An augmented data method for the analysis of nosocomial infection data.

Journal article

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.

Authors: Cooper, B; Medley, G; Bradley, S; Scott, G

• Publication status: Published
• Journal: American journal of epidemiology
• Volume: 168
• Issue: 5
• Pages: 548-557
• Publication date: 2008-09-01
• DOI: 10.1093/aje/kwn176
• EISSN: 1476-6256
• ISSN: 0002-9262
• Language: English
• Keywords: Models, Statistical; Hospital Units; Male; Great Britain; Cross Infection; Humans; Markov Chains; Stochastic Processes; Algorithms; Gram-Positive Bacterial Infections; Enterococcus faecalis; Bayes Theorem; Vancomycin Resistance; Prospective Studies; Female; Monte Carlo Method; Disease Outbreaks