334-348.
21. Drummer OH, Gerostamoulos J, Batziris H, Chu M, Caplehorn J, et al. (2004) The involvement of drugs in drivers of motor vehicles killed in Australian road traffic crashes. Accid Anal Prev 36: 239-248.
22. Anderson BK, Larimer ME (2002) Problem drinking and the workplace: An individualized approach to prevention. Psychol Addict Behav 16: 243-251.
23. Alonso F, Pastor JC, Montoro L, Esteban C (2015) Driving under the influence of alcohol: Frequency reasons perceived risk and punishment. Subst Abuse Treat Prev Policy 10: 11.
24. Alonso F, Esteban C, Useche S, Faus M (2017) Smoking while driving: Frequency, motives, perceived risk and punishment. World J Prev Med 5: 1-9.
25. Linnoila M (1974) Effect of drugs and alcohol on psychomotor skills related to driving. Ann Clin Res 6: 7-18.
26. Useche S. Serge A. Alonso F (2015) Risky behaviors and stress indicators between novice and experienced drivers. Am J Appl Psychol 3: 11-14.
27. Mir MU, Khan I, Ahmed B, Razzak JA (2012) Alcohol and marijuana use while driving-An unexpected crash risk in Pakistani commercial drivers: A cross-sectional survey. BMC Public Health 12: 145.
28. Alonso F, Esteban C, Sanmart�­n J, Useche S (2017) Reported prevalence of health conditions that affect drivers. Cog Med 4: 1303920.
29. Whitehill JM, Rivara FP, Moreno MA (2014) Marijuana-using drivers alcohol-using drivers and their passengers: Prevalence and risk factors among underage college students. JAMA Pediatr 168: 618-624.
30. Useche S (2011) An�¡lisis de errores y violaciones de tr�¡nsito en los conductores de Bogot�¡ a trav�©s del DBQ (Driving Behavior Questionnaire) [Errors and traffic violations analysis in Bogota City drivers measured by DBQ (Driving Behaviour Questionnaire)]. Revista de Psicolog�­a Jur�­dica 1: 29-37.
31. Cendales B. Useche S. G�³mez V (2014) Psychosocial work factors blood pressure and psychological strain in male bus operators. Ind Health 52: 279-288.
32. Barrett PR, Horne JA, Reyner LA (2005) Early evening low alcohol intake also worsens sleepiness-related driving impairment. Hum Psychopharmacol 20: 287-290.
33. Azagba S, Sharaf MF (2011) The effect of job stress on smoking and alcohol consumption. Health Econ Rev 1: 15.
34. Keyes K, Hatzenbuehler M, Grant BF, Hasin DS (2012) Stress and alcohol: Epidemiologic evidence. Alcohol Res 34: 391-400.
35. Moore S, Sikora P, Grunberg L, Greenberg E (2007) Work stress and alcohol use: Examining the tension-reduction model as a function of worker's parent's alcohol use. Addict Behav 32: 3114-3121.
36. Cashman CM, Ruotsalainen JH, Greiner BA, Beirne PV, Verbeek JH (2009) Alcohol and drug screening of occupational drivers for preventing injury. Cochrane Database Syst Rev CD006566.
37. Frone MR, Russell M, Cooper ML (1993) Relationship of work-family conflict, gender and alcohol expectancies to alcohol use/abuse. J Organ Behav 14: 545-558.
38. Hagihara A, Tarumi K, Nobutomo K (2003) Positive and negative effects of social support on the relationship between work stress and alcohol consumption. J Stud Alcohol 64: 874-883.
39. Turner AJ, Hick PE (1975) Inhibition of aldehyde reductase by acidic metabolites of the biogenic amines. Biochem Pharmacol 24: 1731-1733.
40. Frone MR (1999) Work stress and alcohol use. Alcohol Res Health 23: 284-291.
41. Karasek R (1988) Demand/control model: A social, emotional and physiological approach to stress risk and active behavior development. ILO Encyclopedia of Occupational Health and Safety, 4th edition, Geneva: Princeton.
42. Schechter J, Green LW, Olsen L, Kruse K, Cargo M (1997) Application of Karasek's demand/control model a Canadian occupational setting including shift workers during a period of reorganization and downsizing. Am J Health Promot 11: 394-399.
43. Lima CT, Farrell M, Prince M (2013) Job strain, hazardous drinking and alcohol-related disorders among Brazilian bank workers. J Stud Alcohol Drugs 74: 212-222.
44. Gimeno D, Amick BC, Barrientos-Guti�©rrez T, Mangione TW (2009) Work organization and drinking: An epidemiological comparison of two psychosocial work exposure models. Int Arch Occup Environ Health 82: 305-317.
