Cost effectiveness of antimicrobial catheters in the intensive care unit : Addressing uncertainty in the decision

Introduction: Some types of antimicrobial-coated central venous catheters (A-CVC) have been shown to be cost-effective in preventing catheter-related bloodstream infection (CR-BSI). However, not all types have been evaluated, and there are concerns over the quality and usefulness of these earlier studies. There is uncertainty amongst clinicians over which, if any, antimicrobial-coated central venous catheters to use. We re-evaluated the cost-effectiveness of all commercially available antimicrobialcoated central venous catheters for prevention of catheter-related bloodstream infection in adult intensive care unit (ICU) patients. Methods: We used a Markov decision model to compare the cost-effectiveness of antimicrobial-coated central venous catheters relative to uncoated catheters. Four catheter types were evaluated; minocycline and rifampicin (MR)-coated catheters; silver, platinum and carbon (SPC)-impregnated catheters; and two chlorhexidine and silver sulfadiazine-coated catheters, one coated on the external surface (CH/SSD (ext)) and the other coated on both surfaces (CH/SSD (int/ext)). The incremental cost per qualityadjusted life-year gained and the expected net monetary benefits were estimated for each. Uncertainty arising from data estimates, data quality and heterogeneity was explored in sensitivity analyses. Results: The baseline analysis, with no consideration of uncertainty, indicated all four types of antimicrobial-coated central venous catheters were cost-saving relative to uncoated catheters. Minocycline and rifampicin-coated catheters prevented 15 infections per 1,000 catheters and generated the greatest health benefits, 1.6 quality-adjusted life-years, and cost-savings, AUD $130,289. After considering uncertainty in the current evidence, the minocycline and rifampicin-coated catheters returned the highest incremental monetary net benefits of $948 per catheter; but there was a 62% probability of error in this conclusion. Although the minocycline and rifampicin-coated catheters had the highest monetary net benefits across multiple scenarios, the decision was always associated with high uncertainty. Conclusions: Current evidence suggests that the cost-effectiveness of using antimicrobial-coated central venous catheters within the ICU is highly uncertain. Policies to prevent catheter-related bloodstream infection amongst ICU patients should consider the cost-effectiveness of competing interventions in the light of this uncertainty. Decision makers would do well to consider the current gaps in knowledge and the complexity of producing good quality evidence in this area.

Complementarity and the uncertainty principle as aesthetic principles : the practice and performance of The Physics Project

Using the generative processes developed over two stages of creative development and the performance of The Physics Project at the Loft at the Creative Industries Precinct at the Queensland University of Technology (QUT) from 5th – 8th April 2006 as a case study, this exegesis considers how the principles of contemporary physics can be reframed as aesthetic principles in the creation of contemporary performance. The Physics Project is an original performance work that melds live performance, video and web-casting and overlaps an exploration of personal identity with the physics of space, time, light and complementarity. It considers the acts of translation between the language of physics and the language of contemporary performance that occur via process and form. This exegesis also examines the devices in contemporary performance making and contemporary performance that extend the reach of the performance, including the integration of the live and the mediated and the use of metanarratives.

