Handwashing is a well known practice to prevent the hospital-acquired infections, and it is also a basic procedure of the nosocomial infection control programs. This research is conducted to use PRECEDE model to investigate the factors affecting nurses¡¦ performance in handwashing practices. We conducted a cross-sectional study to investigate 212 nurses working patients` heaithcare from a medical center of north Taiwan, and a quota sampling method was used to analyze nurses¡¦ handwashing practices. The results of this research includes (1) the major causes affect nursing staffs¡¦ performance in handwashing practices include attitudes (enthusiastic and passive), encouragements from colleagues, and feedback of the nosocomial infection rate or density. (2) different perfessional department and different ward type also affect the performance of nurses¡¦ handwashing practices. (3) nurses washed their hands often after contacting patients¡¦ body substances, and they usually forget to wash their hands after checking patients¡¦ medicine charts. (4) seif-reported factors for poor adherence with handwashing are heavy work loading and taking patients¡¦ needs priority. The suggestions of this research are (1) providing nurses the routine feedback information regarding the changes in nosocomial infection rate versus their performance in handwashing practices. (2) making nursing supervisors and their colleagues to understand the importance of handwashing practice as well as encouragement, reminding, and supporting. (3) the above two suggestions should be executed continually. From the positive feedback, it shall enhance nursing staffs` attitudes to improve their handwashing practices, and then, nursing staffs will be willing to keep excellent performance in handwashing practices.

Every year, 2 million patients in the United States suffer with at least 1 hospital-acquired infection resulting in an estimated 99,000 deaths annually. The purpose of this exploratory single case study was to explore strategies healthcare managers in U.S. hospitals used to reduce hospital-acquired infections. The study included face-to-face, semistructured interviews with 5 healthcare managers from a hospital in Maryland who were successful in reducing these infections. The conceptual framework was human capital theory. Field notes, hospital documents, and transcribed interviews were analyzed to identify themes regarding strategies used by healthcare managers. The data analysis and coding process resulted in 5 major themes: use of HAI-related data; implementation of detailed cleaning method; implementation of define, measure, analyze, implement, and control; education and training of staff; and implementation of the Antimicrobial Stewardship Program. The findings from this study might benefit healthcare managers in implementing and sustaining successful strategies to reduce hospital-acquired infections. The implications for positive social change included reducing hospital-acquired infections, thereby leading to fewer hospitalization days for patients and a faster recovery time to return to normal life. Reducing hospital acquired infections might reduce patient deaths related to the infections.

Hospital Acquired Infections (HAI) are a global patient safety challenge, costly to treat, and affect hundreds of millions of patients annually worldwide. It has been shown that the majority of HAI are transferred to patients by caregivers' hands and therefore, can be prevented by proper hand hygiene (HH). However, many factors including cognitive load, cause caregivers to forget to cleanse their hands. Hand hygiene compliance among caregivers remains low around the world. In this thesis I showed that it is possible to build a wearable accelerometer-based HH reminder system to identify ongoing nursing activities with the patient, indicate the high-risk activities, and prompt the caregivers to clean their hands. Eight subjects participated in this study, each wearing five wireless accelerometer sensors on the wrist, upper arms and the back. A pattern recognition approach was used to classify six nursing activities offline. Time-domain features that included mean, standard deviation, energy, and correlation among accelerometer axes were found to be suitable features. On average, 1-Nearest Neighbour classifier was able to classify the activities with 84% accuracy. A novel algorithm was developed to adaptively segment the accelerometer signals to identify the start and stop time of each nursing activity. The overall accuracy of the algorithm for a total of 96 events performed by 8 subjects was approximately 87%. The accuracy was higher than 91% for 5 out of 8 subjects. The sequence of nursing activities was modelled by an 18-state Markov Chain. The model was evaluated by recently published data. The simulation results showed that the high-risk of cross-contamination decreases exponentially by frequency of HH and this happens more rapidly up to 50%-60% hand hygiene rate. It was also found that if the caregiver enters the room with high-risk of transferring infection to the current patient, given the assumptions in this study, only 55% HH is capable of reducing the risk of infection transfer to the lowest level. This may help to prevent the next patient from acquiring infection, preventing an infection outbreak. The model is also capable of simulating the effects of the imperfect HH on the risk of cross-contamination.

