Internal medicine journal · 2012
Predicting failure to return to work.
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Mills R
Аннотация
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.
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.