Family history (FH) is a risk factor for many diseases. Disease guidelines often include family history as important in assessing chronic disease risks, but the empirical evidence base to inform the routine use of family history in primary care in practice appears largely lacking. An environmental scan of how family history is represented in prevention guidelines for five conditions showed that, while family history is often included in guidelines, there is variation in the definition used, recommendation given and evidence cited. A dataset on cardiovascular health in women was analyzed to examine whether family history offers useful discrimination value above standard risk factors. Regression results showed that family history is an independent risk predictor for coronary heart disease which improves discrimination beyond classical clinical factors. However, the absolute amount of discriminatory ability alone or with other factors is moderate at best, raising issues regarding clinical utility.

The objective of this work is to develop a Bayesian Network (BN) model to produce environmental risk maps for oil and gas site developments and to demonstrate the model’s scalability from a point to a collection of points. To reach this objective, a benchmark BN model was formulated as a “proof of concept” using Aquifers, Ecoregions and Land Use / Land Cover maps as local and independent input variables. This model was then used to evaluate the probabilistic geographical distribution of the Environmental Sensibility of Oil and Gas (O&G) developments for a given study area. A Risk index associated with the development of O&G operation activities based on the spatial environmental sensibility was also mapped. To facilitate the Risk assessment, these input variables (maps) were discretized into three hazard levels: high, moderate and low. A Geographical Information System (GIS) platform was used (ESRI ArcMap 10), to gather, modify and display the data for the analysis. Once the variables were defined and the hazard data was included on feature classes (layer shapefile format), Python 2.6 software was used as the computational platform to calculate the probabilistic state of all the Bayesian Network’s variables. This allowed to define Risk scenarios both on prognostic and diagnostic analysis and to measure the impact of changes or interventions in terms of uncertainty. The resulting Python – ESRI ArcMap computational script was called “BN+GIS, which populated maps describing the spatial variability of the states of the Environmental Sensibility and of the corresponding Risk index. The latter in particular, represents a tool for decision makers to choose the most suitable location for placing a drilling rig, since it integrates three fundamental environmental variables. Also, results show that is possible to back propagate the information from the Environmental Sensibility to define the inherent triggering scenarios (hazard variables). A case of study is presented to illustrate the applicability of the proposed methodology on a specific geographical setting. The Barnett Shale was chosen as a benchmark study area because sufficient information on this region was available, and the importance that it holds on the latest developments of unconventional plays in the country. The main contribution of this work relies in combining Bayesian Networks and GIS to define environmental Risk scenarios that can facilitate decision-making for O&G stakeholders such as land owners, industry operators, regulators and Non-Governmental Organizations (NGOs), before and during the development of a given site.

This study sought to characterize the risk to human health from dietary mercury (Hg) exposure in two Inuit communities, Repulse Bay and lgloolik. Hg exposure was estimated using food frequency questionnaires. Body burden was determined by measuring Hg in hair samples. Estimated average daily Hg exposures for women of child-bearing age were 21.3 mug in Repulse Bay, and 6.0 mug in lgloolik; the general population exposures were 43.1 mug in Repulse Bay and 23.2 mug in lgloolik. Average hair concentrations were 2.1 mg/kg and 2.8 mg/kg in Repulse Bay, 2.1 mg/kg and 6.2 mg/kg in lgloolik for women of child-bearing age and the general population respectively. No participants had concentrations above the "at risk" concentration of 30 mg/kg. Significant correlations were observed between hair Hg concentration and traditional food intake. These results do not necessitate direct intervention, but validate continued monitoring of Hg exposure in Nunavut.

