RFP_2016-N-17753_Solicitation.docx

RFP 2016-N-17753 Page 88 of 88

SOLICITATION, OFFER AND AWARDPAGES

1. THIS CONTRACT IS A RATED ORDER

UNDER DPAS (15 CFR 700)

RATING

2. CONTRACT NO.

There are no clauses/provisions included in this section.

3. SOLICITATION NO.

2016-N-17753

6. REQUISITION/PURCHASE NO.

00HCUCGB-2016-93336

7. ISSUED BY

There are no clauses/provisions included in this section.

Centers for Disease Control and Prevention Acquisition and Assistance Branch 1 2920 Brandywine Road, MS E-15 Atlanta, GA 30341-5539

Approved as to Form and Legality: _____________________________

NOTE: In sealed bid There are no clauses/provisions included in this section. solicitations “offer” and “offeror” mean “bid” and “bidder.”

SOLICITATION

(Hour)

CAUTION -- LATE Submissions, Modifications, and Withdrawals: See Section L, Provision No. 52.214-7 or 52.215-1. All offers are subject to all terms and conditions contained in this solicitation.

10. FOR INFORMATION

CALL:

A. NAME

Jerry W. Outley

B. TELEPHONE (NO COLLECT CALLS)

AREA CODE NUMBER: EXT:

(770) 488-2831

C. E-MAIL ADDRESS

Jmo4@cdc.gov

11. TABLE OF CONTENTS

(x)PAGE(S)

SEC.

SEC.

PAGE(S)

DESCRIPTION

(x)

DESCRIPTION

XK

REPRESENTATIONS, CERTIFICATIONS, AND

OFFER (Must be fully completed by offeror)

NOTE: Item 12 does not apply if the solicitation includes the provisions at 52.214-16, Minimum Bid Acceptance Period.

12. In compliance with the above, the undersigned agrees, if this offer is accepted within calendar days (60 calendar days unless a differentCALENDAR DAYS

(Type or Print)

15B. TELEPHONE NO.

AREA CODE NUMBER EXT.

18. OFFER DATE

AWARD (To be completed by Government)

22. AUTHORITY FOR USING OTHER THAN FULL AND OPEN COMPETITION:

21. ACCOUNTING AND APPROPRIATION

Centers for Disease Control and Prevention Acquisition and Assistance Branch 1 2920 Brandywine Road, MS E-15 Atlanta, GA 30341-5539

Centers for Disease Control and Prevention (FMO) PO Box 15580 404-718-8100

Atlanta, GA 30333-0080

26. NAME OF CONTRACTING OFFICER (Type or print)

27. UNITED STATES OF AMERICA

(Signature of Contracting Officer)

28. AWARD DATE

IMPORTANT -- Award will be made on this form, or on Standard Form 26, or by other authorized official written notice.

RFP 2016-N-17753 Page 68 of 88

FAR (48 CFR) 53.214©

Section B – Supplies or Services And Prices

ITEM

SUPPLIES / SERVICES

QTY / UNIT

UNIT PRICE

EXTENDED PRICE

1 Job

Travel CLIN is Cost Reimbursement

B.1 TYPE OF CONTRACT

This is a Definitive, Firm Fixed Price type contract as defined in FAR 16.502

Section C - Description/Specification/Work Statement

PERFORMANCE WORK STATEMENT (PWS)

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

C.1 – BACKGROUND INCLUDING PUBLIC HEALTH IMPACT:

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.

C.2 – PROJECT OBJECTIVE:

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.

C.3 – SCOPE OF WORK:

A. Work overview:

The contractor shall furnish all necessary labor, facilities, supplies, and equipment 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.

C.4 – TECHNICAL REQUIRMENTS:

TASKS and Subtasks:

Independently, and not as an agent of the Government, the Contractor shall furnish to the Government all necessary personnel, facilities, supplies, and equipment to complete the following tasks:

Task 1: Convene a kickoff meeting:

Within 2 weeks of contract award, the Contractor shall arrange a conference call with CDC Contracting Officer Representative (COR) and other CDC staff to discuss the goals and the details of the implementation plans for the project. The Contractor shall prepare the agenda in collaboration with COR and meeting notes.

