Wholesale and Retail Trade
Participating core and specialty programs: Center for Occupational Robotics Research, Center for Workers’ Compensation Studies, Exposure Assessment, National Center for Productive Aging and Work, Prevention through Design, Safe●Skilled●Ready Workforce, Small Business Assistance, and Surveillance.
Employers, insurers, trade associations, healthcare providers, equipment manufacturers, and safety and health professionals use NIOSH information to prevent musculoskeletal disorders among older workers in wholesale and retail trade.
|Health Outcome||Research Focus||Worker Population*||Research Type|
|A||Musculoskeletal disorders||Aging workforce (e.g., physical capacity, return to work, economics)||Furniture workers, appliance stores, gardening, food and beverage subsectors; small businesses; vulnerable workers||Surveillance research Translation|
Activity Goal 4.6.1 (Translation Research): Conduct translation research to understand barriers and aids to implementing effective interventions for musculoskeletal disorders among aging workers in wholesale and retail trade.
Activity Goal 4.6.2 (Surveillance Research): Develop/enhance surveillance methods to better monitor trends in risk factors for musculoskeletal disorders and for preclinical musculoskeletal pain symptoms among aging wholesale and retail trade workers.
Injuries from overexertion continue to account for the majority of musculoskeletal disorders (MSDs) (36%) in the wholesale and retail trade sector [Bhattacharya 2017]. These injuries are typically associated with manual materials handling which involves lifting, bending, pushing, and carrying goods that often exceed the physical capacities of wholesale and retail workers. Although older workers are well trained and have learned how to avoid injuries, when they are injured their recovery times are typically longer and they are often subjected to restricted work routines that affects their salaries in many cases. According to BLS , the average non-fatal injury rate for MSDs in both wholesale and retail have both declined over the past decade. The injury rate was 36.5 per 10,000 full-time workers in 2014 compared with 39.9 in 2004 in wholesale. For retail, the injury rate was 35.3 per 10,000 full-time workers in 2014, down from 43.9% in 2004. While the reductions are encouraging, there is still work to be done. The injury rates in WRT are still higher than the average for all industries (31.9 per 10,000 full-time workers in 2014). The retail sub-sectors with the highest rates of injury include building materials and gardening stores; general merchandise (department) stores; food and beverage stores; and furniture and home furnishing stores. In wholesale trade, merchants of nondurable goods have the highest MSD rates. MSD injuries are also costly, averaging $9,743 per case in 2014 [Bhattacharya 2014]. Using the BLS estimate of approximately 63,000 reported cases in (2014) of MSDs, the total cost would be $596 million. MSDs have a large economic impact on society that includes the cost of treatment and the related indirect costs of productivity losses. Workers, their families, employers, and tax payers share this burden.
NIOSH is uniquely positioned to make a difference for the health and safety of workers in wholesale and retail trade due to the partnerships it has developed. NIOSH is the leading U.S. federal entity investigating the causes of MSDs and back injury, the primary reason for injury-related days away from work. Surveillance data are needed to provide information on the effectiveness of return-to-work (RTW) programs, especially for aging workers. While there are numerous RTW programs providing different strategies for returning workers to their jobs following a workplace injury, effective surveillance systems are needed to assess each of the different return-to-work programs. Second, the surveillance data needs to be analyzed to identify and prioritize the criterion used to determine if an injured employee is fit to return-to-work without increasing the risk for a subsequent injury. Third, characteristics of injured workers that influence the success of a RTW program need to be identified. Consideration of psychosocial risk factors in addition to physical risk factors for MSDs and the implementation of effective interventions to mitigate these factors is instrumental to the success of a RTW program. Understanding why effective interventions are not widely used to prevent MSDs in the first place is an area in need of translational research.
Employers, insurers, researchers, safety and health professionals, and equipment manufacturers use NIOSH information to implement cost-effective and risk mitigating interventions for MSDs in the wholesale and retail trade sector.
|Health Outcome||Research Focus||Worker Population*||Research Type|
|A||Musculoskeletal disorders (MSDs)||Emerging technologies
(E.g., robotics/exoskeleton, economics, wearable sensing technology)
|Non-store retailers, non-standard workers, small businesses, vulnerable workers||Basic/etiologic
Activity Goal 4.7.1 (Basic/Etiologic Research): Conduct basic/etiologic research to better understand relationship between emerging technologies and musculoskeletal disorders among wholesale and retail trade workers.
