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Work-related injury and illness among older truck drivers in Australia: A population based, retrospective cohort study
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Work-related injury and illness among older truck drivers in Australia: A population based, retrospective cohort study Sharon Newnama, , Ting Xiab, Sjaan Koppela, Alex Collieb ⁎
a b
Monash University Accident Research Centre, 21 Alliance Lane, Monash University, VIC 3800, Australia Monash University, Insurance Work and Health Group, Faculty of Medicine Nursing and Health Sciences, 553 St Kilda Road, Melbourne, VIC 3004, Australia
ABSTRACT
The professional truck driver population is aging in Australia and internationally. However, there is currently a gap in knowledge related to the morbidity of workers in the transport industry. Understanding the health and wellbeing of workers employed in the transport industry should be a priority to ensure the appropriate allocation of resources to prevention and rehabilitation efforts. This study explored the landscape of work-related injury and disease in the Australian transport industry, by measuring injury and illness resulting in time loss in truck drivers by age group. The study used a population based, retrospective cohort study based on claim data collected from the National Dataset for Compensation-based Statistics in Australia. Analysis on a total of 120,742 accepted workers’ compensation claims was performed to characterize the distribution of workers’ compensation claims by four time periods (2004–2006, 2007–2009, 2010–2012, and 2013–2015), age groups, and jurisdictions. Three key findings were identified: the relative risk of workers’ compensation claims increased with age; older truck drivers (i.e., 65 years and over) did not have significantly higher rates of musculoskeletal injury (MSK) or fracture injuries, and; older truck drivers had a significantly larger proportion of neurological injury compared to younger age groups. The findings of this research support the need for context sensitive, multi-domain, interventions targeted at older truck drivers in order to both prevent work-related injury and disease and reduce the burden of disability once an injury or disease has occurred.
1. Introduction Road freight has been identified as the most dangerous industry in Australia, with the highest death rate of its employees compared to that of other industries (Safe Work Australia, 2016). To illustrate, Safe Work Australia reported in 2016 that more than one quarter of fatalities occurred in the Transport, postal and warehousing industry (47 fatalities), followed by Agriculture, forestry and fishing (44 fatalities) and Construction (35 fatalities) (Safe Work Australia, 2016). The magnitude of the problem in the transport industry is likely to worsen - given the national freight task is projected to double by 2030 (IBIS, 2017). Thus, targeted intervention is required to ensure the safety, health and wellbeing of employees within the industry. There is currently a gap in knowledge related to the morbidity of workers in the transport industry, with most research focused on the unique set of health risk factors related to the nature of the work environment. For example, Apostolopoulos et al. (2010) conducted a review of the research literature and found a range of morbidity risks in the transport industry ranging from long work hours to sedentary lifestyles to exposure to noise and vibration. This research has informed understanding of the adverse working conditions within the transport industry. Despite this knowledge, the epidemiology of injury and illness in the transport industry has not received substantive attention. Understanding the health and wellbeing of workers employed in the ⁎
transport industry should be a priority to ensure the appropriate allocation of resources to prevention and rehabilitation efforts. One group of transport workers that have received minimal attention are older truck drivers. The professional truck driver population is aging in Australia (ATA, 2017). It has been projected that the rate of truck driver recruitment in the Australian road freight industry will need to increase by 150% to account for the increase in demand for road freight services and to replace retiring and/or aging truck drivers (Department of Transport Victoria, 2010). Similar issues have been identified in the United States where it has been estimated that over 850,000 new drivers will be needed in the future to address the growing shortage of drivers (ATA, 2017). These figures suggest that policy and practice should focus on strategies to retain older truck drivers in the industry for as long as (safely) possible. It has been established that older drivers, including older truck drivers, are susceptible to a range of issues related to age-related functional decline in sensory, cognitive and physical abilities (Janke, 1994; Stelmach and Nahom, 1992). Although some research has found that the risk of crash involvement increases commensurate with drivers’ age (Chen et al., 2014; Duke et al., 2010), Newnam and colleagues (Newnam, et al., 2018) recently reported that older truck drivers (i.e., 60+ years) were not significantly more likely to be involved in crashes or sustain serious injury compared to middle-aged (35–59) truck drivers. These findings are consistent with other research findings
Corresponding author at: Monash Injury Research Institute, Building 70, Monash University, VIC 3800, Australia. E-mail address: [email protected] (S. Newnam).
https://doi.org/10.1016/j.ssci.2018.10.028 Received 8 August 2018; Received in revised form 30 October 2018; Accepted 30 October 2018 0925-7535/ © 2018 Elsevier Ltd. All rights reserved.
Please cite this article as: Newnam, S., Safety Science, https://doi.org/10.1016/j.ssci.2018.10.028
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(National Center for Statistics and Analysis, 2017; Pickrell et al., 2016) which suggest that older truck drivers do not pose an increased safety risk in terms of crash involvement. This research, combined with the aging industry profile, suggests an urgent need to focus on the health of older truck drivers to ensure the appropriate allocation of resources to promoting their health and wellbeing; in turn, keeping them in the workplace for as long as (safely) possible. This study explored the landscape of work-related injury and disease in the Australian transport industry, by measuring injury and illness resulting in time loss in truck drivers by age group. More specifically, this research categorised the data by distribution of injury types, mechanisms of injury and body part sustained following the injury and calculated the relative risk for older truck drivers (i.e., 60+ years) compared to their younger counterparts. The objective of this study was to identify the unique challenges facing older truck drivers, so to inform recommendations to improve the health and wellbeing of this valued workforce.
at date of injury, year of injury, occupation (Australian and New Zealand Standard Classification of Occupations (ANZSCO), Type of Occurrence Classification System (TOOCS) codes for injury nature, mechanism and bodily location. Age was converted to a categorical variable by 10-year groupings. Older truck drivers were defined as those aged 65 years and over. For claims resulting in time loss (at least one hour of paid income compensation), the duration of time lost was calculated by dividing the total number of compensated hours by the average weekly number of hours worked prior to claim (Collie et al., 2016). To calculate the incidence of injury and disease, we also accessed data on the number of workers covered by workers’ compensation in Australia. This was derived from Labour Force data by the Australian Bureau of Statistics (ABS) and provided by Safe Work Australia. Truck drivers were identified using the ANZSCO codes (3-digit code 733) (ABS, 2013). To account for coding differences between the workers’ compensation systems, types of work-related injury and disease were categorized using a modified version of the TOOCS version 3 which incorporates the nature, mechanism and bodily location of the primary injury (Gray and Collie, 2017). We focused on the following six major categories: fractures, musculoskeletal injury (MSK), neurological injury, psychological injury, other traumatic injury, and other diseases.
