Does the Upper-Limb Work Instability Scale Predict Transitions Out of Work Among Injured Workers?
Kenneth Tang1, Dorcas E Beaton2, Sheilah Hogg-Johnson3
1Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada; Musculoskeletal Health and Outcomes Research, Li Ka Shing Knowledge Institute of St. Michael's Hospital, Toronto, Ontario, Canada; Institute for Work & Health, Toronto, Ontario, Canada; School of Rehabilitation Science, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada.
Objective:
To investigate the predictive ability of the Upper-Limb Work Instability Scale (UL-WIS) for transitioning out of work among injured workers with chronic, work-related upper extremity disorders (WRUEDs).
Design:
Secondary analysis of a 12-month cohort study with data collection at baseline and 3-, 6-, and 12-month follow-up. Survey questionnaires were used to collect data on an array of sociodemographic, health-related, and work-related variables.
Setting:
Upper extremity specialty clinics.
Participants:
Injured workers (N=356) with WRUEDs who were working at the time of initial clinic attendance.
Interventions:
Not applicable.
Main Outcome Measure:
Transitioning out of work.
Results:
Multivariable logistic regression that considered 9 potential confounders revealed baseline UL-WIS (range, 0-17) to be a statistically significant predictor of a subsequent transition out of work (adjusted odds ratio, 1.18; 95% confidence interval [CI], 1.07-1.31; P=.001). An assessment of predictive values across the UL-WIS score range identified cut-scores of <6 (negative predictive value, .81; 95% CI, .62-.94) and >15 (positive predictive value, .80; 95% CI, .52-.96), differentiating the scale into 3 bands representing low, moderate, and high risk of exiting work.
Conclusions:
The UL-WIS was shown to be an independent predictor of poor work sustainability among injured workers with chronic WRUEDs; however, when applied as a standalone tool in clinical settings, some limits to its predictive accuracy should also be recognized.
