THE EPIDEMIOLOGY AND HEALTH ECONOMICS OF ANKLE INJURIES

The thesis focuses on a central theme of the epidemiology and health economics of ankle sprains to inform health policy and the provision of health services. It describes the burden, prognosis, resource utilization, and costs attributed to these injuries. The first manuscript systematically reviewed 34 studies on the direct and indirect costs of treating ankle and foot injuries. The overall costs per patient ranged from $2,075- $3,799 (2014 USD) for ankle sprains; $290-$20,132 for ankle fractures; and $6,345-$45,731 for foot fractures, reflecting differences in injury severity, treatment methods, and study characteristics. The second manuscript provided an epidemiological and economic profile of non-fracture ankle and foot injuries in Ontario using linked databases from the Institute for Clinical Evaluative Sciences. The incidence rate of ankle sprains was 16.9/1,000 person-years. Annually, ankle and foot injuries cost $21,685,876 (2015 CAD). The mean expenses per case were $99.98 (95% CI, $99.70-100.26) for any injury. Costs ranged from $133.78-$210.75 for ankle sprains and $1,497.12-$1,755.69 for dislocations. The third manuscript explored the impact of body mass index on recovery from medically attended grade 1 and 2 ankle sprains using the Foot and Ankle Outcome Score. Data came from a randomized controlled trial of a physiotherapy intervention in Kingston, Ontario. At six months, the odds ratio of recovery for participants with obesity was 0.60 (0.37-0.97) before adjustment and 0.74 (0.43-1.29) after adjustment compared to non-overweight participants. The fourth manuscript used trial data to examine the health-related quality of life among ankle sprain patients using the Health Utilities Index version 3 (HUI-3). The greatest improvements in scores were seen at one month post-injury (HUI-3: 0.88, 95% CI: 0.86-0.90). Individuals with grade 2 sprains had significantly lower ambulation scores than those with grade 1 sprains (0.70 vs. 0.84; p<0.05). The final manuscript used trial data to describe the financial burden (direct and indirect costs) of ankle sprains. The overall mean costs were $1,508 (SD: $1,452) at one month and increased to $2,206 (SD: $3,419) at six months. Individuals with more severe injuries at baseline had significantly higher (p<0.001) costs compared to individuals with less severe injuries, after controlling for confounders.

Utilizing “Big Data” to Improve the Hotel Sector’s Energy Efficiency: Lessons from Recent Economics Research

Hotel chains have access to a treasure trove of “big data” on individual hotels’ monthly electricity and water consumption. Benchmarked comparisons of hotels within a specific chain create the opportunity to cost-effectively improve the environmental performance of specific hotels. This paper describes a simple approach for using such data to achieve the joint goals of reducing operating expenditure and achieving broad sustainability goals. In recent years, energy economists have used such “big data” to generate insights about the energy consumption of the residential, commercial, and industrial sectors. Lessons from these studies are directly applicable for the hotel sector. A hotel’s administrative data provide a “laboratory” for conducting random control trials to establish what works in enhancing hotel energy efficiency.

Economics of Ethical and Prosocial Behavior: Experiments on Social Responsibility, Ignorance, and Cheating

In this thesis I experimentally investigate prosocial and ethical behavior in economic interactions. The thesis consists of three experimental research papers that have a broad range of research questions on social responsibility, ignorance and cheating. With these experiments I aim to better understand when and why people behave ethically and/or prosocially and which consequences it has on their own and other players’ payoffs, and on overall efficiency. The results from the three experimental studies suggest that (i) donations to charity by employees are rewarded in an experimental setting, and the effect is driven by reciprocal concerns; (ii) there is a significant fraction of people who decide not to know about negative consequences of own actions, and the sorting of social agents of a low type into ignorance drives self-interested behavior of ignorant agents; and (iii) if the possibility of being exposed as a liar is small, the tendency to lie increases with incentives, indicating that some people have positive and finite costs of lying. Furthermore, when the participants lie, they lie to the full extent, which suggests that the intrinsic cost of lying is fixed.

