Abstract
BackgroundTargeted interventions for the long-term sick-listed may prevent permanent exclusion from the labour force. We aimed to develop a prediction method for identifying high risk groups for continued or recurrent long-term sickness absence, unemployment, or disability among persons on long-term sick leave.MethodsWe obtained individual characteristics and follow-up data from the Danish Register of Sickness Absence Compensation Benefits and Social Transfer Payments (RSS) during 2004 to 2010 for 189,279 Danes who experienced a period of long-term sickness absence (4+ weeks). In a learning data set, statistical prediction methods were built using logistic regression and a discrete event simulation approach for a one year prediction horizon. Personalized risk profiles were obtained for five outcomes: employment, unemployment, recurrent sickness absence, continuous long-term sickness absence, and early retirement from the labour market. Predictor variables included gender, age, socio-economic position, job type, chronic disease status, history of sickness absence, and prior history of unemployment. Separate models were built for times of economic growth (2005–2007) and times of recession (2008–2010). The accuracy of the prediction models was assessed with analyses of Receiver Operating Characteristic (ROC) curves and the Brier score in an independent validation data set.ResultsIn comparison with a null model which ignored the predictor variables, logistic regression achieved only moderate prediction accuracy for the five outcome states. Results obtained with discrete event simulation were comparable with logistic regression.ConclusionsOnly moderate prediction accuracy could be achieved using the selected information from the Danish register RSS. Other variables need to be included in order to establish a prediction method which provides more accurate risk profiles for long-term sick-listed persons.
Highlights
Targeted interventions for the long-term sick-listed may prevent permanent exclusion from the labour force
With this design we are limited to comparing the prediction abilities of logistic regression and discrete event simulation when both are trained in a random sample which contains roughly 2/3 of the available data and are evaluated in the remaining 1/3 of the data
The sample consists of about 20% more women than men and the occurrence of the initial long-term sickness absence is higher in the economic growth period for both
Summary
Targeted interventions for the long-term sick-listed may prevent permanent exclusion from the labour force. Much is known about risk factors for sickness absence and future disability Using this knowledge for the prediction of future labour market outcomes and for making a risk profile assessing the future long-term sick-listed persons to return to work, become unemployed, or qualify for a disability pension, has not been explored [7]. A profiling tool was established in Denmark [10] to identify unemployed workers who are at risk of ending up in long-term unemployment It has since become part of the Danish national labour market policy. The purpose of the present work is to develop a prediction method for long-term sick-listed workers in Denmark, based on statistical models The application of this method is intended to result in a profiling method with the potential to provide guidance to caseworkers in local municipalities, when making decisions regarding the allocation of resources
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