SOLICITATION NOTICE
R -- Diabetes Prevention Computer Simulation Models
- Notice Date
- 4/5/2016
- Notice Type
- Presolicitation
- NAICS
- 541519
— Other Computer Related Services
- Contracting Office
- Department of Health and Human Services, Centers for Disease Control and Prevention, Procurement and Grants Office (Atlanta), 2920 Brandywine Road, Room 3000, Atlanta, Georgia, 30341-4146
- ZIP Code
- 30341-4146
- Solicitation Number
- 2016-N-17753
- Archive Date
- 5/5/2016
- Point of Contact
- Jerry W. Outley,
- E-Mail Address
-
jmo4@cdc.gov
(jmo4@cdc.gov)
- Small Business Set-Aside
- N/A
- Description
- Pre-Solicitation Notice for Solicitation 2016-N-17753 Project Title: "PPHF 2016: Diabetes Prevention - Developing and Validating two Computer Simulation Models to Predict the Long-term Health and Economic Outcomes of Interventions for Preventing Diabetes and its Complications - Financed Solely by 2016 Prevention and Public Health Funds" 1. Introduction. This is a Pre-solicitation Notice for a Full and Open Competition (unrestricted) under North American Industry Classification System (NAICS) code 541519 "Other Computer Related Services", with a small business size standard of $25 Million. When released, the solicitation number will be 2014-N-17753. The solicitation Request for Proposal (RFP) will be made available on the Internet at http://www.fbo.gov on or about April 20, 2016. Request for Proposals will not be provided to interested parties in hard copy form. Telephone requests will not be honored. Interested parties are responsible for checking the website regularly for the release of the RFP and for other procurement-related documents. The information provided in this pre-solicitation is for information purposes only. If there are any differences in the information provided here and the actual solicitation, the information provided in the actual solicitation shall govern. 2. Background for the Acquisition. Diabetes is the sixth leading cause of death among Americans. In 2012 the disease cost the nation $176 billion in direct medical costs and $69 billion in indirect costs due to lost productivity. Approximately 29.1 million people or 9.3% of the U.S. population had diabetes in 2012. In addition, an estimated 86 million Americans aged 20 years or older had pre-diabetes, a condition in which glucose levels are higher than normal but below the level of defined diabetes. Diabetes is a leading cause of new cases of blindness, end-stage renal disease, and lower extremity amputations. Diabetes increases the risk of heart attack or stroke twofold to fourfold. The National Center for Chronic Disease Prevention and Health Promotion (NCCDPHP), Centers for Disease Control and Prevention (CDC) is committed to achieving a world free of the devastation of diabetes by (1) reducing the number of people with diabetes through primary prevention and (2) enabling people with diabetes to have a long, healthy and satisfying life by preventing complications, disabilities, and the burden associated with diabetes. To accomplish this goal in the face of escalating health care costs, NCCDPHP needs information on the long-term health and economic impact of interventions/policies used for the prevention and control of diabetes to guide its public policy and program decisions. Computer modeling is a valuable tool to generate information needed for guiding public health policy and program decisions. For the prevention and control of diabetes, a computer model can be used to answer questions at both the individual and population levels. These questions address the long-term health and economic burden of diabetes, cost and cost-effectiveness of interventions, comparative effectiveness of interventions, health and economic outcomes of implementing public health and clinical guidelines, and health and economic outcomes of quality of care improvements. There are two major forms of diabetes, type 1 and 2. Type 1 diabetes is an autoimmune disease that destroys the insulin-producing beta cells in the pancreas, preventing the body from producing enough insulin to adequately regulate blood glucose levels. Type 2 diabetes is a metabolic disorder that results in high blood glucose levels due to the body being ineffective at using the insulin it has produced and/or being unable to produce enough insulin. Risk factors and treatments for the two diabetes types differ. In addition, the ability to prevent the two diabetes types differs. Type 2 diabetes is preventable while type 1 diabetes generally is not. Because of these differences, simulation models that are specific to each type of diabetes are needed for guiding public health policy and program decisions. Although a few type 1 diabetes computer simulation models are available, they either lack comprehensiveness in terms of number of short- and long-term complications and the number of disease states, or they are out of date or are not specifically built for the US health care setting. A new, comprehensive, type 1 diabetes model that reflects current treatment practices in the US health care setting is needed to assess the long-term health and economic impact of interventions/policies used for the control of type 1 diabetes to guide NCCDPHP's policy and program decisions. NCCDPHP's Division of Diabetes Translation (DDT), in collaboration with other organizations, has previously developed a type 2 diabetes simulation model (CDC-DDT model). In the past decade, this model has been used to assess the cost-effectiveness of a variety of type 2 diabetes prevention and management interventions and has produced a significant scientific and public health impact. However, the dynamic environment of diabetes care, and major advances in the science of simulation modeling, have led to challenges and bottlenecks in further development, revision and application of the current model. First, the current CDC-DDT model was developed primarily based on data from the United Kingdom Prospective Diabetes Study (UKPDS), which was conducted almost three decades ago. As such, the management of hemoglobin A1c, blood pressure, and cholesterol as implemented in the trial was based on the United Kingdom (UK) health care setting at the time of the study. Many newer drug therapies and medical devices have since become available thanks to advances in medical technology. Thus, both the cost and effect of the drug therapies used in the original UKPDS study are no longer applicable to clinical practices in the United States today. Second, the