BUILDING EFFICIENT AND COST-EFFECTIVE CLOUD-BASED BIG DATA MANAGEMENT SYSTEMS

In today’s big data world, data is being produced in massive volumes, at great velocity and from a variety of different sources such as mobile devices, sensors, a plethora of small devices hooked to the internet (Internet of Things), social networks, communication networks and many others. Interactive querying and large-scale analytics are being increasingly used to derive value out of this big data. A large portion of this data is being stored and processed in the Cloud due the several advantages provided by the Cloud such as scalability, elasticity, availability, low cost of ownership and the overall economies of scale. There is thus, a growing need for large-scale cloud-based data management systems that can support real-time ingest, storage and processing of large volumes of heterogeneous data. However, in the pay-as-you-go Cloud environment, the cost of analytics can grow linearly with the time and resources required. Reducing the cost of data analytics in the Cloud thus remains a primary challenge. In my dissertation research, I have focused on building efficient and cost-effective cloud-based data management systems for different application domains that are predominant in cloud computing environments. In the first part of my dissertation, I address the problem of reducing the cost of transactional workloads on relational databases to support database-as-a-service in the Cloud. The primary challenges in supporting such workloads include choosing how to partition the data across a large number of machines, minimizing the number of distributed transactions, providing high data availability, and tolerating failures gracefully. I have designed, built and evaluated SWORD, an end-to-end scalable online transaction processing system, that utilizes workload-aware data placement and replication to minimize the number of distributed transactions that incorporates a suite of novel techniques to significantly reduce the overheads incurred both during the initial placement of data, and during query execution at runtime. In the second part of my dissertation, I focus on sampling-based progressive analytics as a means to reduce the cost of data analytics in the relational domain. Sampling has been traditionally used by data scientists to get progressive answers to complex analytical tasks over large volumes of data. Typically, this involves manually extracting samples of increasing data size (progressive samples) for exploratory querying. This provides the data scientists with user control, repeatable semantics, and result provenance. However, such solutions result in tedious workflows that preclude the reuse of work across samples. On the other hand, existing approximate query processing systems report early results, but do not offer the above benefits for complex ad-hoc queries. I propose a new progressive data-parallel computation framework, NOW!, that provides support for progressive analytics over big data. In particular, NOW! enables progressive relational (SQL) query support in the Cloud using unique progress semantics that allow efficient and deterministic query processing over samples providing meaningful early results and provenance to data scientists. NOW! enables the provision of early results using significantly fewer resources thereby enabling a substantial reduction in the cost incurred during such analytics. Finally, I propose NSCALE, a system for efficient and cost-effective complex analytics on large-scale graph-structured data in the Cloud. The system is based on the key observation that a wide range of complex analysis tasks over graph data require processing and reasoning about a large number of multi-hop neighborhoods or subgraphs in the graph; examples include ego network analysis, motif counting in biological networks, finding social circles in social networks, personalized recommendations, link prediction, etc. These tasks are not well served by existing vertex-centric graph processing frameworks whose computation and execution models limit the user program to directly access the state of a single vertex, resulting in high execution overheads. Further, the lack of support for extracting the relevant portions of the graph that are of interest to an analysis task and loading it onto distributed memory leads to poor scalability. NSCALE allows users to write programs at the level of neighborhoods or subgraphs rather than at the level of vertices, and to declaratively specify the subgraphs of interest. It enables the efficient distributed execution of these neighborhood-centric complex analysis tasks over largescale graphs, while minimizing resource consumption and communication cost, thereby substantially reducing the overall cost of graph data analytics in the Cloud. The results of our extensive experimental evaluation of these prototypes with several real-world data sets and applications validate the effectiveness of our techniques which provide orders-of-magnitude reductions in the overheads of distributed data querying and analysis in the Cloud.

Implications of retaining woody regrowth for carbon sequestration for an extensive grazing beef business: a bio-economic modelling case study

A bio-economic modelling framework (GRASP-ENTERPRISE) was used to assess the implications of retaining woody regrowth for carbon sequestration on a case study beef grazing property in northern Australia. Five carbon farming scenarios, ranging from 0% to 100% of the property regrowth retained for carbon sequestration, were simulated over a 20-year period (1993–2012). Dedicating regrowth on the property for carbon sequestration reduced pasture (up to 40%) and herd productivity (up to 20%), and resulted in financial losses (up to 24% reduction in total gross margin). A net carbon income (income after grazing management expenses are removed) of $2–4 per t CO2-e was required to offset economic losses of retaining regrowth on a moderately productive (~8 ha adult equivalent–1) property where income was from the sale of weaners. A higher opportunity cost ($ t–1 CO2-e) of retaining woody regrowth is likely for feeder steer or finishing operations, with improved cattle prices, and where the substantial transaction and reporting costs are included. Although uncertainty remains around the price received for carbon farming activities, this study demonstrated that a conservatively stocked breeding operation can achieve positive production, environmental and economic outcomes, including net carbon stock. This study was based on a beef enterprise in central Queensland’s grazing lands, however, the approach and learnings are expected to be applicable across northern Australia where regrowth is present.