Integrated Bayesian network frameworks for modelling complex ecological issues

Ecological problems are typically multi faceted and need to be addressed from a scientific and a management perspective. There is a wealth of modelling and simulation software available, each designed to address a particular aspect of the issue of concern. Choosing the appropriate tool, making sense of the disparate outputs, and taking decisions when little or no empirical data is available, are everyday challenges facing the ecologist and environmental manager. Bayesian Networks provide a statistical modelling framework that enables analysis and integration of information in its own right as well as integration of a variety of models addressing different aspects of a common overall problem. There has been increased interest in the use of BNs to model environmental systems and issues of concern. However, the development of more sophisticated BNs, utilising dynamic and object oriented (OO) features, is still at the frontier of ecological research. Such features are particularly appealing in an ecological context, since the underlying facts are often spatial and temporal in nature. This thesis focuses on an integrated BN approach which facilitates OO modelling. Our research devises a new heuristic method, the Iterative Bayesian Network Development Cycle (IBNDC), for the development of BN models within a multi-field and multi-expert context. Expert elicitation is a popular method used to quantify BNs when data is sparse, but expert knowledge is abundant. The resulting BNs need to be substantiated and validated taking this uncertainty into account. Our research demonstrates the application of the IBNDC approach to support these aspects of BN modelling. The complex nature of environmental issues makes them ideal case studies for the proposed integrated approach to modelling. Moreover, they lend themselves to a series of integrated sub-networks describing different scientific components, combining scientific and management perspectives, or pooling similar contributions developed in different locations by different research groups. In southern Africa the two largest free-ranging cheetah (Acinonyx jubatus) populations are in Namibia and Botswana, where the majority of cheetahs are located outside protected areas. Consequently, cheetah conservation in these two countries is focussed primarily on the free-ranging populations as well as the mitigation of conflict between humans and cheetahs. In contrast, in neighbouring South Africa, the majority of cheetahs are found in fenced reserves. Nonetheless, conflict between humans and cheetahs remains an issue here. Conservation effort in South Africa is also focussed on managing the geographically isolated cheetah populations as one large meta-population. Relocation is one option among a suite of tools used to resolve human-cheetah conflict in southern Africa. Successfully relocating captured problem cheetahs, and maintaining a viable free-ranging cheetah population, are two environmental issues in cheetah conservation forming the first case study in this thesis. The second case study involves the initiation of blooms of Lyngbya majuscula, a blue-green algae, in Deception Bay, Australia. L. majuscula is a toxic algal bloom which has severe health, ecological and economic impacts on the community located in the vicinity of this algal bloom. Deception Bay is an important tourist destination with its proximity to Brisbane, Australia’s third largest city. Lyngbya is one of several algae considered to be a Harmful Algal Bloom (HAB). This group of algae includes other widespread blooms such as red tides. The occurrence of Lyngbya blooms is not a local phenomenon, but blooms of this toxic weed occur in coastal waters worldwide. With the increase in frequency and extent of these HAB blooms, it is important to gain a better understanding of the underlying factors contributing to the initiation and sustenance of these blooms. This knowledge will contribute to better management practices and the identification of those management actions which could prevent or diminish the severity of these blooms.

Willingness to buy GM food products : the role of uncertainty orientation, consumer risk perceptions and information search in consumers from Australia

Genetically modified (GM) food products are the source of much controversy and in the context of consumer behaviour, the way in which consumers perceive such food products is of paramount importance both theoretically and practically. Despite this, relatively little research has focused on GM food products from a consumer perspective, and as such, this study seeks to better understand what effects consumer willingness to buy GM food products in Australian consumers.

Uncertainty based MDO of UAS using HAPMOEA

CFD has been successfully used in the optimisation of aerodynamic surfaces using a given set of parameters such as Mach numbers and angle of attack. While carrying out a multidisciplinary design optimisation one deals with situations where the parameters have some uncertain attached. Any optimisation carried out for fixed values of input parameters gives a design which may be totally unacceptable under off-design conditions. The challenge is to develop a robust design procedure which takes into account the fluctuations in the input parameters. In this work, we attempt this using a modified Taguchi approach. This is incorporated into an evolutionary algorithm with many features developed in house. The method is tested for an UCAV design which simultaneously handles aerodynamics, electromagnetics and maneuverability. Results demonstrate that the method has considerable potential.

