Foodborne illnesses place a burden on the entire society. One strategy to lower the costs of foodborne illnesses is to reduce the prevalence of foodborne pathogens through interventions along the food supply chain. There is an ongoing trend that food safety systems are moving towards performance-based regimes, which rely on the implementation of food safety standards. However, the implementation of food safety standards has not garnered much interest in the Canadian policy environment. The assessment of food safety interventions to achieve a standard is challenging as the underlying biological processes are complex, the costs of administering such interventions are not abundantly clear and the set of available interventions is changing. This thesis investigates the cost-effectiveness of food safety interventions and specifically the applicability of a food safety standard. First, a theoretical model is developed to investigate how; in theory cost-minimization can be used to identify the most cost-effective way to reduce foodborne pathogens with the utilization of a food safety standard. Then, a specific framework is developed for Campylobacter in chicken that consists of interrelated simulation models that represent the level and flow of pathogens through a commodity supply chain, the impact of alternative interventions on pathogen load and their costs. The case study focus is Ontario, Canada. Different interventions are compared and evaluated based on their compliance with a food safety standard. The applicability of different cost-effectiveness measures is assessed.

This research investigates factors that influence severity of speed-related crashes on mainline roadway segments, with a particular emphasis on comparing single-vehicle and multiple-vehicle incidents and distinguishing between crashes involving legal speed limit violations and those exceeding the maximum safe travel speed as determined by law enforcement. Additionally, it examines significant factors related to classifying a crash as exceeding the maximum safe travel speed. Using crash data from the Traffic Records Electronic Data System (TREDS) for Virginia for 2023, the research employs both Ordinal and Nominal Logistic Regression models for analysis. The findings reveal that higher vehicle speeds before a crash significantly increase crash severity level across all scenarios. Rain and snow/sleet weather conditions exhibit significant impacts on crash outcomes, with adverse conditions often leading to increased severity levels. Roadway characteristics in terms of presence of medians and road surface conditions, are also found to be significant, as are. the driver-related factors of age, safety equipment used, EMS transport after the crash, and vehicle type. The study's comparative analysis between single and multiple vehicles speeding crashes, as well as speeding beyond legal limits and exceeding maximum safe travel speed highlights the contextual differences in crash severity determinants. The findings on classifying crashes as exceeding maximum safe travel speed highlight conditions that influence this designation as well as factors that can lead to inconsistencies in that classification. For example, environmental conditions like rain or snow, certain crash types, and work zone crashes may result in subjective assessments rather than objective determinations. The research offers valuable insights for informing targeted road safety strategies within the Safe System framework – targeted at reducing the severity of speed-related crashes for mainline road segments. The findings support implementing comprehensive strategies that address the complex interplay of speed, road conditions, vehicle characteristics, and driver factors in mitigating crash severity. / Master of Science / This research explores how speeding affects the severity of car crashes, seeking to understand why some accidents are more dangerous than others. By analyzing crash data from Virginia in 2023, the study looks at different types of crash scenarios – those involving just one vehicle and those involving multiple vehicles – and examines how factors like weather, road conditions, vehicle and driver characteristics contribute to the seriousness of these crashes. The research compares crashes where drivers exceed the legal speed limit with those where they drive faster than is safe under the given road conditions. Additionally, it investigates key factors that potentially influence law enforcement at the scene to designate that a crash involves a driver exceeding the safest speed for road and traffic conditions. The findings show that driving at higher speeds before a crash significantly increases the chances of severe injuries or fatalities. The study indicates how weather conditions, design characteristics of roads, or the condition of the road surface, impact crash severity. Driver age and whether drivers were under the influence of alcohol or drugs, and whether vehicle safety equipment like seatbelts were used, are significant in determining the severity of a crash. The findings on classifying crashes as exceeding maximum safe travel speed highlight conditions that influence this designation as well as factors that can lead to inconsistencies in that classification. This research is important because it provides insights for improving road safety.

Medication administration errors (MAEs) may lead to adverse drug events, patient morbidity, prolonged hospital stays, and increased readmission rates, and may contribute to major financial losses for the health system. MAEs are the most common type of error occurring within the health care setting leading to an estimated 7,000 patient deaths every year. Interventions have been designed to prevent MAEs including education for nurses who administer medications; however, little effort has been made to design systematic educational programs that are based on local needs and contexts. The purpose of this project was to identify internal and external factors related to MAEs at the practice site, develop an education program tailored to the factors contributing to MAEs, and implement the program using a pretest posttest design. The Iowa model was used to guide the project. The 26 nurse participants who responded to an initial survey indicated that nurses felt distractions and interruptions during medication administration, and hesitancy to ask for help or to report medication errors increased MAE risks. After the education program, the pretest and posttest results were analyzed and revealed improvement in knowledge and confidence of medication administration (M = 3.2 pre, M = 3.7 post, p < .05). Open-ended question responses suggested a need for dedicated time for preparation and administration of medications without interruptions. Positive social change is possible as nurses become knowledgeable and confident about medication administration safety and as patients are protected from injury secondary to MAEs.

Healthcare systems require continuous monitoring of risk to prevent adverse events. Risk analysis is a time consuming activity that depends on the background of analysts and available data. Patient safety data is often incomplete and biased. This research proposes systematic approaches to monitor risk in healthcare using available patient safety data. The methodologies combine traditional healthcare risk analysis methods with safety theory concepts, in an innovative manner, to allocate available evidence to potential risk sources throughout the system. We propose the use of data mining to analyze near-miss reports and guide the identification of risk sources. In addition, we propose a Maximum-Entropy based approach to monitor risk sources and prioritize investigation efforts accordingly. The products of this research are intended to facilitate risk analysis and allow for timely identification of risks to prevent harm to patients.