A systematic review of the cost-effectiveness of targeted therapies for metastatic non-small cell lung cancer (NSCLC)

Background: Non-small cell lung cancer (NSCLC) imposes a substantial burden on patients, health care systems and society due to increasing incidence and poor survival rates. In recent years, advances in the treatment of metastatic NSCLC have resulted from the introduction of targeted therapies. However, the application of these new agents increases treatment costs considerably. The objective of this article is to review the economic evidence of targeted therapies in metastatic NSCLC. Methods: A systematic literature review was conducted to identify cost-effectiveness (CE) as well as cost-utility studies. Medline, Embase, SciSearch, Cochrane, and 9 other databases were searched from 2000 through April 2013 (including update) for full-text publications. The quality of the studies was assessed via the validated Quality of Health Economic Studies (QHES) instrument. Results: Nineteen studies (including update) involving the MoAb bevacizumab and the Tyrosine-kinase inhibitors erlotinib and gefitinib met all inclusion criteria. The majority of studies analyzed the CE of first-line maintenance and second-line treatment with erlotinib. Five studies dealt with bevacizumab in first-line regimes. Gefitinib and pharmacogenomic profiling were each covered by only two studies. Furthermore, the available evidence was of only fair quality. Conclusion: First-line maintenance treatment with erlotinib compared to Best Supportive Care (BSC) can be considered cost-effective. In comparison to docetaxel, erlotinib is likely to be cost-effective in subsequent treatment regimens as well. The insights for bevacizumab are miscellaneous. There are findings that gefitinib is cost-effective in first- and second-line treatment, however, based on only two studies. The role of pharmacogenomic testing needs to be evaluated. Therefore, future research should improve the available evidence and consider pharmacogenomic profiling as specified by the European Medicines Agency. Upcoming agents like crizotinib and afatinib need to be analyzed as well. © Lange et al.

Evidence that risk adjustment is unnecessary in estimates of the User cost of money

Investors value the special attributes of monetary assets (e.g., exchangeability, liquidity, and safety) and pay a premium for holding them in the form of a lower return rate -- The user cost of holding monetary assets can be measured approximately by the difference between the returns on illiquid risky assets and those of safer liquid assets -- A more appropriate measure should adjust this difference by the differential risk of the assets in question -- We investigate the impact that time non-separable preferences has on the estimation of the risk-adjusted user cost of money -- Using U.K. data from 1965Q1 to 2011Q1, we estimate a habit-based asset pricing model with money in the utility function and find that the risk adjustment for risky monetary assets is negligible -- Thus, researchers can dispense with risk adjusting the user cost of money in constructing monetary aggregate indexes

What Transaction Costs are Acceptable in Life Insurance Products from the Policyholders' Viewpoint?

Purpose - The purpose of this paper is to analyze what transaction costs are acceptable for customers in different investments. In this study, two life insurance contracts, a mutual fund and a risk-free investment, as alternative investment forms are considered. The first two products under scrutiny are a life insurance investment with a point-to-point capital guarantee and a participating contract with an annual interest rate guarantee and participation in the insurer's surplus. The policyholder assesses the various investment opportunities using different utility measures. For selected types of risk profiles, the utility position and the investor's preference for the various investments are assessed. Based on this analysis, the authors study which cost levels can make all of the products equally rewarding for the investor. Design/methodology/approach - The paper notes the risk-neutral valuation calibration using empirical data utility and performance measurement dynamics underlying: geometric Brownian motion numerical examples via Monte Carlo simulation. Findings - In the first step, the financial performance of the various saving opportunities under different assumptions of the investor's utility measurement is studied. In the second step, the authors calculate the level of transaction costs that are allowed in the various products to make all of the investment opportunities equally rewarding from the investor's point of view. A comparison of these results with transaction costs that are common in the market shows that insurance companies must be careful with respect to the level of transaction costs that they pass on to their customers to provide attractive payoff distributions. Originality/value - To the best of the authors' knowledge, their research question - i.e. which transaction costs for life insurance products would be acceptable from the customer's point of view - has not been studied in the above described context so far.

A COST-BENEFIT ANALYSIS OF A 25% RPS IN MICHIGAN

Michigan depends heavily on fossil fuels to generate electricity. Compared with fossil fuels, electricity generation from renewable energy produces less pollutants emissions. A Renewable Portfolio Standard (RPS) is a mandate that requires electric utilities to generate a certain amount of electricity from renewable energy sources. This thesis applies the Cost-Benefits Analysis (CBA) method to investigate the impacts of implementing a 25% in Michigan by 2025. It is found that a 25% RPS will create about $20.12 billion in net benefits to the State. Moreover, if current tax credit policies will not change until 2025, its net present value will increase to about $26.59 billion. Based on the results of this CBA, a 25% RPS should be approved. The result of future studies on the same issue can be improved if more state specific data become available.