Purpose: The purpose of this dissertation was to examine the determinants of unplanned emergency department (ED) use by home care clients, the profile of older ED patients, their transitions from the ED, as well as the determinants of post discharge outcomes among older ED patients. The goal of this dissertation was to create theoretically driven, evidence-based, and practical risk identification methods for home care and the ED. Methods: First, a multi-year, census-level cohort study was conducted on home care clients in two Canadian provinces (N=617,035). Census-level data from RAI-HC assessments were linked to census-level ED records. A needs-based decision tree model – the ED Model – informed by the Andersen Behavioural Model, was created using decision tree analyses. The final model was validated on a separate data partition and compared to the ERA Index and the CARS. Multilevel analyses were conducted to test regional variation in model performance. Disease stratified analyses were also conducted to test model generalizability across common disease classes. Regression analyses determined the effect of predisposing and enabling factors within ED Model strata. Second, a multi-site, multi-province prospective cohort study was conducted, termed the Management of Older Persons in Emergency Departments (MOPED) Study, using a clinically representative sample of 2,101 older ED patients. The interRAI ED-CA was used to assess older ED patients, and a 90-day disposition was collected. The profile of older ED patents was examined. Best-subset regression analyses identified person-level determinants of acute inpatient admission. Two needs-based decision tree models – the ALC/LTC and ED Revisit Models – were created using decision tree analyses to determine the risk of ALC designation or LTC placement, and unplanned repeat ED visits, respectively. Both models were validated on separate data partitions. Multilevel analyses were conducted to test site-level variation in the models’ performance. Results: Overall, 41.2% of home care clients had at least one unplanned emergency department visit within 6 months of an assessment. Previous ED use, cardio-respiratory symptoms, cardiac conditions, and mood symptoms featured heavily in the ED Model. The ED Model provided moderate risk differentiation and clinical utility. It achieved an area under the curve of 0.62 (95% CI: 0.61-0.62) and showed clear differentiation in Kaplan-Meier plots using validation data. Multi-level analyses showed no regional variation. The ED Model significantly outperformed the similar tools specific to primary care with respect to overall accuracy and perceived clinical utility. Predisposing and enabling characteristics provided little added differentiation beyond evaluated need. The majority of older ED patients were dependent on others for basic tasks of daily living, and many had fragile informal care or lived alone. Triage acuity generally did not differentiate chronic geriatric disabilities and conditions. Previous ED or hospital use was associated with chronic geriatric disabilities and conditions as well as informal caregiver distress. The Admission Model found that multiple factors were associated with admission to inpatient acute care, including: acuity, instability, changes in ADL function, cognition, nutrition, and anhedonia. Overall, 20.7% of older ED patients admitted to acute care were designated ALC or discharged to LTC; whereas 39.5% of older ED patients discharged home had one or more repeat ED visits within 90 days. Cognitive, functional, and informal care indicators were predictive of ALC/LTC; whereas functional status and symptoms were predictive of repeat ED use. The ALC/LTC and ED Revisit Models provided good risk differentiation, achieving AUC’s of 0.74 (95% CI: 0.69-0.79) and 0.69 (95% CI: 0.63-0.74), respectively. The ALC/LTC and ED Revisit Models showed clear differentiation in Kaplan-Meier plots. Multi-level analyses showed no site-level variation in each models’ performance. Conclusions: This dissertation produced tangible and empirically-based risk assessment models for clinical practice in home care and the ED. The models developed in this dissertation can support the targeting of preventative services as well as better communication strategies between the ED and community supportive care, primary care, and inpatient acute care. Key questions related to the prevention of the risk pathways identified in each risk assessment model remain unanswered, and should be a focus of future research.

