The Compensation Research Database (CRD) was established by the Institute for Safety Compensation and Recovery Research (ISCRR) at Monash University in 2009 and comprised administrative data from workplace injuries and illnesses that resulted in a compensation claim to WorkSafe Victoria (WSV) since 1985 . WSV acts as the state’s health and safety regulator, and also as the manager of Victoria’s workers (no-fault) compensation scheme; WSV has taken over management of the CRD .
The Victorian Admitted Episodes Dataset (VAED) is a compilation of demographic, administrative and clinical data on all admitted patient episodes of care provided by public and private hospitals, rehabilitation centres, extended care facilities and day procedure centres in Victoria . The dataset is maintained by the Victorian Government Department of Health and Human Services (DHHS) Health Data Standards and Systems (HDSS) unit for morbidity monitoring, casemix-based funding and analysis purposes in accordance with several healthcare reporting agreements. Diagnosis data are coded in accordance with the Australian Coding Standards using the ICD-10-AM health classification system (Australian modified version of the current World Health Organisation’s International Classification of Diseases) .
Claims data (with injury onset in 2008/09) of truck drivers per ANZSCO classification (7331) and other occupational drivers (the various classifications are listed below) were compared to the compensation claims data of all other (non-driver) Victorian workers. For analysing pre-injury health, cases were only selected if they also had a hospital-recorded admission within five years prior to their injury (based on the injury onset date recorded in their workers’ compensation claim). The age of the workers selected for analysis were limited to those over 18 years, due to driving being the focus of the study.
The Australian and New Zealand Standard Classification of Occupations (ANZSCO) code for truck drivers is 733111 . The ‘other occupational driver’ category included the ANZSCO occupational codes of automobile and taxi drivers (731,199, 73,112), bus and coach drivers (731,211, 731,212, 731,213), train drivers (731,311), tram drivers (731,312), and delivery drivers (732,111) .
Research data was sourced via a data linkage method, linking workers’ compensation claims for injury with hospital admissions data. WorkSafe Victoria compensation claims data were sourced from the Institute for Safety Compensation and Recovery Research (ISCRR) Compensation Research Database (CRD) . Data linkage was conducted by the Centre for Victorian Data Linkage (CVDL) located at the Victorian Department of Health and Human Services (currently Department of Health). The CVDL linked the WorkSafe claims data with hospital admissions data, specifically the Victorian Admitted Episodes Dataset (VAED). The data used in the study captured all claims made in 2008/09 (based on affliction year). The hospital admissions data included five years’ pre-injury data relating to these claimants.
Hospital admissions data
A range of health status variables, lifestyle-related conditions and chronic diseases were selected from the Victorian hospital admissions database if they appeared anywhere in the patient’s record, which can include up to 40 diagnosis-related codes. The group coding for the selected health conditions and chronic diseases was determined from various sources including peer-review publications, government health reports, as well as refinements and inclusions made by the authors [34,35,36,37,38]. Diseases arising from the cardiovascular system have long been implicated as a concern amongst professional drivers [18, 21, 23, 39, 40]. Cardiovascular-related conditions included in this analysis include: atrial fibrillation, chronic pulmonary disease, hypertension, myocardial infarction, peripheral vascular disease, and stroke/transient ischemic attack. The irregular nature of professional driving has also been implicated in contributing to other health factors such as those relating to sleep , as well as to diabetes . In addition, several lifestyle concerns have been associated with the occupation of professional driving such as an increased rate of smoking, alcohol and drug use [14, 15, 41, 42], as well as higher incidences of stress and obesity [12, 16, 41]. These health and lifestyle conditions and chronic diseases are captured in the recorded ICD-10-AM diagnosis codes in the Victorian hospital admissions database. Some chronic conditions such as hypertension, diabetes, or depression may not be captured in the hospital admissions records if they were considered not relevant to the admission.
The category codes included in the current study are: atrial fibrillation (ICD-10-AM code I48), chronic pulmonary disease (I27.8, I27.9, J40 – J44, J46 – J47, J60 – J67, J68.4, J70.1, J70.3), diabetes (E10 – E14), hypertension (I10 – I13, I15), myocardial infarction (all types) (I21 – I22, I25.2), peripheral vascular disease (I70 – I71, I73, I77.1, I79.0, I79.2, K55.1, K55.8, K55.9, Z98.8, Z95.9), sleep disorders (G47) and stroke or transient ischemic attack (G45.0 – G45.3, G45.8 – G45.9, H34.1, I60 –I61, I63 – I64).
The category codes related to lifestyle conditions included in the current study are: alcohol misuse/abuse (F10, E24.4, E51.2, E52, G31.2, G40.5, G62.1, G72.1, I42.6, K29.2, K70, K85.2, K86.0, O35.4, R78.0, T51, X45, X65, Y15, Y90, Y91, Z04.0, Z50.2, Z71.4, Z72.1, Z86.41), drug use/abuse (F11 – F16, F18, F19, X41, X42, X61, X62, Y11, Y12, T40, T42.3, T42.4, T42.6, T42.7, T43.3, T43.5, T43.6, T43.8, T43.9, R78.2 – R78.5, Z50.3, Z71.5, Z72.2, Z86.42), obesity (E66), stress (F43, Z73.3, R45.7), and tobacco use (F17, T65.2, Z50.8, Z58.7, Z72.0, Z71.6, Z81.2,Z86.43).
Workers compensation claims data
Workers’ details included in the analysis were: gender; age at time of injury; age group at time of injury; Australian and New Zealand Standard Classification of Occupations (ANZSCO) occupation type ; Accessibility/Remoteness Index of Australia (ARIA)  based on workers’ postcodes and recoded into variables of metropolitan/non metropolitan; and Index of Relative Socio-Economic Advantage and Disadvantage (IRSAD) , and coded by state percentile as well as by state decile. Decile 10 represents the most advantaged population-based decile on a scale of 1 to 10 .
Employer details featured included the size of the organisation (small, medium, large) or whether it was a government workplace. Employee details were also captured including total weekly earnings pre-injury, and total hours worked per week pre-injury. Details of the workplace injury included in the study were ‘Mechanisms of Injury’ and ‘First Body Location of Injury’.
Retrospective analysis of information collected in Victoria, Australia, comprised work-related injury data recorded over a one-year period in addition to pre-injury hospital admissions data recorded over a five-year period. Data extraction and preparation was carried out using SAS 9.4  and the descriptive analyses and modelling were carried out using IBM SPSS Statistics 25 . Binary logistic regression was conducted in SPSS to predict outcomes (i.e. disease prevalence, and harmful lifestyle factors) amongst truck drivers versus other occupational drivers, as well as versus all other workers. The model was adjusted for socio-demographic factors such as age, work factors and geographic region. Binary logistic regression was performed on a series of dependent variables including atrial fibrillation, chronic pulmonary disease, diabetes, hypertension, myocardial infarction, peripheral vascular disease, sleep disorders, stroke/transient ischemic attack; in addition to the lifestyle variables of alcohol misuse/abuse, drug misuse/abuse, obesity, stress and tobacco use. The independent variables were occupation (truck driver/other occupational driver/non-driver), injury age, weekly earnings, weekly hours worked, ARIA (metropolitan/non-metropolitan) and IRSAD.