2. Method 2.1. The Australian compensation context Australia has approximately 24.1 million people and a labor force of 12.4 million workers (ABS, 2017). It is a compulsory for the majority of employers in Australia to pay workers’ compensation premiums to cover their workers in the event of a work-related injury or illness. Each of states and territories have developed their own workers' compensation scheme and there are three Commonwealth (i.e., national) schemes. In this study, 7 of the 11 major Australian compensation systems (representing more than 90% of the labor force) were included, including the states of New South Wales, Victoria, Queensland, Western Australia, South Australia and Tasmania as well as the Northern Territory. Despite differences in workers’ compensation legislation between jurisdictions, the basic obligations of an employer regarding workers’ compensation are the same and all of the schemes provide funding and support for healthcare and rehabilitation, income compensation during the period off work, and lump sum payments in the case of permanent impairment or death (Safe Work Australia, 2016). Australian workers’ compensation schemes do not generally provide coverage for self-employed workers; thus, work-related injuries and diseases of self-employed workers are under-represented in national data. This contextual information is important to consider in the interpretation of the results and generalization to truck drivers in other countries under different compensation scheme structures.
2.4. Analysis strategy Descriptive analyses were performed to characterize the distribution of workers’ compensation claims by four time periods (2004–2006, 2007–2009, 2010–2012, and 2013–2015), age groups, and jurisdictions. The most common type and mechanism of injury were also described using descriptive analyses. Given the count nature of the data, a Poisson distribution was assumed. Since the likelihood ratio test suggested that the data were over-dispersed, a negative binomial regression was used to determine relative risks (RRs) and 95% confidence intervals (95% CI) for the comparison of claim rates across age groups. The 35–44 year old age group was set as the reference group. The regression model was performed on all accepted claim data by truck drivers over the study period, and adjusted for time period and jurisdiction. A series of additional regression models adjusted for time period and jurisdiction were conducted to investigate the differences in the RR of a particular type of injury across age groups. Time loss calculations limited the maximum duration of cumulative time loss to 260 weeks (5 years), and restricted included claims up to the end of the 2012 financial year to allow for a minimum follow-up period of three years for all claims. Quantile regression was used to explore the differences in median duration of time loss by age groups. All analyses were conducted using Stata IC/14 (StataCorp, 2015).
2.2. Compensation data We conducted a population based, retrospective cohort study based on claim data collected from the National Dataset for Compensationbased Statistics (NDS) (Australia, 2004). The NDS is compiled from workers’ compensation claims data from all nine of the state, territory and Commonwealth workers’ compensation systems. The database contains information on the injured worker, their employer, job characteristics, injury or disease details, and claims outcomes. Ethics approval for use of NDS claims data were received from Monash University Human Research Ethics Committee (approval number 201710758-14006).
3. Results In total, there were 120,742 accepted workers’ compensation claims over the 12-year study period. The mean age of truck drivers was 44.5 years. The largest group of claims were from the 35–54 years age group, accounting for nearly 60% of total claims (see Table 1). The oldest age group of truck drivers (i.e., aged 65+ years) recorded the smallest percentage of total claims (2.66%), followed by the youngest age group (i.e., aged ≤ 24 years, 4.04%). The total number of claims gradually decreased over the study period, and the overall incidence decreased from 77.72 to 56.16 per 1,000 workers per year (Table 1; adjusted RR: 0.77, 95% CI: 0.81–0.87; for 2013–2015 compared to 2004–2006). In general, the relative risk of workers’ compensation claims increased with age. The highest rates were observed in the older truck driver group (79.53 per 1000 workers per year), with a 26% increased risk compared to the 35–44 year old age group (adjusted RR: 1.26, 95% CI: 1.10–1.44). Fig. 1 shows the median and interquartile range (IQR) of compensated time for all accepted workers’ compensation claims in truck
2.3. Study population and measurement Data drawn for this study were restricted to ‘accepted’ claims (i.e., those have been accepted for payment) lodged by working age adults (≥15 years) with a date of lodgment between the 2004 and 2015 financial years (1/07/2003 to 30/06/2015). Data from commonwealth workers’ compensation systems were excluded from this study because of an incomplete sub-database. Individual data extracted included: age 2
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Table 1 Number and rate of accepted workers’ compensation claims in HV drivers, 2004 to 2015.
NOTE: Shaded cells indicate statistically significant difference compared to 35–44 year old age group (RR with 95% CIs).
drivers by age group from 2004 to 2012. The median duration of time loss due to work-related injury and illness increased steadily with age. Compared with the 35–44 year old age group (3.2 weeks, IQR: 1.0–11.6), older truck drivers had a significantly longer median duration of time loss, reaching 6.6 weeks (IQR: 2.0.–19.9, Coef: 3.40, 95%CI: 2.86–3.94). The youngest age group had the shortest median time loss (2.0 weeks, IQR: 0.8–6.2), which decreased by 1.21 (95%CI: −1.57 to −0.85) compared with the 35–44 year old age group. Fig. 2 shows the distribution of the most common injury type,
mechanism and body part in truck drivers by age group. Generally, MSK injury was the most common type of injury for all truck drivers. However, it should be noted that older truck drivers had a noticeably larger proportion of neurological injury claims than other age groups. In addition, the percentage of claims due to neurological injury tended to increase with age. Body stressing was the most common mechanism of injury across age groups, however, claims due to sound and pressure were disproportionately larger for older truck drivers. Consistent with this result, ‘head’ related injury was found to be the most common injury among older truck drivers. Table 2 provides the rates and relative risk of work-related injury and illness for the most common type of injury. The overall rate of fracture injury in truck drivers was 6.31 per 1000 workers. Older truck drivers had a slightly higher rate of fracture injury than drivers in the 35–44 year old age group (adjusted RR: 1.03, 95% CI: 0.89–1.20), but this was not statistically significant. For MSK injury, the overall rate was 41.79 per 1000 worker. The rate was 18% lower for the oldest and 32% lower for the youngest age group (adjusted RR: 0.82, 95% CI: 0.72–0.95; adjusted RR: 0.68, 95% CI: 0.60–0.77) compared to the 35–44 year old age group. The overall rates of neurological and other diseases were 2.30 and 2.02 per 1000 workers respectively, and the rates increased steadily with age. For neurological injury, the rate reached 19.11 among older truck drivers, which was nearly 15 times higher compared to the 35–44 year old age group (adjusted RR: 15.2, 95% CI: 12.31–18.80). Psychological injury had the lowest overall rate compared to other types of injury. Furthermore, the rate of psychological peaked in the 35–44 year old age group. Relative to the comparator group, the risk of psychological injury was 60% and 70% lower among the oldest and youngest age groups, respectively (adjusted RR: 0.41, 95% CI:
Fig. 1. Quantile regression analysis of median duration and IQR of time loss in weeks due to work-related injury in HV drivers by age group (2004–2012). 3
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Fig. 2. Injury type, mechanism and body part in truck drivers by age group.