45. Heikkil�¤ K, Nyberg ST, Fransson EI, Alfredsson L, De Bacquer D, et al. (2012) Job strain and tobacco smoking: An individual-participant data meta-analysis of 166,130 adults in 15 European studies. PLoS ONE 7: e35463.
46. Kouvonen A, Kivimaki M, Virtanen M, Pentti J, Vahtera J (2005) Work stress, smoking status and smoking intensity: An observational study of 46 190 employees. J Epidemiol Community Health 59: 63-69.
47. Kawakami N, Haratani T, Araki S (1998) Job strain and arterial blood pressure, serum cholesterol and smoking as risk factors for coronary heart disease in Japan. Int Arch Occup Environ Health 71: 429-432.
48. Alonso F, Esteban C, Useche S, Serge A (2017) Perception of the impact of certain health conditions on driving performance. Public Health International 2: 1-7.
49. Berger JT, Rosner F, Kark P, Bennett AJ (2000) Reporting by physicians of impaired drivers and potentially impaired drivers. J Gen Intern Med 15: 667-672.
50. Alonso F, Esteban C, Sanmart�­n J, Useche SA (2016) Consistency between the subjective perception of feeling indisposed. the decision to drive and driving performance. Science Journal of Public Health 4: 482-488.
51. Ragland DR. Greiner BA. Krause N. Holman BL. Fisher JM (1995) Occupational and non-occupational correlates of alcohol consumption in urban transit drivers. Prev Med 24: 634-645.
52. Useche S, Alonso F, Cendales B, Autukeviciute R, Serge A (2017) Burnout. Job strain and road accidents in the field of public transportation: The case of city bus drivers. J Environ Occup Sci 6: 1-7.
53. Santos JA, Lu JL (2016) Occupational safety conditions of bus drivers in Metro Manila: The Philippines. Int J Occup Saf Ergon 22: 508-513.
54. Ragland DR, Greiner BA, Yen IH, Fisher JM (2000) Occupational stress factors and alcohol-related behavior in urban transit operators. Alcohol Clin Exp Res 24: 1011-1019.
55. Karasek R, Brisson C, Kawakami N, Houtman I, Bongers P, et al. (1998) The job content questionnaire (JCQ): An instrument for internationally comparative assessments of psychosocial job characteristics. J Occup Health Psychol 3: 322-355.
56. G�³mez V (2011) Assessment of psychosocial stressor at work: Psychometric properties of the Spanish version of the JCQ (Job Content Questionnaire) in Colombian workers. Revista Latinoamericana de Psicologi�´a 43: 329-342.
57. Scott-Parker B, Oviedo-Trespalacios O (2017) Young driver risky behaviour and predictors of crash risk in Australia, New Zealand and Colombia: Same but different? Accid Anal Prev 99: 30-38.
58. Brison RJ (1990) Risk of automobile accidents in cigarette smokers. Can J Public Health 81: 102-106.
59. Mangiaracina G, Palumbo L (2007) Smoking while driving and its consequences on road safety. Ann Ig 19: 253-267.
60. Sobngwi-Tambekou JL, Brown TG, Bhatti JA, et al. (2016) Driving under the influence of alcohol in professional drivers in Cameroon. Traffic Inj Prev 17: 73-78.
61. Goon S, Bipasha MS (2014) Prevalence and pattern of smoking among bus drivers of Dhaka. Bangladesh. Tob Use Insights 7: 21-25.
62. Djindji��� N, Jovanovi��� J, Djindji��� B, Jovanovi��� M, Pesi��� M, et al. (2013) Work stress related lipid disorders and arterial hypertension in professional drivers - A cross-sectional study. Vojnosanit Pregl 70: 561-568.
63. Norza-C�©spedes EH, Granados-Le�³n EL, Useche-Hern�¡ndez SA, Romero-Hern�¡ndez M, Moreno-Rodr�­guez J (2014) Componentes descriptivos y explicativos de la accidentalidad vial en Colombia: incidencia del factor humano. Revista Criminalidad 56: 157-187.
64. Valbuena SJ (2011) Muertes y lesiones no fatales por accidentes de transporte. Colombia [Non-fatal injuries and deaths from transportation accidents. Colombia]. Bogot�¡: Instituto Nacional de Medicina Legal y Ciencias Forenses.
65. Sanchez MJ, Forero S (2004) Estudio de las condiciones de trabajo de los conductores de los veh�­culos de carga en Colombia para proponer mejoras en los puestos de trabajo [Study of the working conditions of drivers of freight vehicles in Colombia to propose improvements in the workplaces], Thesis. Bogot�¡: Pontificia Universidad Javeriana.