Activity Goal 4.7.2 (Intervention Research): Conduct intervention studies to develop and assess the effectiveness of interventions to utilize emerging technologies to reduce musculoskeletal disorders among wholesale and retail trade workers.
The wholesale and retail trade (WRT) sector is the second largest of the ten industry sectors comprising the National Occupational Research Agenda (NORA). Because of its size and range of establishments, WRT continues to burden the economy with an annual average (2004–2015) of 683,300 injuries/illnesses. About 30% of the reported injuries/illnesses were severe enough that those employees experienced combinations of lost work-time and restricted work, affecting their overall health and well-being [Bhattarcharya, 2017]. Robots, exoskeletons, and wearable sensor technologies present new challenges to employers and safety practitioners who must assess the role of new technology in injury/illness cases. Although there is little data available about the role of emerging technologies in WRT workplaces, it is clear that there are hundreds of injuries in labor intensive jobs such as manual material handling that are attributed to emerging technologies. As an example, robots are being used in fulfillment centers, a WRT sector growth area, and exoskeletons are being tested in building materials and gardening stores. The introduction of robotics and automation in general are considered as labor saving devices that will reduce the number of overexertion injuries or musculoskeletal disorders (MSDs) in the workplace. Unfortunately, there have been few studies conducted to test this hypothesis. This push to add more sophisticated robotic devices (exoskeletons and electromechanical devices) in the workplace has created situations where human operators and robots work side-by-side (i.e. cobots, working posture controller, the body extender, full-body, hybrid production systems) [Antonelli and Bruno 2017; Nguyen et al. 2017; de Looze et al 2016; Fontana et al 2014].
Novel man-machine interactions bring into the workplace a unique set of potential health hazards: some dangerous tasks disappear, but new ones are generated. Many of the root causes (etiological causes) of the risks inherent in hybrid production systems have not been clearly defined nor have the economic benefits achieved through the use of these systems been fully identified. Research is needed to study the causes of accident involving automation and workers who operate in adjacent work spaces. More information is also needed about the workers’ responses to the presence of this technology. Workers that have physical limitations or speak another language (vulnerable populations) are likely to be at a greater risk in working adjacent to these automated or computerized systems. At present, there is not enough empirical evidence on the nature and causes of mishaps occurring in automated operations to effectively guide injury-prevention and loss control activities. In addition, research is needed to assess the effectiveness of automated systems or robots/exoskeletons as interventions designed to reduce the physical demands of jobs; and, thus MSDs. More extensive research is needed about the effectiveness of these robotic interventions in reducing musculoskeletal disorders, but at the same time, research must take into consideration the potential safety-related injuries due to the presence of these automated/robotic systems in the workplace.
Antonelli D, Bruno G . Human-robot collaboration using industrial robots. 2nd International Conference on Electrical, Automation and Mechanical Engineering (AME 2017). Adv Eng Res 86:99-102.
Bhattarcharya A, Anderson VP, Pfirman DM . An Examination of Changes in Injury/Illnesses Rates for the WRT Sector 2004–2015. Poster presented at the Work, Stress and Health 2017, Minneapolis, MN, June 7-10.
Bhattacharya, A . BLS html data download and calculations, https://data.bls.gov/gqt/RequestDataexternal icon
BLS . Table R4. Number of nonfatal occupational injuries and illnesses involving days away from work by industry and selected events or exposures leading to injury or illness, private industry, 2014. Washington, DC; U.S. Department of Labor, Bureau of Labor Statistics, https://www.bls.gov/iif/oshwc/osh/case/ostb4370.pdfpdf iconexternal icon.
Nguyen TD, Pilz C, Krüger J . The working posture controller—automated assessment and optimisation of the working posture during the process. In: Duffy V. (eds) Advances in Applied Digital Human Modeling and Simulation. Advances in Intelligent Systems and Computing, vol 481.
de Looze MP, Bosch T, Krause F, Stadler KS, O’Sullivan LS . Exoskeletons for industrial application and their potential effects on physical work load. Ergonomics 59(5):671-681.
Fontana M, Vertechy R, Marcheschi S, Salsedo F, Bergamasco M . The body extender: a full-body exoskeleton for the transport and handling of heavy loads. IEEE Robotics and Automation Magazine Vol. 21(4):33-44.