Department of Epidemiology and Biostatistics, Amsterdam Public Health Research Institute, VU University Medical Center, De Boelelaan 1089a, 1081 HV, Amsterdam, The Netherlands.
ArboNed Occupational Health Service, Utrecht, The Netherlands.
J Occup Rehabil. 2019 Sep;29(3):617-624. doi: 10.1007/s10926-018-09825-y.
Purpose The aim of this study was to develop prediction models to determine the risk of sick leave due to musculoskeletal disorders (MSD) in non-sick listed employees and to compare models for short-term (i.e., 3 months) and long-term (i.e., 12 months) predictions. Methods Cohort study including 49,158 Dutch employees who participated in occupational health checks between 2009 and 2015 and sick leave data recorded during 12 months follow-up. Prediction models for MSD sick leave within 3 and 12 months after the health check were developed with logistic regression analysis using routinely assessed health check variables. The performance of the prediction models was evaluated with explained variance (Nagelkerke's R-square), calibration (Hosmer-Lemeshow test) and discrimination (area under the receiver operating characteristic curve, AUC) measures. Results A total of 376 (0.8%) and 1193 (2.4%) employees had MSD sick leave within 3 and 12 months after the health check. The prediction models included similar predictor variables (educational level, musculoskeletal complaints, distress, supervisor social support, work-home interference, intrinsic motivation, development opportunities, and work pace). The explained variances were 7.6% and 8.8% for the model with 3 and 12 months follow-up, respectively. Both prediction models showed adequate calibration and discriminated between employees with and without MSD sick leave 3 months (AUC = 0.761; Interquartile range [IQR] 0.759-0.763) and 12 months (AUC = 0.740; IQR 0.738-0.741) after the health check. Conclusion The prediction models could be used to determine the risk of MSD sick leave in non-sick listed employees and invite them to preventive consultations with occupational health providers.
目的 本研究旨在开发预测模型，以确定非病假员工因肌肉骨骼疾病（MSD）请病假的风险，并比较短期（即 3 个月）和长期（即 12 个月）预测的模型。
方法 本队列研究纳入了 2009 年至 2015 年间参加职业健康检查的 49158 名荷兰员工和在 12 个月随访期间记录的病假数据。使用逻辑回归分析，基于常规评估的健康检查变量，为健康检查后 3 个月和 12 个月内的 MSD 病假开发预测模型。使用解释方差（Nagelkerke 的 R 平方）、校准（Hosmer-Lemeshow 检验）和区分（接收者操作特征曲线下面积，AUC）来评估预测模型的性能。
结果 共有 376 名（0.8%）和 1193 名（2.4%）员工在健康检查后 3 个月和 12 个月内出现 MSD 病假。预测模型包括类似的预测变量（教育程度、肌肉骨骼投诉、困扰、主管社会支持、工作与家庭干扰、内在动机、发展机会和工作节奏）。具有 3 个月和 12 个月随访的模型的解释方差分别为 7.6%和 8.8%。两个预测模型都显示出良好的校准度，并在健康检查后 3 个月（AUC=0.761；四分位距[IQR]0.759-0.763）和 12 个月（AUC=0.740；IQR 0.738-0.741）时区分了患有和不患有 MSD 病假的员工。
结论 该预测模型可用于确定非病假员工的 MSD 病假风险，并邀请他们接受职业健康提供者的预防咨询。
