Roelen Corne A M, Heymans Martijn W, Twisk Jos W R, Laaksonen Mikko, Pallesen Ståle, Magerøy Nils, Moen Bente E, Bjorvatn Bjørn
1 Department of Health Sciences, VU University, The Netherlands
1 Department of Health Sciences, VU University, The Netherlands.
Eur J Public Health. 2015 Aug;25(4):668-72. doi: 10.1093/eurpub/cku192. Epub 2014 Dec 1.
Self-rated health (SRH) has been found to predict sickness absence (SA). The present study investigated the effect of replacing single-item SRH by a multi-item health measure on SA predictions.
Longitudinal study of 2059 Norwegian nurses with assessments in three waves each separated by 1 year. Health was measured by single-item SRH and multi-item SF-12 in waves 1 and 2. SA was self-reported in all three waves and high SA was defined as more than or equal to 31 SA days within the last 12 months. Predictions of high SA by a model including age, prior SA and single-item SRH were compared with predictions by a model including age, prior SA and multi-item SF-12. Both models were bootstrapped to correct for over-optimism and prospectively validated for their predictions in a new time frame.
1253 nurses (61%) had complete data for analysis. The SF-12 model predicted the risk of high SA more accurately (χ(2) = 4.294; df = 8) and was more stable over time than the SRH model (model χ(2) = 14.495; df = 8). Both prediction models correctly discriminated between high-risk and low-risk individuals in 73% of the cases at wave 2 and in 71% of the cases at wave 3.
The accuracy of predictions increased when single-item SRH was replaced by multi-item SF-12, but the discriminative ability did not improve. Single-item SRH suffices to identify employees at increased risk of high SA.
自评健康状况(SRH)已被发现可预测病假缺勤(SA)。本研究调查了用多项目健康测量指标替代单项目SRH对SA预测的影响。
对2059名挪威护士进行纵向研究,分三次进行评估,每次间隔1年。在第1波和第2波中,分别用单项目SRH和多项目SF - 12测量健康状况。在所有三次评估中均采用自我报告的方式获取SA数据,高SA定义为过去12个月内病假天数大于或等于31天。将包含年龄、既往SA和单项目SRH的模型对高SA的预测与包含年龄、既往SA和多项目SF - 12的模型的预测进行比较。对两个模型均进行了自抽样以校正过度乐观偏差,并在新的时间框架内对其预测进行前瞻性验证。
1253名护士(61%)有完整数据用于分析。SF - 12模型对高SA风险的预测更准确(χ(2)=4.294;自由度=8),且随时间推移比SRH模型更稳定(模型χ(2)=14.495;自由度=8)。在第2波中,两个预测模型在73%的案例中能正确区分高风险和低风险个体;在第3波中,这一比例为71%。
当用多项目SF - 12替代单项目SRH时,预测的准确性有所提高,但判别能力并未改善。单项目SRH足以识别出高SA风险增加的员工。