0.26–0.65; adjusted RR: 0.30, 95% CI: 0.21–0.43). Other traumatic injury had the second highest injury rate among all truck drivers (16.57 per 1000 workers). Compared with the 35–44 year old age group, the rate was lower for other age groups except the older truck driver age
group. Older truck drivers had a higher rate of other traumatic injury (16.14 per 1000 workers), but the difference in injury risk compared with the 35–44 year old age group was not statistically significant (adjusted RR: 1.05, 95% CI: 0.91–1.21). 4
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Table 2 Adjusted RRs for risk of injury by age groups in the HV drivers, 2004 to 2015.
Note: Shaded cells indicate statistically significant difference compared to 35–44 year old age group (RR with 95% CIs).
biomechanical tolerances to injury are lower than those of younger persons (Mackay, 1998; Viano, Culver, Evans, Frick, & Scott, 1990), primarily due to reductions in bone and neuromuscular strength and fracture tolerance (Dejeammes & Ramet, 1996; Padmanaban, 2001). A possible explanation of these findings is self-regulation. Meyer (2004) found that older drivers can be highly adaptive and can compensate for deficiencies in certain areas by adapting their behavior (i.e., changing the conditions in which they drive) to minimize their crash risk (for a review see Koppel and Charlton, 2013). This study also found that older truck drivers had a significantly larger proportion of neurological injury compared to younger age groups and that the percentage of these claims increased with age. In fact, claims due to ‘sound and pressure’ were 15 times greater in older age drivers compared to the 35–44 year old age group. Although it is well known that truck drivers are susceptible to traffic noise (e.g., engine and road noise) for long durations, the magnitude of this problem was surprising. Noise-induced hearing loss is common amongst truck drivers (e.g., Alizadeh et al., 2016; Karimi et al., 2010), particularly older truck drivers (Alizadeh et al., 2016, Collee et al., 2011) with some risks associated with factors such as poor road surfaces, conditions of the vehicle and less mature occupational health and safety systems (Fuente and Hickson, 2011). There is also research to suggest that the association between noise-induced hearing loss and these risk factors being more prevalent in developing countries (Alizadeh et al., 2016; Karimi et al., 2010).
4. Discussion The aim of this study was to explore the landscape of work-related injury and illness in Australian truck drivers. The unique aspect of this research was that the data were categorized by age group which allowed insight into the unique challenges facing older truck drivers. The findings of the current study support several recommendations to improve the safety, health and wellbeing of this valued workforce not only in Australia but internationally. It is important to note that the results of this study are unlikely to be isolated to the Australian context. Indeed, the Australian truck driving workplace share similarities to many other countries, particularly developed nations (e.g., U.K., U.S., Canada, etc.); thus, the following recommendations for review and revision of policy and practice can be generalized at an international level. Although older truck drivers were identified as contributing the smallest proportion of total claims, the relative risk of workers’ compensation claims increased with age. In fact, this study found that older truck drivers had the greatest risk when compared to the 35–44 year old age group. This finding is consistent with research conducted in the safety context which has shown that the risk of a highway fatality involving heavy vehicles increases commensurate with drivers’ age and that drivers aged 65 years and older are at a 4.3 times greater risk of being killed in crash compared with drivers aged 15–19 years (Chen et al., 2014). The results also suggested that older truck drivers had significantly greater periods of time off work compared to their younger counterparts. This finding is consistent with the OECD report on Aging and Transport (OECD, 2001) that states that the most critical safety issue for older drivers is their frailty and associated injury susceptibility. These results suggest that targeted intervention is required to address prevention, disability and recovery for older truck drivers. Contrary to expectations, older truck drivers were not found to have significantly higher rates of MSK or fracture injuries. This finding was surprising considering that research has shown that older adults’
4.1. Practical implications Overall, the findings of this research demonstrate the need for a systems approach to addressing the health and wellbeing of older truck drivers. This conclusion is consistent with previous research in the safety context which has identified that the safety of drivers is best managed by identifying intervention at multiple levels of the system 5
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including the vehicle/environment, individual-drivers, company management, supply chain, government and regulatory levels (e.g., Newnam and Goode, 2015; Newnam et al., 2017; Thompson et al., 2015). In support, multi-domain interventions have also been found to be effective at reducing time off work in people with musculoskeletal, pain-related and mental health conditions (Cullen et al., 2017). Such approaches require the integration of healthcare with workplace modifications, and coordination of services delivered to injured or ill workers. However, it should be noted that such interventions could be challenging to implement given the remote nature of the driving task (i.e., Newnam and Goode, 2015) and unique working conditions (e.g., long working hours; Apostolopoulos et al., 2010). These challenges need to be considered in any implementation effort. In regards to individual drivers, the results support a recommendation for self-screening assessment tools in the workplace. Self-screening tools within regular workplace health and safety programs may help to assist older truck drivers and supervisors in identifying and managing any decline in functional and/or cognitive performance over time. Such an approach could also help drivers to better self-regulate their on-road behavior (e.g., Molnar et al., 2015). One good example of such a program is the SAFER Driving self-screening instrument, developed by the University of Michigan Transportation Research Institute (UMTRI) ( Molnar et al., 2010). SAFER Driving is a validated, web-based, self-screening instrument focused on “health concerns” that affect driving – that is, the symptoms that people experience due to medical conditions, medications used to treat them, and the general aging process. By linking the severity of health concerns to their effects on critical driving skills, the instrument provides feedback on general self-awareness and recommendations for behavioral changes, further evaluation, and vehicle modifications. Future research could focus on adapting this tool for use within the workplace to identify not only the health concerns affecting driving ability but factors within and across levels of the transportation system (e.g., scheduling, workloads; Newnam and Goode, 2015; Newnam et al., 2017)) that influence the health and safety of older truck drivers. Consistent with the findings of this study, a modified tool could also incorporate and prioritize screening of hearing with regular monitoring of results over time. The results of this study also inform several recommendations focused on reducing the burden of claims associated with noise-related hearing loss. First, the results suggest that selecting vehicles with superior noise controlling measures could help in reducing the significant burden associated with compensation. To support this intervention approach, company management could encourage driving on quality road surfaces through introducing journey planning practices. The significance of noise-related claims identified for older truck drivers in this study also suggest an urgent need for the review and revision of noise-related risk controls within health and safety laws and regulations.
