Biostatistics (2004), 5, 2, pp. 223-237
Biostatistics Vol. 5 No. 2 © Oxford University Press 2004; all rights reserved.
The analysis of hospital infection data using hidden Markov models
Department of Epidemiology, Kresge Building, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA
ben.cooper{at}hpa.org.uk
Department of Epidemiology, Kresge Building, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA
To whom correspondence should be addressed. Current address: Statistics, Modelling and Economics Unit, 61 Colindale Ave, London NW9 5EQ, UK.
Surveillance data for communicable nosocomial pathogens usually consist of short time series of low-numbered counts of infected patients. These often show overdispersion and autocorrelation. To date, almost all analyses of such data have ignored the communicable nature of the organisms and have used methods appropriate only for independent outcomes. Inferences that depend on such analyses cannot be considered reliable when patient-to-patient transmission is important.
We propose a new method for analysing these data based on a mechanistic model of the epidemic process. Since important nosocomial pathogens are often carried asymptomatically with overt infection developing in only a proportion of patients, the epidemic process is usually only partially observed by routine surveillance data. We therefore develop a ‘structured’ hidden Markov model where the underlying Markov chain is generated by a simple transmission model.
We apply both structured and standard (unstructured) hidden Markov models to time series for three important pathogens. We find that both methods can offer marked improvements over currently used approaches when nosocomial spread is important. Compared to the standard hidden Markov model, the new approach is more parsimonious, is more biologically plausible, and allows key epidemiological parameters to be estimated.
Keywords: Count data; Hidden Markov models; Hospital epidemiology; Interrupted time series; SIS epidemic model; Time series
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