Infection Control

Health care associated infection is coupled with significant morbidity and mortality. Prevention and control of infection is indispensable part of health care delivery system. Knowledge of Preventing HAI can help health care providers to make informed and therapeutic decisions thereby prevent or reduce these infections. Infection control is continuously evolving science that is constantly being updated and enhanced. The book will be very useful for all health care professionals to combat with health care associated infections

Epidemiology Insights

This book represents an overview on the diverse threads of epidemiological research, brings together the expertise and enthusiasm of an international panel of leading researchers to provide a state-of-the art overview of the field. Topics include the epidemiology of dermatomycoses and Candida spp. infections, the epidemiology molecular of methicillin-resistant Staphylococcus aureus (MRSA) isolated from humans and animals, the epidemiology of varied manifestations neuro-psychiatric, virology and epidemiology, epidemiology of wildlife tuberculosis, epidemiologic approaches to the study of microbial quality of milk and milk products, Cox proportional hazards model, epidemiology of lymphoid malignancy, epidemiology of primary immunodeficiency diseases and genetic epidemiology family-based. Written by experts from around the globe, this book is reading for clinicians, researchers and students, who intend to address these issues

Infection Control Insights for Hospital Animal-Assisted Intervention Program Implementation: From Stakeholder Perspectives to Microbial Dynamics

Background: While animal-assisted intervention (AAI) programs have shown significant benefits to patients, there are concerns regarding their use in healthcare settings limiting utilization. This works aims to enhance the adoption and use of hospital AAI programs and understand the positive and negative outcomes of implemented control measures. We hypothesize that a One Health framework will aid in the understanding and improvement of hospital AAI infection control concerns. This dissertation will 1) collect perspectives on concerns and control measures to understand perceived risks, and 2) examine microbial dynamics to understand actual risks. Methods: The first two chapters are literature reviews to identify knowledge gaps and provide rationale for the thesis research. The next two chapters are based on a qualitative study interviewing key stakeholders in hospital AAI programs. The last two chapters describe research that sampled for both hospital pathogens and whole microbial communities to pilot test a canine decolonization approach as an infection control intervention. Results: The literature reviews revealed a lack of data on the risks associated with hospital AAI, and a One Health approach can be used to address this knowledge gap. The qualitative findings indicated occupational health benefits are limited by administrative and infection risk barriers, but these could be overcome through collaboration and leadership. Microbial findings suggest the canine decolonization intervention blocked the microbial contribution from the therapy dog and reduced rare microbiota on the dog, yet did not prevent all microbial sharing, indicating the dog as only one possible pathway for transmission. Conclusions: The results from this thesis support the hypothesis that a holistic One Health approach can assist in understanding and designing interventions to improve hospital AAI programs. The qualitative findings stress the importance of understanding practical considerations for program implementation. In the quantitative study, allocation of the relative contribution for all potential microbial transmission pathways, and the determination of potentially negative unintended consequences of infection control policies, can inform the design of appropriate and effective control measures. This thesis suggests that a One Health framework should be used for future research in hospital AAI to ensure the sustainability of these valuable programs

Improving Antibiotic Resistant Infection Transmission Situational Awareness in Enclosed Facilities with a Novel Graphical User Interface for Tactical Biosurveillance

Serious challenges associated with antibiotic resistant infections (ABRIs) force healthcare practitioners (HCP) to seek innovative approaches that will slow the emergence of new ABRIs and prevent their spread. It was realized that traditional approaches to infection prevention based on education, retrospective reports, and biosurveillance often fail to ensure reliable compliance with infection prevention guidelines and real-time problem solving. The objective of this original research was to develop and test the conceptual design of a situational awareness (SA)-oriented information system for coping with healthcare-associated infection transmission. Constantly changing patterns in spatial distribution of patients, prevalence of infectious cases, clustering of contacts, and frequency of contacts may compromise the effectiveness of infection prevention and control in hospitals. It was hypothesized that providing HCPs with a graphical user interface (GUI) to visualize spatial information on the risks of exposure to ABRIs would effectively increase HCPs’ SA. Increased SA may enhance biosurveillance and result in tactical decisions leading to better patient outcomes. The study employed a mixed qualitative-quantitative research method encompassing conceptualization of GUI content, transcription of electronic health record and biosurveillance data into GUI visual artifacts, and evaluation of the GUI’s impact on HCPs’ perception and comprehension of the conditions that increase the risk of ABRI transmission. The study provided pilot evidence that visualization of spatial disease distribution and spatially-linked exposures and interventions significantly increases HCPs’ SA when compared to current practice. The research demonstrates that the SA-oriented GUI enables the HCPs to promptly answer the question, “At a given location, what are the risks of infection transmission there?” This research provides a new form of medical knowledge representation for spatial population-based decision-making within enclosed environments. The next steps include rapid application development and further hypothesis testing concerning the impact of this GUI on decsion-making

Outcomes of Staphylococcus aureus bacteremia

Master'sMASTER OF SCIENC

Statistical inference and modelling for nosocomial infections and the incorporation of whole genome sequence data

Healthcare-associated infections (HCAIs) remain a problem worldwide, and can cause severe illness and death. The increasing level of antibiotic resistance among bacteria that cause HCAIs limits infection treatment options, and is a major concern. Statistical modelling is a vital tool in developing an understanding of HCAI transmission dynamics. In this thesis, stochastic epidemic models are developed and used with the aim of investigating methicillin-resistant Staphylococcus aureus (MRSA) transmission and intervention measures in hospital wards. A detailed analysis of MRSA transmission and the effectiveness of patient isolation was performed, using data collected from several general medical wards in London. A Markov chain Monte Carlo (MCMC) algorithm was used to derive parameter estimates, accounting for unobserved transmission dynamics. A clear reduction in transmission associated with the use of patient isolation was estimated. A Bayesian framework offers considerable benefits and flexibility when dealing with missing data; however, model comparison is difficult, and existing methods are far from universally accepted. Two commonly used Bayesian model selection tools, reversible jump MCMC and the deviance information criterion (DIC), were thoroughly investigated in a transmission model setting, using both simulated and real data. The collection of whole genome sequence (WGS) data is becoming easier, faster and cheaper than ever before. With WGS data likely to become abundant in the near future, the development of sophisticated analytical tools and models to exploit such genetic information is of great importance. New methods were developed to model MRSA transmission, using both genetic and epidemiological data, allowing for the reconstruction of transmission networks and simultaneous estimation of key transmission parameters. This approach was tested with simulated data and employed on WGS data collected from two Thai intensive care units. This work offers much scope for future investigations into genetic diversity and more complex transmission models, once suitable data become available