Economic analysis of a diabetes-specific nutritional meal replacement for patients with type 2 diabetes.

This study extends nutritional intervention results reported by short-term clinical trials of a diabetes-specific nutritional meal replacement by assessing the ten-year impact of the interventions on patient outcomes and costs compared to usual care. We developed and validated a computer simulation of type 2 diabetes based on published data from major clinical trials. The model tracks patients through microvascular and macrovascular health states and reports cumulative costs and quality adjusted life years. We modeled different scenarios that include a diabetes-specific nutritional meal replacement as part of a structured lifestyle intervention, and also as the only difference between the intervention and usual care treatment groups, and compared them to usual care with diet and physical activity recommendations. We used sensitivity analysis to explore the robustness of results. When a diabetes-specific nutritional meal replacement is the only treatment difference and is considered an equal cost meal replacement, the diabetes-specific nutritional meal replacement interventions are less costly and more effective than usual care. As an added cost meal replacement, the diabetes-specific nutritional meal replacement has an incremental cost-effectiveness ratio between $50,414 and $55,036 depending on improvement in percent glycated hemoglobin. A hypothetical lifestyle intervention using a diabetes-specific nutritional meal replacement has an incremental cost-effectiveness ratio of $47,917. The diabetes-specific nutritional meal replacement was found to be cost-effective under the various conditions simulated.

Long-term cost-effectiveness of disease management in systolic heart failure.

BACKGROUND: Although congestive heart failure (CHF) is a primary target for disease management programs, previous studies have generated mixed results regarding the effectiveness and cost savings of disease management when applied to CHF. OBJECTIVE: We estimated the long-term impact of systolic heart failure disease management from the results of an 18-month clinical trial. METHODS: We used data generated from the trial (starting population distributions, resource utilization, mortality rates, and transition probabilities) in a Markov model to project results of continuing the disease management program for the patients' lifetimes. Outputs included distribution of illness severity, mortality, resource consumption, and the cost of resources consumed. Both cost and effectiveness were discounted at a rate of 3% per year. Cost-effectiveness was computed as cost per quality-adjusted life year (QALY) gained. RESULTS: Model results were validated against trial data and indicated that, over their lifetimes, patients experienced a lifespan extension of 51 days. Combined discounted lifetime program and medical costs were $4850 higher in the disease management group than the control group, but the program had a favorable long-term discounted cost-effectiveness of $43,650/QALY. These results are robust to assumptions regarding mortality rates, the impact of aging on the cost of care, the discount rate, utility values, and the targeted population. CONCLUSIONS: Estimation of the clinical benefits and financial burden of disease management can be enhanced by model-based analyses to project costs and effectiveness. Our results suggest that disease management of heart failure patients can be cost-effective over the long term.

Responding to simulated pandemic influenza in San Antonio, Texas.

OBJECTIVE: To describe the results of a simulation study of the spread of pandemic influenza, the effects of public health measures on the simulated pandemic, and the resultant adequacy of the surge capacity of the hospital infrastructure and to investigate the adequacy of key elements of the national pandemic influenza plan to reduce the overall attack rate so that surge capacity would not be overwhelmed. DESIGN: We used 2 discrete-event simulation models: the first model simulates the contact and disease transmission process, as affected by public health interventions, to produce a stream of arriving patients, and the second model simulates the diagnosis and treatment process and determines patient outcomes. SETTING: Hypothetical scenarios were based on the response plans, infrastructure, and demographic data of the population of San Antonio, Texas. RESULTS: Use of a mix of strategies, including social distancing, antiviral medications, and targeted vaccination, may limit the overall attack rate so that demand for care would not exceed the capacity of the infrastructure. Additional simulations to assess social distancing as a sole mitigation strategy suggest that a reduction of infectious community contacts to half of normal levels would have to occur within approximately 7 days. CONCLUSIONS: Under ideal conditions, the mix of strategies may limit demand, which can then be met by community surge capacity. Given inadequate supplies of vaccines and antiviral medications, aggressive social distancing alone might allow for the control of a local epidemic without reliance on outside support.

Using models and data to support optimization of the Military Health System: a case study in an intensive care unit.

Optimizing healthcare delivery--improving processes to reduce impediments to care--is an important goal of the Military Health System. Models and data can be effective tools to assist managers in achieving this goal. This paper illustrates this utility with a case study of the intensive care unit (ICU) at the US Air Force's Wilford Hall Medical Center. A discrete-event simulation demonstrates how the integration of corporate data and ICU data through a model can help identify changes intended to improve ICU performance. Results of the analysis describe impacts of ICU size and bed mix, operating policies, and the deployment of ICU staff on measures of occupancy, congestion, and physician training needs.