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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.

References

Suetens, C, Latour, K, Kärki, T, et al. Prevalence of healthcare-associated infections, estimated incidence and composite antimicrobial resistance index in acute care hospitals and long-term care facilities: results from two European point prevalence surveys, 2016 to 2017. Eurosurveillance 2018;23(46). doi: 10.2807/1560-7917.ES.2018.23.46.1800516.CrossRefGoogle ScholarPubMed
Metsini, A, Vazquez, M, Sommerstein, R, et al. Point prevalence of healthcare-associated infections and antibiotic use in three large Swiss acute-care hospitals. Swiss Med Wkly 2018;148:w14617.Google ScholarPubMed
Zingg, W, Metsini, A, Balmelli, C, et al. National point prevalence survey on healthcare-associated infections in acute care hospitals, Switzerland, 2017. Eurosurveillance 2019;24(32). doi: 10.2807/1560-7917.ES.2019.24.32.1800603.Google Scholar
Zingg, W, Metsini, A, Gardiol, C, et al. Antimicrobial use in acute care hospitals: national point prevalence survey on healthcare-associated infections and antimicrobial use, Switzerland, 2017. Eurosurveillance 2019;24(33). doi: 10.2807/1560-7917.ES.2019.24.33.1900015.Google Scholar
Wolkewitz, M, Schumacher, M, Rücker, G, Harbarth, S, Beyersmann, J. Estimands to quantify prolonged hospital stay associated with nosocomial infections. BMC Med Res Method 2019;19(1):111.CrossRefGoogle ScholarPubMed
Wolkewitz, M, Allignol, A, Harbarth, S, de Angelis, G, Schumacher, M, Beyersmann, J. Time-dependent study entries and exposures in cohort studies can easily be sources of different and avoidable types of bias. J Clin Epidemiol 2012;65:1171–1180.CrossRefGoogle Scholar
Wolkewitz, M, Mandel, M, Palomar-Martinez, M, Alvarez-Lerma, F, Olaechea-Astigarraga, P, Schumacher, M. Methodological challenges in using point-prevalence versus cohort data in risk factor analyses of nosocomial infections. Ann Epidemiol 2018;28:475–480.CrossRefGoogle ScholarPubMed
Wolkewitz, M, von Cube, M, Schumacher, M. Multistate modeling to analyze nosocomial infection data: an introduction and demonstration. Infect Control Hosp Epidemiol 2017;38:953–959.CrossRefGoogle ScholarPubMed
Point prevalence survey of healthcare-associated infections and antimicrobial use in European acute-care hospitals. Protocol version 5.3. European Centre for Disease Prevention and Control website. https://ecdc.europa.eu/sites/portal/files/media/en/publications/Publications/PPS-HAI-antimicrobial-use-EU-acute-care-hospitals-V5-3.pdf. Published 2016. Accessed June 30, 2021.Google Scholar
Doerken, S, Mandel, M, Zingg, W, Wolkewitz, M. Use of prevalence data to study sepsis incidence and mortality in intensive care units. Lancet Infect Dis 2018;18(3):252.CrossRefGoogle ScholarPubMed
Asgharian, M, Wolfson, DB, Zhang, X. Checking stationarity of the incidence rate using prevalent cohort survival data. Statist Med 2006;25:1751–1767.CrossRefGoogle ScholarPubMed
Kalbfleisch, JD, Prentice, RL. The Statistical Analysis of Failure Time Data. Hoboken, NJ: John Wiley & Sons; 2011.Google Scholar
Buuren, S, Groothuis-Oudshoorn, K. mice: multivariate imputation by chained equations in R. J Stat Softw 2010:1–68.CrossRefGoogle Scholar
Fluss, R, Mandel, M, Freedman, LS, et al. Correction of sampling bias in a cross-sectional study of post-surgical complications. Stat Med 2013;32:2467–2478.CrossRefGoogle Scholar
De Uña-Álvarez, J. Nonparametric estimation under length-biased sampling and type I censoring: a moment-based approach. Ann Inst Stat Math 2004;56:667–681.CrossRefGoogle Scholar
Wang, MC. Length bias. Encyclopedia of Biostatistics. Armitage, P. and Colton, T., editors. Hoboken, NJ: Wiley Online Library; 2005.Google Scholar
Schulgen, G, Schumacher, M. Estimation of prolongation of hospital stay attributable to nosocomial infections: new approaches based on multistate models. Lifetime Data Anal 1996;2:219–240.CrossRefGoogle ScholarPubMed
Allignol, A, Schumacher, M, Beyersmann, J. Empirical transition matrix of multi-state models: the etm package. J Stat Softw 2011a;38(4):1–15.CrossRefGoogle Scholar
Allignol, A, Schumacher, M, Beyersmann, J. Estimating summary functionals in multistate models with an application to hospital infection data. Comput Stat 2011b;26:181–197.CrossRefGoogle Scholar
Von Elm, E, Altman, DG, Egger, M, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. J Clin Epidemiol 2008;61:344–349.CrossRefGoogle ScholarPubMed
Cheng, J, Karambelkar, B, Xie, Y. leaflet: Create Interactive Web Maps with the JavaScript ‘Leaflet’ Library. R package version 2.0.3. https://CRAN.R-project.org/package=leaflet. Published 2019. Accessed June 30, 2021.Google Scholar
Rhame, FS, Sudderth, WD. Incidence and prevalence as used in the analysis of the occurrence of nosocomial infections. Am J Epidemiol 1981;113:1–11.CrossRefGoogle ScholarPubMed
Mandel, M, Fluss, R. Nonparametric estimation of the probability of illness in the illness-death model under cross-sectional sampling. Biometrika 2009;96:861–872.CrossRefGoogle Scholar
Point prevalence survey of healthcare-associated infections and antimicrobial use in European acute-care hospitals 2011–2012. European Centre for Disease Prevention and Control website. https://www.ecdc.europa.eu/sites/default/files/media/en/publications/Publications/healthcare-associated-infections-antimicrobial-use-PPS.pdf. Published 2013. Accessed June 30, 2021.Google Scholar
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