Chronic Disease Data To Action
Data to action means using different types of data to select, inform, improve, and adapt public health programs and policies. Essentially, using data to make more impactful decisions. It is a continuous cycle of learning and improvement that can be represented in a cycle of 6 stages, known as the data-to-action framework.
Stage 1 Assess Current State of Available Data
Start by identifying what you know and don’t know.
Applying The Data To Action Framework
Diabetes Communications Campaign Example
As part of a diabetes communications campaign focused on communities with limited access to prevention and care services, your team turns to the Data to Action framework to guide your approach. You begin by refining your key question: how can you identify individuals who may not be managing their diabetes well enough to avoid serious complications like cardiovascular disease or neuropathy? To answer this, you explore potential proxy indicators of inadequate diabetes control, such as co-morbidities, geographic disparities, demographic patterns, and gaps in preventative care behaviors.
You prioritize near-real-time data sources like electronic health records (EHRs) and health information exchanges (HIEs), which can provide current diagnosis and visit data, and correlate those data with population-based survey results and other data sources related to diabetes risk factors to help identify trends across time and populations. Grounded in your analyses, you engage a diverse set of partners to deepen your understanding of community needs and shape your outreach strategy. These partners include community-based organizations, parks and recreation departments, public safety, urban planning teams, federally qualified health centers (FQHCs), and local hospitals.
Through this collaborative process, you identify communities at higher risk for pre-diabetes or undiagnosed diabetes. Drawing on these insights, you develop targeted messaging and programming to increase screening, improve early detection, and connect individuals to appropriate care. This approach helps ensure that your campaign is not only data-driven but also grounded in community realities and aligned with broader chronic disease surveillance goals.
What Data Is Available?
The following are data sources that you may already have access to — or can work with your partners to gain access to — to answer your chronic disease questions. Click on the system name for more information.
Clinical and Vital Records
Survey and Demographic Data
Data Aggregators and Equity Tools
Accessing Data Quality
Data are the foundation for informed chronic disease decision-making. The most appropriate data sources will depend on the questions or information you and your partners want to address. Keep the following considerations in mind when utilizing data sources.
Completeness in chronic disease surveillance can be described as the extent to which all possible observations and variables are recorded.
Missing data and incomplete patient records limit the ability of chronic disease practitioners to assess the representativeness of data and use those data as a foundation for their interventions and actions. Analyzing incomplete datasets can also lead to incorrect interpretations. Consider supplementing with behavioral health, health surveys, linking to other data systems to inform decision-making.
Example
In the Pacific Islands, chronic disease rates are sometimes hindered by uncertainty around population denominators. STLTs can leverage the National Association for Public Health Statistics and Information Systems’ (NAPHSIS) State and Territorial Exchange of Vital Events (STEVE) to improve access to and integrate more complete morality records.
Timeliness in data collection can be described as the time between data collection and reporting. Many chronic disease surveillance systems report data once a year or longer.
State and county-level data are typically collected annually or every few years and may not fully reflect the ‘current state’ of chronic diseases within a jurisdiction. Public health decision-making might be based on data collected years ago, which may not be as helpful for all potential public health actions relating to chronic diseases.
Example
To improve chronic disease surveillance capacity and provide timely data, Michigan's Department of Health and Human Services (MDHS) created CHRONICLE, the Chronic Disease Registry Linking Electronic Health Record Data. CHRONICLE is a near-real-time disease monitoring system designed to harness electronic health record (EHR) data and existing health information exchange (HIE) infrastructure for transformative public health surveillance.
Data standardization is bringing data into a consistent format and structure so it can be easily shared, compared, and analyzed across different systems and organizations. It is important for STLTs to use standardization to promote data interoperability between STLTs and partner organizations, including hospital EHR systems. Please see “Interoperability” and “FHIR” for more information.
Currently, there are no nationally mandated standard data elements specific for chronic disease surveillance.
Example
The Multi-state EHR-based Network for Disease Surveillance (MENDS) is addressing this lack of national standards by developing and promoting a common framework for using electronic health record (EHR) data in public health. MENDS uses the PCORnet Common Data Model to standardize how clinical data is structured and coded, and developed standardized surveillance indicators for chronic disease.
Toolkit Navigation
Foundational Concepts
Understand the core principles, key terminology, and initiatives grounding CSTE’s Chronic Disease Surveillance Data Modernization Strategic Plan
Implementation Actions and Strategies
Learn about the strategies and objectives outlined in STE’s Chronic Disease Surveillance Data Modernization Strategic Plan and explore tools for implementation
Implementation Stories
Draw from real-world examples of chronic disease surveillance across a range of jurisdictions with varying levels of experience and resources
Community and Collaboration
Identify and cultivate partnerships with other practitioners working on chronic disease surveillance modernization