other factors beyond demographic information. Finally, it was not possible to identify individuals who made more than one claim. There may be unique characteristics associated with multiple claimers that could inform targeted prevention efforts. 5. Conclusion The aim of this study was to explore the landscape of work-related injury and illness in Australian truck drivers. The unique aspect of this research was that data were categorized by age group which allowed insight into the unique challenges facing older truck drivers. The findings of the current study support several recommendations to improve the safety, health and wellbeing of this valued workforce not only in Australia but internationally. The results of this study are unlikely to be isolated to the Australian context. The selection of heavy vehicles and road conditions share similarities to many other countries, particularly developed nations (e.g., U.K., U.S., Canada, etc.); thus, the following recommendations for review and revision of policy and practice can be generalized at an international level. This study is the first to explore the indicators of health and wellness in older truck drivers using Australian compensation claims data. The results of the study provided insight into the unique challenges facing older truck drivers and offer several recommendations for the review and revision into policy and practice to improve their safety, health and wellbeing. These recommendations should be considered from an international perspective as the findings are likely to be generalized to other developed nations. The findings of this study also offer directions for future research focused on the development of tools to identify and manage the individual health needs of older truck drivers. This research and associated implications are important to ensure employers can keep this valued cohort of workers on the road for as long as they are safe. References ABS, 2013. Australian and New Zealand Standard Classification of Occupations. Version 1.2. Canberra, ACT Australian Bureau of Statistics. Work Australia. ABS, 2017. Labour Force, Australia. http://www.abs.gov.au/ausstats/[email protected]/mf/ 6202.0. Alizadeh, Ahmad, Etemadinezhad, Siavash, Charati, Jamshid Yazdani, Mohamadiyan, Mahmood, 2016. Noise-induced hearing loss in bus and truck drivers in Mazandaran province, 2011. Int. J. Occupat. Saf. Ergon. 22 (2), 193–198. Apostolopoulos, Y., Sönmez, S., Shattell, M.M., Belzer, M., 2010. Worksite-induced morbidities among truck drivers in the United States. J. Am. Assoc. Occupat. Health Nurses 58 (7), 285–296. ATA, 2017. Truck Driver Shortage Analysis. http://progressive2.acs.playstream.com/ truckline/progressive/ATAs%20Driver%20Shortage%20Report%202017.pdf. Chen, G.X., Amandus, H.E., Wu, N., 2014. Occupatonal fatalities among driver/sales workers and truck drivers in the United States, 2003–2008. Am. J. Ind. Med. 57 (7), 800–809. Collee, A., Legarnd, C., Govaerts, B., et al., 2011. Occupational expo- sure to noise and the prevalence of hearing loss in a Belgian military population: a cross-sectional study. Noise Health 13 (50), 64–70. Collie, A., Lane, T.J., Hassani-Mahmooei, B., Thompson, J., Mcleod, C., 2016. Does time off work after injury vary by jurisdiction? A comparative study of eight Australian workers' compensation systems. BMJ Open 6, e010910. Cullen, et al., 2017. Effectiveness of workplace interventions in return-to-work for musculoskeletal, pain-related and mental health conditions: an update of the evidence and messages for practitioners. J. Occup. Rehabil. 1–15. Dejeammes, M., Ramet, M., 1996. Aging process and safety enhancement of car occupants. In: Proceedings: International Technical Conference on the Enhanced Safety of Vehicles. National Highway Traffic Safety Administration, pp. 1189–1196. Department of Transport Victoria, 2010, A Workforce Strategy for Road Freight Drivers. Duke, J., Guest, M., Boggess, M., 2010. Age-related safety in professional heavy vehicle drivers: a literature review. Accid. Anal. Prevent. 42, 364–371. Fuente, A., Hickson, L., 2011. Noise-induced hearing loss in Asia. Int. J. Audiol. 50 (Suppl 1), S3–S10. Gray, S.E., Collie, A., 2017. The nature and burden of occupational injury among first responder occupations: a retrospective cohort study in Australian workers. Injury 48 (11), 2470–2477. IBIS World Industry, 2017. Report 14610, Road Freight transport – Australian Market research report. Retrieved from https://www.ibisworld.com.au/industry-trends/ market-research-reports/transport-postal-warehousing/road/road-freight-transport. html. Janke, M., 1994. Age-related Disabilities that may Impair Driving and their Assessment: Literature Review. California Department of Motor Vehicles, Sacramento.
4.2. Strengths and limitations The strength of study was use of a database with population coverage of compensable work-related injury and disease at a national level as well as population level denominator data. Use of standardized coding system also allowed comparisons within and across occupational, industry categories and across injury types. However, this study has several limitations that should be acknowledged. First, by providing detailed analyses of specific injuries by age group, caution should be taken when interpreting some of the results because of the relatively small group sizes (e.g., 19 claims due to psychological injury in older truck drivers). Second, some workers with work-related conditions may choose not to make workers’ compensation claims, or may not be eligible. Thus, the NDS is unlikely to represent all cases of work-related injury and illness in truck drivers. Third, the database contains limited information on workplace factors contributing to injury and illness and 6
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S. Newnam et al. Karimi, A., Nasiri, S., Kazerooni, F.K., 2010. Noise induced hearing loss risk assessment in truck drivers. Noise Health 12 (46), 49–55. Koppel, S.N., Charlton, J.L., 2013. Behavioural Adaptation and Older Drivers. Behavioural Adaptation and Road Safety: Theory, Evidence and Action. CRC Press. Mackay, M., 1998. Occupant protection and vehicle design. Paper presented at the Proceedings from the Association for the Advancement of Automotive Medicine Course on the Biomechanics of Impact Trauma, Los Angeles, USA. Meyer, J., 2004. Personal vehicle transportation. In: Pew, Richard W., Hemel, Van, Susan, B. (Eds.), Technology for Adaptive Aging. The National Academies Press, Washington, D.C.. Molnar, L.J., Eby, D.W., Kartje, P.S., St. Louis, R.M., 2010. Increasing self-awarenessamong older drivers: the role of self-screening. J. Saf. Res. 41, 367–373. Molnar, L.J., Eby, D.W., Zhang, L., Zanier, N., St. Louis, R.M., Kostyniuk, L.P., 2015. SelfRegulation of Driving by Older Adults: A Synthesis of the Literature and Framework for Future Research. Washington, D.C. National Center for Statistics and Analysis, 2017. 2016 Fatal motor vehicle crashes: Overview (Traffic Safety Facts Research Note. Report No. DOT HS 812 456). National Highway Traffic Safety Administration, Washington, DC. Newnam, S., Goode, N., 2015. Don’t blame the driver: a systems analysis of the causes of road freight crashes. Accid. Anal. Prevent. 76, 141–151. Newnam, S., Lewis, I., Warmerdam, A., 2014. Modifying behaviour to reduce overspeeding in work-related drivers: an objective approach. Accid. Anal. Prev. 64, 23–29. Newnam, S., Goode, N., Salmon, P., Stevenson, M., 2017. Reforming the road freight transportation system using systems thinking: an investigation of Coronial inquests in
Australia. Accid. Anal. Prev. 101, 28–36. Newnam, S., Blower, D., Molnar, L., Eby, D., Koppel, S., 2018. Exploring crash characteristics and injury outcomes among older truck drivers: an analysis of truck-involved crash data in the United States. Saf. Sci. 106, 140–145. OECD, 2001. Ageing and Transport: Mobility Needs and Safety Issues. Organization for Economic. Padmanaban, J., 2001. Crash injury experience of elderly drivers. Paper Presented at the Aging and Driving Symposium. Association for the Advancement of Automotive Medicine, Des Plaines, IL. Pickrell, T.M., Li, R., K, S., 2016. Occupant restraint use in 2015: Results from the NOPUS controlled intersection study (Report No. DOT HS 812 330). National Highway Traffic Safety Administration, Washington, DC. Safe Work Australia, 2004. National Data Set for Compensation-based Statistics. Canberra. Safe Work Australia, 2016. Work-related Fatalities. https://www.safeworkaustralia.gov. au/statistics-and-research/statistics/fatalities/fatality-statistics#work-relatedfatalities. Stelmach, G.E., Nahom, A., 1992. Cognitive-motor abilities of the elderly driver. Hum. Factors 34 (1), 53–65. Stratacorp, 2015. Stata Statistical Software: Release 14. StataCorp LP, College Station, TX. Thompson, J., Newnam, S., Stevenson, M., 2015. A model for exploring the relationship between payment structures, fatigue, crash risk, and regulatory response in a heavyvehicle transport system. Transport. Res. Part A: Policy Pract. 82, 204–215. Viano, D.C., Culver, C.C., Evans, L., Frick, M., Scott, R., 1990. Involvement of older drivers in multivehicle side-impact crashes. Accid. Anal. Prev. 22, 177–188.
