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首页> 外文期刊>Journal of occupational rehabilitation >Development of Prediction Models for Sickness Absence Due to Mental Disorders in the General Working Population
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Development of Prediction Models for Sickness Absence Due to Mental Disorders in the General Working Population

机译：疾病缺席预测模型的发展普遍存在人口

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PurposeThis study investigated if and how occupational health survey variables can be used to identify workers at risk of long-term sickness absence (LTSA) due to mental disorders.MethodsCohort study including 53,833 non-sicklisted participants in occupational health surveys between 2010 and 2013. Twenty-seven survey variables were included in a backward stepwise logistic regression analysis with mental LTSA at 1-year follow-up as outcome variable. The same variables were also used for decision tree analysis. Discrimination between participants with and without mental LTSA during follow-up was investigated by using the area under the receiver operating characteristic curve (AUC); the AUC was internally validated in 100 bootstrap samples.Results30,857 (57%) participants had complete data for analysis; 450 (1.5%) participants had mental LTSA during follow-up. Discrimination by an 11-predictor logistic regression model (gender, marital status, economic sector, years employed at the company, role clarity, cognitive demands, learning opportunities, co-worker support, social support from family/friends, work satisfaction, and distress) was AUC = 0.713 (95% CI 0.692-0.732). A 3-node decision tree (distress, gender, work satisfaction, and work pace) also discriminated between participants with and without mental LTSA at follow-up (AUC = 0.709; 95% CI 0.615-0.804).ConclusionsAn 11-predictor regression model and a 3-node decision tree equally well identified workers at risk of mental LTSA. The decision tree provides better insight into the mental LTSA risk groups and is easier to use in occupational health care practice.
机译：目的本研究调查了职业健康调查变量是否以及如何用于识别因精神障碍而长期缺勤（LTSA）风险的工人。方法：在2010年至2013年间，对53833名未列入疾病名单的职业健康调查参与者进行短期研究。27个调查变量被纳入一项反向逐步logistic回归分析，以1年随访时的心理LTSA作为结果变量。同样的变量也用于决策树分析。通过使用受试者工作特征曲线（AUC）下的面积，调查了随访期间患有和未患有精神LTSA的受试者之间的差异；AUC在100个引导样本中进行了内部验证。结果30857名（57%）参与者有完整的数据可供分析；450名（1.5%）参与者在随访期间出现精神LTSA。通过11个预测因素的逻辑回归模型（性别、婚姻状况、经济部门、在公司工作年限、角色清晰性、认知需求、学习机会、同事支持、家人/朋友的社会支持、工作满意度和痛苦）得出的歧视AUC=0.713（95%可信区间0.692-0.732）。一个三节点决策树（痛苦、性别、工作满意度和工作节奏）也可以在随访中区分有无心理LTSA的参与者（AUC=0.709；95%可信区间0.615-0.804）。结论SAN 11预测回归模型和三节点决策树同样能很好地识别出有心理LTSA风险的工人。决策树能更好地洞察LTSA心理风险群体，更易于在职业卫生保健实践中使用。

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