Predictors of disability pension in long-term sickness absence
Predictors of disability pension in long-term sickness absence
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DOI：
10.1093/eurpub/14.4.398
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发表时间：
2004-12-01
影响因子：
4.4
通讯作者：
Mæland, JG
中科院分区：
文献类型：
--
作者：
Gjesdal, S;Ringdal, PR;Mæland, JG
背景资料：虽然已经确定了残疾养恤金（DP）的几个社会人口预测因素，但对医疗方面的重要性知之甚少。研究方法：挪威长期患病缺席者的代表性样本，2043名妇女和1585名男子，详细的诊断信息的基础上，国际初级保健分类（ICPC）进行了5年的随访。从挪威DP登记处获得DP授予日期，并将其用作考克斯多变量回归分析中的因变量。医疗和社会人口因素作为解释变量输入。结果：Kaplan-Meier估计的5年DP的风险为22.9%的全样本，22.5%的男性和23.3%的女性。因精神疾病而请病假的男性残疾风险增加。除了妊娠相关病例，其未来DP的风险非常低，女性中的主要诊断组之间没有显著差异。以前的病假增加了残疾的风险，但只有在列入前4年的20周以上的总缺席显着。年龄是未来DP的最强预测因子。增加收入会降低风险，但不是线性的。纳入前一年的失业状况增加了妇女的残疾风险，但对男子没有影响。在肌肉骨骼疾病的病例中（样本的54.5%），在考克斯回归分析中确定了具有不同残疾风险的亚组，具有性别特异性模式。结论：除了以前已知的社会人口预测因素，医疗变量是重要的，在确定疾病缺席与DP的风险增加。
Background: While several socio-demographic predictors of disability pension (DP) have been identified, less is known about the importance of the medical aspects. Methods: A representative sample of Norwegian long-term sickness absentees, 2043 women and 1585 men, with detailed diagnostic information based on the International Classification of Primary Care (ICPC) was followed up for 5 years. The date of granting DP was obtained from the Norwegian DP-register and used as the dependent variable in Cox multivariate regression analysis. Medical and socio-demographic factors were entered as explanatory variables. Results: Kaplan-Meier estimates of the 5 year risk of DP were 22.9% for the full sample, 22.5% for men and 23.3% for the women. Men on sick leave for mental health disorders had an increased disability risk. Except for pregnancy-related cases, which carried a very low risk for future DP, there was no significant difference between the main diagnostic groups among women. Previous sickness absence increased the disability risk but was significant only for total absence above 20 weeks in the 4 years preceding inclusion. Age was the strongest predictor of future DP. Increasing income decreased the risk, bur not linearly. Unemployment status in the year preceding inclusion increased disability risk for women, but not for men. Among cases with musculoskeletal disorders (54.5% of the sample), subgroups with different disability risks were identified in Cox' regression analysis, with a gender-specific pattern. Conclusion: In addition to previously known socio-demographic predictors, medical variables were important in identifying sickness absentees with an increased risk of DP.