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Safety Science journal homepage: www.elsevier.com/locate/safety
Work-related injury and illness among older truck drivers in Australia: A population based, retrospective cohort study Sharon Newnama, , Ting Xiab, Sjaan Koppela, Alex Collieb ⁎
a b
Monash University Accident Research Centre, 21 Alliance Lane, Monash University, VIC 3800, Australia Monash University, Insurance Work and Health Group, Faculty of Medicine Nursing and Health Sciences, 553 St Kilda Road, Melbourne, VIC 3004, Australia
ABSTRACT
The professional truck driver population is aging in Australia and internationally. However, there is currently a gap in knowledge related to the morbidity of workers in the transport industry. Understanding the health and wellbeing of workers employed in the transport industry should be a priority to ensure the appropriate allocation of resources to prevention and rehabilitation efforts. This study explored the landscape of work-related injury and disease in the Australian transport industry, by measuring injury and illness resulting in time loss in truck drivers by age group. The study used a population based, retrospective cohort study based on claim data collected from the National Dataset for Compensation-based Statistics in Australia. Analysis on a total of 120,742 accepted workers’ compensation claims was performed to characterize the distribution of workers’ compensation claims by four time periods (2004–2006, 2007–2009, 2010–2012, and 2013–2015), age groups, and jurisdictions. Three key findings were identified: the relative risk of workers’ compensation claims increased with age; older truck drivers (i.e., 65 years and over) did not have significantly higher rates of musculoskeletal injury (MSK) or fracture injuries, and; older truck drivers had a significantly larger proportion of neurological injury compared to younger age groups. The findings of this research support the need for context sensitive, multi-domain, interventions targeted at older truck drivers in order to both prevent work-related injury and disease and reduce the burden of disability once an injury or disease has occurred.
1. Introduction Road freight has been identified as the most dangerous industry in Australia, with the highest death rate of its employees compared to that of other industries (Safe Work Australia, 2016). To illustrate, Safe Work Australia reported in 2016 that more than one quarter of fatalities occurred in the Transport, postal and warehousing industry (47 fatalities), followed by Agriculture, forestry and fishing (44 fatalities) and Construction (35 fatalities) (Safe Work Australia, 2016). The magnitude of the problem in the transport industry is likely to worsen - given the national freight task is projected to double by 2030 (IBIS, 2017). Thus, targeted intervention is required to ensure the safety, health and wellbeing of employees within the industry. There is currently a gap in knowledge related to the morbidity of workers in the transport industry, with most research focused on the unique set of health risk factors related to the nature of the work environment. For example, Apostolopoulos et al. (2010) conducted a review of the research literature and found a range of morbidity risks in the transport industry ranging from long work hours to sedentary lifestyles to exposure to noise and vibration. This research has informed understanding of the adverse working conditions within the transport industry. Despite this knowledge, the epidemiology of injury and illness in the transport industry has not received substantive attention. Understanding the health and wellbeing of workers employed in the ⁎
transport industry should be a priority to ensure the appropriate allocation of resources to prevention and rehabilitation efforts. One group of transport workers that have received minimal attention are older truck drivers. The professional truck driver population is aging in Australia (ATA, 2017). It has been projected that the rate of truck driver recruitment in the Australian road freight industry will need to increase by 150% to account for the increase in demand for road freight services and to replace retiring and/or aging truck drivers (Department of Transport Victoria, 2010). Similar issues have been identified in the United States where it has been estimated that over 850,000 new drivers will be needed in the future to address the growing shortage of drivers (ATA, 2017). These figures suggest that policy and practice should focus on strategies to retain older truck drivers in the industry for as long as (safely) possible. It has been established that older drivers, including older truck drivers, are susceptible to a range of issues related to age-related functional decline in sensory, cognitive and physical abilities (Janke, 1994; Stelmach and Nahom, 1992). Although some research has found that the risk of crash involvement increases commensurate with drivers’ age (Chen et al., 2014; Duke et al., 2010), Newnam and colleagues (Newnam, et al., 2018) recently reported that older truck drivers (i.e., 60+ years) were not significantly more likely to be involved in crashes or sustain serious injury compared to middle-aged (35–59) truck drivers. These findings are consistent with other research findings
Corresponding author at: Monash Injury Research Institute, Building 70, Monash University, VIC 3800, Australia. E-mail address: [email protected] (S. Newnam).
https://doi.org/10.1016/j.ssci.2018.10.028 Received 8 August 2018; Received in revised form 30 October 2018; Accepted 30 October 2018 0925-7535/ © 2018 Elsevier Ltd. All rights reserved.
