Introduction: Clostridium difficile infection (CDI) is a common hospital-acquired infection and a financial burden on the healthcare system. There is a need to reduce its impact on patients and the entire health system. More accurate estimates of the financial impact of CDI will assist hospitals in creating better CDI reduction strategies with limited resources. Previous research has not sufficiently accounted for the skewed nature of hospital cost data, baseline patient mortality risk, and the time-varying nature of CDI. Objective: We conducted a retrospective cohort study to estimate the cost impact of hospital-acquired CDI from the hospital perspective, using a number of analytical approaches. Method: We used clinical and administrative data for inpatients treated at The Ottawa Hospital to construct an analytical data set. Our primary outcome was direct costs and our primary exposure was hospital-acquired CDI. We performed the following analyses: Ordinary least square regression and generalized linear regression as time-fixed methods, and Kaplan-Meier survival curve and Cox regression models as time-varying methods. Results: A total of 49,888 admissions were included in this study (mean (SD) age of 64.6 ± 17.8 years, median (IQR) baseline mortality risk of 0.04 (0.01-0.14)). 360 (0.73%) patients developed CDI. Estimates of incremental cost due to CDI were substantially higher when using time-fixed methods than time-varying methods. Using methods that appropriately account for the time-varying nature of the exposure, the estimated incremental cost due to CDI was $8,997 per patient. In contrast, estimates from time-fixed methods ranged from $49,150 to $55,962: about a six fold difference. Conclusion: Estimates of hospital costs are strongly influenced by the time-varying nature of CDI as well as baseline mortality risk. If studies do not account for these factors, it is likely that the impact of hospital-acquired CDI will be overestimated.

Introduction: Clostridium difficile infection (CDI) is a common hospital-acquired infection and a financial burden on the healthcare system. There is a need to reduce its impact on patients and the entire health system. More accurate estimates of the financial impact of CDI will assist hospitals in creating better CDI reduction strategies with limited resources. Previous research has not sufficiently accounted for the skewed nature of hospital cost data, baseline patient mortality risk, and the time-varying nature of CDI. Objective: We conducted a retrospective cohort study to estimate the cost impact of hospital-acquired CDI from the hospital perspective, using a number of analytical approaches. Method: We used clinical and administrative data for inpatients treated at The Ottawa Hospital to construct an analytical data set. Our primary outcome was direct costs and our primary exposure was hospital-acquired CDI. We performed the following analyses: Ordinary least square regression and generalized linear regression as time-fixed methods, and Kaplan-Meier survival curve and Cox regression models as time-varying methods. Results: A total of 49,888 admissions were included in this study (mean (SD) age of 64.6 ± 17.8 years, median (IQR) baseline mortality risk of 0.04 (0.01-0.14)). 360 (0.73%) patients developed CDI. Estimates of incremental cost due to CDI were substantially higher when using time-fixed methods than time-varying methods. Using methods that appropriately account for the time-varying nature of the exposure, the estimated incremental cost due to CDI was $8,997 per patient. In contrast, estimates from time-fixed methods ranged from $49,150 to $55,962: about a six fold difference. Conclusion: Estimates of hospital costs are strongly influenced by the time-varying nature of CDI as well as baseline mortality risk. If studies do not account for these factors, it is likely that the impact of hospital-acquired CDI will be overestimated.

A smart grid cost/benefit analysis answers a series of economic questions that address the incremental benefits of each stage or decision point. Each stage of the economic analysis provides information about the incremental benefits of that stage with respect to the previous stage. With this approach stages that provide little or no economic benefits can be identified. In this study there are series of applications,-including quasi-steady state power flows over time-varying loads and costs of service, Monte Carlo simulations, reconfiguration for restoration, and coordinated control - that are used to evaluate the cost-benefits of a series of smart grid investments. In the electric power system planning process, engineers seek to identify the most cost-effective means of serving the load within reliability and power quality criteria. In order to accurately assess the cost of a given project, the feeder losses must be calculated. In the past, the feeder losses were estimated based upon the peak load and a calculated load factor for the year. The cost of these losses would then be calculated based upon an expected, fixed per-kWh generation cost. This dissertation presents a more accurate means of calculating the cost of losses, using hourly feeder load information and time-varying electric energy cost data. The work here attempts to quantify the improvement in high accuracy and presents an example where the economic evaluation of a planning project requires the more accurate loss calculation. Smart grid investments can also affect response to equipment failures where there are two types of responses to consider -blue-sky day and storm. Storm response and power restoration can be very expensive for electric utilities. The deployment of automated switches can benefit the utility by decreasing storm restoration hours. The automated switches also improve system reliably by decreasing customer interruption duration. In this dissertation a Monte Carlo simulation is used to mimic storm equipment failure events, followed by reconfiguration for restoration and power flow evaluations. The Monte Carlo simulation is driven by actual storm statistics taken from 89 different storms, where equipment failure rates are time varying. The customer outage status and durations are examined. Changes in reliability for the system with and without automated switching devices are investigated. Time varying coordinated control of Conservation Voltage Reduction (CVR) is implemented. The coordinated control runs in the control center and makes use of measurements from throughout the system to determine control settings that move the system toward optimum performance as the load varies. The coordinated control provides set points to local controllers. A major difference between the coordinated control and local control is the set points provided by the coordinated control are time varying. Reduction of energy and losses of coordinated control are compared with local control. Also eliminating low voltage problems with coordinated control are addressed. An overall economic study is implemented in the final stage of the work. A series of five evaluations of the economic benefits of smart grid automation investments are investigated. Here benefits that can be quantified in terms of dollar savings are considered here referred to as "hard dollar" benefits. Smart Grid investment evaluations to be considered include investments in improved efficiency, more cost effective use of existing system capacity with automated switches, and coordinated control of capacitor banks and voltage regulators. These Smart Grid evaluations are sequentially ordered, resulting in a series of incremental hard dollar benefits. Hard dollar benefits come from improved efficiency, delaying large capital equipment investments, shortened storm restoration times, and reduced customer energy use. The evaluation shows that when time varying loads are considered in the design, investments in automation can improve performance and significantly lower costs resulting in "hard dollar" savings. / Ph. D.