Longitudinal Associations between Anatomical Regions of Pain and Work Conditions: A Study from The SwePain Cohort.
Elena DragiotiBjörn GerdleBritt LarssonPublished in: International journal of environmental research and public health (2019)
We investigated the time-based associations between workload (physical and mechanical), psychosocial work stressors (demands, control, and support), and the number of anatomical regions with pain (ARP). This population-based study with a two-year follow-up included 11,386 responders (5125 men, 6261 women; mean age: 48.8 years; SD: 18.5) living in south-eastern Sweden. Predictive associations were assessed through generalised linear models, and changes over time were examined using a generalised estimating equation. The results of both models were reported as parameter estimates (B) with 95% confidence interval (CIs). Mean changes in the number of ARP, workload, and psychosocial work stressors were stable over time. High mechanical workload and job demands were likely associated with the number of ARP at the two-year follow-up. In the reverse prospective model, we found that the number of ARP was also associated with high physical and mechanical workload and low job control and support. In the two time-based models of changes, we found a reciprocal association between number of ARP and mechanical workload. Our results add epidemiological evidence to the associations between work conditions and the extent of pain on the body. Components of work conditions, including job demands and mechanical strain, must be considered when organisations and health policy makers plan and employ ergonomic evaluations to minimise workplace hazards in the general population.
Keyphrases
- mental health
- chronic pain
- pain management
- public health
- healthcare
- neuropathic pain
- social support
- physical activity
- polycystic ovary syndrome
- adipose tissue
- spinal cord injury
- metabolic syndrome
- skeletal muscle
- spinal cord
- insulin resistance
- health information
- health promotion
- risk assessment
- single molecule
- neural network
- human health