Work stress and health problems of professional drivers: a hazardous formula for their safety outcomes

Introduction

Work (or job) stress is a key predictor of adverse health and organizational outcomes (Stansfeld & Candy, 2006; Gilboa et al., 2008; Largo-Wight et al., 2011; O’Neill & Davis, 2011; Viswesvaran, Ones & Schmidt, 1996; Hoboubi et al., 2017). Particularly, anxiety, depression (Stansfeld & Candy, 2006; Woo & Postolache, 2008), psychosomatic symptoms (Kuiper, Van Der Beek & Meijman, 1998; Nakao, 2010) and general psychological strain (O’Neill & Davis, 2011) have been previously associated with the exposition to work stressors. Regarding physical health, work stress has been proved to be a consistent predictor of cardiovascular diseases (Kang et al., 2005; Habibi, Poorabdian & Shakerian, 2015; Poorabdian et al., 2013), musculoskeletal problems (Lundberg, 2003; Szeto & Lam, 2007), diabetes mellitus (Heraclides et al., 2009, 2012) and obesity (Schulte et al., 2007; Nyberg et al., 2012). Furthermore, in the organizational field, work stress has been related to turnover intention (O’Neill & Davis, 2011), absenteeism (Seamonds, 1982), sickness absence and presenteeism (Woo et al., 1999).

Psychological stress takes place when an individual perceives that the environmental demands exceed his/her adaptive capacity (Cohen, Janicki-Deverts & Miller, 2007). The Job Demands–Control (JDC) model (Karasek, 1998) is the worldwide most frequently used approach to research on work stress. Meta-analytic reviews have shown that, either jointly or separately, high demands and low decision latitude (skill discretion + decision authority) are significant risk factors that may lead to negative physical and mental health outcomes (Karasek, 1998; Stansfeld & Candy, 2006). Besides, high strain jobs (i.e., with high job demands and low decision latitude) which also have low levels of social support (from colleagues and supervisors) are associated with the highest risk for stress-related disease (Johnson & Hall, 1998).

Regarding professional drivers, different studies report a consistent association between job strain and poor health, fatigue, absenteeism and medical disability (Evans, 1994; Habibi, Poorabdian & Shakerian, 2015; Nyberg et al., 2012). In their 50 years’ literature review, Tse, Flin & Mearns (2006) found that task-related features of professional drivers (e.g., traffic congestion, time pressure, shift patterns, social isolation) are linked to high levels of psychophysiological stress. Santos & Lu (2016) suggest that typical stressors of professional drivers, such as working overtime and working shifts, increase the risk of traffic accidents, aggressive driving, fatigue, back pain, cough and colds. Furthermore, numerous researches have demonstrated that certain health conditions (e.g., poor mental health and cardiovascular problems) of professional drivers may affect their capacity to safely operate motor vehicles (Vernon et al., 2002; Abu Dabrh et al., 2014; Alavi et al., 2017; Hilton et al., 2009), and therefore increase their risk of road accidents, occupational injury and even early deaths (Knutsson, 2003; Zivkovic et al., 2005; Wong et al., 2013). Likewise, medications highly consumed by professional drivers, such as analgesics, anti-depressive and anxiolytic drugs (Orriols et al., 2009; Dow, Gaudet & Turmel, 2013), could substantially impair their driving performance. In other words, both sickness itself and the medications used to treat it may compromise the operational safety of professional drivers, which explains the need of conducting research on their stress-related diseases (Houlden et al., 2015).

Regarding health conditions that most frequently affect this occupational group, numerous studies have found professional drivers at high risk for: hypertension (Ragland et al., 1987; Jovanovic et al., 1998; Shin et al., 2013), musculoskeletal/ergonomic problems linked to prolonged inadequate postures (Netterstrom & Juel, 1988; Alperovitch-Najenson et al., 2010; Bulduk et al., 2014), cancer (Hansen, Raaschou-Nielsen & Olsen, 1998; Soll-Johanning et al., 1998) gastrointestinal (Koda et al., 2000) and metabolic disorders (Mansur et al., 2015), chronic fatigue (National Academies of Sciences, Engineering, and Medicine, 2016; Useche, Cendales & Gómez, 2017) and mental health problems (Hentschel et al., 1993; Shattell et al., 2012).

