Stochastic modelling and Bayesian inference for the effect of antimicrobial treatments on transmission and carriage of nosocomial pathogens - Verykouki, Eleni (2013)
Nosocomial pathogens are usually organisms such as fungi and bacteria that are associated with infections caused in a hospital environment. Examples include Clostridium difficile, Pseudomonas aeruginosa, Vancomycin-resistant enterococcus and Methicillin-resistant Staphylococcus aureus (MRSA). MRSA, like most of the nosocomial pathogens, is resistant to antibiotics and is one of the most serious causes of infections. In this thesis we assess the effects of antibiotics and antiseptics on carriage and transmission of MRSA. We use highly detailed patient level data taken from two Intensive Care Unit (ICU) wards in St. Guys and Thomas’s hospital in London, where patients were receiving daily antimicrobial treatment and a decolonisation protocol was used. We work in discrete time and employ three different patient-level stochastic models in a Bayesian framework to explore the effectiveness of antimicrobial treatment on MRSA in discrete time. We also develop suitable methods of model assessment. The first two models assume that there is no transmission between patients in the ICU wards. Initially a Markov model is used, assuming perfect swab test specificity and sensitivity, to describe the colonisation status of an individual on a daily basis. Results are obtained using Gaussian random walk Metropolis- Hastings algorithms. We find some evidence that decolonisation treatment and Oxazolidinone have a positive effect in clearing MRSA carriage. The second model is a hidden Markov model and assumes perfect swab test specificity but imperfect sensitivity. We obtain the results using data- augmented Markov Chain Monte Carlo (MCMC) algorithms to make inference for the unobserved patient colonisation states. We find evidence that the Antiseptic treatment used during the decolonisation period is effective in the clearance of MRSA carriage. In the third case we assume that there is MRSA transmission between the patients in the ICUs. We use three different stochastic transmission models which overcome many of the unrealistic assumptions of other models. A data- augmented MCMC algorithm is employed in order to estimate the transmission rates of MRSA between the patients assuming imperfect swab test sensitivity. We found no or limited evidence that antibiotic use affects the transmission process, whereas antiseptic treatment was found to have an effect.