Common cause failures (CCF) are a significant contributor to risk in complex technological systems, such as nuclear power plants. Many probabilistic parametric models have been developed to quantify the systems subject to the CCF. Existing models include the beta factor model, the multiple Greek letter model, the basic parameter model, the alpha factor model and the binomial failure rate model. These models are often only capable of providing a point estimate, when there are limited CCF data available. Some recent studies have proposed a Bayesian approach to quantify the uncertainties in CCF modeling, but they are limited in addressing the uncertainty in the common failure factors only. This thesis presents a multivariate Poisson model for CCF modeling, which combines the modeling of individual and common cause failures into one process. The key idea of the approach is that failures in a common cause component group of n components are decomposed into superposition of k (>n) independent Poisson processes. Empirical Bayes method is utilized for simultaneously estimating the independent and common cause failure rates which are mutually exclusive. In addition, the conventional CCF parameters can be evaluated using the outcomes of the new approach. Moreover, the uncertainties in the CCF modeling can also be addressed in an integrated manner. The failure rate is estimated as the mean value of the posterior density function while the variance of the posterior represents the variation of the estimate. A MATLAB program of the Monte Carlo simulation was developed to check the behavior of the proposed multivariate Poisson (MVP) model. Superiority over the traditional CCF models has been illustrated. Furthermore, due to the rarity of the CCF data observed at one nuclear power plant, data of the target plant alone are insufficient to produce reliable estimates of the failure rates. Data mapping has been developed to make use of the data from source plants of different sizes. In this thesis, data mapping is combined with EB approach to partially assimilate information from source plants and also respect the data of the target plant. Two case studies are presented using different database. The results are compared to the empirical values provided by the United States Nuclear Regulatory Commission (USNRC).

With the severity of cardiovascular disease (CVD) and the related mortality rate to this disease, new methods are necessary for risk assessment and treatment prior to the onset of the disease. The current paradigm in CVD risk assessment has shifted towards the multivariate approach over the individual use of traditional risk factors or lipid measurements. Through a combination of analytical techniques and multivariate statistical analysis, a novel method of cardiovascular risk assessment was developed. The analytical techniques employed include density gradient ultracentrifugation (DGU) and matrix assisted laser desorption ionization mass spectrometry (MALDI-MS) applied to human serum. These techniques provided detailed information about the characterization of the lipoproteins and their structural components, specifically the apolipoproteins belonging to high density lipoproteins (HDL). This information when combined with multivariate statistical analysis provided a method that accurately identified the presence of CVD in clinical studies between cohorts of subjects that had been previously diagnosed with CVD and cohorts of subjects that had been identified as healthy controls (CTRL) based on a clear angiography. The lipoprotein density profiles were divided into subclasses based on their density and measured using a fluorescent probe to tag the lipoprotein particles. Use of multiple ethylenediaminetetraacetic acid (EDTA) based solutes allowed for the manipulation of the density gradient formation in order to separate the lipoproteins by specific density ranges in order to achieve better baseline separation of the profiles. Application of the integrated fluorescence intensities for each subclass of lipoprotein to linear discriminant analysis/sliced inverse regression (LDA/SIR) and quadratic discriminant analysis (QDA) yielded an advanced and accurate form of risk assessment for CVD. This method was found to be highly accurate as well as identify potential atherogenic lipoprotein subclasses through studying the LDA/SIR prediction equation generated. It was also shown that the LDA/SIR equation could be used to monitor medical treatment and lifestyle change for their effects on the risk assessment model. Further study into the atherogenicity of HDL through analysis of the apolipoproteins using MALDI-MS led to identification of potential risk factors that could be added to the statistical analyses. These risk factors included mass differences in the Apolipoprotein A-I (Apo A-I) and Apolipoprotein C-I (Apo C-I) between CVD and CTRL samples as well as the presence of specific mass peaks related to Apolipoprotein A-II (Apo A-II) that were primarily found in the CVD samples. These differences, in addition to the lipoprotein density profile data, were found to increase the potential accuracy of CVD risk assessment. The combination of these methods has shown great potential in the assessment of CVD risk as well as the ability to increase researchers' understanding of the nature of VD and how to treat it.

