[1]宋秋月,易东,伍亚舟.基于纵向数据线性混合效应模型的老年人抑郁影响因素研究[J].第三军医大学学报,2019,41(04):384-387.
 SONG Qiuyue,YI Dong,WU Yazhou.Factors contributing to depression in the elderly: a longitudinal data analysis based on linear mixed effect [J].J Third Mil Med Univ,2019,41(04):384-387.
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基于纵向数据线性混合效应模型的老年人抑郁影响因素研究(/HTML )
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《第三军医大学学报》[ISSN:1000-5404/CN:51-1095/R]

卷:
41卷
期数:
2019年第04期
页码:
384-387
栏目:
医学心理学
出版日期:
2019-02-28

文章信息/Info

Title:
Factors contributing to depression in the elderly: a longitudinal data analysis based on linear mixed effect
 
作者:
宋秋月易东伍亚舟
陆军军医大学(第三军医大学)军事预防医学系卫生统计学教研室
Author(s):
SONG Qiuyue YI Dong WU Yazhou

Department of Health Statistics, Faculty of Military Preventive Medicine, Army Medical University (Third Military Medical University), Chongqing, 400038, China
 

关键词:
线性混合效应模型纵向数据老年抑郁
Keywords:
linear mixed effect model longitudinal data elderly depression    
分类号:
R195.4; R339.34; R749.4
文献标志码:
A
摘要:

目的 应用线性混合效应(linear mixed effect,LME)模型对老年抑郁量表得分情况进行拟合分析,探讨老年人抑郁情况及其影响因素。方法 数据来源于美国国家阿尔茨海默合作中心(National Alzheimer’s Coordinating Center,NACC),采用简版老年人抑郁量表对老年人抑郁情绪进行调查,从2011年进行首次调查的1 345人中筛选出连续随访4年共计230人;根据赤池信息准则(Akaike information criterion,AIC)、贝叶斯信息准则(Bayesian information criterions,BIC)值最小化原则,选择合适的方差协方差结构(UN)对该数据进行拟合和模型参数估计,利用SAS软件中的MIXED模块对该纵向数据进行建模与分析。结果 LME模型结果显示,受教育年限越长(β=-0.103,P=0.016),简版老年抑郁量表(Geriatric Depression Scale,GDS)得分越低;离婚的老年人相比于在婚状态的老年人GDS得分高(β=0.742,P=0.025),独居的老年人比在婚状态的GDS得分高(β=1.495,P=0.024);复杂活动需要帮助的老年人相比于完全能自理的老年人GDS得分更高(β=0.420,P=0.036);近2年经常感到沮丧的老年人GDS得分更高(β=1.176,P<0.0001);痴呆状态的老年人比正常认知老年人GDS得分高(β=1.068,P=0.003),MCI状态的老年人也比正常状态的老年人GDS得分高(β=1.020,P=0.001)。结论 线性混合效应模型能有效地处理纵向数据;低文化水平、低自理能力、低认知状态、离婚是造成老年抑郁的重要因素。

Abstract:

Objective To investigate the prevalence of depression in the elderly and identify the contributing factors by fitting the Geriatric Depression Scale (GDS) scores using a linear mixed effect (LME) model.  Methods The data were obtained from a longitudinal study by the United States National Alzheimer’s Coordinating Center (NACC). Starting from 2011, this study investigated the prevalence of depression using Short GDS initially among 1 345 elderly subjects, from whom 230 were followed up continuously for 4 years. According to the minimization principle of the values of Akaike information criterion (AIC) and Bayesian Information Criteria (BIC), a variance-covariance structure was chosen to fit the data and estimate the model, and the MIXED module in SAS software was used to model and analyze the longitudinal data. Results Analysis of the LME model showed that a longer education time was associated with a lower GDS score (β=-0.103, P=0.016). The elderly who divorced (β=0.742, P=0.025) and those who lived alone (β=1.495, P=0.024) were more likely to have a higher GDS score than those in marriage. The elderly who required assistance for complex activities had higher GDS scores than those who were completely independent (β=0.420, P=0.036). The elderly reporting frequent depressive feelings in the past 2 years had higher GDS scores (β=1.176, P<0.000 1). The elderly patients with dementia (β=1.068, P=0.003) and those with mild cognitive impairment (β=1.020, P=0.001) had higher GDS scores than the elderly with normal cognitive function. Conclusion The LME model allows efficient analysis of the longitudinal data from this elderly cohort. A lower level of education, a lowered self-care ability, cognitive impairment and divorce are all important factors contributing to depression in the elderly.

 

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更新日期/Last Update: 2019-02-22