Longitudinal trajectories of work absence: bridging the gap between health and work

缺勤的纵向轨迹：弥合健康与工作之间的差距

• 批准号: 2436894
• 金额: --
• 依托单位: Keele University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2436894
• 关键词: Longitudinal; trajectories; work; absence; bridging

Absence from work due to ill-health is one of the biggest causes of productivity loss across the UK. This PhD proposal aims to compare statistical methods to model common patterns (trajectories) of work absence using a large national electronic health record database; and determine health and sociodemographic characteristic associated with the resultant trajectories. Simple statistical approaches such as cumulative duration of absence will be compared to more complex methods such as latent class growth analysis and growth mixture modelling. Understanding patterns of absence would allow clinicians (and potentially workplaces) to identify those employees who may need further support, earlier than would otherwise be possible. By understanding the profiles of the patients/employees within these trajectories this support can be tailored to their specific needs, therefore making interventions more likely to succeed and hence preventing the potential for adverse economic and social outcomes for the individual, the economy and the wider society.

由于健康状况不佳而缺勤是全英国生产率下降的最大原因之一。该博士提案旨在比较统计方法，以利用大型国家电子健康记录数据库对缺勤的常见模式（轨迹）进行建模；并确定与由此产生的轨迹相关的健康和社会人口特征。简单的统计方法，如累计缺席时间，将与更复杂的方法，如潜在类别增长分析和增长混合模型进行比较。了解缺勤模式将使临床医生（以及潜在的工作场所）能够比其他方式更早地识别那些可能需要进一步支持的员工。通过了解患者/员工在这些轨迹中的概况，这种支持可以根据他们的具体需求进行定制，从而使干预措施更有可能成功，从而防止对个人、经济和更广泛的社会产生不利的经济和社会结果。