Water supply systems aim, among other objectives, to protect public health by reducing the concentration of, and potentially eliminating, microorganisms pathogenic to human beings. Yet, because water supply systems are engineered systems facing variable conditions, such as raw water quality or treatment process performance, the quality of the drinking water produced also exhibits variability. The reliability of a treatment system is defined in this context as the probability of producing drinking water that complies with existing microbial quality standards. This thesis examines the concept of reliability for two physicochemical treatment technologies, conventional rapid granular filtration and ultrafiltration, used to remove the protozoan pathogen Cryptosporidium parvum from drinking water. First, fault tree analysis is used as a method of identifying technical hazards related to the operation of these two technologies and to propose ways of minimizing the probability of failure of the systems. This method is used to compile operators’ knowledge into a single logical diagram and allows the identification of important processes which require efficient monitoring and maintenance practices. Second, an existing quantitative microbial risk assessment model is extended to be used in a reliability analysis. The extended model is used to quantify the reliability of the ultrafiltration system, for which performance is based on full-scale operational data, and to compare it with the reliability of rapid granular filtration systems, for which performance is based on previously published data. This method allows for a sound comparison of the reliability of the two technologies. Several issues remain to be addressed regarding the approaches used to quantify the different input variables of the model. The approaches proposed herein can be applied to other water treatment technologies, to aid in prioritizing interventions to improve system reliability at the operational level, and to determine the data needs for further refinements of the estimates of important variables.

The use of on-site wastewater treatment systems (OWTS) for the treatment and dispersal of domestic effluent is common in urban fringe areas which are not serviced by centralised wastewater collection systems. However, due to inappropriate siting and soil characteristics, the failure of these systems has become a common scenario. The current standards and guidelines adopted by many local authorities for assessing suitable site and soil conditions for OWTS are increasingly coming under scrutiny due to the public health and environmental impacts caused by poorly performing systems, in particular septic tank-soil adsorption systems. In order to achieve sustainable on-site wastewater treatment with minimal impacts on the environment and public health, more appropriate means of assessment are required. The research described in the thesis details the processes adopted for the development and implementation of an integrated risk based approach to OWTS siting, design and management. This involved detailed investigations into resolution of some of the inherent deficiencies identified in the existing OWTS codes and guidelines, including more thorough site and soil assessment and data analysis, integration of the key risk facets of OWTS siting and design, environmental and public health, and the incorporation of scientific knowledge into the assessment processes. The research undertaken focused on four key research areas; (i) assessment of soil suitability for providing adequate treatment and dispersal of domestic wastewater; (ii) contamination of ground and surface waters as a result of OWTS failure and the major factors influencing contaminant fate and transport; (iii) assessment of environmental and public health risks due to poor OWTS performance; and (iv) the development of an integrated risk assessment framework for OWTS siting, design and management. The research conducted was multidisciplinary in nature, with detailed investigations of the physical, chemical and biological processes involved in on-site wastewater treatment and dispersal. This involved extensive field investigations, sampling, laboratory testing and detailed data analysis across the fields of soil science, groundwater and surface water quality, chemical and microbiological contamination, and contaminant fate and transport processes. The interactions between these different disciplines can be complex, resulting in large amounts of data being generated from the numerous field investigations and sampling processes undertaken. In order to understand the complex relationships that can occur, multivariate statistical techniques were utilised. The use of these techniques were extremely beneficial, as not only were the respective relationships between investigated parameters identified, but adequate decisions based on the respective correlations were formulated. This allowed a more appropriate assessment of the influential factors, and subsequently the inherent hazards related to OWTS, to be conducted. The primary research objectives for this research were investigated through a series of scientific papers centred on these key research disciplines. The assessment of soil suitability was achieved through extensive soil sampling throughout the study area and detailed laboratory testing and data analysis. The studies undertaken are described in Chapters 3, 4 and 5. Paper 1 (Framework for soil suitability evaluation for sewage effluent renovation) outlines a framework for assessing the renovation ability of the major soil groups located throughout Southeast Queensland. This framework formed the basis for the assessment of OWTS siting and design risks employed in the developed risk framework. Paper 2 (Use of Chemometric Methods and Multicriteria Decision-Making for Site Selection for Sustainable On-site Sewage Effluent Disposal) details and justifies the multivariate data analysis techniques used in establishing the soil framework. Paper 3 (Assessment of soil suitability for effluent renovation using undisturbed soil columns) describes investigations of the use of undisturbed soil cores for the assessment of long term soil renovation ability. This study was undertaken to validate the research outcomes achieved through the established framework developed in Paper 1. Papers 4, 5 and 6 (Chapters 6 - 8) focus on contamination issues of ground and surface waters resulting from poor OWTS treatment performance, and the different factors that influence pollutant fate and transport. The investigation of ground and surface water contamination, detailed in Paper 4 (Assessment of High Density of Onsite Wastewater Treatment Systems on a Shallow Groundwater Coastal Aquifer using PCA) and Paper 5 (Environmental and anthropogenic factors affecting fecal coliforms and E. coli in ground and surface waters in a coastal environment) was achieved through extensive ground and surface water sampling and testing from several monitored study sites. Analysis of the resulting data indicated that several key factors, including rainfall, site and soil conditions and on-site system density can significantly influence the fate and transportation of pollutants emitted from OWTS. An additional issue found during this research in assessing faecal contamination of water resources was the necessity to ensure that the respective sources of contamination were actually OWTS. The inherent difficulty in identifying the actual source of contamination was resolved by employing a source tracking method, namely antibiotic resistance analysis of faecal coliforms (Paper 6; Sourcing fecal pollution from onsite wastewater treatment systems in surface waters using antibiotic resistance analysis). Finally, Paper 7 (Integrated Risk Framework for On-site Wastewater Treatment Systems) describes the development of the final generic integrated risk assessment framework and how the outcomes, as discussed through the previous 6 papers, were combined to assess the environmental and public health risks inherent in OWTS siting and design. The outcomes of this research have significantly contributed to knowledge of best practice in OWTS siting, design and management. The developed soil suitability framework allows more appropriate assessment of soil characteristics for providing effluent renovation. This is generally not done in the current assessment techniques for OWTS. Additionally, the use of this framework incorporates scientific knowledge into the assessment of OWTS, allowing a more rigorous and scientifically robust assessment process. The processes and techniques used in the soil suitability framework, although based on the common soil types typical of South East Queensland, can be implemented in other regions, provided appropriate soil information is collected for the area of interest. The integrated risk framework has also been developed on a generic level, allowing easy implementation into most assessment processes. This gives the framework the flexibility to be developed for other areas specifically targeting the most influential OWTS siting and design factors, and the potential environmental and public health hazards within those regions. The resulting research outcomes achieved through the studies undertaken were subsequently applied to a case study for the development of the integrated risk framework for the Gold Coast region. The developed framework, based on scientific research, has allowed a more appropriate means of assessing site suitability for OWTS and appropriate management and mitigation of the environmental and public health risks inherent with poor OWTS performance

