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Predicting failure to return to work
Author(s) -
Mills R.
Publication year - 2012
Publication title -
internal medicine journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.596
H-Index - 70
eISSN - 1445-5994
pISSN - 1444-0903
DOI - 10.1111/j.1445-5994.2011.02639.x
Subject(s) - medicine , workers' compensation , compensation (psychology) , outcome (game theory) , descriptive statistics , covariate , work (physics) , proportional hazards model , regression analysis , variance (accounting) , actuarial science , operations management , statistics , surgery , psychology , social psychology , engineering , mechanical engineering , mathematics , mathematical economics , accounting , business
Aim: The research question is: is it possible to predict, at the time of workers' compensation claim lodgement, which workers will have a prolonged return to work (RTW) outcome? This paper illustrates how a traditional analytic approach to the analysis of an existing large database can be insufficient to answer the research question, and suggests an alternative data management and analysis approach. Methods: This paper retrospectively analyses 9018 workers' compensation claims from two different workers' compensation jurisdictions in Australia (two data sets) over a 4‐month period in 2007. De‐identified data, submitted at the time of claim lodgement, were compared with RTW outcomes for up to 3 months. Analysis consisted of descriptive, parametric (analysis of variance and multiple regression), survival (proportional hazards) and data mining (partitioning) analysis. Results: No significant associations were found on parametric analysis. Multiple associations were found between the predictor variables and RTW outcome on survival analysis, with marked differences being found between some sub‐groups on partitioning – where diagnosis was found to be the strongest discriminator (particularly neck and shoulder injuries). There was a consistent trend for female gender to be associated with a prolonged RTW outcome. The supplied data were not sufficient to enable the development of a predictive model. Conclusion: If we want to predict early who will have a prolonged RTW in Australia, workers' compensation claim forms should be redesigned, data management improved and specialised analytic techniques used.