Factors associated with the duration of disability benefits claims among Canadian workers: a retrospective cohort study
Journal Articles
Overview
Research
Identity
Additional Document Info
View All
Overview
abstract
BACKGROUND: Disability insurance protects workers from total loss of income in case of a disabling injury or illness by providing wage-replacement benefits. To better inform early identification of claims at risk of prolonged recovery, we explored predictors of the duration of disability benefits claims. METHODS: We conducted a retrospective cohort study using claims data provided by SSQ Life Insurance Company Inc., a private Canadian disability insurer. We examined all claims SSQ approved for short- and long-term disability benefits from Jan. 1, 2007, to Mar. 31, 2014, and evaluated the association between 9 variables and duration of short- and long-term disability benefits using Cox proportional hazards regression analyses. RESULTS: For both short- (n = 70 776) and long-term disability (n = 22 205) claims, and across all disorders, older age, female sex, heavy job demands, presence of comorbidity, attending an independent medical evaluation, receipt of rehabilitation therapy and longer time to claim approval were associated with longer claim duration. Higher predisability salary was associated with longer short-term disability claim duration. Quebec residency was associated with longer short-term disability claim duration among workers with psychological disorders, but shorter short-term disability claim duration among those with musculoskeletal complaints and other illnesses. For long-term disability claims, however, residing in Quebec was associated with shorter claim duration, although the size of the association differed across clinical conditions. INTERPRETATION: The factors we found to be associated with the duration of short- and long-term disability claims may be helpful to identify claims at risk of prolonged recovery. Our study has limitations, however, and well-designed prospective studies are needed to confirm our findings and identify other promising predictors.