Classification trees for decision making in long-term care.
BACKGROUND
The purpose of the study was to develop a classification tool predicting a requirement for nursing home care in a population of nursing home applicants. In long-term care services, the objectives of classification mechanisms will include the prevention of inappropriate nursing home admission.


METHODS
We studied 295 nursing home applicants residing in the Lower North Shore Area of Sydney, a high socioeconomic status area of Sydney, Australia. The predictor variables examined included: demographic data, social work assessment data, the presence of dementia and incontinence, the Barthel Index of Activities of Daily Living, and the Mini-Mental State Examination.


RESULTS
Classification analysis using the C4.5 Program resulted in several classification trees for a decision for nursing home care with sensitivities greater than 70%. The best classification tree was one which combined the scores of the Barthel Index and the Mini-Mental State Examination.


CONCLUSIONS
Classification trees in their simplicity of design and application have advantages over other analytical methods of classification. Classification analysis and the trees examined in this study may have future useful application in decision making for long-term care.
Quartararo
M
M
Department of Public Health, University of Sydney, Australia.
Glasziou
P
P
Kerr
C B
CB
D002965:Classification; D003663:Decision Trees; D006801:Humans; D008134:Long-Term Care; D009735:Nursing Homes; D012680:Sensitivity and Specificity