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Mixture Model to Assess the Extent of Cross-Transmission of Multidrug-Resistant Pathogens in Hospitals

Published online by Cambridge University Press:  02 January 2015

Rafael T. Mikolajczyk ,
Göran Kauermann ,
Ulrich Sagel  and
Mirjam Kretzschmar
Rafael T. Mikolajczyk*
Affiliation:
School of Public Health, University of Bielefeld, Bielefeld, Germany
Göran Kauermann
Affiliation:
Faculty of Economics and Business Administration, University of Bielefeld, Bielefeld, Germany
Ulrich Sagel
Affiliation:
Institute of Medical Microbiology and Hygiene, Linz, Austria
Mirjam Kretzschmar
Affiliation:
Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, The Netherlands Centre for Infectious Disease Control, RIVM, Bilthoven, The Netherlands
*
School of Public Health, University of Bielefeld, P.O.Box 100131, 33501 Bielefeld, Germany (rmikolajczyk@uni-bielefeld.de)
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Abstract

Objective.

Creation of a mixture model based on Poisson processes for assessment of the extent of cross-transmission of multidrug-resistant pathogens in the hospital.

Methods.

We propose a 2-component mixture of Poisson processes to describe the time series of detected cases of colonization. The first component describes the admission process of patients with colonization, and the second describes the cross-transmission. The data set used to illustrate the method consists of the routinely collected records for methicillin-resistant Staphylococcus aureus (MRSA), imipenem-resistant Pseudomonas aeruginosa, and multidrug-resistant Acinetobacter baumannii over a period of 3 years in a German tertiary care hospital.

Results.

For MRSA and multidrug-resistant A. baumannii, cross-transmission was estimated to be responsible for more than 80% of cases; for imipenem-resistant P. aeruginosa, cross-transmission was estimated to be responsible for 59% of cases. For new cases observed within a window of less than 28 days for MRSA and multidrug-resistant A. baumannii or 40 days for imipenem-resistant P. aeruginosa, there was a 50% or greater probability that the cause was cross-transmission.

Conclusions.

The proposed method offers a solution to assessing of the extent of cross-transmission, which can be of clinical use. The method can be applied using freely available software (the package FlexMix in R) and it requires relatively little data.

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 30 , Issue 8 , August 2009 , pp. 730 - 736
Copyright
Copyright © The Society for Healthcare Epidemiology of America 2009

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