Task 2: Progress meetings and travel to CDC Atlanta:

The Contractor assigned project lead shall arrange to meet with the CDC project team for in-person meetings in Atlanta, GA, each quarter, to address progress and consult with technical monitors on key decisions related to the development of each model. Meetings shall be scheduled as required by the COR but shall not exceed 4 meetings per year. The Contractor shall arrange bi-weekly teleconference calls with the COR and CDC project team to discuss progress and development of the project activities.

Task 3: OMB Clearance Package The Contractor shall develop and submit a complete package required for OMB clearance to CDC COR if applicable.

Task 4: Conduct a literature review of existing diabetes computer simulation models and other advanced modeling/simulation methods

4.a Conduct a detailed review of existing diabetes computer simulation models:

The Contractor shall conduct a detailed review, including published literature and other appropriate sources (e.g., websites, informative interviews), to document and evaluate the existing diabetes computer simulation models. Within 3 months of the receipt of the contract, the Contractor shall submit to CDC COR a written report that include the results of the review. Specifically, the report shall include at least the following components for each reviewed model:

1. Purpose of the model

2. Structure of the model including the modules included and components in each module

3. Simulation methods/approaches used

4. Risk equations used and their data sources

5. Cost equations used and their data sources

6. Health utility equations used and their data sources

7. Algorithms for combining these equations

8. Programming language used to program the model

9. Methods to handle the uncertainties

10. Strengths and weakness of the models

11. Summary of the reviewed models

12. Recommendations for advancing the field of diabetes modeling

4.b Conduct a systematic literature review of advanced modeling/simulation methods that can be used for building diabetes simulation models:

The Contractor shall conduct a systematic literature review of advanced modeling/simulation methods that can be used for building diabetes simulation models. These methods either have been used in building chronic disease models or could be potentially used but have not been used in disease models. Within 4 months of the receipt of the contract, the Contractor shall submit to CDC COR a written report that includes the results of the literature review. Specifically, the report shall include at least the following components:

1. Descriptions of each approach/method

2. Feasibility of using each approach/method in developing the type 1 and type 2 diabetes simulation models

3. Advantages and disadvantages of adopting each approach/method for developing the type 1 and type 2 diabetes simulation models

4. Recommendations for using or not using the reviewed approaches/methods

Task 5: Convene an External Scientific Evaluation Committee (ESEC) for developing the type 1 and type 2 diabetes models:

The Contractor shall identify 5-6 best-qualified experts, either from US or internationally, in various aspects of chronic disease/diabetes modeling to form an External Scientific Evaluation Committee (ESEC) for the development of the diabetes models.

(1) Within 4 months of the receipt of the contract, the Contractor shall convene the first in-person meeting of the ESEC. At this meeting, the Contractor shall present the reports developed under Tasks 3a and 3b, along with the work plan described in the contract. The Panel shall conduct evaluations and provide recommendations on the work plan and reports. The Contractor shall revise the work plan based upon the recommendations of the ESEC and CDC COR and submit the final plan (see task 5).

(2) Within 22 months after completion of the first in-person meeting of the ESEC, the Contractor shall convene the second meeting to present the report developed in Tasks 6a and 6b (refer to Tasks 6a and 6b below for details) to the ESEC. The ESEC shall review all components of the models, in particular all mathematical equations and model parameters developed for the model, and algorithms for combining these equations and parameters. The Contractor shall revise the methods and analyses per ESEC and CDC COR recommendations. Within 5 days of the closure of the meeting, the Contractor shall submit minutes of the meeting to CDC COR.

(3) Within 32 months after the second in-person meeting of the ESEC, the Contractor shall convene the third meeting to present to ESEC and CDC project team on the working beta versions of the models. The ESEC shall review the software and its functionalities provide recommendations for refinement. The Contractor shall revise the beta versions per ESEC and CDC COR recommendations. Within 5 days of the closure of the meeting, the Contractor shall submit minutes of the meeting to CDC COR.

Task 6: Final work plan

Within 6 months of the receipt of the contract, but after the first ESEC meeting, the Contractor shall submit the final detailed work plan that reflects the recommendations made by the ESEC and CDC COR on the structure of the model, mathematical equations and parameters used, data sources that will be used for developing key mathematical equations, algorithms for combining these equations, program languages for developing the computer software, computing environments required for running the software.

