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多维老年评估在社会保障体系中的潜在影响。

The potential impact of multidimesional geriatric assessment in the social security system.

机构信息

Department of Medicine and Health Sciences "V. Tiberio", University of Molise, Via De Sanctis snc, 86100, Campobasso, Italy.

Operative Unit INPS of Bari 2, Bari, Italy.

出版信息

Aging Clin Exp Res. 2018 Oct;30(10):1225-1232. doi: 10.1007/s40520-017-0889-2. Epub 2018 Jan 12.

Abstract

AIM

To evaluate the efficacy of multidimensional geriatric assessment (MGA/CGA) in patients over 65 years old in predicting the release of the accompaniment allowance (AA) indemnity by a Local Medico-Legal Committee (MLC-NHS) and by the National Institute of Social Security Committee (MLC-INPS).

METHODS

In a longitudinal observational study, 200 Italian elder citizens requesting AA were first evaluated by MLC-NHS and later by MLC-INPS. Only MLC-INPS performed a MGA/CGA (including SPMSQ, Barthel Index, GDS-SF, and CIRS). This report was written according to the STROBE guidelines.

RESULTS

The data analysis was performed on January 2016. The evaluation by the MLC-NHS and by the MLC-INPS was in agreement in 66% of cases. In the 28%, the AA benefit was recognized by the MLC-NHS, but not by the MLC-INPS. By the multivariate analysis, the best predictors of the AA release, by the MLC-NHS, were represented by gender and the Barthel Index score. The presence of carcinoma, the Barthel Index score, and the SPMQ score were the best predictors for the AA release by MLC-INPS.

CONCLUSIONS

MGA/CGA could be useful in saving financial resources reducing the risk of incorrect indemnity release. It can improve the accuracy of the impairment assessment in social security system.

摘要

目的

评估多维老年评估(MGA/CGA)在 65 岁以上患者中的疗效,以预测地方医疗法律委员会(MLC-NHS)和国家社会保险委员会(MLC-INPS)对陪伴津贴(AA)赔偿的释放。

方法

在一项纵向观察性研究中,首先由 MLC-NHS 评估 200 名意大利老年公民的 AA 申请,然后由 MLC-INPS 评估。只有 MLC-INPS 进行了 MGA/CGA(包括 SPMSQ、巴氏量表、GDS-SF 和 CIRS)。本报告根据 STROBE 指南编写。

结果

数据分析于 2016 年 1 月进行。MLC-NHS 和 MLC-INPS 的评估结果在 66%的病例中一致。在 28%的病例中,AA 福利被 MLC-NHS 认可,但未被 MLC-INPS 认可。通过多变量分析,MLC-NHS 对 AA 释放的最佳预测因素是性别和巴氏量表评分。存在癌、巴氏量表评分和 SPMQ 评分是 MLC-INPS 释放 AA 的最佳预测因素。

结论

MGA/CGA 可用于节省财政资源,降低不正确赔偿发放的风险。它可以提高社会保险系统中损伤评估的准确性。

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