Open Access Research article

Selecting long-term care facilities with high use of acute hospitalisations: issues and options

Joanna B Broad1*, Toni Ashton2, Thomas Lumley3, Michal Boyd145, Ngaire Kerse2 and Martin J Connolly15

Author Affiliations

1 Freemasons’ Department of Geriatric Medicine, University of Auckland, C/- WDHB, Box 93503, Takapuna, Auckland 0740, New Zealand

2 School of Population Health, University of Auckland, Auckland, New Zealand

3 Department of Statistics, University of Auckland, Auckland, New Zealand

4 School of Nursing, University of Auckland, Auckland, New Zealand

5 Health of Older People, Waitemata District Health Board, Auckland, New Zealand

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BMC Medical Research Methodology 2014, 14:93  doi:10.1186/1471-2288-14-93

Published: 22 July 2014



This paper considers approaches to the question “Which long-term care facilities have residents with high use of acute hospitalisations?” It compares four methods of identifying long-term care facilities with high use of acute hospitalisations by demonstrating four selection methods, identifies key factors to be resolved when deciding which methods to employ, and discusses their appropriateness for different research questions.


OPAL was a census-type survey of aged care facilities and residents in Auckland, New Zealand, in 2008. It collected information about facility management and resident demographics, needs and care. Survey records (149 aged care facilities, 6271 residents) were linked to hospital and mortality records routinely assembled by health authorities. The main ranking endpoint was acute hospitalisations for diagnoses that were classified as potentially avoidable. Facilities were ranked using 1) simple event counts per person, 2) event rates per year of resident follow-up, 3) statistical model of rates using four predictors, and 4) change in ranks between methods 2) and 3). A generalized mixed model was used for Method 3 to handle the clustered nature of the data.


3048 potentially avoidable hospitalisations were observed during 22 months’ follow-up. The same “top ten” facilities were selected by Methods 1 and 2. The statistical model (Method 3), predicting rates from resident and facility characteristics, ranked facilities differently than these two simple methods. The change-in-ranks method identified a very different set of “top ten” facilities. All methods showed a continuum of use, with no clear distinction between facilities with higher use.


Choice of selection method should depend upon the purpose of selection. To monitor performance during a period of change, a recent simple rate, count per resident, or even count per bed, may suffice. To find high–use facilities regardless of resident needs, recent history of admissions is highly predictive. To target a few high-use facilities that have high rates after considering facility and resident characteristics, model residuals or a large increase in rank may be preferable.

Long-term care; Risk assessment; Hospitalization; Health services for the aged; facility selection; Research design