Title
A Predictive Model of Days from Infection to Discharge in Patients with Healthcare Associated Urinary Tract Infections (HAUTI): A Structural Equation Modelling Approach
Document Type
Article
Publication Date
11-2017
Early Online Version
8-12-2017
JOURNAL
Journal of Hospital Infection
VOLUME NUMBER
97
ISSUE NUMBER
3
PAGE NUMBERS
282-287
ISSN
0195-6701
Embargo Period
8-14-2018
ANZSRC / FoR Code
111004 Clinical Nursing: Tertiary (Rehabilitative)| 111716 Preventive Medicine
Avondale Research Centre
Lifestyle Research Centre
Reportable Items (HERDC/ERA)
C1
Abstract
Background
Length of stay (LOS) in hospital is an important component of describing how costs change in relation to healthcare-associated infection and this variable underpins models used to evaluate cost. It this therefore imperative that estimations of LOS associated with infections are performed accurately.
Aim
To test the relationships between the size of hospital, age, and patient comorbidity on days from admission to infection and days from infection to discharge in patients with a healthcare-associated urinary tract infection (HAUTI), using structural equation modelling (SEM).
Methods
A non-current cohort study in eight hospitals in New South Wales, Australia. All patients admitted to the hospital for >48 h and who acquired a HAUTI were included.
Findings
From the 162,503 eligible patient admissions, 2821 (1.73%) acquired a HAUTI. SEM showed that the proposed model had acceptable fit indices for the combined sample (GFI = 1.00; AGFI = 1.00; NFI = 1.00; CFI = 1.00; RMSEA = 0.000). The main findings showed that age of patient had a direct association with days from admission to infection and with days from infection to discharge. Patient comorbidity had direct links to the variables days from admission to infection and days from infection to discharge. Multi-group analysis indicated that the age of male patients was more influential on the factor days from admission to infection when compared to female patients. Furthermore, the number of comorbidities was significantly more influential on days from admission to infection in male patients than in female patients.
Conclusion
As the first published study to use SEM to explore a healthcare-associated infection and the predictors of days from infection to discharge in hospital, we can confirm that accounting for the timing of infection during hospitalization is important and that patient comorbidity influences the timing of infection.
Link to publisher version (DOI)
https://doi.org/10.1016/j.jhin.2017.08.006
Peer Review
Before publication
Recommended Citation
Mitchell, B. G., Anderson, M., & Ferguson, J. K. (2017). A predictive model of days from infection to discharge in patients with healthcare associated urinary tract infections: A structural equation modelling approach. Journal of Hospital Infection, 97(3), 282-287. doi: 10.1016/j.jhin.2017.08.006.
Comments
Due to copyright restrictions this article is unavailable for download.
© 2017 The Healthcare Infection Society. Published by Elsevier Ltd. All rights reserved.
This article may be accessed from the publisher here.
Staff and Students of Avondale College may access this article via a library PRIMO search here.