Incorporating uncertainty in environmental models informed by imagery

In this thesis, the issue of incorporating uncertainty for environmental modelling informed by imagery is explored by considering uncertainty in deterministic modelling, measurement uncertainty and uncertainty in image composition. Incorporating uncertainty in deterministic modelling is extended for use with imagery using the Bayesian melding approach. In the application presented, slope steepness is shown to be the main contributor to total uncertainty in the Revised Universal Soil Loss Equation. A spatial sampling procedure is also proposed to assist in implementing Bayesian melding given the increased data size with models informed by imagery. Measurement error models are another approach to incorporating uncertainty when data is informed by imagery. These models for measurement uncertainty, considered in a Bayesian conditional independence framework, are applied to ecological data generated from imagery. The models are shown to be appropriate and useful in certain situations. Measurement uncertainty is also considered in the context of change detection when two images are not co-registered. An approach for detecting change in two successive images is proposed that is not affected by registration. The procedure uses the Kolmogorov-Smirnov test on homogeneous segments of an image to detect change, with the homogeneous segments determined using a Bayesian mixture model of pixel values. Using the mixture model to segment an image also allows for uncertainty in the composition of an image. This thesis concludes by comparing several different Bayesian image segmentation approaches that allow for uncertainty regarding the allocation of pixels to different ground components. Each segmentation approach is applied to a data set of chlorophyll values and shown to have different benefits and drawbacks depending on the aims of the analysis.

Merging fact with fiction : a study of year 9 students' developing scientific literacy through the writing of hybridised scientific narratives on a socioscientific issue

International assessments of student science achievement, and growing evidence of students' waning interest in school science, have ensured that the development of scientific literacy continues to remain an important educational priority. Furthermore, researchers have called for teaching and learning strategies to engage students in the learning of science, particularly in the middle years of schooling. This study extends previous national and international research that has established a link between writing and learning science. Specifically, it investigates the learning experiences of eight intact Year 9 science classes as they engage in the writing of short stories that merge scientific and narrative genres (i.e., hybridised scientific narratives) about the socioscientific issue of biosecurity. This study employed a triangulation mixed methods research design, generating both quantitative and qualitative data, in order to investigate three research questions that examined the extent to which the students' participation in the study enhanced their scientific literacy; the extent to which the students demonstrated conceptual understanding of related scientific concepts through their written artefacts and in interviews about the artefacts; and the extent to which the students' participation in the project influenced their attitudes toward science and science learning. Three aspects of scientific literacy were investigated in this study: conceptual science understandings (a derived sense of scientific literacy), the students' transformation of scientific information in written stories about biosecurity (simple and expanded fundamental senses of scientific literacy), and attitudes toward science and science learning. The stories written by students in a selected case study class (N=26) were analysed quantitatively using a series of specifically-designed matrices that produce numerical scores that reflect students' developing fundamental and derived senses of scientific literacy. All students (N=152) also completed a Likert-style instrument (i.e., BioQuiz), pretest and posttest, that examined their interest in learning science, science self-efficacy, their perceived personal and general value of science, their familiarity with biosecurity issues, and their attitudes toward biosecurity. Socioscientific issues (SSI) education served as a theoretical framework for this study. It sought to investigate an alternative discourse with which students can engage in the context of SSI education, and the role of positive attitudes in engaging students in the negotiation of socioscientific issues. Results of the study have revealed that writing BioStories enhanced selected aspects of the participants' attitudes toward science and science learning, and their awareness and conceptual understanding of issues relating to biosecurity. Furthermore, the students' written artefacts alone did not provide an accurate representation of the level of their conceptual science understandings. An examination of these artefacts in combination with interviews about the students' written work provided a more comprehensive assessment of their developing scientific literacy. These findings support extensive calls for the utilisation of diversified writing-to-learn strategies in the science classroom, and therefore make a significant contribution to the writing-to-learn science literature, particularly in relation to the use of hybridised scientific genres. At the same time, this study presents the argument that the writing of hybridised scientific narratives such as BioStories can be used to complement the types of written discourse with which students engage in the negotiation of socioscientific issues, namely, argumentation, as the development of positive attitudes toward science and science learning can encourage students' participation in the discourse of science. The implications of this study for curricular design and implementation, and for further research, are also discussed.