Please cite this article as: Newnam, S., Safety Science, https://doi.org/10.1016/j.ssci.2018.10.028
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(National Center for Statistics and Analysis, 2017; Pickrell et al., 2016) which suggest that older truck drivers do not pose an increased safety risk in terms of crash involvement. This research, combined with the aging industry profile, suggests an urgent need to focus on the health of older truck drivers to ensure the appropriate allocation of resources to promoting their health and wellbeing; in turn, keeping them in the workplace for as long as (safely) possible. This study explored the landscape of work-related injury and disease in the Australian transport industry, by measuring injury and illness resulting in time loss in truck drivers by age group. More specifically, this research categorised the data by distribution of injury types, mechanisms of injury and body part sustained following the injury and calculated the relative risk for older truck drivers (i.e., 60+ years) compared to their younger counterparts. The objective of this study was to identify the unique challenges facing older truck drivers, so to inform recommendations to improve the health and wellbeing of this valued workforce.
at date of injury, year of injury, occupation (Australian and New Zealand Standard Classification of Occupations (ANZSCO), Type of Occurrence Classification System (TOOCS) codes for injury nature, mechanism and bodily location. Age was converted to a categorical variable by 10-year groupings. Older truck drivers were defined as those aged 65 years and over. For claims resulting in time loss (at least one hour of paid income compensation), the duration of time lost was calculated by dividing the total number of compensated hours by the average weekly number of hours worked prior to claim (Collie et al., 2016). To calculate the incidence of injury and disease, we also accessed data on the number of workers covered by workers’ compensation in Australia. This was derived from Labour Force data by the Australian Bureau of Statistics (ABS) and provided by Safe Work Australia. Truck drivers were identified using the ANZSCO codes (3-digit code 733) (ABS, 2013). To account for coding differences between the workers’ compensation systems, types of work-related injury and disease were categorized using a modified version of the TOOCS version 3 which incorporates the nature, mechanism and bodily location of the primary injury (Gray and Collie, 2017). We focused on the following six major categories: fractures, musculoskeletal injury (MSK), neurological injury, psychological injury, other traumatic injury, and other diseases.
2. Method 2.1. The Australian compensation context Australia has approximately 24.1 million people and a labor force of 12.4 million workers (ABS, 2017). It is a compulsory for the majority of employers in Australia to pay workers’ compensation premiums to cover their workers in the event of a work-related injury or illness. Each of states and territories have developed their own workers' compensation scheme and there are three Commonwealth (i.e., national) schemes. In this study, 7 of the 11 major Australian compensation systems (representing more than 90% of the labor force) were included, including the states of New South Wales, Victoria, Queensland, Western Australia, South Australia and Tasmania as well as the Northern Territory. Despite differences in workers’ compensation legislation between jurisdictions, the basic obligations of an employer regarding workers’ compensation are the same and all of the schemes provide funding and support for healthcare and rehabilitation, income compensation during the period off work, and lump sum payments in the case of permanent impairment or death (Safe Work Australia, 2016). Australian workers’ compensation schemes do not generally provide coverage for self-employed workers; thus, work-related injuries and diseases of self-employed workers are under-represented in national data. This contextual information is important to consider in the interpretation of the results and generalization to truck drivers in other countries under different compensation scheme structures.
2.4. Analysis strategy Descriptive analyses were performed to characterize the distribution of workers’ compensation claims by four time periods (2004–2006, 2007–2009, 2010–2012, and 2013–2015), age groups, and jurisdictions. The most common type and mechanism of injury were also described using descriptive analyses. Given the count nature of the data, a Poisson distribution was assumed. Since the likelihood ratio test suggested that the data were over-dispersed, a negative binomial regression was used to determine relative risks (RRs) and 95% confidence intervals (95% CI) for the comparison of claim rates across age groups. The 35–44 year old age group was set as the reference group. The regression model was performed on all accepted claim data by truck drivers over the study period, and adjusted for time period and jurisdiction. A series of additional regression models adjusted for time period and jurisdiction were conducted to investigate the differences in the RR of a particular type of injury across age groups. Time loss calculations limited the maximum duration of cumulative time loss to 260 weeks (5 years), and restricted included claims up to the end of the 2012 financial year to allow for a minimum follow-up period of three years for all claims. Quantile regression was used to explore the differences in median duration of time loss by age groups. All analyses were conducted using Stata IC/14 (StataCorp, 2015).
2.2. Compensation data We conducted a population based, retrospective cohort study based on claim data collected from the National Dataset for Compensationbased Statistics (NDS) (Australia, 2004). The NDS is compiled from workers’ compensation claims data from all nine of the state, territory and Commonwealth workers’ compensation systems. The database contains information on the injured worker, their employer, job characteristics, injury or disease details, and claims outcomes. Ethics approval for use of NDS claims data were received from Monash University Human Research Ethics Committee (approval number 201710758-14006).
3. Results In total, there were 120,742 accepted workers’ compensation claims over the 12-year study period. The mean age of truck drivers was 44.5 years. The largest group of claims were from the 35–54 years age group, accounting for nearly 60% of total claims (see Table 1). The oldest age group of truck drivers (i.e., aged 65+ years) recorded the smallest percentage of total claims (2.66%), followed by the youngest age group (i.e., aged ≤ 24 years, 4.04%). The total number of claims gradually decreased over the study period, and the overall incidence decreased from 77.72 to 56.16 per 1,000 workers per year (Table 1; adjusted RR: 0.77, 95% CI: 0.81–0.87; for 2013–2015 compared to 2004–2006). In general, the relative risk of workers’ compensation claims increased with age. The highest rates were observed in the older truck driver group (79.53 per 1000 workers per year), with a 26% increased risk compared to the 35–44 year old age group (adjusted RR: 1.26, 95% CI: 1.10–1.44). Fig. 1 shows the median and interquartile range (IQR) of compensated time for all accepted workers’ compensation claims in truck
2.3. Study population and measurement Data drawn for this study were restricted to ‘accepted’ claims (i.e., those have been accepted for payment) lodged by working age adults (≥15 years) with a date of lodgment between the 2004 and 2015 financial years (1/07/2003 to 30/06/2015). Data from commonwealth workers’ compensation systems were excluded from this study because of an incomplete sub-database. Individual data extracted included: age 2
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Table 1 Number and rate of accepted workers’ compensation claims in HV drivers, 2004 to 2015.