Etiologically, stress-related dysfunctions of the hypothalamic pituitary adrenocortical axis (HPA) (Brotman, Golden & Wittstein, 2007) and sympathetic nervous system (SNS) (Lundberg et al., 1994; Collet et al., 1997; Krantz, Forsman & Lundberg, 2004) underlies the association between the professional drivers’ adverse work condition and negative health outcomes. In particular, chronic stress induce HPA and SNS overactivity, which in turn is associated with metabolic disorders (Bose, Oliván & Laferrère, 2009; Vicennati et al., 2009), cardiovascular disease (Kaye et al., 1995; Esler & Kaye, 2000; Goldstein & McEwen, 2002; Lovallo & Gerin, 2003; Carney, Freedland & Veith, 2005; Lundberg, 2005; Malpas, 2010; Miller, Chen & Zhou, 2007; Parati & Esler, 2012) and psychological strain (Hoehn-Saric & McLeod, 1988; Jacobson & Sapolsky, 1991; Veith et al., 1994; Thayer, Friedman & Borkovec, 1996; Holsboer & Barden, 1996; Friedman & Thayer, 1998; Ströhle & Holsboer, 2003; Vreeburg et al., 2009).

Furthermore, shift work, overtime and long periods of physical inactivity may increase the risk for metabolic disorders, sedentary habits and obesity (Taylor & Dorn, 2006), which in turn may lead to hypertension and atherosclerosis (Chen et al., 2010; Pop et al., 2015). Empirical studies have demonstrated that between 19% and 74% of professional drivers develop metabolic syndrome, 5–48% hypertension, 7–46% dyslipidemia and 1–22% diabetes (Erin Mabry et al., 2016). Besides, drivers of city buses, minibuses and taxis have the highest percentage of smokers among professional drivers (82.9) (Ebrahimi, Delvarianzadeh & Saadat, 2016). Likewise, a recent study has found that 20.3% of professional drivers have the habit of actively consuming tobacco, and 27.9% of regularly drinking alcohol (Useche et al., 2017b).

Materials and Methods

Results

Professional drivers’ health outcomes and behaviors

All specific groups in the full-sample analyses reported BMI scores over 25 (upper limit for healthy subjects) (as seen in Table 4). The groups with higher mean values of BMI and BMI Prime were composed of those drivers belonging to samples of Study 1, -city bus drivers- (x̄ = 26.62; SD = 3.12 for BMI, and x̄ = 1.065; SD = 0.12 for BMI Prime), and Study 3 -urban bus, taxi and intercity bus drivers- (x̄ = 26.30; SD = 3.93 for BMI, and x̄ = 1.052; SD = 0.16 for BMI Prime). The full-sample’s average index was x̄ = 25.87 (SD = 3.60) for BMI, and x̄ = 1.034 (SD = 0.14) for BMI prime indicator.

Table 4:

Body mass index (BMI) coefficients of professional drivers.

Study	BMI					BMI prime
	Mean	SD	95% CI	Min	Max	Mean	SD	95% CI	Min	Max
Total	25.87	3.60	25.75–25.99	15.06	62.44	1.034	0.144	1.030–1.040	0.600	2.500
Study 1	26.62	3.12	26.20–27.03	19.38	35.91	1.065	0.125	1.048–1.081	0.780	1.440
Study 2	25.76	3.72	25.59–25.93	16.02	61.59	1.030	0.149	1.024–1.037	0.640	2.460
Study 3	26.30	3.93	26.01–26.59	15.06	62.44	1.052	0.157	1.040–1.064	0.600	2.500
Study 4	25.40	2.64	24.93–25.88	18.52	33.91	1.016	0.106	0.997–1.035	0.740	1.360
Study 5	25.48	2.87	25.22–25.73	17.05	35.92	1.019	0.115	1.009–1.029	0.680	1.440

DOI: 10.7717/peerj.6249/table-4

Note:

Body mass index (BMI) is a commonly used coefficient to determine the healthy relationship between the height and the weight of individuals. A value greater than 25 indicates overweight.

Regarding BMI values, it was found that 45.9% of professional drivers composing the full sample were “overweight,” and 10.7% “obese.” Furthermore, only 39.9% of them had a “normal weight,” and 3.1% were “underweight” (see Fig. 1). In reference to the specific groups, city bus operators (Study 1) constitute the group with the highest percentages of obesity (15.8%), whereas bus rapid transit drivers have the lowest ones (Study 4), with only 5.0% of the participants suffering from obesity.

The highest prevalence rates within the full sample of professional drivers were: sedentary lifestyle (global prevalence of 43.4%), smoking (21.2%) and self-reported overweight (18%), as shown in Fig. 2. It is worth mentioning that self-reported prevalence of overweight conditions (18%) substantially differs from the BMI-based categorization (45.9%).

