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The impact of healthcare associated infections on mortality and length of stay in Singapore—A time-varying analysis

Published online by Cambridge University Press:  15 July 2020

Yiying Cai Open the ORCID record for Yiying Cai [Opens in a new window] ,
Jamie Jay-May Lo ,
Indumathi Venkatachalam ,
Andrea L. Kwa ,
Paul A. Tambyah ,
Li Yang Hsu ,
Adrian Barnett ,
Kalisvar Marimuthu  and
Nicholas Graves
Yiying Cai
Affiliation:
Department of Pharmacy, Singapore General Hospital, Singapore Department of Pharmacy, Faculty of Science, National University of Singapore, Singapore
Jamie Jay-May Lo
Affiliation:
Saw Swee Hock School of Public Health, National University of Singapore and National University Health Systems, Singapore
Indumathi Venkatachalam
Affiliation:
Department of Infectious Diseases, Singapore General Hospital, Singapore
Andrea L. Kwa
Affiliation:
Department of Pharmacy, Singapore General Hospital, Singapore Emerging Infectious Diseases, Duke-NUS Medical School, Singapore SingHealth Duke-NUS Medicine Academic Programme, Singapore
Paul A. Tambyah
Affiliation:
Division of Infectious Diseases, National University Health Systems, Singapore Yong Loo Lin School of Medicine, National University of Singapore, Singapore
Li Yang Hsu
Affiliation:
Saw Swee Hock School of Public Health, National University of Singapore and National University Health Systems, Singapore
Adrian Barnett
Affiliation:
School of Public Health and Social Work, Queensland University of Technology, Queensland, Australia
Kalisvar Marimuthu
Affiliation:
Division of Infectious Diseases, National University Health Systems, Singapore National Centre for Infectious Diseases, Tan Tock Seng Hospital, Singapore
Nicholas Graves*
Affiliation:
Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore
*
Author for correspondence: Nicholas Graves, E-mail: graves@duke-nus.edu.sg
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Abstract

Objective:

Methods that include the time-varying nature of healthcare-associated infections (HAIs) avoid biases when estimating increased risk of death and excess length of stay. We determined the excess mortality risk and length of stay associated with HAIs among inpatients in Singapore using a multistate model that accommodates the timing of key events.

Design:

Analysis of existing prospective cohort study data.

Setting:

Seven public acute-care hospitals in Singapore.

Patients:

Inpatients reviewed in a HAI point-prevalence survey (PPS) conducted between June 2015 and February 2016.

Methods:

We modeled each patient’s admission over time using 4 states: susceptible with no HAI, infected, died, and discharged alive. We estimated the excess mortality risk and length of stay associated with HAIs, with adjustment for the baseline characteristics between the groups for mortality risk.

Results:

We included 4,428 patients, of whom 469 had ≥1 HAI. Using a multistate model, the expected excess length of stay due to any HAI was 1.68 days (95% confidence interval [CI], 1.15–2.21 days). Surgical site infections were associated with the longest excess length of stay of 4.68 days (95% CI, 2.60–6.76 days). After adjusting for baseline differences, HAIs were associated with increased hazards of in-hospital mortality (adjusted hazard ratio [aHR], 1.32; 95% CI, 1.09–1.65) and decreased hazards in being discharged (aHR, 0.75; 95% CI, 0.67–0.84).

Conclusions:

HAIs are associated with increased length of hospital stay and mortality in hospitalized patients. Avoiding nosocomial infections can improve patient outcomes and free valuable bed days.

Type
Original Article
Information
Infection Control & Hospital Epidemiology , Volume 41 , Issue 11 , November 2020 , pp. 1315 - 1320
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Copyright
© 2020 by The Society for Healthcare Epidemiology of America. All rights reserved.

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