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Identifying clusters of falls-related hospital admissions to inform population targets for prioritising falls prevention programmes
  1. Caroline F Finch1,
  2. Karen Stephan2,
  3. Anna Wong Shee1,
  4. Keith Hill3,
  5. Terry P Haines4,
  6. Lindy Clemson5,
  7. Lesley Day2
  1. 1Australian Centre for Research into Injury In Sport and its Prevention (ACRISP), Federation University Australia, Ballarat, Victoria, Australia
  2. 2Monash Injury Research Institute (MIRI), Monash University, Clayton, Victoria, Australia
  3. 3Faculty of Health Sciences, School of Physiotherapy and Exercise Science, Curtin University, Perth, Western Australia, Australia
  4. 4Allied Health Research Unit, Monash Health, Monash University, Clayton, Victoria, Australia
  5. 5Ageing, Work & Health Research Unit, Faculty of Health Sciences, The University of Sydney, Sydney, New South Wales, Australia
  1. Correspondence to Professor Caroline Finch, Australian Centre for Research into Injury In Sport and its Prevention (ACRISP), Federation University Australia, PO Box 663, Ballarat, Victoria, Australia; c.finch{at}


Background There has been limited research investigating the relationship between injurious falls and hospital resource use. The aims of this study were to identify clusters of community-dwelling older people in the general population who are at increased risk of being admitted to hospital following a fall and how those clusters differed in their use of hospital resources.

Methods Analysis of routinely collected hospital admissions data relating to 45 374 fall-related admissions in Victorian community-dwelling older adults aged ≥65 years that occurred during 2008/2009 to 2010/2011. Fall-related admission episodes were identified based on being admitted from a private residence to hospital with a principal diagnosis of injury (International Classification of Diseases (ICD)-10-AM codes S00 to T75) and having a first external cause of a fall (ICD-10-AM codes W00 to W19). A cluster analysis was performed to identify homogeneous groups using demographic details of patients and information on the presence of comorbidities. Hospital length of stay (LOS) was compared across clusters using competing risks regression.

Results Clusters based on area of residence, demographic factors (age, gender, marital status, country of birth) and the presence of comorbidities were identified. Clusters representing hospitalised fallers with comorbidities were associated with longer LOS compared with other cluster groups. Clusters delineated by demographic factors were also associated with increased LOS.

Conclusions All patients with comorbidity, and older women without comorbidities, stay in hospital longer following a fall and hence consume a disproportionate share of hospital resources. These findings have important implications for the targeting of falls prevention interventions for community-dwelling older people.

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