Predicting future intensive care demand in Australia

Corke, Charlie, de Leeuw, Evelyne, Lo, Sing Kai and George, Carol 2009, Predicting future intensive care demand in Australia, Critical care and resuscitation, vol. 11, no. 4, pp. 257-260.

Attached Files
Name Description MIMEType Size Downloads

Title Predicting future intensive care demand in Australia
Author(s) Corke, Charlie
de Leeuw, Evelyne
Lo, Sing Kai
George, Carol
Journal name Critical care and resuscitation
Volume number 11
Issue number 4
Start page 257
End page 260
Publisher Australasian Academy of Critical Care Medicine
Place of publication Bedford Park, S. A.
Publication date 2009-12
ISSN 1441-2772
Summary BACKGROUND: Predicting future demand for intensive care is vital to planning the allocation of resources.

METHOD: Mathematical modelling using the autoregressive integrated moving average (ARIMA) was applied to intensive care data from the Australian and New Zealand Intensive Care Society (ANZICS) Core Database and population projections from the Australian Bureau of Statistics to forecast future demand in Australian intensive care.

RESULTS: The model forecasts an increase in ICU demand of over 50% by 2020, with current total ICU bed-days (in 2007) of 471 358, predicted to increase to 643 160 by 2020. An increased rate of ICU use by patients older than 80 years was also noted, with the average bed-days per 10 000 population for this group increasing from 396 in 2006 to 741 in 2007.

CONCLUSION: An increase in demand of the forecast magnitude could not be accommodated within current ICU capacity. Significant action will be required.
Language eng
Field of Research 111003 Clinical Nursing: Secondary (Acute Care)
HERDC Research category C1 Refereed article in a scholarly journal
Persistent URL http://hdl.handle.net/10536/DRO/DU:30022840

Document type: Journal Article
Collection: School of Medicine
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 1 times in TR Web of Science
Scopus Citation Count Cited 2 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 465 Abstract Views, 0 File Downloads  -  Detailed Statistics
Created: Tue, 02 Feb 2010, 09:10:17 EST by Evelyne de Leeuw

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.