Email updates

Keep up to date with the latest news and content from BMC Geriatrics and BioMed Central.

Open Access Highly Accessed Open Badges Research article

Predicting nursing home admission in the U.S: a meta-analysis

Joseph E Gaugler1*, Sue Duval2, Keith A Anderson3 and Robert L Kane4

Author Affiliations

1 Center on Aging, Center for Gerontological Nursing, School of Nursing, University of Minnesota, 6-150 Weaver-Densford Hall, 1331, 308 Harvard Street S.E., Minneapolis, MN, 55455 USA

2 Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Division of Epidemiology & Community Health, 1300 South Second Street, Suite 300, Minneapolis, MN 55454-1015 USA

3 College of Social Work, The Ohio State University, 1947 N. College Road, Columbus, OH, 43210, USA

4 Division of Health Policy and Management, University of Minnesota School of Public Health, Mayo Mail Code 197, 420 Delaware Street SE, Minneapolis, MN 55455 USA

For all author emails, please log on.

BMC Geriatrics 2007, 7:13  doi:10.1186/1471-2318-7-13

Published: 19 June 2007



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.