NOTE: Shaded cells indicate statistically significant difference compared to 35–44 year old age group (RR with 95% CIs).
drivers by age group from 2004 to 2012. The median duration of time loss due to work-related injury and illness increased steadily with age. Compared with the 35–44 year old age group (3.2 weeks, IQR: 1.0–11.6), older truck drivers had a significantly longer median duration of time loss, reaching 6.6 weeks (IQR: 2.0.–19.9, Coef: 3.40, 95%CI: 2.86–3.94). The youngest age group had the shortest median time loss (2.0 weeks, IQR: 0.8–6.2), which decreased by 1.21 (95%CI: −1.57 to −0.85) compared with the 35–44 year old age group. Fig. 2 shows the distribution of the most common injury type,
mechanism and body part in truck drivers by age group. Generally, MSK injury was the most common type of injury for all truck drivers. However, it should be noted that older truck drivers had a noticeably larger proportion of neurological injury claims than other age groups. In addition, the percentage of claims due to neurological injury tended to increase with age. Body stressing was the most common mechanism of injury across age groups, however, claims due to sound and pressure were disproportionately larger for older truck drivers. Consistent with this result, ‘head’ related injury was found to be the most common injury among older truck drivers. Table 2 provides the rates and relative risk of work-related injury and illness for the most common type of injury. The overall rate of fracture injury in truck drivers was 6.31 per 1000 workers. Older truck drivers had a slightly higher rate of fracture injury than drivers in the 35–44 year old age group (adjusted RR: 1.03, 95% CI: 0.89–1.20), but this was not statistically significant. For MSK injury, the overall rate was 41.79 per 1000 worker. The rate was 18% lower for the oldest and 32% lower for the youngest age group (adjusted RR: 0.82, 95% CI: 0.72–0.95; adjusted RR: 0.68, 95% CI: 0.60–0.77) compared to the 35–44 year old age group. The overall rates of neurological and other diseases were 2.30 and 2.02 per 1000 workers respectively, and the rates increased steadily with age. For neurological injury, the rate reached 19.11 among older truck drivers, which was nearly 15 times higher compared to the 35–44 year old age group (adjusted RR: 15.2, 95% CI: 12.31–18.80). Psychological injury had the lowest overall rate compared to other types of injury. Furthermore, the rate of psychological peaked in the 35–44 year old age group. Relative to the comparator group, the risk of psychological injury was 60% and 70% lower among the oldest and youngest age groups, respectively (adjusted RR: 0.41, 95% CI:
Fig. 1. Quantile regression analysis of median duration and IQR of time loss in weeks due to work-related injury in HV drivers by age group (2004–2012). 3
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Fig. 2. Injury type, mechanism and body part in truck drivers by age group.
0.26–0.65; adjusted RR: 0.30, 95% CI: 0.21–0.43). Other traumatic injury had the second highest injury rate among all truck drivers (16.57 per 1000 workers). Compared with the 35–44 year old age group, the rate was lower for other age groups except the older truck driver age
group. Older truck drivers had a higher rate of other traumatic injury (16.14 per 1000 workers), but the difference in injury risk compared with the 35–44 year old age group was not statistically significant (adjusted RR: 1.05, 95% CI: 0.91–1.21). 4
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Table 2 Adjusted RRs for risk of injury by age groups in the HV drivers, 2004 to 2015.
Note: Shaded cells indicate statistically significant difference compared to 35–44 year old age group (RR with 95% CIs).
biomechanical tolerances to injury are lower than those of younger persons (Mackay, 1998; Viano, Culver, Evans, Frick, & Scott, 1990), primarily due to reductions in bone and neuromuscular strength and fracture tolerance (Dejeammes & Ramet, 1996; Padmanaban, 2001). A possible explanation of these findings is self-regulation. Meyer (2004) found that older drivers can be highly adaptive and can compensate for deficiencies in certain areas by adapting their behavior (i.e., changing the conditions in which they drive) to minimize their crash risk (for a review see Koppel and Charlton, 2013). This study also found that older truck drivers had a significantly larger proportion of neurological injury compared to younger age groups and that the percentage of these claims increased with age. In fact, claims due to ‘sound and pressure’ were 15 times greater in older age drivers compared to the 35–44 year old age group. Although it is well known that truck drivers are susceptible to traffic noise (e.g., engine and road noise) for long durations, the magnitude of this problem was surprising. Noise-induced hearing loss is common amongst truck drivers (e.g., Alizadeh et al., 2016; Karimi et al., 2010), particularly older truck drivers (Alizadeh et al., 2016, Collee et al., 2011) with some risks associated with factors such as poor road surfaces, conditions of the vehicle and less mature occupational health and safety systems (Fuente and Hickson, 2011). There is also research to suggest that the association between noise-induced hearing loss and these risk factors being more prevalent in developing countries (Alizadeh et al., 2016; Karimi et al., 2010).
4. Discussion The aim of this study was to explore the landscape of work-related injury and illness in Australian truck drivers. The unique aspect of this research was that the data were categorized by age group which allowed insight into the unique challenges facing older truck drivers. The findings of the current study support several recommendations to improve the safety, health and wellbeing of this valued workforce not only in Australia but internationally. It is important to note that the results of this study are unlikely to be isolated to the Australian context. Indeed, the Australian truck driving workplace share similarities to many other countries, particularly developed nations (e.g., U.K., U.S., Canada, etc.); thus, the following recommendations for review and revision of policy and practice can be generalized at an international level. Although older truck drivers were identified as contributing the smallest proportion of total claims, the relative risk of workers’ compensation claims increased with age. In fact, this study found that older truck drivers had the greatest risk when compared to the 35–44 year old age group. This finding is consistent with research conducted in the safety context which has shown that the risk of a highway fatality involving heavy vehicles increases commensurate with drivers’ age and that drivers aged 65 years and older are at a 4.3 times greater risk of being killed in crash compared with drivers aged 15–19 years (Chen et al., 2014). The results also suggested that older truck drivers had significantly greater periods of time off work compared to their younger counterparts. This finding is consistent with the OECD report on Aging and Transport (OECD, 2001) that states that the most critical safety issue for older drivers is their frailty and associated injury susceptibility. These results suggest that targeted intervention is required to address prevention, disability and recovery for older truck drivers. Contrary to expectations, older truck drivers were not found to have significantly higher rates of MSK or fracture injuries. This finding was surprising considering that research has shown that older adults’
4.1. Practical implications Overall, the findings of this research demonstrate the need for a systems approach to addressing the health and wellbeing of older truck drivers. This conclusion is consistent with previous research in the safety context which has identified that the safety of drivers is best managed by identifying intervention at multiple levels of the system 5
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including the vehicle/environment, individual-drivers, company management, supply chain, government and regulatory levels (e.g., Newnam and Goode, 2015; Newnam et al., 2017; Thompson et al., 2015). In support, multi-domain interventions have also been found to be effective at reducing time off work in people with musculoskeletal, pain-related and mental health conditions (Cullen et al., 2017). Such approaches require the integration of healthcare with workplace modifications, and coordination of services delivered to injured or ill workers. However, it should be noted that such interventions could be challenging to implement given the remote nature of the driving task (i.e., Newnam and Goode, 2015) and unique working conditions (e.g., long working hours; Apostolopoulos et al., 2010). These challenges need to be considered in any implementation effort. In regards to individual drivers, the results support a recommendation for self-screening assessment tools in the workplace. Self-screening tools within regular workplace health and safety programs may help to assist older truck drivers and supervisors in identifying and managing any decline in functional and/or cognitive performance over time. Such an approach could also help drivers to better self-regulate their on-road behavior (e.g., Molnar et al., 2015). One good example of such a program is the SAFER Driving self-screening instrument, developed by the University of Michigan Transportation Research Institute (UMTRI) ( Molnar et al., 2010). SAFER Driving is a validated, web-based, self-screening instrument focused on “health concerns” that affect driving – that is, the symptoms that people experience due to medical conditions, medications used to treat them, and the general aging process. By linking the severity of health concerns to their effects on critical driving skills, the instrument provides feedback on general self-awareness and recommendations for behavioral changes, further evaluation, and vehicle modifications. Future research could focus on adapting this tool for use within the workplace to identify not only the health concerns affecting driving ability but factors within and across levels of the transportation system (e.g., scheduling, workloads; Newnam and Goode, 2015; Newnam et al., 2017)) that influence the health and safety of older truck drivers. Consistent with the findings of this study, a modified tool could also incorporate and prioritize screening of hearing with regular monitoring of results over time. The results of this study also inform several recommendations focused on reducing the burden of claims associated with noise-related hearing loss. First, the results suggest that selecting vehicles with superior noise controlling measures could help in reducing the significant burden associated with compensation. To support this intervention approach, company management could encourage driving on quality road surfaces through introducing journey planning practices. The significance of noise-related claims identified for older truck drivers in this study also suggest an urgent need for the review and revision of noise-related risk controls within health and safety laws and regulations.
