Predicting nursing home admission in the U.S: a meta-analysis.
Predicting nursing home admission in the U.S: a meta-analysis.
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DOI：
10.1186/1471-2318-7-13
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发表时间：
2007-06-19
期刊：
影响因子：
4.1
通讯作者：
Kane, Robert L
中科院分区：
文献类型：
--
作者：
Gaugler, Joseph E;Duval, Sue;Anderson, Keith A;Kane, Robert L
虽然现有的综述已经确定了疗养院入院的显著预测因素，但这项荟萃分析试图提供更综合的实证结果来确定预测因素。本研究旨在建立社会人口学、功能、认知、服务使用和非正式支持指标之间的汇集经验关联，以预测美国老年人中的养老院入院情况。通过搜索MEDLINE、SqueccINFO、CINAHL和数字论文数据库中的关键词，检索发表在英语中的研究，关键词分别为：“疗养院安置”、“疗养院条目”、“疗养院入院”和“预测者/制度化”。包括这些关键字的任何报告都被检索到。还搜索了检索到的文章的参考书目。选定的研究包括代表美国老年人口的全国性或地区性抽样框架。在确定的736份相关报告中，包括使用纵向设计和基于社区的样本的12个数据来源的77份报告。通过标准化方案提取疗养院入院人数、随访时间、样本特征、分析类型、统计调整和潜在风险因素等信息。随机效应模型被用来分别汇集来自各个数据源的Logistic模型和Cox回归模型的结果。有3项或3项以上的日常生活依赖活动(汇总优势比[OR]=3.25；95%可信区间[CI]，2.56~4.09)、认知障碍(OR=2.54；CI，1.44~4.51)和有过养老院经历(OR=3.47；CI，1.89~6.37)是养老院入院的最强预测因素。这些汇集的关联提供了详细的经验信息，说明哪些变量是NH入院的最强预测因素(例如，3个或更多的ADL依赖、认知障碍、既往使用NH)。这些结果可以用来作为构建和验证预后工具的权重，以估计多年期间NH进入的风险。
While existing reviews have identified significant predictors of nursing home admission, this meta-analysis attempted to provide more integrated empirical findings to identify predictors. The present study aimed to generate pooled empirical associations for sociodemographic, functional, cognitive, service use, and informal support indicators that predict nursing home admission among older adults in the U.S. Studies published in English were retrieved by searching the MEDLINE, PSYCINFO, CINAHL, and Digital Dissertations databases using the keywords: "nursing home placement," "nursing home entry," "nursing home admission," and "predictors/institutionalization." Any reports including these key words were retrieved. Bibliographies of retrieved articles were also searched. Selected studies included sampling frames that were nationally- or regionally-representative of the U.S. older population. Of 736 relevant reports identified, 77 reports across 12 data sources were included that used longitudinal designs and community-based samples. Information on number of nursing home admissions, length of follow-up, sample characteristics, analysis type, statistical adjustment, and potential risk factors were extracted with standardized protocols. Random effects models were used to separately pool the logistic and Cox regression model results from the individual data sources. Among the strongest predictors of nursing home admission were 3 or more activities of daily living dependencies (summary odds ratio [OR] = 3.25; 95% confidence interval [CI], 2.56–4.09), cognitive impairment (OR = 2.54; CI, 1.44–4.51), and prior nursing home use (OR = 3.47; CI, 1.89–6.37). The pooled associations provided detailed empirical information as to which variables emerged as the strongest predictors of NH admission (e.g., 3 or more ADL dependencies, cognitive impairment, prior NH use). These results could be utilized as weights in the construction and validation of prognostic tools to estimate risk for NH entry over a multi-year period.