Development and validation of a prediction model for unemployment and work disability among 55 950 Dutch workers

Background:  This study developed prediction models for involuntary exit from paid employment through unemployment and disability benefits and examined if predictors and discriminative ability of these models differ between five common chronic diseases. 

 Methods:  Data from workers in the Lifelines Cohort Study (n = 55 950) were enriched with monthly information on employment status from Statistics Netherlands. Potential predictors included sociodemographic factors, chronic diseases, unhealthy behaviours and working conditions. Data were analyzed using cause-specific Cox regression analyses. Models were evaluated with the C-index and the positive and negative predictive values (PPV and NPV, respectively). The developed models were externally validated using data from the Study on Transitions in Employment, Ability and Motivation. 

 Results:  Being female, low education, depression, smoking, obesity, low development possibilities and low social support were predictors of unemployment and disability. Low meaning of work and low physical activity increased the risk for unemployment, while all chronic diseases increased the risk of disability benefits. The discriminative ability of the models of the development and validation cohort were low for unemployment (c = 0.62; c = 0.60) and disability benefits (c = 0.68; c = 0.75). After stratification for specific chronic diseases, the discriminative ability of models predicting disability benefits improved for cardiovascular disease (c = 0.81), chronic obstructive pulmonary disease (c = 0.74) and diabetes mellitus type 2 (c = 0.74). The PPV was low while the NPV was high for all models. 

 Conclusion:  Taking workers' particular disease into account may contribute to an improved prediction of disability benefits, yet risk factors are better examined at the population level rather than at the individual level.