66. Buitrago JR, Norza-C�©spedes EH, Ruiz H (2015) Conductores en estado de embriaguez en Colombia y la implementaci�³n de la Ley 1696 de 2013 [Driving under the influence of alcohol in Colombia, and the implementation of Act ("Ley") 1696 of 2013]. Revista Criminalidad 57: 27-40.
67. Emdad R, Belkic K, Theorell T, Cizinsky S (1998) What prevents professional drivers from following physicians' cardiologic advice? Psychother Psychosom 67: 226-240.
68. Ansari-Moghaddam A, Ansari H, Mohammadi M, Tabatabaei SM, Pishevare-Mofrad M, et al. (2016) Prevalence of addiction and smoking and their relationship with blood parameters among drivers in Zahedan. Int J High Risk Behav Addict 5: e27554.
69. Hansen J, Raaschou-Nielsen O, Olsen JH (1998) Increased risk of lung cancer among different types of professional drivers in Denmark. Occup Environ Med 55: 115-118.
70. Lundborg P (2007) Does smoking increase sick leave? Evidence using register data on Swedish workers. Tob Control 16: 114-118.
71. Mansur Ade P, Rocha MA, Leyton V, Takada JY, Avakian SD, et al. (2015) Risk factors for cardiovascular disease. Metabolic syndrome and sleepiness in truck drivers. Arq Bras Cardiol 105: 560-565.
72. Rosso GL, Montomoli C, Candura SM (2016) Poor weight control. alcoholic beverage consumption and sudden sleep onset at the wheel among Italian truck drivers: A preliminary pilot study. Int J Occup Med Environ Health 29: 405-416.
73. Fell JC, Tippetts AS, Voas RB (2009) Fatal traffic crashes involving drinking drivers: What have we learned? Ann Adv Automot Med 53: 63-76.
74. Summala H, Mikkola T (1994) Fatal accidents among car and truck drivers: Effects of fatigue, age and alcohol consumption. Hum Factors 36: 315-326.
75. Pampel FC (2006) Global patterns and determinants of sex differences in smoking. Int J Comp Sociol 47: 466-487.
76. Global youth tobacco survey collaborating group (2003) Differences in worldwide tobacco use by gender: Findings from the global youth tobacco survey. J Sch Health 73: 207-215.
77. Norza E, Romero M, Moreno J, D�­az R, Useche S, et al. (2013) Caracterizaci�³n de la accidentalidad en Colombia: An�¡lisis del fen�³meno desde el estudio del factor humano [Characterization of accident in Colombia: Analysis of the phenomenon from the study of the human factor], Polic�­a Nacional de Colombia.
78. Ayyagari P, Sindelar JL (2010) The impact of job stress on smoking and quitting: Evidence from the HRS. BEJ Econom Anal Policy 10.
79. Vasse RM, Nijhuis FJ, Kok G (1998) Associations between work stress, alcohol consumption and sickness absence. Addiction 93: 231-241.
80. Hartley LR, El Hassani J (1994) Stress, violations and accidents. Appl Ergon 25: 221-230.
81. Taylor AH. Dorn L (2006) Stress, Fatigue, Health and risk of road traffic accidents among professional drivers: The contribution of physical inactivity. Annu Rev Public Health 27:371-391.
82. Papadakaki M, Kontogiannis T, Tzamalouka G, Darviri C, Chliaoutakis J (2008) Exploring the effects of lifestyle. sleep factors and driving behaviors on sleep-related road risk: A study of Greek drivers. Accid Anal Prev 40: 2029-2036.
83. Useche S, Alonso F (2017) The importance of fatigue-monitoring as a tool for the intelligent transport systems (ITS). EC Neurology 5: 71-73.
84. Shattell M, Apostolopoulos Y, Collins C, S�¶nmez S, Fehrenbacher C (2012) Trucking organization and mental health disorders of truck drivers. Issues Ment Health Nurs 33: 436-444.
85. Iavicoli S, Cesana G, Dollard M, Leka S, Sauter SL (2015) Psychosocial factors and workers' health and safety. Biomed Res Int 2015: 628749.
86. Tse JLM, Flin R, Mearns K (2006) Bus driver well-being review: 50 years of research. Transportation Res F-Traf 9: 89-114.
87. Dingus TA (2014) Estimates of prevalence and risk associated with inattention and distraction based upon in situ naturalistic data. Ann Adv Automot Med 58: 60-68.
88. Griffin R, Huisingh C, McGwin G (2014) Prevalence of and factors associated with distraction among public transit bus drivers. Traffic Inj Prev 15: 720-725.
89. Stutts JC, Hunter WW (2006) Driver inattention. driver distraction and traffic crashes. ITE J 73: 34-45.
90. Najaf P, Isaai MT, Lavasani M, Thill J-C (2016) Evaluating traffic safety policies for developing countries based on equity considerations. Journal of Transportation Safety and Security 9: 178-203.