Hostname: page-component-848d4c4894-ttngx Total loading time: 0 Render date: 2024-05-18T03:02:39.874Z Has data issue: false hasContentIssue false
  • English
  • Français

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)
Get access Rights & Permissions [Opens in a new window]

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

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Tiemersma, EW, Bronzwaer, SL, Lyytikainen, O, et al.Methicillin-resistant Staphylococcus aureus in Europe, 1999-2002. Emerg Infect Dis 2004;10:1627–1634.CrossRefGoogle ScholarPubMed
2.Griffiths, C, Lamagni, TL, Crowcroft, NS, Duckworth, G, Rooney, C. Trends in MRSA in England and Wales: analysis of morbidity and mortality data for 1993-2002. Health Stat Q 2004:15–22.Google Scholar
3.Cooper, BS, Stone, SP, Kibbler, CC, et al.Systematic review of isolation policies in the hospital management of methicillin-resistant Staphylococcus aureus: a review of the literature with epidemiological and economic modelling. Health Technol Assess 2003;7:1–194.Google Scholar
4.Cosgrove, SE, Qi, Y, Kaye, KS, Harbarth, S, Karchmer, AW, Carmeli, Y. The impact of methicillin resistance in Staphylococcus aureus bacteremia on patient outcomes: mortality, length of stay, and hospital charges. Infect Control Hosp Epidemiol 2005;26:166–174.CrossRefGoogle ScholarPubMed
5.Wertheim, HF, Vos, MC, Boelens, HA, et al.Low prevalence of methicillin-resistant Staphylococcus aureus (MRSA) at hospital admission in the Netherlands: the value of search and destroy and restrictive antibiotic use. J Hosp Infect 2004;56:321–325.Google Scholar
6.Wernitz, MH, Keck, S, Swidsinski, S, Schulz, S, Veit, SK. Cost analysis of a hospital-wide selective screening programme for methicillin-resistant Staphylococcus aureus (MRSA) carriers in the context of diagnosis related groups (DRG) payment. Clin Microbiol Infect 2005;11:466–471.Google Scholar
7.Anonymous. Strain Typing. In: Winn, WC, Allen, SD, Janda, WM, et al., eds. Koneman's color atlas and textbook of diagnostic microbiology. 6th ed. Philadelphia, PA: Lippincott Williams & Wilkins; 2006:156–159.Google Scholar
8.Hori, S, Sunley, R, Tami, A, Grundmann, H. The Nottingham Staphylococcus aureus population study: prevalence of MRSA among the elderly in a university hospital. J Hosp Infect 2002;50:25–29.CrossRefGoogle ScholarPubMed
9.Grundmann, H, Hori, S, Tanner, G. Determining confidence intervals when measuring genetic diversity and the discriminatory abilities of typing methods for microorganisms. J Clin Microbiol 2001;39:4190–4192.CrossRefGoogle ScholarPubMed
10.Pelupessy, I, Bonten, MJ, Diekmann, O. How to assess the relative importance of different colonization routes of pathogens within hospital settings. Proc Natl Acad Sci USA 2002;99:5601–5605.CrossRefGoogle ScholarPubMed
11.Forrester, M, Pettitt, AN. Use of stochastic epidemic modeling to quantify transmission rates of colonization with methicillin-resistant Staphylococcus aureus in an intensive care unit. Infect Control Hosp Epidemiol 2005;26:598–606.CrossRefGoogle Scholar
12.Forrester, ML, Pettitt, AN, Gibson, GJ. Bayesian inference of hospital-acquired infectious diseases and control measures given imperfect surveillance data. Biostatistics 2007;8:383–401.Google Scholar
13.Mikolajczyk, RT, Sagel, U, Bornemann, R, Kramer, A, Kretzschmar, M. A statistical method for estimating the proportion of cases resulting from cross-transmission of multi-resistant pathogens in an intensive care unit. J Hosp Infect 2007;65:149–155.Google Scholar
14.Anonymous. Surveillance of nosocomial infections and recording of pathogens with specific resistances and multiresistances [in German]. Bundes-gesundheitsbl-Gesundheitsforsch-Gesundheitsschutz 2000;43:887–890.Google Scholar
15.Leisch, F. FlexMix: A general framework for finite mixture models and latent class regression in R. J Stat Softw 2004;11:1–18.CrossRefGoogle Scholar
16.Louis, TA. Finding observed information using the EM algorithm. J R Stat Soc Series B Stat Methodol 1982;44:98–130.Google Scholar
17.Grundmann, H, Barwolff, S, Tami, A, et al.How many infections are caused by patient-to-patient transmission in intensive care units? Crit Care Med 2005;33:946–951.Google Scholar
18.Bootsma, MC, Bonten, MJ, Nijssen, S, Fluit, AC, Diekmann, O. An algorithm to estimate the importance of bacterial acquisition routes in hospital settings. Am J Epidemiol 2007;166:841–851.CrossRefGoogle ScholarPubMed
19.Cooper, B, Lipsitch, M. The analysis of hospital infection data using hidden Markov models. Biostatistics 2004;5:223–237.Google Scholar