Prediction models to identify workers at risk of sick leave due to low-back pain in the Dutch construction industry.

Objective The aim of this study was to develop a prediction model based on variables measured in occupational health checks to identify non-sick listed workers at risk of sick leave due to non-specific low-back pain (LBP). Methods This cohort study comprised manual (N=22 648) and non-manual (N=9735) construction workers who participated in occupational health checks between 2010 and 2013. Occupational health check variables were used as potential predictors and LBP sick leave was recorded during 1-year follow-up. The prediction model was developed with logistic regression analysis among the manual construction workers and validated in non-manual construction workers. The performance of the prediction model was evaluated with explained variances (Nagelkerke's R-square), calibration (Hosmer-Lemeshow test), and discrimination (area under the receiver operating curve, AUC) measures. Results During follow-up, 178 (0.79%) manual and 17 (0.17%) non-manual construction workers reported LBP sick leave. Backward selection resulted in a model with pain/stiffness in the back, physician-diagnosed musculoskeletal disorders/injuries, postural physical demands, feeling healthy, vitality, and organization of work as predictor variables. The Nagelkerke's R-square was 3.6%; calibration was adequate, but discrimination was poor (AUC=0.692; 95% CI 0.568-0.815). Conclusions A prediction model based on occupational health check variables does not identify non-sick listed workers at increased risk of LBP sick leave correctly. The model could be used to exclude the workers at the lowest risk on LBP sick leave from costly preventive interventions.

Re: Amelsvoort et al. "Approaches for predicting long-term sickness absence".

We would like to thank Van Amelsvoort et al (1) for the interest in our study (2) and take the opportunity to clarify here that none of the workers were sick-listed when they participated in the baseline health survey. We mentioned in the abstract that incident (ie, not prevalent) long-term sickness absence was retrieved from an occupational health register (2). Our explanation of how to interpret the area under the receiver operating characteristic (ROC) curve as measure of discrimination between workers with and without long-term sickness absence might have given the impression that the study population was a mix of workers with and without sickness absence. Throughout the paper, however, workers with long-term sickness absence refer to those not sick-listed at baseline who had incident long-term sickness absence during 1-year follow-up. We agree with the authors that instruments to predict long-term sickness absence for workers still at work (secondary prevention) should be distinguished from instruments for workers already on sick leave (tertiary prevention). The objective of our study was to investigate the Work Ability Index (WAI) as an instrument to predict future long-term sickness absence in non-sick-listed workers, ie, as an instrument for secondary prevention. Therefore, the term "screening" was used in the appropriate context. Van Amelsvoort et al (1) raise an interesting point when they state that including the outcome (sickness absence) as predictor in the model will shift the focus towards the prediction of recurrent sickness absence. Obviously, sickness absence is useless for predicting the first long-term sickness absence episode of an individual who has just finished education and enters the workforce. During working life, workers develop a sickness absence history either without sickness absence episodes (ie, zero-absenteeism) or with successive sickness absence episodes. In the latter case, Navarro et al (3) recommended to use statistical techniques for recurrent rather than independent events. A worker's sickness absence history is the strongest predictor of future sickness absence episodes (4, 5). From that perspective, it would be a missed opportunity not to include past sickness absence as variable in prediction models for future long-term sickness absence.

Gender and age differences in the recurrence of sickness absence due to common mental disorders: a longitudinal study.

BACKGROUND: Common mental disorders (CMDs) are an important cause of sickness absence and long-term work disability. Although CMDs are known to have high recurrence rates, little is known about the recurrence of sickness absence due to CMDs. The aim of this study was to investigate the recurrence of sickness absence due to CMDs, including distress, adjustment disorders, depressive disorders and anxiety disorders, according to age, in male and female employees in the Netherlands. METHODS: Data on sickness absence episodes due to CMDs were obtained for 137,172 employees working in the Dutch Post and Telecommunication companies between 2001 and 2007. The incidence density (ID) and recurrence density (RD) of sickness absence due to CMDs was calculated per 1000 person-years in men and women in the age-groups of < 35 years, 35-44 years, 45-54 years, and > or = 55 years. RESULTS: The ID of one episode of CMDs sickness absence was 25.0 per 1000 person-years, and the RD was 76.7 per 1000 person-years. Sickness absence due to psychiatric disorders (anxiety and depression) does not have a higher recurrence density of sickness absence due to any CMDs as compared to stress-related disorders (distress and adjustment disorders): 81.6 versus 76.0 per 1000 person-years. The ID of sickness absence due to CMDs was higher in women than in men, but the RD was similar. Recurrences were more frequent in women < 35 years and in women between 35 and 44 years of age. We observed no differences between age groups in men. Recurrences among employees with recurrent episodes occurred within 3 years in 90% of cases and the median time-to-onset of recurrence was 11 (10-13) months in men and 10 (9-12) months in women. CONCLUSIONS: Employees who have been absent from work due to CMDs are at increased risk of recurrent sickness absence due to CMDs and should be monitored after they return to work. The RD was similar in men and in women. In women < 45 years the RD was higher than in women > or = 45 years. In men no age differences were observed.