Discussion

The results of this study, aimed at describing the working conditions and the health status of professional drivers, and evaluating the association of the JDC model of stress with their self-reported health and safety, provide some support to the hypothesis on the relationship between work stressors and adverse health outcomes. In particular, the performed correlational and multivariate analyses suggest that de JDC model of stress is associated with the professional drivers’ mental health, traffic accidents and fines, but not with other physical and behavioral health outcomes which are highly prevalent among this occupational group, such as hypertension, dyslipidemia, diabetes, being overweight, smoking and sedentary behavior. To this extend, our results are just partially consistent with the accumulated evidence on the associations of psychosocial work conditions with physical and mental health (Bhatt & Seema, 2012; Chung & Wu, 2013), well-being (Bawa & Srivastav, 2013), self-care and healthy behaviors (Facey, 2010), job satisfaction (De Croon et al., 2002), driving performance (Gilboa et al., 2008; Useche, Gómez & Cendales, 2017), and safety records of professional drivers (Taylor & Dorn, 2006; Yamada et al., 2008; Thayer et al., 2010; Useche et al., 2018b).

The non-significant results on the association between the JDC model and some health outcomes may be due to different factors. First, some studies based on single occupational groups have reported problems detecting associations between the JDC model and health outcomes due to the lack of variability in the working conditions of their samples (Lundberg, 2005). It is also possible that the standard version of the JCQ is not sufficiently sensitive to the specific stressors of professional drivers. Regarding this issue, Gardell, Aronsson & Barklöf (1982), and Choi et al. (2017) have developed models based on the JDC model and the job demand-resources model, which operationalize workload and work resources based on specific working conditions of professional drivers such as long driving and work overtime, irregular shifts, conflicts with passengers, prolonged sedentary work, time pressure, in-vehicle ergonomic hazards, short intervals between stops, high passenger load, high traffic load, constant threat avoidance vigilance, and short layover time. Additionally, the model of Choi et al. (2017) includes contextual factors (e.g., low wages and work intensification) and sociodemographic factors (age, sex, education, health behaviors), which can be central in order to understand work stress-related health outcomes among professional drivers.

In comparison with other transport operators, such as American and Japanese professional drivers, the Colombian sample of this study report a higher job strain prevalence (Albright et al., 1992). These trends are consistent with the evidence on the working conditions disparities between developed and developing countries (Ortiz & Juárez-García, 2016). It would be interesting to compare the sample of this study with other drivers from countries with similar socioeconomic profiles. However, there are little research on the professional drivers working conditions’ in developing countries. To the best of our knowledge, the studies included in this pooled analysis represent all the available evidence on the JDC profile of professional drivers in Latin America. Nevertheless, taking into account the labor, economic, safety and road infrastructure problems of many developing countries, it can be hypothesized that the professional drivers included in this pooled analysis are exposed to particularly risky working conditions.

Regarding the associations between work stress and health among professional drivers, it was found that more hours of daily driving (intensity of the task) are associated with an increased Job Strain, together with road crashes and traffic fines. This tendency has also been documented by other empirical studies. For instance, Rowden et al. (2011), and Useche, Cendales & Gómez (2017) found a positive association between work-related stress and risky behaviors at the wheel in professional drivers. This association is stronger among drivers with a relatively lower experience (Useche, Serge & Alonso, 2015). In addition, previous studies have linked work stress with risky driving factors, such as road aggressiveness (Sansone & Sansone, 2010) and anxious driving behavior (Clapp et al., 2011). Finally, Taylor & Dorn (2006) also suggest that work stressors are associated with impairments in the driving performance, representing a significant risk for both drivers and the general road safety.

Furthermore, work-related stress of professional drivers has been associated with other adverse outcomes such as burnout, cardio-metabolic disease (Cohen, Kessler & Gordon, 1995; Brosschot, Pieper & Thayer, 2005; Spruill, 2010; Apostolopoulos et al., 2016) and poor mental and physical self-reported health (Stoynev & Minkova, 1997; Chung & Wong, 2011; Chung & Wu, 2013; Ihlström, Kecklund & Anund, 2017). Regarding mid and long-term outcomes, work stress may also explain worse results in, for instance, job adjustment (Schjott, 2002), job satisfaction (De Croon et al., 2002), and perceived well-being (O’Neill & Davis, 2011). Other studies dealing with the health profile of this occupational group have documented a relatively high prevalence of physical and mental disorders, such as acute fatigue (20.6%), respiratory illnesses (11.1%), musculoskeletal or ergonomic disturbances (4.3%), depression (1.2%) and stress symptomatology (1.2%) (Alonso et al., 2017a). Nevertheless, the observed rates of professional drivers specifically working in the field of public transportation are usually worse due to their high exposition to specific stressors such as road traffic and negative interactions with passengers (Santos & Lu, 2016). Furthermore, city bus drivers (Study 1) were the group which presented the highest mean score of psychological demands (x̄ = 36.28, SD = 6.14) combined with a relatively lower mean of perceived control at work (x̄ = 75.39; SD = 12.52).

