Abstract
BackgroundWhile 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.MethodsStudies 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.ResultsOf 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).ConclusionThe 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.
Highlights
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 pooled associations provided detailed empirical information as to which variables emerged as the strongest predictors of nursing home (NH) admission (e.g., 3 or more activity of daily living (ADL) dependencies, cognitive impairment, prior NH use)
Relying on multiple and representative sources of data, we have identified a number of indicators that predict NH admission over multi-year intervals
Summary
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. The cost of nursing home (NH) care for persons 65 years of age and over is estimated to be roughly 150 billion dollars by 2007 in the United States (U.S.). Several comprehensive literature reviews have summarized studies predicting NH admission among older adults [5,6,7,8,9]. These reviews address "long-term" predictors of NH admission, or those factors that influence NH entry 1 year or more in the future. Additional predictors of NH admission move beyond the older adult to capture the care received (e.g., unavailable family caregiver; community-based service use) or community contexts (lower NH bed supply)
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