Prevention strategies for sickness absence: sick individuals or sick populations?

Sickness absence mesmerizes many researchers, given the numerous publications on risk factors for sickness absence. A large variety of risk factors have been identified, including work-related risk factors such as physical work demands and psychosocial work factors (1,2), unhealthy behaviors such as lack pf physical activity and smoking (3,4), and chronic health problems (5). A logical next step seems to be the development of a prediction model, whereby an individual ’s profile on risk factors is converted into a probability on future sickness absence. In the past few years, several prediction models have been developed and validated. It is in intriguing question how to use these models in occupational health practice to identify workers at risk for prolonged sickness absence reliably and to act on this. The recent prediction models differ with respect to target populations, number, and type of predictors, and definition of sickness absence to be predicted. A large Finnish study among public sector employees with a 12-year follow-up used 17 factors in a prediction model for short-term sickness absence (≥10 days) and 14 predictors for long-term sickness absence (≥90 days). The performance of the model, evaluated by the C-index (also known as the area under the curve), for short-term sickness absence was 0.65 and for long-term sickness absence 0.74, representing poor-to-moderate model performance. For workers with higher risk levels, the positive predictive values were ...
Source: Scandinavian Journal of Work, Environment and Health - Category: Occupational Health Tags: Editorial Source Type: research