Objective: The purpose of this study was to develop a model that evaluates the impact of policy changes on the number of workers' compensation lost-time back claims in Ontario, Canada, over a 30-year timeframe. The model was used to test the hypothesis that a theory- and policy-driven model would be sufficient in reproducing historical claims data in a robust manner and that policy changes would have a major impact on modeled data.
Methods: The model was developed using system dynamics methods in the Vensim simulation program. The theoretical effects of policies for compensation benefit levels and experience rating fees were modeled. The model was built and validated using historical claims data from 1980 to 2009. Sensitivity analysis was used to evaluate the modeled data at extreme end points of variable input and timeframes. The degree of predictive value of the modeled data was measured by the coefficient of determination, root mean square error, and Theil's inequality coefficients.
Results: Correlation between modeled data and actual data was found to be meaningful (R(2) = 0.934), and the modeled data were stable at extreme end points. Among the effects explored, policy changes were found to be relatively minor drivers of back claims data, accounting for a 13% improvement in error. Simulation results suggested that unemployment, number of no-lost-time claims, number of injuries per worker, and recovery rate from back injuries outside of claims management to be sensitive drivers of back claims data.
Conclusion: A robust systems-based model was developed and tested for use in future policy research in Ontario's workers' compensation. The study findings suggest that certain areas within and outside the workers' compensation system need to be considered when evaluating and changing policies around back claims.
Keywords: Back Pain; Economics; Nonlinear Models; Policy; Workers' Compensation.
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