Department of Public and Occupational Health, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Van der Boechorststraat 7, NL, 1081 BT, Amsterdam, The Netherlands.
Dutch Institute of Employee Benefit Schemes (UWV), Amsterdam, The Netherlands.
J Occup Rehabil. 2020 Sep;30(3):371-380. doi: 10.1007/s10926-020-09874-2.
Purpose Today, decreasing numbers of workers in Europe are employed in standard employment relationships. Temporary contracts and job insecurity have become more common. This study among workers without an employment contract aimed to (i) predict risk of long-term sickness absence and (ii) identify distinct subgroups of sick-listed workers. Methods 437 individuals without an employment contract who were granted a sickness absence benefit for at least two weeks were followed for 1 year. We used registration data and self-reported questionnaires on sociodemographics, work-related, health-related and psychosocial factors. Both were retrieved from the databases of the Dutch Social Security Institute and measured at the time of entry into the benefit. We used logistic regression analysis to identify individuals at risk of long-term sickness absence. Latent class analysis was used to identify homogenous subgroups of individuals. Results Almost one-third of the study population (n = 133; 30%) was still at sickness absence at 1-year follow-up. The final prediction model showed fair discrimination between individuals with and without long-term sickness absence (optimism adjusted AUC to correct for overfitting = 0.761). Four subgroups of individuals were identified based on predicted risk of long-term sickness absence, self-reported expectations about recovery and return to work, reason of sickness absence and coping skills. Conclusion The logistic regression model could be used to identify individuals at risk of long-term sickness absence. Identification of risk groups can aid professionals to offer tailored return to work interventions.
目的 如今，欧洲从事标准雇佣关系的工人数量正在减少。临时合同和工作不稳定变得越来越普遍。本研究旨在调查无雇佣合同的工人：（i）预测长期病假风险，（ii）确定请病假工人的不同亚组。
方法 437 名请病假至少两周的无雇佣合同工人随访 1 年。我们使用登记数据和自我报告的问卷收集与社会人口统计学、工作相关、健康相关和心理社会因素相关的数据。这些数据均来自荷兰社会保障研究所的数据库，在进入福利期时进行测量。我们使用逻辑回归分析来识别长期病假风险高的个体。使用潜在类别分析来识别同质的个体亚组。
结果 在研究人群中，近三分之一（n=133；30%）在 1 年随访时仍处于病假状态。最终的预测模型显示出区分长期病假和非长期病假个体的良好区分度（校正过度拟合的乐观调整 AUC 为 0.761）。根据长期病假的预测风险、对康复和重返工作岗位的自我报告期望、病假原因和应对技巧，确定了 4 个个体亚组。
结论 逻辑回归模型可用于识别长期病假风险高的个体。识别风险组可以帮助专业人员提供定制的重返工作干预措施。
