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From prevalence to incidence - a new approach in the hospital setting

View ORCID ProfileNiklas Willrich, Sebastian Haller, Tim Eckmanns, Benedikt Zacher, Tommi Kärki, Diamantis Plachouras, Alessandro Cassini, Carl Suetens, Michael Behnke, Petra Gastmeier, Jan Walter
doi: https://doi.org/10.1101/554725
Niklas Willrich
1Department for Infectious Disease Epidemiology, Robert Koch Institute (RKI), Berlin, Germany
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Sebastian Haller
1Department for Infectious Disease Epidemiology, Robert Koch Institute (RKI), Berlin, Germany
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Tim Eckmanns
1Department for Infectious Disease Epidemiology, Robert Koch Institute (RKI), Berlin, Germany
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Benedikt Zacher
1Department for Infectious Disease Epidemiology, Robert Koch Institute (RKI), Berlin, Germany
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Tommi Kärki
2European Centre for Disease Prevention and Control (ECDC), Solna, Sweden
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Diamantis Plachouras
2European Centre for Disease Prevention and Control (ECDC), Solna, Sweden
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Alessandro Cassini
2European Centre for Disease Prevention and Control (ECDC), Solna, Sweden
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Carl Suetens
2European Centre for Disease Prevention and Control (ECDC), Solna, Sweden
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Michael Behnke
3Institute of Hygiene and Environmental Medicine, Charité University Medicine Berlin, Berlin, Germany
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Petra Gastmeier
3Institute of Hygiene and Environmental Medicine, Charité University Medicine Berlin, Berlin, Germany
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Jan Walter
1Department for Infectious Disease Epidemiology, Robert Koch Institute (RKI), Berlin, Germany
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Abstract

Point-prevalence surveys (PPSs) are often used to estimate the prevalence of healthcare-associated infections (HAIs). Methods for estimating incidence of HAIs from prevalence have been developed, but application of these methods is often difficult because key quantities, like the average length of infection, cannot be derived directly from the data available in a PPS. We propose a new theory-based method to estimate incidence from prevalence data dealing with these limitations and compare it to other estimation methods in a simulation study. In contrast to previous methods, our method does not depend on any assumptions on the underlying distributions of length of infection and length of stay. As a basis for the simulation study we use data from the second study of nosocomial infections in Germany (Nosokomiale Infektionen in Deutschland, Erfassung und Prävention - NIDEP2) and the European surveillance of HAIs in intensive care units (HAI-Net ICU). The new method compares favourably with the other estimation methods and has the advantage of being consistent in its behaviour across the different setups. It is implemented in an R-package prevtoinc which will be freely available on CRAN (http://cran.r-project.org/).

  • Abbreviations

    CDC–
    Centers for Disease Control and Prevention
    ECDC–
    European for Disease Control and Prevention
    HAI–
    healthcare-associated infection
    HAI-Net ICU–
    European surveillance of HAIs in intensive care units
    NIDEP2–
    second study of nosocomial infections in Germany (Nosokomiale Infektionen in Deutschland, Erfassung und Prävention)
    PPS–
    point-prevalence survey RMSD - root mean squared deviation
  • Copyright 
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    Posted February 22, 2019.
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    From prevalence to incidence - a new approach in the hospital setting
    Niklas Willrich, Sebastian Haller, Tim Eckmanns, Benedikt Zacher, Tommi Kärki, Diamantis Plachouras, Alessandro Cassini, Carl Suetens, Michael Behnke, Petra Gastmeier, Jan Walter
    bioRxiv 554725; doi: https://doi.org/10.1101/554725
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    From prevalence to incidence - a new approach in the hospital setting
    Niklas Willrich, Sebastian Haller, Tim Eckmanns, Benedikt Zacher, Tommi Kärki, Diamantis Plachouras, Alessandro Cassini, Carl Suetens, Michael Behnke, Petra Gastmeier, Jan Walter
    bioRxiv 554725; doi: https://doi.org/10.1101/554725

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