Multidrug-resistant tuberculosis (MDR-TB) is a public health problem in several countries such as Angola, India, China, Kenya, and Nigeria. Due to the increasing high burden of MDR-TB, most of these countries do not have adequate capacities to manage MDR-TB patients effectively. This study investigated the effect of model of care; human immunodeficiency virus comorbidity; and demographic factors such as age, gender, and marital status on the treatment outcomes of MDR-TB patients in Nigeria. The study was based on the analysis of secondary data of 402 MDR-TB patients accessed from the data systems of the National Tuberculosis, Buruli Ulcer, and Leprosy Control Program. The theoretical framework for this study was the health belief model. The results of the study showed that treatment outcomes were similar for hospital and community-based models of care. Age was the only factor found to be significantly associated with treatment outcomes; age > than 40 years was a predictor of unsuccessful treatment outcomes among MDR-TB patients at a p-value of 0.026. In the multivariate logistics regression analysis, age and model of care were found to be significantly associated with treatment outcomes at p-values of 0.043 and 0.048, respectively. Marital status, gender, and HIV comorbidity were not significantly associated with treatment outcomes. Implications of the findings of this study for social change in a health care program include opportunities to help reduce the number of patients on waiting lists for MDR-TB treatment. These strategies may ultimately help to reduce the spread of MDR-TB infection as well as the mortality associated with late treatment.

The effectiveness of treatments for degenerative spine conditions, where the primary symptom is back pain, is typically determined using patient-reported quality of life (QoL) measures. However, patients may adjust their internal standards when scoring QoL based on factors other than their health. This response shift phenomenon could confound the interpretation of study data and impact effectiveness conclusions. In the current study, response shift was examined using structural equation modeling (SEM) and previously collected clinical trial data comparing 2 minimally invasive medical devices in lumbar spinal stenosis patients through 1 year postintervention. In subject QoL results, reprioritization shift between 3 months and 12 months that could confound standard analysis was identified. Treatment group did not influence response shift identified at 12 months. SEM provided an effective and practical tool for clinical investigators to assess response shift in available clinical study data. As response shift could lead to invalid conclusions when QoL measures are analyzed, clinical investigators should include response shift assessment in the design of clinical trials. This research into how response shift phenomenon can impact clinical trial results improves the ability of clinical investigators to interpret clinical trial data, potentially preventing erroneous conclusions. This research may also assist researchers and government regulators in the identification and reimbursement of beneficial, cost-effective medical treatments for patients worldwide. For clinical research designers, this study demonstrates a practical application of response shift assessment.

The rise of market globalization creates challenges for business executives seeking to pursue foreign direct investment (FDI) in least developed countries (LDC), such as Afghanistan. Multinational corporate (MNC) executives need strategies that will improve the timely delivery of minerals for mining projects in LDCs. Guided by the force field analysis theory, the purpose of this holistic, single-case study was to explore the strategies that 5 MNC executives in Beijing, China, used to improve the timely delivery of minerals associated with the Aynak copper mine project in Afghanistan. Semistructured interviews were used to elicit detailed narratives from MNC executives about their experiences to develop strategies for mining projects in LDCs. A review of company documents, as well as member-checking of initial interview transcripts, helped to bolster the trustworthiness of final interpretations. Study results included 2 themes. Theme 1 was determinants of mine investment strategies in LDCs that included an exploration of driving forces, restraining forces, neutral forces, and the effect of those forces. Theme 2 was FDI strategies for copper mine projects in LDCs that included the comparison of cost leadership strategy, differentiation strategy, and combination of cost leadership and differentiation strategies. By implementing a cost leadership strategy and best practices, MNC executives were able to achieve greater success to improve timely delivery of minerals associated with FDI copper mine projects in LDCs. Social implications include ongoing efforts of Afghan government leaders to implement effective economic policies that decrease unemployment while reducing poverty.

