Occupational health profile of Canadian Maritimes truck drivers

2.1 Procedures

In coordination with the Atlantic Provinces Trucking Association (APTA), formal letters to trucking companies across Canadian Maritime provinces were sent for recruitment. Five trucking companies (4 from NB, 1 from NS, 0 from PEI) responded regarding willingness to participate in the study. Interested companies then contacted and coordinated their employees for involvement in the study. One hundred and four male, heavy vehicle drivers volunteered for the study (65 from NB, 39 from NS, 0 from PEI). To be eligible, participants were required to have a valid commercial driving license and be a regional (Maritimes/Atlantic provinces), national (Canada) and/or international (Canada, US, Mexico) truck driver. No participants were excluded from this study.

2.2 Data collection

The project was approved by the University’s ethics review committee. Upon arrival at a mobile data collection unit, next to the trucking terminals of the interested companies, volunteers were briefed on the procedures of the experiment, read a consent form and gave written informed consent. Participants were measured for height and weight following a standard anthropometric measurement protocol [19]. Using a Lead II electrode placement according to Einthoven’s triangle configuration [20], a 3-lead electrocardiograph (ECG) was placed on the participant before they sat in a quiet and dimly lit room for a five-minute baseline recording of ECG. Next, participants answered health and demographic questionnaires. A research assistant was present at all times to answer any questions that participants may have had during the data collection process.

Participants in this study self-reported age, years of trucking experience and years of education. Also, nine occupational health indicators were measured. Seven were self-reported via questionnaire and two were physical measurements. Participants self-reported if they had a diagnosis of diabetes (type I, II), high blood pressure, lipidemia (and/or hypercholesterolemia), gastrointestinal disorders (Crohn’s disease, ulcerative colitis, irritable bowel, intestinal and stomach ulcers), back problems, psychosocial disorders (mood and/or anxiety disorders) or respiratory problems (chronic obstructive pulmonary disease).

Through physical measures, the presence of obesity and the risk of cardiovascular disease (CVD) were assessed. For this, weight (kilograms) and height (meters) were measured with a 450KL Physician Beam scale (Health O Meter Professional, USA) from which the body mass index (BMI) was calculated (kg/m²). Participants were considered to be obese if their BMI exceeded 30 kg/m². For the assessment of CVD risk, a single time-domain measure of heart rate variability (HRV) was computed from a five-minute baseline. According to Dekker et al. [21], standard deviation of normal successive beats (SDNN) values lower than 24 milliseconds are associated with an increased risk of CVD. In this study, participants were considered to be at risk of CVD if their SDNN value was lower than 24 milliseconds. In order to obtain SDNN value, a three-lead ECG (MLA2340, AdInstruments (AdI), United States of America) was used to collect, condition (i.e. amplification, filtering, converting) and record heart signals (from the five-minute baseline) with the help of the Bio Amp unit (FE132, AdI) and an eight-channel PowerLab unit (PL3508, AdI). LabChart software (version 7, AdI) was used for data collection, data analysis and calculation of SDNN values.

Participants were divided into one of four groups after completing a health-questionnaire and completing a physical health evaluation: 1) no health conditions (0-HC) (N = 25) (participants presenting no physical health condition); 2) one health condition (1-HC) (N = 28) (participants with only one self-reported or measured physical health condition); 3) two health conditions (2-HC) (N = 21) (participants with two self-reported or measured physical health conditions); or 4) multiple health conditions (X-HC) (N = 30) (participants with more than two self-reported or measured physical health conditions).

2.3 Analysis

Descriptive statistics were used to describe the prevalence of the nine health conditions and demographic data (i.e. number, proportion, 95% confidence intervals). Prior to multivariate analysis of variance, the normality of multivariate distribution of dependent variables (age, truck driving experience, education) and the equality of variance-covariance across the groups (0-HC, 1-HC, 2-HC, X-HC), was assessed by Box’s test of equality of covariance. The value of p obtained from the Box’s test was higher than 0.05 for all variables, confirming that the assumption of normality and equality was met. To examine the multivariate differences between different truck driver groups, the data set was split into four groups (0-HC, 1-HC, 2-HC, X-HC) and a MANOVA analysis was conducted to compare group means between dependent variables (age, truck driving experience, education). The MANOVA analysis was conducted to measure the influence of demographic variables (age, truck driving experience, education) across the groups (0-HC, 1-HC, 2-HC, X-HC). A Bonferroni post-hoc test was used explore group differences when MANOVA yield significance (p < 0.05). In addition, from the residuals obtain from MANOVA, the strength of the relationship between dependent variables was calculated with the Sums of Squares and Cross Products (SSCP) method. The statistical methods used in this paper were based on Sheskin’s statistical handbook [22]. All results from the inferential tests were considered to be statistically significant if p < 0.05. SPSS (version 25, IBM Corporation, Chicago, USA) statistical software was used to conduct statistical analyses.

