Predicting long-term sickness absence among retail workers after four days of sick-listing

• Main Authors: Corné AM Roelen (Author), Erwin M Speklé (Author), Birgit I Lissenberg-Witte (Author), Martijn W Heymans (Author), Willem van Rhenen (Author), Frederieke G Schaafsma (Author)
• Format: Book
• Published: Nordic Association of Occupational Safety and Health (NOROSH), 2022-09-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 0355-3140; 1795-990X; 10.5271/sjweh.4041
• Keywords: prediction model; roc analysis; sick-listing; external validation; sick leave; risk assessment; sickness absence; occupational health service; long-term sickness absence; Public aspects of medicine; RA1-1270
• Source: Scandinavian Journal of Work, Environment & Health, Vol 48, Iss 7, Pp 579-585 (2022)

OBJECTIVE: This study tested and validated an existing tool for its ability to predict the risk of long-term (ie, ≥6 weeks) sickness absence (LTSA) after four days of sick-listing. METHODS: A 9-item tool is completed online on the fourth day of sick-listing. The tool was tested in a sample (N=13 597) of food retail workers who reported sick between March and May 2017. It was validated in a new sample (N=104 698) of workers (83% retail) who reported sick between January 2020 and April 2021. LTSA risk predictions were calibrated with the Hosmer-Lemeshow (H-L) test; non-significant H-L P-values indicated adequate calibration. Discrimination between workers with and without LTSA was investigated with the area (AUC) under the receiver operating characteristic (ROC) curve. RESULTS: The data of 2203 (16%) workers in the test sample and 14 226 (13%) workers in the validation sample was available for analysis. In the test sample, the tool together with age and sex predicted LTSA (H-L test P=0.59) and discriminated between workers with and without LTSA [AUC 0.85, 95% confidence interval (CI) 0.83-0.87]. In the validation sample, LTSA risk predictions were adequate (H-L test P=0.13) and discrimination was excellent (AUC 0.91, 95% CI 0.90-0.92). The ROC curve had an optimal cut-off at a predicted 36% LTSA risk, with sensitivity 0.85 and specificity 0.83. CONCLUSION: The existing 9-item tool can be used to invite sick-listed retail workers with a ≥36% LTSA risk for expedited consultations. Further studies are needed to determine LTSA cut-off risks for other economic sectors.