A Predictive Model of Days from Infection to Discharge in Patients with Healthcare Associated Urinary Tract Infections (HAUTI): A Structural Equation Modelling Approach

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avondale-bepress-to-dspace.facultyNursing
avondale-bepress-to-dspace.peer_review_statusPeer reviewed before publication
avondale-bepress.abstract<p><h3>Background</h3></p> <p>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. <h3>Aim</h3></p> <p>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). <h3>Methods</h3></p> <p>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. <h3>Findings</h3></p> <p>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. <h3>Conclusion</h3></p> <p>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.</p>
avondale-bepress.articleid1150
avondale-bepress.authorsBrett G Mitchell
avondale-bepress.authorsMalcolm Anderson
avondale-bepress.authorsJohn K Ferguson
avondale-bepress.context-key10599896
avondale-bepress.coverpage-urlhttps://research.avondale.edu.au/nh_papers/146
avondale-bepress.document-typearticle
avondale-bepress.field.author_faculty_disciplineNursing
avondale-bepress.field.avon_earlyonline2017-08-12T00:00:00-07:00
avondale-bepress.field.comments<p>Due to copyright restrictions this article is unavailable for download.</p> <p>© 2017 The Healthcare Infection Society. Published by Elsevier Ltd. All rights reserved.</p> <p>This article may be accessed from the publisher<a href="https://doi.org/10.1016/j.jhin.2017.08.006"> here.</a></p> <p>Staff and Students of Avondale College may access this article via a library PRIMO search <a href="http://primo.unilinc.edu.au/primo_library/libweb/action/search.do?mode=Advanced&vid=AVN">here.</a></p>
avondale-bepress.field.custom_citation<p>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. <em>Journal of Hospital Infection, 97</em>(3), 282-287. doi: 10.1016/j.jhin.2017.08.006.</p>
avondale-bepress.field.doihttps://doi.org/10.1016/j.jhin.2017.08.006
avondale-bepress.field.email_boxtrue
avondale-bepress.field.embargo_date2018-08-14T00:00:00Z
avondale-bepress.field.field_of_education06 Health
avondale-bepress.field.for111004 Clinical Nursing: Tertiary (Rehabilitative)
avondale-bepress.field.issn0195-6701
avondale-bepress.field.issue_number3
avondale-bepress.field.journalJournal of Hospital Infection
avondale-bepress.field.page_numbers282-287
avondale-bepress.field.peer_reviewBefore publication
avondale-bepress.field.publication_date2017-11-01T00:00:00Z
avondale-bepress.field.reportable_itemsC1
avondale-bepress.field.research_centreLifestyle Research Centre
avondale-bepress.field.source_fulltext_urlhttps://doi.org/10.1016/j.jhin.2017.08.006
avondale-bepress.field.source_publication<p>This article was originally published as:</p> <p>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. <em>Journal of Hospital Infection, 97</em>(3), 282-287. doi: 10.1016/j.jhin.2017.08.006.</p> <p>ISSN: 0195-6701</p>
avondale-bepress.field.staff_classificationPermanent
avondale-bepress.field.volume_number97
avondale-bepress.keywordshealthcare-associated infections; infection control; infection prevention; hospital infections
avondale-bepress.label146
avondale-bepress.publication-date2017-11-01T00:00:00Z
avondale-bepress.publication-titleNursing and Health Papers and Journal Articles
avondale-bepress.statepublished
avondale-bepress.submission-date2017-08-14T20:42:07Z
avondale-bepress.submission-pathnh_papers/146
avondale-bepress.titleA Predictive Model of Days from Infection to Discharge in Patients with Healthcare Associated Urinary Tract Infections (HAUTI): A Structural Equation Modelling Approach
avondale-bepress.typearticle
dc.contributor.authorFerguson, John K.
dc.contributor.authorAnderson, Malcolm
dc.contributor.authorMitchell, Brett G.
dc.date.accessioned2023-11-01T00:25:07Z
dc.date.available2023-11-01T00:25:07Z
dc.date.issued2017-11-01
dc.date.submitted2017-08-14T20:42:07Z
dc.description.abstract<p><h3>Background</h3></p> <p>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. <h3>Aim</h3></p> <p>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). <h3>Methods</h3></p> <p>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. <h3>Findings</h3></p> <p>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. <h3>Conclusion</h3></p> <p>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.</p>
dc.description.versionBefore publication
dc.identifier.citation<p>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. <em>Journal of Hospital Infection, 97</em>(3), 282-287. doi: 10.1016/j.jhin.2017.08.006.</p>
dc.identifier.doihttps://doi.org/10.1016/j.jhin.2017.08.006
dc.identifier.issn0195-6701
dc.identifier.urihttps://research.avondale.edu.au/handle/123456789/10599896
dc.language.isoen_us
dc.provenance<p>This article was originally published as:</p> <p>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. <em>Journal of Hospital Infection, 97</em>(3), 282-287. doi: 10.1016/j.jhin.2017.08.006.</p> <p>ISSN: 0195-6701</p>
dc.rights<p>Due to copyright restrictions this article is unavailable for download.</p> <p>© 2017 The Healthcare Infection Society. Published by Elsevier Ltd. All rights reserved.</p> <p>This article may be accessed from the publisher<a href="https://doi.org/10.1016/j.jhin.2017.08.006"> here.</a></p> <p>Staff and Students of Avondale College may access this article via a library PRIMO search <a href="http://primo.unilinc.edu.au/primo_library/libweb/action/search.do?mode=Advanced&vid=AVN">here.</a></p>
dc.subjecthealthcare-associated infections; infection control; infection prevention; hospital infections
dc.titleA Predictive Model of Days from Infection to Discharge in Patients with Healthcare Associated Urinary Tract Infections (HAUTI): A Structural Equation Modelling Approach
dc.typeJournal Article
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