A process for the management of physical infrastructure

Physical infrastructure assets are important components of our society and our economy. They are usually designed to last for many years, are expected to be heavily used during their lifetime, carry considerable load, and are exposed to the natural environment. They are also normally major structures, and therefore present a heavy investment, requiring constant management over their life cycle to ensure that they perform as required by their owners and users. Given a complex and varied infrastructure life cycle, constraints on available resources, and continuing requirements for effectiveness and efficiency, good management of infrastructure is important. While there is often no one best management approach, the choice of options is improved by better identification and analysis of the issues, by the ability to prioritise objectives, and by a scientific approach to the analysis process. The abilities to better understand the effect of inputs in the infrastructure life cycle on results, to minimise uncertainty, and to better evaluate the effect of decisions in a complex environment, are important in allocating scarce resources and making sound decisions. Through the development of an infrastructure management modelling and analysis methodology, this thesis provides a process that assists the infrastructure manager in the analysis, prioritisation and decision making process. This is achieved through the use of practical, relatively simple tools, integrated in a modular flexible framework that aims to provide an understanding of the interactions and issues in the infrastructure management process. The methodology uses a combination of flowcharting and analysis techniques. It first charts the infrastructure management process and its underlying infrastructure life cycle through the time interaction diagram, a graphical flowcharting methodology that is an extension of methodologies for modelling data flows in information systems. This process divides the infrastructure management process over time into self contained modules that are based on a particular set of activities, the information flows between which are defined by the interfaces and relationships between them. The modular approach also permits more detailed analysis, or aggregation, as the case may be. It also forms the basis of ext~nding the infrastructure modelling and analysis process to infrastructure networks, through using individual infrastructure assets and their related projects as the basis of the network analysis process. It is recognised that the infrastructure manager is required to meet, and balance, a number of different objectives, and therefore a number of high level outcome goals for the infrastructure management process have been developed, based on common purpose or measurement scales. These goals form the basis of classifYing the larger set of multiple objectives for analysis purposes. A two stage approach that rationalises then weights objectives, using a paired comparison process, ensures that the objectives required to be met are both kept to the minimum number required and are fairly weighted. Qualitative variables are incorporated into the weighting and scoring process, utility functions being proposed where there is risk, or a trade-off situation applies. Variability is considered important in the infrastructure life cycle, the approach used being based on analytical principles but incorporating randomness in variables where required. The modular design of the process permits alternative processes to be used within particular modules, if this is considered a more appropriate way of analysis, provided boundary conditions and requirements for linkages to other modules, are met. Development and use of the methodology has highlighted a number of infrastructure life cycle issues, including data and information aspects, and consequences of change over the life cycle, as well as variability and the other matters discussed above. It has also highlighted the requirement to use judgment where required, and for organisations that own and manage infrastructure to retain intellectual knowledge regarding that infrastructure. It is considered that the methodology discussed in this thesis, which to the author's knowledge has not been developed elsewhere, may be used for the analysis of alternatives, planning, prioritisation of a number of projects, and identification of the principal issues in the infrastructure life cycle.

On the data consumption benefits of accepting increased uncertainty

In the context of learning paradigms of identification in the limit, we address the question: why is uncertainty sometimes desirable? We use mind change bounds on the output hypotheses as a measure of uncertainty, and interpret ‘desirable’ as reduction in data memorization, also defined in terms of mind change bounds. The resulting model is closely related to iterative learning with bounded mind change complexity, but the dual use of mind change bounds — for hypotheses and for data — is a key distinctive feature of our approach. We show that situations exists where the more mind changes the learner is willing to accept, the lesser the amount of data it needs to remember in order to converge to the correct hypothesis. We also investigate relationships between our model and learning from good examples, set-driven, monotonic and strong-monotonic learners, as well as class-comprising versus class-preserving learnability.

