Original article
K-Means Cluster Analysis of Rehabilitation Service Users in the Home Health Care System of Ontario: Examining the Heterogeneity of a Complex Geriatric Population

Presented to the Gerontological Society of America, November 21, 2011, Boston, MA; and the Canadian Association on Gerontology, December 3, 2010, Montreal, QC, Canada.
https://doi.org/10.1016/j.apmr.2012.05.026Get rights and content

Abstract

Armstrong JJ, Zhu M, Hirdes JP, Stolee P. K-means cluster analysis of rehabilitation service users in the home health care system of Ontario: examining the heterogeneity of a complex geriatric population.

Objective

To examine the heterogeneity of home care clients who use rehabilitation services by using the K-means algorithm to identify previously unknown patterns of clinical characteristics.

Design

Observational study of secondary data.

Setting

Home care system.

Participants

Assessment information was collected on 150,253 home care clients using the provincially mandated Resident Assessment Instrument–Home Care (RAI-HC) data system.

Interventions

Not applicable.

Main Outcome Measures

Assessment information from every long-stay (>60d) home care client that entered the home care system between 2005 and 2008 and used rehabilitation services within 3 months of their initial assessment was analyzed. The K-means clustering algorithm was applied using 37 variables from the RAI-HC assessment.

Results

The K-means cluster analysis resulted in the identification of 7 relatively homogeneous subgroups that differed on characteristics such as age, sex, cognition, and functional impairment. Client profiles were created to illustrate the diversity of this geriatric population.

Conclusions

The K-means algorithm provided a useful way to segment a heterogeneous rehabilitation client population into more homogeneous subgroups. This analysis provides an enhanced understanding of client characteristics and needs, and could enable more appropriate targeting of rehabilitation services for home care clients.

Section snippets

Methods

In this article, we aimed to explore the heterogeneity of home care clients who use rehabilitation services, discover previously unidentified patterns of clinical characteristics, and create client profiles to illustrate the different subgroups found within this complex client population. This study used data collected based on the Resident Assessment Instrument–Home Care (RAI-HC).43, 44 The RAI-HC assessment system has been mandated for use for all clients expected to use home care services

Results

The first column in table 1 presents the variables used in the cluster analyses, as well as the full sample baseline demographic, functional, and health characteristics. For the entire sample, the average age ± SD was 76.8±13.2 years, 12.6% were diagnosed with dementia (Alzheimer's and non-Alzheimer's dementias), 5.6% had a previous hip fracture, 19.1% had stroke, and two thirds of these clients were women (66.7%). The majority of clients had daily pain (60.8%), arthritis (55.4%), and unsteady

Discussion

The findings of this cluster analysis demonstrate that rehabilitation service users in the home care system are a heterogeneous group that can be grouped into smaller, more homogeneous clusters based on available health information. By applying the K-means clustering algorithm, we were able to identify 7 relatively homogeneous subgroups from within the entire population of rehabilitation services users in the Ontario home care system.

This article illustrates the use of an alternative approach

Conclusions

This investigation identified 7 subgroups of rehabilitation service users within the long-stay home care client population in Ontario. This work supports the idea that older home care clients form a diverse, heterogeneous population and clustering methodologies can be used to further our understanding of the patterns or groups that naturally form within the rehabilitation client population. Researchers can use cluster analyses within large administrative databases to focus on pattern discovery

References (52)

  • T.M. Gill et al.

    A program to prevent functional decline in physically frail, elderly persons who live at home

    N Engl J Med

    (2002)
  • L.N. Gitlin et al.

    Effect of an in-home occupational and physical therapy intervention on reducing mortality in functionally vulnerable older people: preliminary findings

    J Am Geriatr Soc

    (2006)
  • L.N. Gitlin et al.

    A randomized trial of a multicomponent home intervention to reduce functional difficulties in older adults

    J Am Geriatr Soc

    (2006)
  • R. Kuisma

    A randomized, controlled comparison of home versus institutional rehabilitation of patients with hip fracture

    Clin Rehabil

    (2002)
  • M.D. Landry et al.

    Analysis of factors affecting demand for rehabilitation services in Ontario, Canada: a health-policy perspective

    Disabil Rehabil

    (2008)

  • Home care in Ontario - facts & figures

  • S.N. Hastings et al.

    Frailty predicts some but not all adverse outcomes in older adults discharged from the emergency department

    J Am Geriatr Soc

    (2008)
  • G.E. Alkema et al.

    Characteristics associated with home- and community-based service utilization for Medicare managed care consumers

    Gerontologist

    (2006)
  • M. Luppa et al.

    Prediction of institutionalization in the elderlyA systematic review

    Age Ageing

    (2009)
  • R. Hébert et al.

    Factors associated with functional decline and improvement in a very elderly community-dwelling population

    Am J Epidemiol

    (1999)
  • F. Formiga et al.

    Risk factors for functional decline in nonagenarians: a one-year follow-up - the Nonasantfeliu study

    Gerontology

    (2007)
  • K.E. Ensrud et al.

    A comparison of frailty indexes for the prediction of falls, disability, fractures, and mortality in older men

    J Am Geriatr Soc

    (2009)
  • A.M. Kulminski et al.

    Cumulative deficits better characterize susceptibility to death in elderly people than phenotypic frailty: lessons from the cardiovascular health study

    J Am Geriatr Soc

    (2008)
  • L. Lafortune et al.

    Health state profiles and service utilization in community-living elderly

    Med Care

    (2009)
  • D.A. Luke

    Getting the big picture in community science: methods that capture context

    Am J Community Psychol

    (2005)
  • R.L. Kravitz et al.

    Evidence-based medicine, heterogeneity of treatment effects, and the trouble with averages

    Milbank Q

    (2004)

    • Cited by (0)

    Supported by the Canadian Institutes of Health Research (grant no. ETG-92249).

    No commercial party having a direct financial interest in the results of the research supporting this article has or will confer a benefit on the authors or on any organization with which the authors are associated.

    In-press corrected proof published online on Aug 1, 2012, at www.archives-pmr.org.

    View full text
    Copyright © 2012 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.