Abstract：
OBJECTIVE: It is difficult to predict which employees, in particular those with musculoskeletal pain, will return to work quickly without additional vocational advice and support, which employees will require this support and what levels of support are most appropriate. Consequently, there is no way of ensuring the right individuals are directed towards the right services to support their occupational health needs. The aim of this review will be to identify prognostic factors for duration of work absence in those already absent and examine the utility of prognostic models for work absence.
METHODS: Eight databases were search using a combination of subject headings and key words focusing on work absence, musculoskeletal pain and prognosis. Two authors independently assessed the eligibility of studies, extracted data from all eligible studies and assessed risk of bias using the QUIPS or PROBAST tools, an adapted GRADE was used to assess the strength of the evidence. To make sense of the data prognostic variables were grouped according to categories from the Disability Prevention Framework and the SWiM framework was utilised to synthesise findings.
RESULTS: A total of 23 studies were included in the review, including 13 prognostic models and a total of 110 individual prognostic factors. Overall, the evidence for all prognostic factors was weak, although there was some evidence that older age and better recovery expectations were protective of future absence and that previous absence was likely to predict future absences. There was weak evidence for any of the prognostic models in determining future sickness absence.
CONCLUSIONS: Analysis was difficult due to the wide range of measures of both prognostic factors and outcome and the differing timescales for follow-up. Future research should ensure that consistent measures are employed and where possible these should be in-line with those suggested by Ravinskaya et al. (2023).
METHODS: Eight databases were search using a combination of subject headings and key words focusing on work absence, musculoskeletal pain and prognosis. Two authors independently assessed the eligibility of studies, extracted data from all eligible studies and assessed risk of bias using the QUIPS or PROBAST tools, an adapted GRADE was used to assess the strength of the evidence. To make sense of the data prognostic variables were grouped according to categories from the Disability Prevention Framework and the SWiM framework was utilised to synthesise findings.
RESULTS: A total of 23 studies were included in the review, including 13 prognostic models and a total of 110 individual prognostic factors. Overall, the evidence for all prognostic factors was weak, although there was some evidence that older age and better recovery expectations were protective of future absence and that previous absence was likely to predict future absences. There was weak evidence for any of the prognostic models in determining future sickness absence.
CONCLUSIONS: Analysis was difficult due to the wide range of measures of both prognostic factors and outcome and the differing timescales for follow-up. Future research should ensure that consistent measures are employed and where possible these should be in-line with those suggested by Ravinskaya et al. (2023).
摘要：
目标：很难预测哪些员工，特别是那些肌肉骨骼疼痛的人，将在没有额外职业建议和支持的情况下迅速恢复工作，哪些员工需要这种支持，什么级别的支持是最合适的。因此,没有办法确保适当的个人被引导到适当的服务来支持他们的职业健康需求。这篇综述的目的是确定已经缺勤的人缺勤时间的预后因素，并检查缺勤的预后模型的实用性。
方法：使用主题词和关键词组合搜索了八个数据库，重点是工作缺勤，肌肉骨骼疼痛和预后。两位作者独立评估了研究的资格，从所有符合条件的研究中提取数据，并使用QUIPS或PROBAST工具评估偏倚风险，采用适应等级来评估证据的强度.为了理解数据，根据残疾预防框架的类别对预后变量进行了分组，并利用SWiM框架来综合研究结果。
结果：本综述共纳入23项研究，包括13个预后模型和总共110个个体预后因素。总的来说,所有预后因素的证据都很弱，尽管有一些证据表明，年龄较大和更好的康复预期可以保护未来的缺勤，并且以前的缺勤可能预测未来的缺勤。在确定未来的疾病缺席方面，任何预后模型的证据都很薄弱。
结论：分析是困难的，因为对预后因素和结果的测量范围很广，而且随访时间不同。未来的研究应确保采用一致的措施，并在可能的情况下与Ravinskaya等人建议的措施一致。(2023)。
方法：使用主题词和关键词组合搜索了八个数据库，重点是工作缺勤，肌肉骨骼疼痛和预后。两位作者独立评估了研究的资格，从所有符合条件的研究中提取数据，并使用QUIPS或PROBAST工具评估偏倚风险，采用适应等级来评估证据的强度.为了理解数据，根据残疾预防框架的类别对预后变量进行了分组，并利用SWiM框架来综合研究结果。
结果：本综述共纳入23项研究，包括13个预后模型和总共110个个体预后因素。总的来说,所有预后因素的证据都很弱，尽管有一些证据表明，年龄较大和更好的康复预期可以保护未来的缺勤，并且以前的缺勤可能预测未来的缺勤。在确定未来的疾病缺席方面，任何预后模型的证据都很薄弱。
结论：分析是困难的，因为对预后因素和结果的测量范围很广，而且随访时间不同。未来的研究应确保采用一致的措施，并在可能的情况下与Ravinskaya等人建议的措施一致。(2023)。