Writing stories to enhance scientific literacy

In response to international concerns about scientific literacy and students’ waning interest in school science, this study investigated the effects of a science-writing project about the socioscientific issue of biosecurity on the development of students’ scientific literacy. Students generated two BioStories each that merged scientific information with the narrative storylines in the project. The study was conducted in two phases. In the exploratory phase, a qualitative case study of a 6th grade class involving classroom observations and interviews informed the design of the second, confirmatory phase of the study, which was conducted at a different school. This phase involved a mixed methods approach featuring a quasi-experimental design with two classes of Australian middle school students (i.e., 6th grade, 11 years of age, n=55). The results support the argument that writing the sequence of stories helped the students become more familiar with biosecurity issues, develop a deeper understanding of related biological concepts, and improve their interest in science. On the basis of these findings, teachers should be encouraged to engage their students in the practice of writing about socioscientific issues (SSI) in a way that integrates scientific information into narrative storylines. Extending the practice to older students, and exploring additional issues related to writing about SSI are recommended for further research.

Metal Contaminants in Leachate from Sanitary Landfills

The uncontrolled disposal of solid wastes poses an immediate threat to public health and a long term threat to the environmental well being of future generations. Solid waste is waste resulting from human activities that is solid and unwanted (Peavy et al., 1985). If unmanaged, dumped solid wastes generate liquid and gaseous emissions that are detrimental to the environment. This can lead to a serious form of contamination known as metal contamination, which poses a risk to human health and ecosystems. For example, some heavy metals (cadmium, chromium compounds, and nickel tetracarbonyl) are known to be highly toxic, and are aggressive at elevated concentrations. Iron, copper, and manganese can cause staining, and aluminium causes depositions and discolorations. In addition, calcium and magnesium cause hardness in water causing scale deposition and scum formation. Though not a metal but a metalloid, arsenic is poisonous at relatively high concentrations and when diluted at low concentrations causes skin cancer. Normally, metal contaminants are found in a dissolved form in the liquid percolating through landfills. Because average metal concentrations from full-scale landfills, test cells, and laboratory studies have tended to be generally low, metal contamination originating from landfills is not generally considered a major concern (Kjeldsen et al., 2002; Christensen et al., 1999). However, a number of factors make it necessary to take a closer look at metal contaminants from landfills. One of these factors relates to variability. Landfill leachate can have different qualities depending on the weather and operating conditions. Therefore, at one moment in time, metal contaminant concentrations may be quite low, but at a later time these concentrations could be quite high. Also, these conditions relate to the amount of leachate that is being generated. Another factor is biodiversity. It cannot be assumed that a particular metal contaminant is harmless to flora and fauna (including micro organisms) just because it is harmless to human health. This has significant implications for ecosystems and the environment. Finally, there is the moral factor. Because uncertainty surrounds the potential effects of metal contamination, it is appropriate to take precautions to prevent it from taking place. Consequently, it is necessary to have good scientific knowledge (empirically supported) to adequately understand the extent of the problem and improve the way waste is being disposed of

Bayesian methodology incorporating expert judgment for ranking countermeasure effectiveness under uncertainty: Example applied to at grade railroad crossings in Korea

This paper describes the formalization and application of a methodology to evaluate the safety benefit of countermeasures in the face of uncertainty. To illustrate the methodology, 18 countermeasures for improving safety of at grade railroad crossings (AGRXs) in the Republic of Korea are considered. Akin to “stated preference” methods in travel survey research, the methodology applies random selection and laws of large numbers to derive accident modification factor (AMF) densities from expert opinions. In a full Bayesian analysis framework, the collective opinions in the form of AMF densities (data likelihood) are combined with prior knowledge (AMF density priors) for the 18 countermeasures to obtain ‘best’ estimates of AMFs (AMF posterior credible intervals). The countermeasures are then compared and recommended based on the largest safety returns with minimum risk (uncertainty). To the author's knowledge the complete methodology is new and has not previously been applied or reported in the literature. The results demonstrate that the methodology is able to discern anticipated safety benefit differences across candidate countermeasures. For the 18 at grade railroad crossings considered in this analysis, it was found that the top three performing countermeasures for reducing crashes are in-vehicle warning systems, obstacle detection systems, and constant warning time systems.