Chronic infection with P. aeruginosa is expected in patients with cystic fibrosis (CF), but the ability to delay, prevent, or better manage infection with multiply-resistant P. aeruginosa (MRPA) can potentially increase quality of life and extend survival. The Texas Children’s Hospital CF Care Center has identified an endemic MRPA strain (dominant clone), and this study aimed to identify risk factors for acquisition of the clone as well as determine differences in patient outcome associated with subsequent infection with the clone. The study included 71 patients with CF with documented MRPA infection. Designation of patients as members of the dominant clone or a non-dominant clone group was based on molecular typing by rep-PCR of MRPA isolates from respiratory cultures. Patient data was collected from Port CF, the national patient registry of the CF Foundation. Patient demographic information and clinical parameters prior to MRPA infection were analyzed by logistic regression as potential risk factors. Differences in patient outcome including change in BMI, change in FEV1, and hospitalization rate were evaluated by MANOVA. Recent hospitalization (< 90 days) was a statistically significant (p = 0.035) risk factor for acquisition of the dominant clone. Patients hospitalized < 90 days prior to MRPA diagnosis were four times more likely to be infected with the dominant clone, and patients hospitalized 91-180 days prior were almost three times more likely. Increased hospitalization rates were seen in the dominant clone group both pre- (11 more days/year) and post-infection (14 more days/year) as compared to the non-dominant clone group. Patients infected with the endemic strain exhibited poorer outcomes in terms of nutritional status (3.73% decrease/year in BMI %ile) and lung function (3.7% decrease/year in FEV1 %ile). Significant overlap in hospitalization episodes of patients known to be infected with the dominant clone and patients subsequently infected with the dominant clone was observed. Recent hospitalization was a significant risk factor for infection with the dominant MRPA clone, and following infection, patients infected with the endemic strain exhibited declines in nutritional status and lung function and increased hospitalization rates. The results suggest potentially increased virulence and transmissibility of the endemic MRPA strain.

The primary objective of this dissertation is to utilise, adapt and extend current stochastic models and statistical inference techniques to describe the transmission of nosocomial pathogens, i.e. hospital-acquired pathogens, and multiply-resistant organisms within the hospital setting. The emergence of higher levels of antibiotic resistance is threatening the long term viability of current treatment options and placing greater emphasis on the use of infection control procedures. The relative importance and value of various infection control practices is often debated and there is a lack of quantitative evidence concerning their effectiveness. The methods developed in this dissertation are applied to data of methicillin-resistant Staphylococcus aureus occurrence in intensive care units to quantify the effectiveness of infection control procedures. Analysis of infectious disease or carriage data is complicated by dependencies within the data and partial observation of the transmission process. Dependencies within the data are inherent because the risk of colonisation depends on the number of other colonised individuals. The colonisation times, chain and duration are often not visible to the human eye making only partial observation of the transmission process possible. Within a hospital setting, routine surveillance monitoring permits knowledge of interval-censored colonisation times. However, consideration needs to be given to the possibility of false negative outcomes when relying on observations from routine surveillance monitoring. SI (Susceptible, Infected) models are commonly used to describe community epidemic processes and allow for any inherent dependencies. Statistical inference techniques, such as the expectation-maximisation (EM) algorithm and Markov chain Monte Carlo (MCMC) can be used to estimate the model parameters when only partial observation of the epidemic process is possible. These methods appear well suited for the analysis of hospital infectious disease data but need to be adapted for short patient stays through migration. This thesis focuses on the use of Bayesian statistics to explore the posterior distributions of the unknown parameters. MCMC techniques are introduced to overcome analytical intractability caused by partial observation of the epidemic process. Statistical issues such as model adequacy and MCMC convergence assessment are discussed throughout the thesis. The new methodology allows the quantification of the relative importance of different transmission routes and the benefits of hospital practices, in terms of changed transmission rates. Evidence-based decisions can therefore be made on the impact of infection control procedures which is otherwise difficult on the basis of clinical studies alone. The methods are applied to data describing the occurrence of methicillin-resistant Staphylococcus aureus within intensive care units in hospitals in Brisbane and London