Also, the empirical research performed by Querido et al. (2012) described a set of ergonomic and environmental stressors which are common among transport workers, who are constantly exposed to high demands (long and irregular shifts, time pressure, excessive physical efforts) and low decision latitude in their work (Evans & Johansson, 1998; Evans & Carrère, 1991; Evans, 1994). In addition, most of the epidemiological studies dealing with professional drivers have problematized their typically poor working conditions, and their high risk for negative health outcomes (Siu et al., 2012), such as high blood pressure, muscle-skeletal disorders and high cholesterol (Querido et al., 2012; Landsbergis et al., 2013). Consistently with the aforementioned evidence, this study found that these health outcomes are highly prevalent among Colombian professional drivers.

Finally, it is worth mentioning the existence of positive evidence-based interventions that have proved the importance of improving the individual and occupational risk factors of professional drivers, such as: (a) poor physical and mental health indicators (Emdad et al., 1998; Ronchese & Bovenzi, 2012; Ihlström, Kecklund & Anund, 2017); (b) psychosocial work factors and wear indicators, including acute and chronic fatigue, stress and psychological strain (Netterstrom & Juel, 1988, 1989; Hege et al., 2018); (c) lifestyle factors, such as low physical activity and health-related risky behaviors (Taylor & Dorn, 2006; Alonso et al., 2017b). Some of these risk factors can be addressed through the spreading of information, the improvement of awareness and the fostering of early attention to the problem, all in order to prevent the chronic work-related causes of disease (Anderson, 1992; Karazman et al., 2000), together with potential injuries and occupational accidents (Winkleby et al., 1988; Taylor & Dorn, 2006).

Conclusions

Summarizing, it is possible to conclude that in the case of professional drivers, work-related stress is consistently associated with mental health and safety outcomes, but not with other physical and behavioral health-related outcomes such as hypertension, dyslipidemia, diabetes, overweight, smoking and sedentary behavior. In light of this findings, it can be concluded that more specific work stress frameworks and measurements, and evidence-based occupational interventions, are needed in order to expand the knowledge on the occupational risk profile of professional drivers and improve the workers’ health and safety in this occupational group.

Limitations of the Study and Further Research

Although the size of the sample used for the pooled analysis was considerably large and the statistical parameters were accurately and tested, some potential sources of bias must be mentioned. In short, the analyzed data were collected through self-report measures, especially regarding common method bias that may potentially inflate variable scores, or relationships between study variables, as documented in other studies addressing health and welfare-related topics, especially in organizational/occupational contexts (Spector, 2006; Wingate, Sng & Loprinzi, 2018). However, summed to the extensive sample size and its heterogeneity, procedural and statistical considered during the data-treatment phase care (in actions such as variable-interaction control performed during regression analyses) contributed to reduce its potential impact on the study findings (Spector & Brannick, 2009; Siemsen, Roth & Oliveira, 2010). Also, and considering that participants may underestimate minor events as traffic crashes, it is suggestible to compare self-reported crash rates with objective data sources, if these data would be available (Yang et al., 2018).

Also, two essential facts related to the collection of data and to the analyses performed in this study should be mentioned. First, the lack of a control group and the exclusive reliance on self-reported measures are important limitations of this research. Additionally, the lack of systematic sampling criteria for each one of the studies included in the pooled data may constitute a source of bias, and it restricts the generalizability of our empirical findings. However, the fact that the five databases included in the pooled analysis used the same variables and measurements guarantees a reduced possibility of bias related to the methodological heterogeneity between studies. Furthermore, the high reliability of the JCQ, and the epidemiologic evidence on the high correlations between self-reported and objective health indicators (such as BMI and hypertension) supports the overall validity of our analyses (Kehoe et al., 1994; Spencer et al., 2002; McAdams, Van Dam & Hu, 2007).

Supplemental Information