Hospital Acquired Infections (HAI) are a global patient safety challenge, costly to treat, and affect hundreds of millions of patients annually worldwide. It has been shown that the majority of HAI are transferred to patients by caregivers' hands and therefore, can be prevented by proper hand hygiene (HH). However, many factors including cognitive load, cause caregivers to forget to cleanse their hands. Hand hygiene compliance among caregivers remains low around the world. In this thesis I showed that it is possible to build a wearable accelerometer-based HH reminder system to identify ongoing nursing activities with the patient, indicate the high-risk activities, and prompt the caregivers to clean their hands. Eight subjects participated in this study, each wearing five wireless accelerometer sensors on the wrist, upper arms and the back. A pattern recognition approach was used to classify six nursing activities offline. Time-domain features that included mean, standard deviation, energy, and correlation among accelerometer axes were found to be suitable features. On average, 1-Nearest Neighbour classifier was able to classify the activities with 84% accuracy. A novel algorithm was developed to adaptively segment the accelerometer signals to identify the start and stop time of each nursing activity. The overall accuracy of the algorithm for a total of 96 events performed by 8 subjects was approximately 87%. The accuracy was higher than 91% for 5 out of 8 subjects. The sequence of nursing activities was modelled by an 18-state Markov Chain. The model was evaluated by recently published data. The simulation results showed that the high-risk of cross-contamination decreases exponentially by frequency of HH and this happens more rapidly up to 50%-60% hand hygiene rate. It was also found that if the caregiver enters the room with high-risk of transferring infection to the current patient, given the assumptions in this study, only 55% HH is capable of reducing the risk of infection transfer to the lowest level. This may help to prevent the next patient from acquiring infection, preventing an infection outbreak. The model is also capable of simulating the effects of the imperfect HH on the risk of cross-contamination.

Malgré les avancées en matière de prévention, Pseudomonas aeruginosa reste un pathogène fréquent et délétère en réanimation. Des facteurs de risque d’acquisition de ce micro-organisme ont déjà pu être identifiés, mais jamais dans un contexte multicentrique et rarement en ajustant sur des caractéristiques du service. Si l’analyse de la littérature était jusqu’alors en faveur d’une forte origine individuelle, la part du contexte d’hospitalisation et des caractéristiques de la réanimation paraît de moins en moins négligeable. Notre travail a permis non seulement de faire un état des lieux concernant les connaissances actuelles sur Pseudomonas aeruginosa en réanimation mais également d’identifier des profils type de patients et services de réanimation plus à risque vis à vis de ce micro-organisme. L’intérêt majeur est non seulement de pouvoir ainsi orienter les cliniciens face à une conjonction d’éléments mais surtout, là où les facteurs patients restent souvent peu modifiables, d’identifier des éléments contextuels d’acquisition sur lesquels il serait possible d’agir afin de réduire le risque infectieux. / Despite major advance in techniques and reinforcement of infection control measures, Pseudomonas aeruginosa remains frequent in intensive care unit (ICU) and is responsible for severe hospital-acquired infections. Several patient and pathogen-specific risk factors have been associated with acquisition of P. aeruginosa in ICUs Nevertheless those risk factors were identified in monocentric studies which rarely took in account the context of cares. If individual risk factors for P. aeruginosa acquisition have appeared to be predominant since then, the role of contextual variables seems to have been underestimated. This thesis provides insight into the epidemiology of P. aeruginosa in ICU, identifies individual and contextual risk factors for P. aeruginosa infection and P. aeruginosa acquisition and emphasizes the interest of contextual variables which gives new perspectives to P. aeruginosa prevention.

The purpose of the study was to develop practice guidelines to promote adherence to infection control standards in a specific hospital in Manzini, Swaziland. It was a qualitative, descriptive and explanatory design which utilised an in-depth unstructured face to face interview data collection method done to nine (9) registered nurse participants. A qualitative content analysis was conducted to identify prominent themes and patterns, smaller units of data were named and coded according to the contents they represented. Four themes emerged from data analysis, they were; working environment for nurses, nurse’s descriptions of infection control standards, nurse’s challenges regarding adherence to infection control standards and nurses expressed needs. The findings revealed possible contributing factors to non-adherence to infection control standards by participants and therefore denoted to a serious need for development of general guidelines to promote adherence to infection control standards, these guidelines were presented. / Health Studies / M. A. (Health Studies)

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

The purpose of the study was to develop practice guidelines to promote adherence to infection control standards in a specific hospital in Manzini, Swaziland. It was a qualitative, descriptive and explanatory design which utilised an in-depth unstructured face to face interview data collection method done to nine (9) registered nurse participants. A qualitative content analysis was conducted to identify prominent themes and patterns, smaller units of data were named and coded according to the contents they represented. Four themes emerged from data analysis, they were; working environment for nurses, nurse’s descriptions of infection control standards, nurse’s challenges regarding adherence to infection control standards and nurses expressed needs. The findings revealed possible contributing factors to non-adherence to infection control standards by participants and therefore denoted to a serious need for development of general guidelines to promote adherence to infection control standards, these guidelines were presented. / Health Studies / M. A. (Health Studies)