Introdução - A crescente resistência antimicrobiana em bactérias responsáveis por infecções hospitalares é um grande desafio à Saúde Pública. as B-lactamases de espectro estendido (ESBL), que hidrolisam a maioria dos compostos B-lactâmicos, são reconhecidas mundialmente como um grande problema para pacientes hospitalizados, devido à localização de seus genes em elementos transferíveis, facilitando sua disseminação. Objetivo - Caracterizar geneticamente cepas de Enterobactérias produtoras de ESBL isoladas de pacientes de um hospital público da cidade de São Paulo. Material e métodos - Todas as cepas de enterobactérias produtoras de ESBL isoladas em um ano foram submetidas a análises moleculares pela PCR, com iniciadores específicos para oito genes bla, e as cepas de Klebsiella pneumoniae ESBL positivas (ESBL-Kp) identificadas nesse período foram comparadas pela técnica de PFGE.Resultados - Os genes, bla(tem), bla(shv), bla(ctx-m), bla(per-2) bla(veb) and bla(ges) foram identificados em 9 espécies: Klebsiella pneumoniae (71,5 por cento), Escherichia coli (13,5 por cento), Morganella morganii (6 por cento), Proteus mirabilis (3 por cento), Klebsiella oxytoca (1,5 por cento), Providencia rettgeri (1,5 por cento), Providencia stuartii (1,5 por cento), Enterobacter aerogenes (0,75 por cento). Os genes bla(per-1) e bla(oxa) não foram detectados. O PFGE revelou 8 perfis moleculares principais em 68,4 por cento das ESBL-Kp, e 31,6 por cento das cepas não estavam relacionadas. Conclusões - Os resultados de PCR revelaram uma grande variedade de grupos de ESBL, e aparentemente este é o primeiro relato de grupos GES e VEB em enterobactérias no Brasil. / Introduction - The increasing antimicrobial resistance in pathogenic bacteria causing nosocomial infections is a major public health challenge. The extended-spectrum β-lactamases (ESBL), which hydrolyze most of β-lactams, are recognized worldwide as a great problem to hospitalized patients, due to the transferable location of their genes, which facilitates their spreading. Objective - Genetically characterize ESBL-producing Enterobacteriaceae strains isolated from patients of a Public Hospital in the city of São Paulo. Material and Methods - All Enterobacteriaceae ESBL-producing strains isolated in an 1-year period were submitted to molecular analysis by PCR with specific primers for eight bla genes, and all ESBL Klebsiella pneumoniae (ESBL-Kp) identified in this period were compared by the PFGE technique. Results - Genes blaTEM, blaSHV, blaCTX-M, blaPER-2, blaVEB and blaGES were identified in 9 species: Klebsiella pneumoniae (71,5%), Escherichia coli (13,5%), Morganella morganii (6%), Proteus mirabilis (3%), Klebsiella oxytoca (1,5%), Providencia rettgeri (1,5%), Providencia stuartii (1,5%), Enterobacter aerogenes (0,75%) and Enterobacter cloacae (0,75%). Genes blaPER-1 and blaOXA were not detected in any strain. PFGE revealed 8 distinct main molecular patterns in 68,4% of ESBL-Kp, and 31,6% of the strains were totally unrelated. Conclusions - PCR results showed a great variety of ESBL groups in the institution, and apparently this is the first report of GES- and VEB-ESBL groups in enterobacteria in Brazil. The results suggest the spread of resistance genes in different strains of ESBL-Kp in some hospital wards, and also that some strongly related clones of these bacteria colonized patients from a neonatal ward in a 3-month period.

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

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