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Effect of Healthcare‐Acquired Infection on Length of Hospital Stay and Cost
Nicholas Graves , PhD, Diana Weinhold , PhD, Edward Tong , BSc (Hons), Frances Birrell , M App Epi, Shane Doidge , G Dip PH, Prabha Ramritu , MPH, Kate Halton , MSc, David Lairson , PhD and Michael Whitby , MPH
Infection Control and Hospital Epidemiology
Vol. 28, No. 3 (March 2007), pp. 280-292
Published by: Cambridge University Press on behalf of The Society for Healthcare Epidemiology of America
Stable URL: http://www.jstor.org/stable/10.1086/512642
Page Count: 13
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Objective. To estimate the independent effect of a single lower respiratory tract infection, urinary tract infection, or other healthcare‐acquired infection on length‐of‐stay and variable costs and to demonstrate the bias from omitted variables that is present in previous estimates. Design. Prospective cohort study. Setting. A tertiary care referral hospital and regional district hospital in southeast Queensland, Australia. Patients. Adults aged 18 years or older with a minimum inpatient stay of 1 night who were admitted to selected clinical specialities. Results. Urinary tract infection was not associated with an increase in length of hospital stay or variable costs. Lower respiratory tract infection was associated with an increase of 2.58 days in the hospital and variable costs of AU$24, whereas other types of infection were associated with an increased length of stay of 2.61 days but not with variable costs. Many other factors were found to be associated with increased length of stay and variable costs alongside healthcare‐acquired infection. The exclusion of these variables caused a positive bias in the estimates of the costs of healthcare‐acquired infection. Conclusions. The existing literature may overstate the costs of healthcare‐acquired infection because of bias, and the existing estimates of excess costs may not make intuitive sense to clinicians and policy makers. Accurate estimates of the costs of healthcare‐acquired infection should be made and used in appropriately designed decision‐analytic economic models (ie, cost‐effectiveness models) that will make valid and believable predictions of the economic value of increased infection control.
© 2007 by The Society for Healthcare Epidemiology of America. All rights reserved.