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Estimating incidence and attributable length of stay of healthcare-associated infections—Modeling the Swiss point-prevalence survey

Published online by Cambridge University Press:  05 August 2021

Sam Doerken Open the ORCID record for Sam Doerken [Opens in a new window] ,
Aliki Metsini ,
Sabina Buyet ,
Aline Wolfensberger ,
Walter Zingg  and
Martin Wolkewitz
Sam Doerken*
Affiliation:
Institute of Medical Biometry and Statistics, Division Methods in Clinical Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany Freiburg Center for Data Analysis and Modeling, Freiburg, Germany
Aliki Metsini
Affiliation:
Swissnoso, Swiss Center for Infection Prevention, Bern, Switzerland Cantonal physician office, State of Geneva, Geneva, Switzerland
Sabina Buyet
Affiliation:
Spital Bülach AG, Bülach, Switzerland
Aline Wolfensberger
Affiliation:
Division of Infectious Diseases and Hospital Epidemiology, University Hospital and University of Zurich, Zurich, Switzerland
Walter Zingg
Affiliation:
Division of Infectious Diseases and Hospital Epidemiology, University Hospital and University of Zurich, Zurich, Switzerland
Martin Wolkewitz
Affiliation:
Institute of Medical Biometry and Statistics, Division Methods in Clinical Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany Freiburg Center for Data Analysis and Modeling, Freiburg, Germany
*
Author for correspondence: Dr Sam Doerken, E-mail: doerken@imbi.uni-freiburg.de
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Abstract

Objectives:

In 2017, a point-prevalence survey was conducted with 12,931 patients in 96 hospitals across Switzerland as part of the national strategy to prevent healthcare-associated infections (HAIs). We present novel statistical methods to assess incidence proportions of HAI and attributable length-of-stay (LOS) in point-prevalence surveys.

Methods:

Follow-up data were collected for a subsample of patients and were used to impute follow-up data for all remaining patients. We used weights to correct length bias in logistic regression and multistate analyses. Methods were also tested in simulation studies.

Results:

The estimated incidence proportion of HAIs during hospital stay and not present at admission was 2.3% (95% confidence intervals [CI], 2.1–2.6), the most common type being lower respiratory tract infections (0.8%; 95% CI, 0.6–1.0). Incidence proportion was highest in patients with a rapidly fatal McCabe score (7.8%; 95% CI, 5.7–10.4). The attributable LOS for all HAI was 6.4 days (95% CI, 5.6–7.3) and highest for surgical site infections (7.1 days, 95% CI, 5.2–9.0). It was longest in the age group of 18–44 years (9.0 days; 95% CI, 5.4–12.6). Risk-factor analysis revealed that McCabe score had no effect on the discharge hazard after infection (hazard ratio [HR], 1.21; 95% CI, 0.89–1.63). Instead, it only influenced the infection hazard (HR, 1.84; 95% CI, 1.39–2.43) and the discharge hazard prior to infection (HR, 0.73; 95% CI, 0.66–0.82).

Conclusions:

In point-prevalence surveys with limited follow-up data, imputation and weighting can be used to estimate incidence proportions and attributable LOS that would otherwise require complete follow-up data.

Type
Original Article
Information
Infection Control & Hospital Epidemiology , Volume 43 , Issue 8 , August 2022 , pp. 1022 - 1031
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Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of The Society for Healthcare Epidemiology of America

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Footnotes

a

Authors of equal contribution.

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