Task 7: Design and build the two diabetes models

7.a Design and build the type 1 diabetes model:

Upon the completion of the literature review (Tasks 2 and 3) and guided by the final agreement of the CDC COR and ESEC, the Contractor shall design and build a comprehensive type 1 diabetes simulation model.. The model shall reflects the most up-to-date knowledge of chronic disease modeling methods, development and progression of type 1 diabetes, development and progression of type 1 diabetes-related complications, and clinical efficacies/effectiveness and cost consequences of interventions for managing and treating type 1 diabetes and its complications and comorbidities. (See the table of deliverables for the completion date for each specific task discussed below).

The developed model shall have at least the following analytical capabilities:

1. To predict long-term health and economic burdens of type 1 diabetes, including prevalence and incidence of type 1 diabetes-related complications in the overall population and subpopulations

2. To conduct cost-effectiveness analysis and comparative effectiveness analysis of

a. public health and clinical interventions

b. public health and clinical guidelines

c. quality of care improvements

3. To conduct cost-effectiveness analysis and comparative effectiveness analysis of interventions, guidelines, care improvement related to

a. management and treatment of type1 diabetes

b. screening for diabetes-related complications in persons with type 1 diabetes

c. management and treatment of diabetes-related complications in persons with type 1 diabetes

d. management and treatment of comorbidities in persons with type 1 diabetes

The model shall be capable of performing analyses listed above in 1 – 3 above at both the individual level and aggregated to the population level, from the following three different perspectives: (1) single payer health care system, (2) private insurer, and (3) society.

The model shall be capable of simulating differences in disease progression and the effect and cost of treatments at the individual level.

The model shall be capable of producing desired statistical properties of simulated outcomes such as confidence intervals, capable of performing formal post-simulation statistical testing, allow for individual level and parameter level random variations, and capable of performing both probabilistic and non-probabilistic sensitivity analyses for all analyses listed above in 1–3.

The model shall be capable of performing all analyses listed above in 1 – 3 in both US and non-US settings. In addition to the US setting, at least one default non-US country setting shall be embedded in the model. The selection of the default non-US country setting shall be determined in consultation with CDC COR. The level of variations in model parameters between the US and non-US setting shall be depend on data availability and be approved by ESEC and CDC COR.

The model shall represent the entire type 1 diabetes population and be able to conduct analysis for subpopulations defined by age, sex, and race/ethnicity.

The model shall consist of at least the following components: mathematical/ statistical equations/parameters, algorithms for combining these equations/parameters, and executable computer software that implements the equations/parameters and algorithms.

(1) Mathematical/ statistical equations/parameters:

The models shall have mathematical/ statistical equations/parameters that quantify the relationship:

A. between the level of key risk factors (e.g., glucose, blood pressure and lipid levels) and important diabetes related complications. The relationship for each complication shall also be further divided into sub-disease categories of the complication if applicable. For example, CVD should be further divided into specific diseases, such as angina, coronary heart disease, myocardial infarction, and congestive heart failure. For some diseases such as stroke, differentiating first occurrence from recurrence may also be needed.

B. between diabetes-related complications and diabetes and non-diabetes deaths.

C. between health utility scores and type 1 diabetes, and each of the type 1 related complications.

D. between medical costs and type 1 diabetes and diabetes-related complications by study perspective if applicable.

E. between the reduction in key risk factors (e.g., glucose, blood pressure and lipid levels) and their treatment intensities.

F. between the treatment cost and treatment intensity for key risk factors (e.g., glycemic, blood pressure and lipid control).

Where possible, variables that refer to the same risk factors and diabetes complications in different mathematical equations should be defined in the same way to ensure their consistency across the equations.

Balancing the pros and cons, the Contractor shall justify the approach and data sources used to develop the mathematical equations. Several potential alternative methods are:

(a) Survival analysis or similar statistical modeling methods

(b) Continuous mathematical functions such as differential equations

(c) Data mining methods or other advanced statistical modeling/predictive analytics methods.

The Contractor shall identify and acquire all of the data that is needed for the model. Data quality will play a critical role in determining the validity and predictive ability of the mathematical equations. For the purpose of developing the mathematical equations, large, accurate, and longitudinal cohort data should be used.

