Department of Public Health, University of Helsinki, Tukholmankatu 8B, P.O. Box 20, Helsinki, 00014, Finland.
BMC Public Health. 2023 Jul 26;23(1):1429. doi: 10.1186/s12889-023-16345-9.
The ageing work force is heterogeneous, following distinct development in work ability. This study aims to identify trajectories of long-term sickness absence (SA) in later careers and to examine potentially modifiable factors associated with the development of SA.
Data comprised of municipal employees of the city of Helsinki aged 50-60 years during 2004-2018 (N = 4729, 80% women). The developmental trajectories of long-term (> 10 working days) SA were examined with Group-based trajectory modelling (GBTM) using SA records of the Social Insurance Institution of Finland during 2004-2018. All-cause and diagnosis-specific (mental disorder- and musculoskeletal disease-related) SA days were analysed. The association of social and health-related factors with trajectory membership was examined using multinomial logistic regression (odds ratios and 95% confidence intervals).
A model with three trajectories was selected for both all-cause and diagnosis-specific SA. Regarding all-cause long-term SA trajectories, 42% had no long-term SA, 46% had low levels of SA, and 12% had a high rate of SA during follow-up. Lower occupational class, reporting smoking, overweight or obesity, moderate or low leisure-time physical activity, and sleep problems were associated with a higher likelihood of belonging to the trajectory with a high rate of SA in both all-cause and diagnosis-specific models.
Most ageing employees have no or little long-term SA. Modifiable factors associated with trajectories with more SA could be targeted when designing and timing interventions in occupational healthcare.
老龄化劳动力是多样化的,工作能力呈现出明显的不同发展趋势。本研究旨在确定长期病假(SA)在职业生涯后期的轨迹,并研究与 SA 发展相关的潜在可改变因素。
本研究的数据来自于赫尔辛基市的市政工作人员,年龄在 50-60 岁之间,时间跨度为 2004-2018 年(N=4729,80%为女性)。使用基于群组的轨迹建模(GBTM),根据芬兰社会保险局在 2004-2018 年期间的 SA 记录,对长期(>10 个工作日)SA 的发展轨迹进行了研究。分析了全因和诊断特异性(精神障碍和肌肉骨骼疾病相关)SA 天数。使用多项逻辑回归(比值比和 95%置信区间)分析了社会和健康相关因素与轨迹成员之间的关系。
选择了一个包含三个轨迹的模型来分析全因和诊断特异性 SA。对于全因长期 SA 轨迹,42%的人没有长期 SA,46%的人有低水平的 SA,12%的人在随访期间有较高的 SA 发生率。较低的职业阶层、吸烟、超重或肥胖、中等或低水平的休闲时间体力活动以及睡眠问题与全因和诊断特异性模型中属于高 SA 发生率轨迹的可能性更高相关。
大多数老年员工没有或很少有长期 SA。在职业健康保健中设计和安排干预措施时,可以针对与更多 SA 相关的轨迹的可改变因素进行干预。