The purpose of this experimental research was to determine whether using delta-MELD as a criterion for the liver transplant patient selection process could improve the U.S. liver allocation system. This research closed a gap in current literature on the utility of delta-MELD for liver transplant patient selection. The frameworks of systems theory, the analytic hierarchy process, and the Kalman filter contributed to the development of 2 simulation models of the liver allocation system: one that used delta-MELD and one that did not use delta-MELD. The research question examined whether using delta-MELD could improve the liver allocation system by reducing the number of patients dropping off the wait list and lowering the average MELD score. Statistical t tests of 2 independent scenarios (allocation with and without delta-MELD), each with 70 runs of 180 simulated days on the liver allocation wait list, did not indicate a significant improvement to the liver allocation system by using delta-MELD for liver allocation. However, observations made from the simulation experiment, such as the median patient wait time being 11 months and delta-MELD being more variable at the end-stage of liver diseases, provided insights into how to improve the model of the liver allocation process. In addition, observations made from the status 1 patient subgroup (patients in ICU with about 7 days to live), which were excluded from this research, suggested including status 1 patients and expanding the simulation timespan from 180 to 360 days to better capture the delta-MELD variability from patients at the end-stage of liver disease. This research provides empirical evidence on the applicability of the delta-MELD criterion for non-status 1 patients, and yields recommendations to include status 1 patients in an improved simulation of the donor liver system while using delta-MELD as criterion.

The environmental uncertainty of federal politics and acquisition outsourcing in competitive markets requires an adaptive decision-analysis structure. Practitioners oriented toward exclusively static methods face severe challenges in understanding qualitative aspects of organizational governance. The purpose of this grounded theory study was to examine and understand behavioral relationship attributes within intuitive, choice, judgment, or preference decision-making processes. The problem addressed in this study was the detrimental effects of organizational citizenship behavior (OCB), compulsory citizenship behavior (CCB), and social exchange theory (SET) on the acquisition management relationship The OCB, CCB, SET dictates that sound business development, relationship acumen, emotional intelligence and perceptiveness transcend pure numerical quantification. Exhibition of relationship-based attributes influence and drive long-term contractual relationships and the sustainability of business organizations. The data collected included historical data and survey responses. Approximately 34,000 acquisition professionals comprised the population-sampling frame. The study sample consisted of 378 survey responses that yielded 294 qualifying respondents with 94 disqualifications that produced a 78% response rate. The Carnegie-Mellon behavioral survey guidelines underpinned questionnaire construction and affirmation of themes. Strauss and Corbin grounded theory and theme generation addressed behavioral decision making under the additive model that inform the development of an organizational social operations and business framework that accounts for intuitive judgment. The study may contribute to positive social change by orienting managers toward behavioral decision making, ensuring responsiveness to the public and federal governance

As a result of the passage of the Affordable Care Act, the behavioral healthcare field is experiencing an increased demand for services. This increase is based on the availability of healthcare coverage to an estimated 13.4 million previously uninsured individuals. To meet this demand for treatment, the workforce of behavioral health therapists must grow. The largest generation entering the workforce, the Millennial Generation - those born after 1980 - is believed to lack commitment to their employers, frequently vacating their positions for the next best offer. The purpose of this case study was to determine factors that both affect retention and contribute to employee turnover among Millennial behavioral health therapists, in the hope of identifying approaches for retaining them in not-for-profit organizations. This exploration used Rousseau's psychological contract theory as the theoretical lens. Secondary data from academic literature, public media, and published surveys were collected and analyzed using open coding to identify patterns and trends. Key variables influencing retention were parity in compensation, organizational culture, the opportunity to advance careers, and make a difference in the community. The implications for social change include informing policy makers and organizational leaders in behavioral healthcare about developing creative methods to increase retention. Recommendations include employer evaluation and improvement in their organizational culture and quality of relationships with their employees. The implementation of these recommendations could result in improved client outcomes, fiscal integrity, and organizational continuity.

