More than 84 million Americans have elevated levels of glycemia. If not treated, the condition leads to type 2 diabetes, the 7th leading cause of death in the U.S. How do we get out ahead of this disease?
One answer is to track the progression toward the disease over time. The data will help to
- Predict who might be at higher risk
- Intervene early to delay disease onset
The Centers for Disease Control and Prevention (CDC) tapped Westat to build a complex database using electronic health records (EHRs) of nearly 5 million people from health plans across the country and following them over 8 years.
Known as the Longitudinal Epidemiologic Assessment of Diabetes Risk (LEADR), the database includes nearly 568 million data records to determine the risk of developing diabetes. The records include a range of factors from patient demographics, medical history, attendance in lifestyle change programs, and more.
By tracking these individuals and studying the risk profiles over time, we can learn what factors lead to elevated glycemia and, ultimately, diabetes.
- Designed and developed a longitudinal data system to capture data elements from disparate EHR systems from across the U.S.
- Captured data from 2010-2017.
- Standardized and harmonized data using a common data model to allow for systematic analysis.
- Created analytic data sets of nearly 2 million patients at greatest risk for type 2 diabetes
- This complex, comprehensive database will provide data on the risk factors leading to diabetes.
- The data will help CDC
- Monitor clinical and lifestyle factors to track trends over time
- Determine how effective interventions are at individual, health care system, community, and policy levels
- Lead to a predictive model of diabetes risk