other factors beyond demographic information. Finally, it was not possible to identify individuals who made more than one claim. There may be unique characteristics associated with multiple claimers that could inform targeted prevention efforts. 5. Conclusion The aim of this study was to explore the landscape of work-related injury and illness in Australian truck drivers. The unique aspect of this research was that data were categorized by age group which allowed insight into the unique challenges facing older truck drivers. The findings of the current study support several recommendations to improve the safety, health and wellbeing of this valued workforce not only in Australia but internationally. The results of this study are unlikely to be isolated to the Australian context. The selection of heavy vehicles and road conditions share similarities to many other countries, particularly developed nations (e.g., U.K., U.S., Canada, etc.); thus, the following recommendations for review and revision of policy and practice can be generalized at an international level. This study is the first to explore the indicators of health and wellness in older truck drivers using Australian compensation claims data. The results of the study provided insight into the unique challenges facing older truck drivers and offer several recommendations for the review and revision into policy and practice to improve their safety, health and wellbeing. These recommendations should be considered from an international perspective as the findings are likely to be generalized to other developed nations. The findings of this study also offer directions for future research focused on the development of tools to identify and manage the individual health needs of older truck drivers. This research and associated implications are important to ensure employers can keep this valued cohort of workers on the road for as long as they are safe. References ABS, 2013. Australian and New Zealand Standard Classification of Occupations. Version 1.2. Canberra, ACT Australian Bureau of Statistics. Work Australia. ABS, 2017. Labour Force, Australia. http://www.abs.gov.au/ausstats/[email protected]/mf/ 6202.0. Alizadeh, Ahmad, Etemadinezhad, Siavash, Charati, Jamshid Yazdani, Mohamadiyan, Mahmood, 2016. Noise-induced hearing loss in bus and truck drivers in Mazandaran province, 2011. Int. J. Occupat. Saf. Ergon. 22 (2), 193–198. Apostolopoulos, Y., Sönmez, S., Shattell, M.M., Belzer, M., 2010. Worksite-induced morbidities among truck drivers in the United States. J. Am. Assoc. Occupat. Health Nurses 58 (7), 285–296. ATA, 2017. Truck Driver Shortage Analysis. http://progressive2.acs.playstream.com/ truckline/progressive/ATAs%20Driver%20Shortage%20Report%202017.pdf. Chen, G.X., Amandus, H.E., Wu, N., 2014. Occupatonal fatalities among driver/sales workers and truck drivers in the United States, 2003–2008. Am. J. Ind. Med. 57 (7), 800–809. Collee, A., Legarnd, C., Govaerts, B., et al., 2011. Occupational expo- sure to noise and the prevalence of hearing loss in a Belgian military population: a cross-sectional study. Noise Health 13 (50), 64–70. Collie, A., Lane, T.J., Hassani-Mahmooei, B., Thompson, J., Mcleod, C., 2016. Does time off work after injury vary by jurisdiction? A comparative study of eight Australian workers' compensation systems. BMJ Open 6, e010910. Cullen, et al., 2017. Effectiveness of workplace interventions in return-to-work for musculoskeletal, pain-related and mental health conditions: an update of the evidence and messages for practitioners. J. Occup. Rehabil. 1–15. Dejeammes, M., Ramet, M., 1996. Aging process and safety enhancement of car occupants. In: Proceedings: International Technical Conference on the Enhanced Safety of Vehicles. National Highway Traffic Safety Administration, pp. 1189–1196. Department of Transport Victoria, 2010, A Workforce Strategy for Road Freight Drivers. Duke, J., Guest, M., Boggess, M., 2010. Age-related safety in professional heavy vehicle drivers: a literature review. Accid. Anal. Prevent. 42, 364–371. Fuente, A., Hickson, L., 2011. Noise-induced hearing loss in Asia. Int. J. Audiol. 50 (Suppl 1), S3–S10. Gray, S.E., Collie, A., 2017. The nature and burden of occupational injury among first responder occupations: a retrospective cohort study in Australian workers. Injury 48 (11), 2470–2477. IBIS World Industry, 2017. Report 14610, Road Freight transport – Australian Market research report. Retrieved from https://www.ibisworld.com.au/industry-trends/ market-research-reports/transport-postal-warehousing/road/road-freight-transport. html. Janke, M., 1994. Age-related Disabilities that may Impair Driving and their Assessment: Literature Review. California Department of Motor Vehicles, Sacramento.
4.2. Strengths and limitations The strength of study was use of a database with population coverage of compensable work-related injury and disease at a national level as well as population level denominator data. Use of standardized coding system also allowed comparisons within and across occupational, industry categories and across injury types. However, this study has several limitations that should be acknowledged. First, by providing detailed analyses of specific injuries by age group, caution should be taken when interpreting some of the results because of the relatively small group sizes (e.g., 19 claims due to psychological injury in older truck drivers). Second, some workers with work-related conditions may choose not to make workers’ compensation claims, or may not be eligible. Thus, the NDS is unlikely to represent all cases of work-related injury and illness in truck drivers. Third, the database contains limited information on workplace factors contributing to injury and illness and 6
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