4.1 Canadian Maritime truck driver’s health status

The primary finding of this investigation is that in this sample of Canadian Maritime Provinces truck drivers operating from NB and NS have a high prevalence of health problems. Three-quarters of participants had at least one health conditions, half were obese and one third had back problems. One fifth had lipidemia and/or high blood pressure as well as one-tenth had type II diabetes and/or gastrointestinal and/or psychosocial disorders. These results are comparable to those of previous Canadian studies [12, 17] as well as the latest North American systematic review of the truck driver literature [6]. Similar to these previous studies, the current analysis shows that weight management problems are prevalent in at least fifty percent of truck drivers in our sample (Table 3). In addition, high blood pressure, diabetes, lipidemia and back problems appear to be similar to the Canadian and North American truck driver populations (Table 3). The fact that relatively similar health profiles have been reported across North America may suggest that the nature of the work puts drivers at high-risk of developing health conditions [10].

To our knowledge, this study is the first to take a direct approach using the measure of heart rate variability (HRV) to assess the risk of CVD among truck drivers. As such, it is difficult to compare to previous literature. The discrepancy between CVD values reported by Angeles et al. [12] at 4 %, Bigelow et al. [17] at 7 % and Crizzle et al. [6] between 3.4 and 4.4 % are results of a direct and non-invasive health indicator measuring the current cardiovascular health and representing the risk of developing future CVD [21, 23]. This is not a self-reported measure of past CVD event. In addition, a meta-analysis published by Hillebrand et al. [23] assessed the association between low HRV and a first CVD event among individuals without knowledge of past CVD. Hillebrand et al. reported an increased risk between 32 and 45 % on fatal or non-fatal CVD among individuals with low HRV. Given the greater accessibility of portable HRV measurement systems via smartphone, HRV appears to be useful in CVD risk identification in truck drivers should be the focus of future research.

The current study observed a comparable prevalence of self-reported health issues as well as similar profiles for age (current study 48.2 years; Angeles et al. [12] 49 % was over 50 years; Bigelow et al. [17] 50.5 years) and truck driving experience (current study 16.2 years; Angeles et al. 79 % was over 10 years; Bigelow et al. 18.4 years) with other publications on Canadian truck drivers. Our data is also comparable to American results. Apostolopoulos et al. [11] reported a mean age of 44 years old and 41 percent had 6 to 15 years’ experience and 33 percent had more than 16 years’ experience. Sieber et al. [14] reported a mean age of 48 years old and had 16 years of truck driving experience. This study reported a lower education status than truck drivers from previous Canadian and American studies. Angeles et al. and Apostolopoulos et al. reported that 89 percent and 91 percent of their respective samples had at least a high school diploma. These differences between educational attainments may be related to the regional (provinces) and age group specificity. Based on the data from Statistics Canada [24], individuals from the Maritimes provinces had a higher rate of individuals without high school diplomas than Ontario for age groups 45 to 54 and 55 to 64 years old (NB 14.3 %, NS 13.0 %, PEI 13.1 % vs. ON 10.6 %; NB 21.7 %, NS 17.9 %, PEI 18.2 % vs. ON 15.1 %, respectively). However, for the age group 35 to 44 years old, the rate remains similar (NB 8.5 %, NS 8.2 %, PEI 7.6 % vs. ON 7.9 %). Data from Statistics Canada [24] indicate similar trends to our results, whereby age is inversely related to the educational attainment. This fits with our data that observed that the group without health condition was younger and better educated than the group of truck drivers with multi-morbidities. Therefore, age and year of truck driving experience seem to be more related to the truck driver profession, but the impact of education appears to be more related to regional and age group (generation) factors. Nonetheless, more research should be completed to understand the role of education on truck drivers since, to our knowledge, there are no other North American studies evaluating the relationship between education level and health conditions among truck drivers.

Future studies should attempt to determine the role of age, years of experience, and education as determinants of health conditions. In the present study, truck drivers who had one or more health conditions showed relatively similar experience, which suggests that not only the employment is a risk factor for developing such condition, but that age might accelerate this phenomenon. Similar to our findings, a study by Schütte et al. (2014) [25] among 8,484 male workers observed an increase in the number of individuals with poor health conditions with increasing age. Still, another study on 2031 respondents [26], observed that even after controlling for age, sex and marital status, an association between education and health was present such that individuals with a high school degree reported better health conditions than those less educated.

4.3 Limitations

A primary limitation is that this was a self-selected sample and is not necessarily representative of the population of Canadian Maritime truck drivers. Bias could be introduced by this approach to sampling. Sleep disorders and sleep apnea was not part of the self-reported measures of this project. The accessibility to health care or family physician was not recorded. In addition, the type of trucking experience was not recorded, it remains unknown if the truck driving experience was built on local, regional, national and international driving exposure (or the rate of exposure of each type). Furthermore, a non-disclosure agreement did not allow us to divulge some information about companies (e.g. the number of drivers per company, company size or what the type of cargo they transported). Additionally, because we had dealt directly with APTA, the number of companies solicited remains unknown and therefore the response rate of the company and the drivers were not documented. However, the five companies who participate in this investigation assured us that they have communicated with all their drivers and, all drivers interested could volunteer for the project. Despite our communication effort, no driver from Prince Edward Island Province participated in this study.