Supercomputers have become increasingly important in recent years due to the growing amount of data available and the increasing demand for quicker results in the scientific community. Since supercomputers carry a high cost to build and maintain, efficiency becomes more important to the owners, administrators, and users of these supercomputers. One important factor in determining the efficiency of a supercomputer is the scheduling of jobs that are submitted by users of the system. Previous work has dealt with optimizing the schedule on the system’s end while the users are blinded from the process. The work presented in this thesis investigates a scheduling system that is implemented at the Oak Ridge National Laboratory (ORNL) supercomputer Kraken with a backfilling policy and attempts to outline the optimal methods from the user’s point of view in the scheduling system, along with using a simulation approach to optimize the priority formula. Normally the user has no idea which scheduling algorithms are used, but the users at ORNL not only know how the scheduling works but they can also view the current activity of the system. This gives an advantage to the users who are willing to benefit from this knowledge by utilizing some elementary game theory to optimize their strategies. The results will show a benefit to both the users, since they will be able to process their jobs sooner, and the system, since it will better utilized with little expense to the administrators, through competition. Queuing models and simulation have been well studied in almost all relevant aspects of the modern world. Higher efficiency is the goal of many researchers in several different fields; the supercomputer queues are no different. Efficient use of the resources makes the system administrator pleased while benefiting the users with more timely results. Studying these queuing models through simulation should help all parties involved by increasing utilization. The simulation will be validated and the utilization improvement will be measured and reported. User defined formulas will be developed for future users to help maximize utilization and minimize wait times.

A model to analyze lean systems reliability is developed based on the premises that lean production systems require four primary resources; materials, equipment, personnel, and schedule (Sawhney, R., Subburaman, K., Sonntag, C., Capizzi, C. and Rao, P.V., 2009). The four primary lean resources were independently analyzed, without due consideration to underlying relationships within each other. In this research, relationships and interaction between root-causes and lean primary resources were conceptualized and developed. The development applied a five-phase approach: 1. review of related literature, 2. decompositions of lean primary resources, 3. selection of consensus root causes items and questionnaire construct, 4. survey design and deployment, 5. collection and analysis of data and relationship and model building. This approach harnessed real world experiences of field experts. The result shows very significant levels of relationships and interactions within root-cause items and primary resources. This result, applied to lean implementation, is expected to enable experts to direct their efforts and resources to prevent undesired events that have the greatest impact on lean system.

Cap-and-trade is the most discussed CO2 emissions control scheme in the U.S. It is a market-based mechanism that has been used previously to successfully reduce the levels of SO2 and NOx emitted by power generators. Since electricity generators are responsible for about 40% of the CO2 emissions in the U.S., the implementation of CO2 cap-and-trade will have a significant impact on electric power generation systems. In particular, cap-and-trade will influence the investment decisions made by power generators. These decisions in turn, will affect electricity prices and demand. If the allowances (or emission permits) created by a cap-and-trade program are auctioned, the government will collect a significant amount of money that can be redistributed back to the electricity market participants to mitigate increases on electricity prices due to cap-and-trade and also, to increase the market share of low-emission generators. In this dissertation, we develop two models to analyze the impact of CO2 cap-and-trade on electric power generation systems. The first model is intended to be used by power generators in a restructured market to evaluate investment decisions under different CO2 cap-and-trade programs for a given time horizon and a given forecast in demand growth. The second model is intended to aid policymakers in developing optimal CO2 revenue redistribution policies via subsidies for low-emission generators. Through the development of these two models, our underlying objective is to provide analysis tools for policymakers and market participants so that they can make informed decisions about the design of cap-and-trade programs and about the market actions they can take if such programs are implemented.

This dissertation focuses on extending solution methods in the area of stochastic optimization. Attention is focused to three specific problems in the field. First, a solution method for mixed integer programs subject to chance constraints is discussed. This class of problems serves as an effective modeling framework for a wide variety of applied problems. Unfortunately, chance constrained mixed integer programs tend to be very challenging to solve. Thus, the aim of this work is to address some of these challenges by exploiting the structure of the deterministic reformulation for the problem. Second, a stochastic program for integrating renewable energy sources into traditional energy systems is developed. As the global push for higher utilization of such green resources increases, such models will prove invaluable to energy system designers. Finally, a process for transforming clinical medical data into a model to assist decision making during the treatment planning phase for palliative chemotherapy is outlined. This work will likely provide decision support tools for oncologists. Moreover, given the new requirements for the usage electronic medical records, such techniques will have applicability to other treatment planning applications in the future.