The Contractor shall identify potential data sources, both available publicly and not available publicly, for developing mathematical equations/parameters to quantify the relationships listed from A to F, above, compare the pros and cons of each data source, and justify the selection of data set used. The best data sources to use for developing the mathematical equations will depend on the equation type (diabetes and diabetes complication risk equations, cost equation and utility equation).

Data collected from large and long-term clinical trials and their follow up such as the Diabetes Control and Complications Trial (DCCT) are most likely the best data sources for developing mathematical equations to quantify the relationship between risk factors such as levels of hemoglobin A1c, blood pressure and cholesterol (ABC control), and diabetes-related complications and mortality. In addition, other large, longitudinal cohort data such as the Pittsburgh Epidemiology of Diabetes Complications (EDC) study can be good sources for developing mathematical equations needed for the model. As some of the optimal data may not be available publicly, ability to access the unpublished original data can play a critical role in the success of constructing the risk equations needed for the model. Thus, collaborations with other study groups are highly recommended to enhance the access to additional, key data for the model’s mathematical equations.

(2) Algorithms for combining these equations:

Algorithms used to combine the mathematical equations/parameters affect the accuracy of the simulated outcomes, their statistical properties, and the efficiency of the computations. Algorithms will also affect how the model is structured. The Contractor shall examine the advantages and disadvantages of each algorithm that could potentially be used and justify the algorithm selected.

The selected algorithms and model structure shall be able to (a) simulate diabetes progression and the development of complications at the individual patient level, (b) define the disease progression by biomarkers, clinical stage or organ functionalities in a meaningful way, (c) consider the potential interaction among different diabetes related complications due to changes in the levels of risk factors for the complications, (d) conduct all analyses listed in the analytical model capacity section, and (e) produced desired statistical properties for the simulated outcomes as specified earlier.

(3) Programming the equations and algorithms in an executable computer software:

The software developed from the model shall be run in a proper computational environment. The Contractor shall examine the alternative computing environments that can run the software, compare the advantages and disadvantages of each environment, and select the most appropriate computational environment.

Potential computational environments might include:

(a) A server with multiple CPU cores and expanded memory space, for example, a server with 128 CPU cores and 128/256 GB RAM

(b) Cloud computation with easy access from the CDC computers

(c) Single desktop or laptop computer with multiple CPU cores

The model shall be programmed in a computer language that is compatible with most common operating systems and can readily be translated into a web-based platform.

Candidates for the programming language include C++, Python, and Java. While there is no restriction on how the model will be programmed, the final platform of the model must also be simple enough for end users who were not involved in the development of the model to run the model as instructed by the user manual.

7.b Design and build the type 2 diabetes model:

Upon the completion of the literature and model reviews (Tasks 2 and 3) and guided by the final agreement of the CDC COR and ESEC, the Contractor shall design and build one of the most innovative and comprehensive type 2 diabetes simulation models in the world. The model shall reflects the most up-to-date knowledge of chronic disease modeling methods, development and progression of type 2 diabetes, development and progression of type 2 diabetes-related complications, and clinical efficacies/effects and cost consequences of interventions used for the prevention of type 2 diabetes and type 2 diabetes-related complications (see the table of deliverables for the completion date for each specific task discussed below).

The developed model shall have at least the following analytical capacity:

1. To predict long-term health and economic burdens of type 2 diabetes, including prevalence and incidence of type 2 diabetes and its complications in the overall population and subpopulations.

2. To conduct cost-effectiveness analysis and comparative effectiveness analysis of:

a. public health and clinical interventions

b. public health and clinical guidelines

c. quality of care improvements

3. To conduct cost-effectiveness analysis and comparative effectiveness analysis of interventions, guidelines and/ or care improvement related to:

a. screening for high risk individuals for the prevention of type 2 diabetes

b. management and treatment of persons with high risk of type 2 diabetes

c. screening for undiagnosed diabetes

d. management and treatment of type 2 diabetes

e. screening for diabetes-related complications in persons with type 2 diabetes

f. management and treatment of diabetes-related complications in persons with type 2 diabetes

g. management and treatment of comorbidities in persons with type 2 diabetes

The model shall be capable of performing analyses listed in 1–3 above at the individual level and aggregated to a population level, from the following three different perspectives: (1)single payer health care system, (2) private insurer, and (3) society.