relationships between risk factors and the progression of diabetes-related complications and mortality as defined in the UKPDS risk equations may no longer hold due to changes in treatments for diabetes and its complications. Recent studies have suggested that UKPDS-based risk predictions consistently overestimate the risk of cardiovascular disease and mortality. In addition, there appears to be increasing inconsistency between UKPDS-based predicted risk and real-world observations of microvascular complications. This mismatch has been found in different patient populations in both the US and other countries, which indicates the equations may be outdated for current type 2 diabetes patients. Third, the mismatch is partly caused by the changing attributes of type 2 diabetes patients. In the UKPDS, patients had a median A1c value of 9.1, which may no longer represent the A1c value of a typical newly diagnosed diabetes patient today. In addition, because of the limited range of A1c values among the UKPDS study participants, applying risk equations based on the UKPDS data could lead to poor outcome predictions for patients whose baseline blood sugar level is not within that A1c range. This uncertainty was demonstrated consistently at the recent Mount Hood Challenge modeling group meeting, where almost all of the UKPDS-based diabetes models produced inaccurate predictions of the cardiovascular outcomes and mortality that were observed in the ACCORD trial. Fourth, from a technical perspective, the current CDC-DDT model is a Markov-based stationary model that simulates disease pathways at a cohort level. As a result, no individual-level random variation is considered in the model. This prevents researchers from examining the stochastic nature and uncertainty of the model predictions. More importantly, as additional mutually-exclusive Markov states have been added to the model over time, the analytical complexity has increased dramatically and computational efficiency has significantly deteriorated. Newer data from large-scale clinical trials have become available since the UKPDS. Data from other landmark studies such as the Diabetes Prevention Program (DPP), Action to Control Cardiovascular Risk in Diabetes (ACCORD), and Look AHEAD (Action for Health in Diabetes) trials provide unique opportunity and feasibility for developing new risk equations for the development of diabetes and diabetes-related complications and mortality. Because these are more recent trials with large numbers of patients receiving contemporary treatment, incorporating these new data into a diabetes model will greatly enhance the model's predictive ability. To fill the gaps described above, it is necessary to develop a new computer simulation model for assessing the long-term health and economic outcomes of interventions and public health policies for the prevention of type 2 diabetes in persons without the disease and the prevention of diabetes-related complications in persons with type 2 diabetes. On completion of the project, NCCDHP will have the two most comprehensive and advanced diabetes computer simulation models in the world. Information and knowledge generated from the computer simulation models will directly inform decision-making related the prevention and control of diabetes of type 2 diabetes and management of type 1 diabetes as well as the prioritization of nation's public health interventions. 3. Purpose: The primary goals of the project are three fold: (1) to develop two independent computer simulation models of type 1 and type 2 diabetes, (2) to conduct internal and external validation studies on the two models using both clinical and real-world data; and (3) to disseminate the developed models among scientific communities and the general public through publications and presentations. 4. Synopsis of Contract requirements: The contractor shall, acting independently and not as an agent of the Government, provide all labor, supervision, equipment, materials, supplies, travel, transportation and perform all work necessary to: 1) Conduct a detailed review of all existing type 1 and type 2 diabetes computer simulation models, published and not published, with the key objectives focusing on the purpose, structure, strengths, and weaknesses of the models and the data sources used for developing different modules /components. 2) Conduct a thorough literature review of chronic disease modeling/simulation methods with the key objectives focusing on identifying new state-of-art modeling methodologies that can be readily adopted for building diabetes simulation models. 3) Identify and obtain the most appropriate clinical and real-world data, both available publicly and not available publicly, to develop a set of mathematical equations for development of type 2 diabetes and all diabetes-related complications, mortality, and changes in medical costs and health utility associated diabetes and diabetes related complications. 4) Design and build two simulation models (one for type 1 diabetes and one for type 2 diabetes) that are capable of addressing a broad range of public health and clinical questions regarding the prevention of type 2 diabetes and the management and treatment of type 1 and type 2 diabetes. 5) Calibrate and validate the simulation model using data that are partially used (internal) and not used (external) for developing the model, following validation guidelines recommended by professional organizations. 6) Develop a web-based platform that provides a user-friendly workbench for incorporating setting-specific inputs and executing (running) the models. 7) Develop complete documentation for the models, including (1) technical reports that describe the modeling methods, data sources and model validations, and (2) user manuals. 8) Develop and publish peer-reviewed manuscripts documenting the mathematical equations that the project will develop for the two models. 9) Develop and publish peer-reviewed manuscripts describing the design, implementation, and validation of the models. Detailed descriptions of the tasks to be accomplished will be provided when the formal RFP is issued.
- Web Link
-
FBO.gov Permalink
(https://www.fbo.gov/spg/HHS/CDCP/PGOA/2016-N-17753/listing.html)
- Place of Performance
- Address: Contractor's Facility, United States
- Record
- SN04074510-W 20160407/160405235640-300b968586ff2872cc4bc2afad2351e4 (fbodaily.com)
- Source
-
FedBizOpps Link to This Notice
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