Project - National Neurological Conditions Surveillance System (NNCSS)
Millions of peopleexternal icon of all ages across the United States face the substantial and sometimes devastating consequences of neurological disorders and conditions.
In 2016, as part of the 21st Century Cures Actexternal icon, Congress authorized Centers for Disease Control and Prevention (CDC) to initiate development of a National Neurological Conditions Surveillance System (NNCSS). Congress has appropriated $5 million for the NNCSS as part of the FY 2019 spending bill for the U.S. Department of Health and Human Services.
- The $5 million appropriated in FY 2019 will enable CDC to begin its NNCSS developmental and implementation work. This will include:
- Exploring data needs and identifying available data sources
- Determining how to build an effective system by identifying the most useful data sources and exploring a variety of methods and approaches
- Collaborating and communicating with stakeholders and Congress about the status of the NNCSS and relevant results
- With this initial investment, consistent with the 21st Century Cures Act, the NNCSS will begin collecting and synthesizing data to help increase understanding of neurological disorders and to support further neurologic research.
- The NNCSS will be developed in three stages, which CDC will carry out in association with partners and stakeholders:
- Demonstrations using two neurological conditions, multiple sclerosis (MS) and Parkinson’s disease (PD), to determine how we can have the biggest impact by exploring complex data sources with innovative analytic methods, and capturing lessons learned. This stage will take two years. To be as efficient as possible, in FY 2019, CDC is evaluating in-house data sources and working to purchase other data sources. In FY 2020, CDC will evaluate the newly purchased data sources and, as resources allow, will purchase and evaluate the final data sources.
- Build out the NNCSS for multiple sclerosis and Parkinson’s disease, as resources allow, using successful approaches from the demonstration projects, and checking methods, costs, and opportunities to determine which approaches will help efficiently extend the NNCSS to other neurological conditions
- Apply these model approaches to extend the NNCSS to other neurological conditions, as resources allow
CDC looks forward to helping to develop greater understanding of neurological disorders and conditions to improve health and economic consequences for those who are affected.
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National Neurological Conditions Surveillance System (NNCSS) Logic Model
The National Neurological Conditions Surveillance System (NNCSS) aims to provide useful estimates of neurological conditions in the United States. In 2016, the United States Congress authorized the Centers for Disease Control and Prevention (CDC) to develop and implement NNCSS as part of the 21st Century Cures Actexternal icon. The logic model depicts resources dedicated to the project, key domains of activity, and desired contributions to meaningful changes or results. CDC developed this logic model to summarize and depict how stakeholders understand project components and the relationships between and among the components. This logic model includes inputs, activities, and outcomes presented in boxes; lines and arrows connect these boxes in a purposeful progression from use of resources (inputs and activities) to results (outcomes).
In calendar year 2019, NNCSS focuses on estimating incidence and prevalence for multiple sclerosis (MS) and Parkinson’s disease (PD). As a starting point, the logic model presents the actual and anticipated resources dedicated to this work as inputs in column one. These inputs include the material and intellectual contributions to this project: interest and support from Congress, states, stakeholder organizations, and the public; CDC leadership of this project and commitment to the work; active and sustained collaboration with stakeholder organizations nationwide; access to diverse subject matter expertise, internal and external to CDC; human and fiscal resources dedicated to this work; existing technical capabilities and infrastructure within CDC; and access to a range of data resources that can be used to develop sound estimates of incidence and prevalence for MS and PD. A large arrow connects these inputs to activities in the second column to show how resources will be used to achieve the project’s intended outcomes in the third column.
Activities presented in a logic model depict what is done to create, or contribute to, desired benefits or changes (i.e., outcomes). (Centers for Disease Control and Prevention, 1999) This logic model includes three boxes of related activities: (a) demonstration project to estimate incidence and prevalence for MS and PD, (b) document demonstration project processes and outcomes, and (c) disseminate work completed to relevant stakeholders via appropriate channels. We intend to summarize knowledge on case definitions for MS and PD; summarize previous work on incidence and prevalence for these conditions; identify and summarize existing data that can be used to calculate incidence and prevalence (i.e., data already in-house or easily obtained); prepare an explanation of options to calculate incidence and prevalence for discussion with stakeholders; calculate initial estimates of prevalence and, to the extent possible, incidence; and explain these estimates in a format suitable to stakeholders. As presented in the graphic, each of these tasks includes meaningful collaboration with diverse stakeholder organizations, government and non-government.
- The second box of activities provides information on how and why CDC will document project processes and outcomes. In public health and allied fields, the ability to identify the key components of an activity or intervention that are effective, and under what conditions, can help us to disentangle the factors that enable or support progress toward desired outcomes.(Linnan, 2002) CDC personnel will work with stakeholders to document and explain work completed and products delivered; articulate options to derive estimates for MS and PD; capture lessons learned to support future work on these and other neurological conditions; and recommend immediate and longer-term activities based on documented achievements and challenges.
