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Identifying clusters of falls-related hospital admissions to inform population targets for prioritising falls prevention programmes

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 followin...

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Autores principales: Finch, Caroline F, Stephan, Karen, Shee, Anna Wong, Hill, Keith, Haines, Terry P, Clemson, Lindy, Day, Lesley
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4518748/
https://www.ncbi.nlm.nih.gov/pubmed/25618735
http://dx.doi.org/10.1136/injuryprev-2014-041351
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author Finch, Caroline F
Stephan, Karen
Shee, Anna Wong
Hill, Keith
Haines, Terry P
Clemson, Lindy
Day, Lesley
author_facet Finch, Caroline F
Stephan, Karen
Shee, Anna Wong
Hill, Keith
Haines, Terry P
Clemson, Lindy
Day, Lesley
author_sort Finch, Caroline F
collection PubMed
description 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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spelling pubmed-45187482015-08-03 Identifying clusters of falls-related hospital admissions to inform population targets for prioritising falls prevention programmes Finch, Caroline F Stephan, Karen Shee, Anna Wong Hill, Keith Haines, Terry P Clemson, Lindy Day, Lesley Inj Prev Original Article 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. BMJ Publishing Group 2015-08 2015-01-24 /pmc/articles/PMC4518748/ /pubmed/25618735 http://dx.doi.org/10.1136/injuryprev-2014-041351 Text en Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
spellingShingle Original Article
Finch, Caroline F
Stephan, Karen
Shee, Anna Wong
Hill, Keith
Haines, Terry P
Clemson, Lindy
Day, Lesley
Identifying clusters of falls-related hospital admissions to inform population targets for prioritising falls prevention programmes
title Identifying clusters of falls-related hospital admissions to inform population targets for prioritising falls prevention programmes
title_full Identifying clusters of falls-related hospital admissions to inform population targets for prioritising falls prevention programmes
title_fullStr Identifying clusters of falls-related hospital admissions to inform population targets for prioritising falls prevention programmes
title_full_unstemmed Identifying clusters of falls-related hospital admissions to inform population targets for prioritising falls prevention programmes
title_short Identifying clusters of falls-related hospital admissions to inform population targets for prioritising falls prevention programmes
title_sort identifying clusters of falls-related hospital admissions to inform population targets for prioritising falls prevention programmes
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4518748/
https://www.ncbi.nlm.nih.gov/pubmed/25618735
http://dx.doi.org/10.1136/injuryprev-2014-041351
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