The model shall be capable of simulating differences in disease progression, and the effect and cost of treatments, at the individual level.

The model shall be capable of producing desired statistical properties of simulated outcomes such as confidence intervals, capable of performing formal post-simulation statistical testing, allow for individual level and parameter level random variations, and be capable of performing both probabilistic and non-probabilistic sensitivity analyses for all analyses listed above in 1 – 3.

The model shall be capable of performing all analyses listed above in 1 – 3 in both US and non-US settings. In addition to the US setting, at least one default non-US country setting shall be embedded in the model. The selection of the default non-US country setting shall be determined in consultation with CDC COR. The level of variation in model parameters between the US and non-US setting shall depend on data availability and be approved by ESEC and CDC COR.

The model shall represent the entire adult population and be able to conduct analysis for subpopulations defined by age, sex, and race/ethnicity.

The model shall consist of at least the following components: mathematical/ statistical equations/parameters, algorithms for combining these equations/parameters, and executable computer software that implements the equations/parameters and algorithms.

(1) Mathematical/ statistical equations/parameters.

The models shall have mathematical/ statistical equations/parameters that quantify the relationship:

A. between type 2 diabetes incidence and risk factors for type 2 diabetes in persons with normal glucose levels with and without other diabetes risk factors.

B. between type 2 diabetes incidence and risk factors for type 2 diabetes in persons with pre-diabetes defined by impaired glucose tolerance (IGT), and /or impaired fasting and/or glucose (IFG), and /or hemoglobin A1c.

C. between the levels key risk factors (e.g., glucose, blood pressure and lipid) and diabetes-related complications, including cardiovascular diseases (CVD), stroke, retinopathy, neuropathy, and nephropathy among persons with newly diagnosed diabetes. The relationship for each of the complications shall also be further divided into sub-disease categories of the complication if applicable. For example, CVD should be further divided into specific disease, such as angina, coronary heart disease, myocardial infarction, and congestive heart failure. For some diseases such as stroke, differentiating first occurrence from recurrence may also be needed.

D. The same sets of mathematical equations described in C shall also be developed for

a. persons who have type 2 diabetes for some specified time such over 5 years,

b. persons who are at high risk of diabetes such as pre-diabetes,

c. persons who have normal glucose levels with and without other diabetes risk factors.

E. between diabetes-related complications and diabetes and non-diabetes deaths.

F. between health utility scores and prediabetes, type 2 diabetes, and each of the type 2 diabetes-related complications.

G. between medical costs and pre-diabetes, type 2 diabetes, and diabetes-related complications by study perspective if applicable;

H. between the reduction in key risk factors (e.g., glucose, blood pressure and lipid levels) and their treatment intensities;

I. between the treatment cost and treatment intensity for key risk factors (e.g., glycemic, blood pressure and lipid levels).

Where possible, variables that refer to the same risk factors and diabetes complications in different mathematical equations should be defined in the same way to ensure their consistency across the equations.

Balancing the pros and cons, the Contractor shall justify the approach and data sources used to develop the mathematical equations. Several potential alternative methods are:

(a) Survival analysis or similar statistical modeling methods

(b) Continuous mathematical functions such as differential equations

(c) Data mining methods or other advanced statistical modeling/predictive analytics methods.

The Contractor shall identify and acquire all of the data that is needed for the model. Data quality will play a critical role in determining the validity and predictive ability of the mathematical equations. For the purpose of developing the mathematical equations, large, accurate, and longitudinal cohort data should be used.

The Contractor shall identify all potential data sources, both available publicly and not available publicly, for developing mathematical equations/parameters to quantify the relationships listed from A to H, above, compare the pros and cons of each data source, and justify the selection of data set used. The best data sources to use for developing the mathematical equations will depend on the equation type (diabetes and diabetes complication risk equation, cost equation and utility equation).