The third box of activities focuses on dissemination of work completed to relevant stakeholders via appropriate channels. Given the importance of this project to a wide range of stakeholders, it is especially important to ensure that products reach intended users and other audiences.
The last section of the logic model includes the intended outcomes of this project over time. Outcomes refer to the expected benefits, changes, or results of the program or project. (MacDonald, 2018)(A.W. Frye, 2012) In this case, the logic model includes four boxes of proximal or short-term outcomes: benefits or changes specific to MS and PD surveillance; benefits or changes relevant to neurological conditions surveillance more generally (i.e., not limited to MS or PD); benefits or changes relevant to CDC surveillance practices or processes; and benefits or changes relevant to collaboration with stakeholder organizations on neurological conditions surveillance. Each of these boxes includes more detailed statements regarding the specific benefits or changes desired. In the first box, benefits or changes specific to MS and PD surveillance, stakeholders expect the activities in the second column to contribute to a better understanding of challenges and information needs specific to MS and PD. And, the project aims to produce usable and useful estimates of incidence and prevalence for the two conditions before the end of year two. The second box in this column includes intended benefits or changes relevant to neurological conditions surveillance more generally: (a) improved knowledge and experience relevant to this work, and (b) innovation in data sources and surveillance methods for neurological conditions. The third box includes benefits or changes specific to CDC practices or processes relevant to public health surveillance. Stakeholders internal to CDC expect that this project will contribute to expanded use of the Agency’s internal data hub capabilities to support public health surveillance. For example, this data hub includes multiple data sets and analytic tools that can be used Agency-wide. In addition, collaboration as part of this demonstration project will produce expanded, cross-functional collaboration that contributes to the agency’s Public Health Surveillance Strategy. The fourth box of outcomes includes benefits or changes relevant to stakeholder collaboration. Specifically, this project is expected to strengthen collaboration among stakeholder organizations to advance and support neurological conditions surveillance. As this collaboration continues, and the products of this work are disseminated widely, stakeholder organizations and the public are expected to use the estimates generated via the demonstration project.
The logic models presents these four boxes of outcomes vertically, connected via straight lines. The circle of arrows behind the boxes is intended to represent that these benefits and changes are not isolated and static—each outcome is related to the others in meaningful ways. Cumulatively, these outcomes contribute to progress toward 2 longer-term benefits or changes presented in the logic model: (a) an increasingly robust National Neurological Conditions Surveillance System, and (b) expanded and improved application of processes and models to provide disease-specific surveillance information for neurological conditions.
As the demonstration project continues to evolve, and additional stakeholders contribute to activities, this logic model can be updated to reflect what is learned over time.
Centers for Disease Control and Prevention. (1999). Framework for program evaluation in public health. Morbidity and Mortality Weekly Report, 48(RR-11), 1-40.
Frye, A., & Hemmer, P. (2012). Program evaluation models and related theories: AMEE guide no. 67. Medical Teacher, 34, 3288-3299.
MacDonald, G. (2018). Checklist of Key Considerations for the Development of Program Logic Models. Retrieved from https://wmich.edu/sites/default/files/attachments/u350/2018/logic-models-macdonald_0.pdfpdf iconexternal icon
Steckler, A., & Linnan, L. (eds.) (2002). Process Evaluation for Public Health Interventions and Research. San Francisco, CA: Jossey-Bass.
Q. How will the demonstration projects/studies work?
A. During the first stage of NNCSS, CDC will conduct demonstration projects for two neurological conditions (Parkinson’s disease and multiple sclerosis), exploring what data must be present to identify someone as a case, what data sources are available for each condition, and whether the value of particular data sources differs according to factors such as the average age at which the condition is diagnosed. This will allow CDC to better understand surveillance tools that could work for each of the conditions and where there are similarities across neurological conditions, as well as where there might be potential economies of scale. During stage two, CDC will build out Parkinson’s disease and multiple sclerosis surveillance which will help develop cost estimates and collect lessons learned that will be important in extending the NNCSS to other neurological conditions going forward during stage three and beyond. At present, Congress has obligated FY 2019 funding for CDC/NNCSS, which should allow CDC to implement its first stage, which will take 2 years. To be as efficient as possible, in FY 2019, CDC is evaluating in-house data sources and working to purchase other data sources. In FY 2020, CDC will evaluate the newly purchased data sources and, as resources allow, will purchase and evaluate the final data sources. Stages two and three will be implemented as resources allow.
Q. How will CDC and NNCSS identify the stakeholders with whom it will work?
A. CDC is looking for opportunities to engage stakeholders as it moves forward with the NNCSS project. We value the perspective, knowledge, expertise, and experience that others will bring to this effort.