A Cost Analysis Of Carbon Dioxide Emission Reduction Strategies For New Plants In Michigan's Electric Power Sector

This thesis attempts to find the least-cost strategy to reduce CO2 emission by replacing coal by other energy sources for electricity generation in the context of the proposed EPA’s regulation on CO2 emissions from existing coal-fired power plants. An ARIMA model is built to forecast coal consumption for electricity generation and its CO2 emissions in Michigan from 2016 to 2020. CO2 emission reduction costs are calculated under three emission reduction scenarios- reduction to 17%, 30% and 50% below the 2005 emission level. The impacts of Production Tax Credit (PTC) and the intermittency of renewable energy are also discussed. The results indicate that in most cases natural gas will be the best alternative to coal for electricity generation to realize CO2 reduction goals; if the PTC for wind power will continue after 2015, a natural gas and wind combination approach could be the best strategy based on the least-cost criterion.

Resource endowment and opportunity cost effects along the stages of entrepreneurship

In this paper, the start-up process is split conceptually into four stages: considering entrepreneurship, intending to start a new business in the next 3 years, nascent entrepreneurship and owning-managing a newly established business. We investigate the determinants of all of these jointly, using a multinomial logit model; it allows for the effects of resources and capabilities to vary across these stages. We employ the Global Entrepreneurship Monitor database for the years 2006–2009, containing 8269 usable observations from respondents drawn from the Lower Layer Super Output Areas in the East Midlands (UK) so that individual observations are linked to space. Our results show that the role of education, experience, and availability of ‘entrepreneurial capital’ in the local neighbourhood varies along the different stages of the entrepreneurial process. In the early stages, the negative (opportunity cost) effect of resources endowment dominates, yet it tends to reverse in the advanced stages, where the positive effect of resources becomes stronger.

The Epidemiology and Health Economics of Ankle Injuries

Thesis (Ph.D, Community Health & Epidemiology) -- Queen's University, 2016-10-03 22:59:05.858

The Economics of Adaptation to Climate Change in Coasts and Oceans: Literature Review, Policy Implications and Research Agenda

Sea level rise and other effects of climate change on oceans and coasts around the world are major reasons to halt the emissions of greenhouse gases to the maximum extent. But historical emissions and sea level rise have already begun so steps to adapt to a world where shorelines, coastal populations, and economies could be dramatically altered are now essential. This presents significant economic challenges in four areas. (1) Large expenditures for adaptation steps may be required but the extent of sea level rise and thus the expenditures are unknowable at this point. Traditional methods for comparing benefits and costs are severely limited, but decisions must still be made. (2) It is not clear where the funding for adaptation will come from, which is a barrier to even starting planning. (3) The extent of economic vulnerability has been illustrated with assessments of risks to current properties, but these likely significantly understate the risks that lie in the future. (4) Market-based solutions to reducing climate change are now generally accepted, but their role in adaptation is less clear. Reviewing the literature addressing each of these points, this paper suggests specific strategies for dealing with uncertainty in assessing the economics of adaptation options, reviews the wide range of options for funding coastal adaption, identifies a number of serious deficiencies in current economic vulnerability studies, and suggests how market based approaches might be used in shaping adaptation strategies. The paper concludes by identifying a research agenda for the economics of coastal adaptation that, if completed, could significantly increase the likelihood of economically efficient coastal adaptation.

Climate Change Adaptation Case Study: Benefit-Cost Analysis of Coastal Flooding Hazard Mitigation

The damage Hurricane Sandy caused had far-reaching repercussions up and down the East Coast of the United States. Vast coastal flooding accompanied the storm, inundating homes, businesses, and utility and emergency facilities. Since the storm, projects to mitigate similar future floods have been scrutinized. Such projects not only need to keep out floodwaters but also be designed to withstand the effect that climate change might have on rising sea levels and increased flood risk. In this study, we develop an economic model to assess the costs and benefits of a berm (sea wall) to mitigate the effects of flooding from a large storm. We account for the lifecycle costs of the project, which include those for the upfront construction of the berm, ongoing maintenance, land acquisition, and wetland and recreation zone construction. Benefits of the project include avoided fatalities, avoided residential and commercial damages, avoided utility and municipal damages, recreational and health benefits, avoided debris removal expenses, and avoided loss of function of key transportation and commercial infrastructure located in the area. Our estimate of the beneficial effects of the berm includes ecosystem services from wetlands and health benefits to the surrounding community from a park and nature system constructed along the berm. To account for the effects of climate change and verify that the project will maintain its effectiveness over the long term, we allow the risk of flooding to increase over time. Over our 50-year time horizon, we double the risk of 100- and 500-year flood events to account for the effects of sea level rise on coastal flooding. Based on the economic analysis, the project is highly cost beneficial over its 50-year timeframe. This analysis demonstrates that climate change adaptation investments can be cost beneficial even though they mitigate the impacts of low-probability, high-consequence events.