Recently competed clinical trials and their follow-ups provide high-quality data on the clinical risk and outcome variables needed for developing mathematical equations for the type 2 diabetes models. Clinical data collected in Diabetes Prevention Program (DPP) and DPP Outcome Study can be used to estimate mathematical equations for predicting type 2 diabetes incidence among persons with pre-diabetes. Quality of life data from the same trial can provide the data needed for estimating the relationship between health utility and prediabetes, diabetes and diabetes-related complications. The trial can also provide data on estimating the effect of lifestyle and metformin intervention on the development of type 2 diabetes. Although the UK Prospective Diabetes Study (UKPDS) provides the data needed to estimate mathematical equations to describe the relationships between the control of glucose, blood pressure and lipid and development of diabetes related complications and morality, the data might be outdated due to changes in management of these risk factors. Contemporary trials such as Action to Control Cardiovascular Risk in Diabetes Trial (ACCORD) study and The Look AHEAD (Action for Health in Diabetes) study and their follow-ups provide the data to estimate the relationship between ABC control and development of diabetes-related complication and morality among patients with a wide range of baseline blood sugar level. Quality-of-life measurement in these trials are good data sources to estimate reductions on health utility for development of different diabetes related complications.

Previous research has demonstrated the feasibility of estimating medical costs associated with pre-diabetes, diabetes progression, and the progression of complications using combined clinical and insurance claim data from large longitudinal cohorts.

Data collected from publicly available cohort studies such as Atherosclerosis Risk in Communities Study (ARIC), Coronary Artery Risk Development in Young Adults Study (CARDIA), Cardiovascular Health Study (CHS), and Health and Retirement Study (HRS) could also be used in estimating the relationship between incidence of diabetes and risk factors in persons with normal glucose level by age groups.

(2) Algorithms for combining these equations:

The Contractor shall examine the advantages and disadvantage of each algorithm that can be potentially used and justify the algorithm selected.

As described for the type 1 diabetes model, the selected algorithm and model structure shall be able to (a) simulate development of type 2 diabetes and its complications at individual patient level, (b) define the disease progression by health state, either by biomarkers, clinical stage or organ functionalities in a meaningful way, (c) consider the potential interaction among different diabetes related complications due to changes in the level of risk factor of the complications (d) conduct all analyses listed in the model analytic capacity section and (e) produced desired statistical properties for the simulated outcomes as specified earlier.

(3) Programming the equations and algorithms in an executable computer software:

The computational environment and program languages needed for the development of the type 2 diabetes model are the same as described above for the type 1 diabetes model.

Task 8: Internal and external model validation and model recalibration:

Within 28 months of receipt of the contract award, the Contractor shall complete internal and external validations of the developed models. For the external validation, under the guidance of the CDC COR and ESECs, the Contractor shall predetermine the goodness-of-fit of the model and justify the extent of mismatch or deviance that is allowed to exist between the model results and the results from external trials or studies (both in magnitude and significance). The procedure used for the internal and external valuation shall be consistent with the best practices recommended by the diabetes modeling guidelines of the American Diabetes Association, the International Society for Pharmacoeconomics and Outcomes Research, and the Society for Medical Decision Making.

Within 32 months of receipt of the contract award, the Contractor shall complete recalibration of the updated model. The Contractor shall recalibrate the model if the results generated by the model are significantly different from the results of external trials.

Task 9: Integrate the two developed models into a web-based environment and develop a web-based cost-effectiveness analysis platform for adaptation:

Within 36 months of the contract award, the Contractor shall integrate the final version of the model into a web-based environment, and make it internet accessible to researchers. Within the web-based environment, the Contractor shall develop a user-friendly, flexible, analytic platform for conducting basic cost-effectiveness analysis and sensitivity analysis. The analytic platform shall be readily available for adapting the models for cost-effectiveness analysis for a variety of populations and settings, such as analysis for minority population and non-US setting.

Task 10: Develop model-related documentation and publications, including progress report, technical reports, user manuals and journal articles:

Within 36 months of receipt of the contract award, the Contractor shall prepare and submit to the CDC COR the final technical report that describes the computational algorithms, parameters, and all other data used in the model (see the table of deliverables for the completion dates for the draft and final reports). The report shall include technical descriptions for all associated modules. The technical report shall include the algorithms, all mathematical equations/parameters used in the model, and methods and data sources used for estimating the mathematical equations/parameters. The technical report shall also include the procedures used for the internal and external validations and the results of the validations. The technical report shall be written in sufficient detail to allow other diabetes modelers to duplicate the model results based on the information provided in the technical report. The draft of the technical report shall be completed and submitted within 25 months of receipt of the contract award.