Q. What has CDC done with NNCSS funding to date?
A. CDC appreciates that Congress has allocated funding for our agency to use its expertise in epidemiology and surveillance to shine a light on the incidence and prevalence of often-neglected neurological conditions and the impact they have on public health.
With the $5 million appropriated in FY 2019, CDC is beginning to develop a National Neurological Conditions Surveillance System (NNCSS). Consistent with the 21st Century Cures Act, CDC plans to collect and synthesize data to help increase understanding of neurological conditions and to support further research. Work includes:
- Identifying data requirements and evaluating existing data sources
- Exploring both standard and innovative methods and additional data sources to derive estimates of neurologic conditions
- Collaborating and communicating with stakeholders and Congress about the status of the NNCSS and relevant results.
In fiscal year 2019, CDC is focusing on the first of three stages, in association with partners and stakeholders. In this first stage, which will take two years, CDC is working on demonstration projects using two neurological conditions—multiple sclerosis and Parkinson’s disease. The intent is to determine how CDC can have the biggest impact by exploring existing data sources and innovative new methods, capturing lessons learned, and identifying approaches to build out the NNCSS for continued surveillance of MS and PD and to efficiently extend the NNCSS to other neurological conditions during its third stage.
Q. How much would it cost to add another condition (e.g., condition X)?
A. One of the main purposes of the stage 1 demonstration projects, using multiple sclerosis and Parkinson’s disease, is to determine how to undertake surveillance in a way that maximizes quality, timeliness, and efficiency. It is important for CDC to complete stage 1 work, and establish meaningful cost estimates for initiating surveillance for a new condition, prior to adding additional conditions. The intent for stage 2 (depending on resources) is to build out the NNCSS to collect surveillance data on an ongoing basis for multiple sclerosis and Parkinson’s disease, using successful approaches from stage 1, and assessing costs and staffing needs for ongoing surveillance. CDC can most effectively extend the NNCSS to additional neurological diseases and conditions during stage 3 (pending resources) by incorporating lessons learned in the first two stages.
Q. What would CDC do next with additional resources?
A. CDC has depended on an FY 2019 appropriation of $5 million to launch and begin implementation of stage 1 of the NNCSS. With funding for FY 2020, CDC would be able to complete year two of stage 1, which will involve a) validating the findings from the data sources assessed in year 1 (to ensure that the prevalence and incidence estimates generated are accurate and complete), and b) assessing the remaining data sources. If additional funding is received in FY 2020, CDC would begin to plan for and initiate stage 2 during the final stages of completing stage 1. Stage 2 involves building out the NNCSS to continue to collect surveillance data for MS and PD, using successful approaches from the stage 1 demonstration projects while standardizing methods and approaches, and assessing costs and opportunities. Assuming adequate resources for stage 3, CDC will use lessons learned from stages 1 and 2 to extend the NNCSS to other neurological conditions.
Q. Are stages 2 and 3 of the NNCSS plan dependent on funding?
A. Yes, CDC will be able to proceed to stages 2 and 3 only if additional funding is appropriated. Stable funding would allow ongoing efforts for PD and MS, without stopping and starting work and losing the benefit of efficient allocation of resources like people and funds. Stable funding will not allow the addition of other neurologic conditions without delaying and diminishing progress on surveillance for PD and MS. If additional funding is not available, CDC will complete as much of stage 1 as possible—primarily assessing and validating the data sources that were acquired with FY 2019 funding.
Q. What is the relationship between the national data collection effort (NNCSS) and the registries? How are they different and what role does each play in getting the information needed to further research?
A. Public health surveillance efforts, such as the NNCSS, gather data on the prevalence and incidence of diseases and conditions. Prevalence is the presence of a disease or condition in the population (i.e., how many people in the population have the disease or condition). Incidence is the measurement of new cases (i.e., the number of new cases of the disease or condition in the population).
Surveillance data, such as prevalence and incidence, can be gathered from a variety of sources including administrative data (e.g., Medicare, Medicaid), mandated reports (e.g., data on reportable conditions), surveys, and registries. There are many benefits to registries. Registries systematically collect data on specific individuals—sometimes a lot of data—and they also may collect biological specimens. The detailed patient information and biological specimens make registries extremely valuable for research, as well as for informing clinical care. Registries can also help researchers locate potential patients for research studies because registries include personally identifiable information.
As valuable as registries are, they also have their challenges if trying to build a surveillance system at the national level. First, collecting and maintaining all of the information in a registry is very expensive and labor-intensive. The expense will be even greater for a system expected to undertake surveillance of numerous conditions. Also, most registries are voluntary, and patients must consent to have their information and biological specimens included. As a result, the registries may be missing significant numbers or types of patients, which could affect the accuracy of national prevalence and incidence estimates.