Division of Physiotherapy, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Huddinge, Sweden; School of Health and Welfare, Dalarna University, Falun, Sweden.
Department of Behavioural Sciences and Learning, Linköping University, Linköping, Sweden.
J Pain. 2021 Oct;22(10):1180-1194. doi: 10.1016/j.jpain.2021.03.145. Epub 2021 Apr 2.
Chronic pain-related sickness absence is an enormous socioeconomic burden globally. Optimized interventions are reliant on a lucid understanding of the distribution of social insurance benefits and their predictors. This register-based observational study analyzed data for a 7-year period from a population-representative sample of 44,241 chronic pain patients eligible for interdisciplinary treatment (IDT) at specialist clinics. Sequence analysis was used to describe the sickness absence over the complete period and to separate the patients into subgroups based on their social insurance benefits over the final 2 years. The predictive performance of features from various domains was then explored with machine learning-based modeling in a nested cross-validation procedure. Our results showed that patients on sickness absence increased from 17% 5 years before to 48% at the time of the IDT assessment, and then decreased to 38% at the end of follow-up. Patients were divided into 3 classes characterized by low sickness absence, sick leave, and disability pension, with eight predictors of class membership being identified. Sickness absence history was the strongest predictor of future sickness absence, while other predictors included a 2008 policy, age, confidence in recovery, and geographical location. Information on these features could guide personalized intervention in the specialized healthcare. PERSPECTIVE: This study describes sickness absence in patients who visited a Swedish pain specialist interdisciplinary treatment clinic during the period 2005 to 2016. Predictors of future sickness absence are also identified that should be considered when adapting IDT programs to the patient's needs.
慢性疼痛相关的病假是全球范围内巨大的社会经济负担。优化干预措施依赖于对社会保险福利的分布及其预测因素的清晰理解。这项基于登记的观察性研究分析了来自一个具有代表性的 44241 名慢性疼痛患者样本的数据，这些患者有资格在专家诊所接受跨学科治疗 (IDT)。序列分析用于描述整个期间的病假情况，并根据患者在最后 2 年的社会保险福利将患者分为亚组。然后，使用基于机器学习的建模在嵌套交叉验证过程中探索来自不同领域的特征的预测性能。我们的结果表明，病假患者的比例从 5 年前的 17%增加到 IDT 评估时的 48%，然后在随访结束时降至 38%。患者分为低病假、病假和残疾抚恤金 3 类，确定了 8 个类别成员资格的预测因素。病假史是未来病假的最强预测因素，而其他预测因素包括 2008 年的一项政策、年龄、对康复的信心和地理位置。这些特征的信息可以指导专门医疗保健中的个性化干预。观点：本研究描述了 2005 年至 2016 年间访问瑞典疼痛专家跨学科治疗诊所的患者的病假情况。还确定了未来病假的预测因素，在根据患者的需求调整 IDT 计划时应考虑这些因素。