Within 36 months of receipt of the contract award, the Contractor shall provide the software and all the programming code used in the software (see the table of deliverables for the completion dates of beta and final versions of the software). The report/document that describes the structure and organization of the software programming code shall also be provided. The final programmed model shall be bug free and efficient in running time.

Within 36 months of receipt of the contract award, the Contractor shall prepare a detailed user manual that provides step-by-step instructions on how to run each of the two models (see the table of deliverables for the completion dates for the draft and final manuals). The manual shall include instructions for the entire model including all associated modules. The instructions provided in the manual shall be clear and easy to follow. The instruction shall also be written in non-technical language as much as possible. The draft of the user manual shall be completed and submitted within 25 months of receipt of the contract award.

The Contractor shall develop at least 5 manuscripts for peer-reviewed publications within 42 months of the date of award, including a minimum of 3 manuscripts on mathematical equations related clinical events, mortality, cost, and health utility; one manuscript on the design and methods and validation for the type 1 model; and 1 manuscript on the design, methods and validation for the type 2 model (also see the table of deliverables for the completion dates for the draft and published manuscripts).

Task 11: Monthly and annual progress reports:

The Contractor shall prepare monthly progress reports detailing work accomplished during each month, problems encountered and recommendations for solving problems identified in the reporting period, activities planned for the next month, and special items of interest. The purpose of these reports is to keep appropriate Government managers apprised of the current status of significant work activities under the Work Authorization. These reports shall document the progress of the project.

The Contractor shall prepare annual progress reports detailing work accomplished during the year and activities planned for the next year, and special items of interest.

The Contractor shall deliver electronically 1) a monthly financial summary report in Excel, and 2) a monthly progress report in Word format. The financial summary report and invoice submitted to CDC shall include charges for satisfactory work completed. Charges shall not exceed the authorized cost limits for labor and other direct costs (ODCs). The Government will not pay unauthorized charges. These reports shall include:

(1) A summary of the progress made under the contract during the reporting period, separated into logical elements of work performed by individual(s). This shall include pertinent data and graphs sufficient to explain any significant results achieved;

(2) An explanation of any technical and/or schedule problems which may have occurred or are expected to arise. The description of any differences between planned and actual technical progress shall include a statement of why the differences occurred or why they are expected to occur and what remedial or alternative actions are planned or recommended. This may include any recommended action by the Government to assist in the resolution of a problem;

(3) Actual versus planned level of effort broken down by individual(s), including cumulative expenditures incurred.

(4) Projected activities and costs for the next reporting period; and

(5) Minutes of status review meetings, if any.

C.5 REPORTING SCHEDULE:

The Contractor shall furnish monthly progress reports detailing current status of the contract, in accordance with Paragraph F.2 of this solicitation. The report shall be narrative in form and shall include a summary of progress toward completion of each task and problems encountered to date, including the Contractor’s assessment of specific impact of such problems on scheduled date of completion of milestones. In addition, the contractor shall provide an overall Program Monthly Progress Report which will summarize the current status of the contract and provide status of contractor’s internal efforts to maintain and improve quality of work conducted on this contract.

Final Report

Within 30 days prior to the expiration date of the contract, a comprehensive final report shall be submitted with one electronic copy to the COR and one copy to the Contracting Officer. The final report shall include a summation of the work performed and results obtained for the entire contract period of performance.

C.6 SPECIAL CONSIDERATIONS:

IT Contractor performance and resulting deliverables must adhere to all federal, HHS, and/or CDC IT security policies and procedures. Based upon the scope of this contract, the SOW must include the appropriate language to address the following IT security topics Standard for Security Configurations; Standard for Encryption Language; and Security requirements for Federal Information technology Resources.

Development or implementation of an electronic information system or any electronic data collection effort conducted in the performance of this contract will be required to complete Certification and Accreditation (C&A) prior to operation resulting in an Authority of Operate (ATO) from CDC. The contractor shall be required to complete all security documentation and materials necessary to obtain an ATO. The contractor shall comply with all applicable HHS, CDC, FISMA, HIPPA, NIST, and other federal policies and regulations in the performance of the security requirements.

All information systems developed, implemented, or maintained in support of this contract must adhere to the security controls outlined in the National Institute of Standards and Technology (NIST) Special Publication 800-53, Recommended Security Controls for Federal…