Safety culture has risen to prominence over the past two decades as a means by which organisations may enhance their safety performance. Safety culture may be conceptualised as an interpretive device that mediates between organisational safety rhetoric and safety programs on the one hand, and local workplace cultures on the other. More recently, risk-awareness has emerged as a cultural approach to safety. Front line workers are encouraged to become risk-aware through programs designed to prompt them to undertake mental or informal risk assessments before commencing work. The problem is that risk-awareness programs have not been the subject of systematic research and the impact of these programs on the culture of safety and the resultant level of risk is unknown. Therefore, this ethnographic study of two sites within a large contract maintenance organisation in Australia explored what impact risk-awareness programs have upon the culture of safety and the resultant level of risk. The researcher spent two months in the field and data was collected through participant observation, semistructured interviews and through a review of organisational documents. This study found that managers focused upon collecting the paperwork associated with the program as proof that workers had a safer workplace, whereas workers preferred to rely upon their common sense rather than the paperwork to keep them safe. As a consequence, the riskawareness program resulted in a culture of paperwork and varying levels of risk reduction because the paperwork associated with the program created an illusion of safety for managers as much as common sense did for workers. The results of this study have implications for safety culture, risk-awareness programs and for organisational learning. They also have implications for organisations wishing to improve their safety culture by encouraging risk-awareness in front-line workers. / Doctor of Philosophy

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