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American Journal of Epidemiology Advance Access originally published online on July 16, 2008
American Journal of Epidemiology 2008 168(5):548-557; doi:10.1093/aje/kwn176
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American Journal of Epidemiology © 2008 The Authors
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

PRACTICE OF EPIDEMIOLOGY

An Augmented Data Method for the Analysis of Nosocomial Infection Data

Ben S. Cooper1, Graham F. Medley2, Susan J. Bradley3 and Geoffrey M. Scott4

1 Statistics, Modelling and Bioinformatics Department, Centre for Infections, Health Protection Agency, London, United Kingdom
2 Department of Biological Sciences, University of Warwick, Coventry, United Kingdom
3 Clinical Haematology, West Hertfordshire Hospitals NHS Trust, Hemel Hempstead, United Kingdom
4 Clinical Microbiology, University College London Hospitals, London, United Kingdom

Correspondence to Dr. Ben S. Cooper, Statistics, Modelling and Bioinformatics Department, Centre for Infections, Health Protection Agency, 61 Colindale Avenue, London NW9 5EQ, United Kingdom (e-mail: ben.cooper{at}hpa.org.uk).

Received for publication January 29, 2008. Accepted for publication May 22, 2008.

The analysis of nosocomial infection data for communicable pathogens is complicated by two facts. First, typical pathogens more commonly cause asymptomatic colonization than overt disease, so transmission can be only imperfectly observed through a sequence of surveillance swabs, which themselves have imperfect sensitivity. Any given set of swab results can therefore be consistent with many different patterns of transmission. Second, data are often highly dependent: the colonization status of one patient affects the risk for others, and, in some wards, repeated admissions are common. Here, the authors present a method for analyzing typical nosocomial infection data consisting of results from arbitrarily timed screening swabs that overcomes these problems and enables simultaneous estimation of transmission and importation parameters, duration of colonization, swab sensitivity, and ward- and patient-level covariates. The method accounts for dependencies by using a mechanistic stochastic transmission model, and it allows for uncertainty in the data by imputing the imperfectly observed colonization status of patients over repeated admissions. The approach uses a Markov chain Monte Carlo algorithm, allowing inference within a Bayesian framework. The method is applied to illustrative data from an interrupted time-series study of vancomycin-resistant enterococci transmission in a hematology ward.

cross infection; drug resistance, microbial; enterococcus; infection control; models, statistical; sensitivity and specificity


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