Diabetes mellitus is one of the most common non-communicable diseases in Germany. According to the Diabetes Network Germany, around seven million people in Germany currently live with diabetes, around 90 to 95 percent of whom have type 2 diabetes.
With the new project "Economics of diabetes prevention and care: Using microsimulation and prediction modeling to support evidence-based decision making in health policy", ECON-Diabetes for short, the Associate Professorshipr of Public Health and Prevention headed by Prof. Dr. Michael Laxy aims to use microsimulation and prediction modeling to support evidence-based decision making in health policy with regard to diabetes prevention and care. The project is funded with 270,000 euros by the German Center for Diabetes Research (DZD), which is supported by the German Federal Ministry of Education and Research. It will run for three years (2023-2025).
"We are taking two different approaches in the project," explains Prof. Laxy. "On the one hand, the long-term cost-effectiveness of measures to prevent and treat diabetes mellitus is to be analyzed. On the other hand, possible complications of diabetes are to be predicted on the basis of routine data on patients."
The first step is to compare, from a health economic perspective, which interventions and policy measures are most efficient in preventing or delaying diabetes in the long term, or which are most efficient in improving the quality of care for those affected. In addition, those interventions that are expected to have the greatest effect at the population level will be prioritized.
"We work with mathematical simulation models that are very 'data hungry,'" says the health scientist Laxy, who holds a doctorate. "In doing so, we try to represent reality in a model from different data sources. This allows us to determine, for example, what the gain in quality of life would be if diabetes were delayed for a few years, what the cost savings would be to the health care system, and how productivity would change as a result. At the end, we can then generate so-called 'if-then' scenarios for different time horizons."
The second step is to continue a line of research that has already been funded by the DZD. Here, machine learning approaches and large cash register data sets will be used to predict which people suffering from diabetes may soon have to reckon with complications such as heart attacks, strokes, etc. Here, the head of the Associate Professorship of Public Health and Prevention sees particular potential for effective stratification of care: "From a health economic perspective, it would be particularly important to take the next therapeutic step with these individuals. Such 'pre-stratification' is not really possible routinely, but with the point-of-care data we have a lot of variables and health information about the patients."
Dr. Anna-Janina Stephan, Research Associate at the Associate Professorship of Public Health and Prevention, explains further: "These data are available in the health care system anyway for all those with statutory health insurance. Making them systematically usable thus promises a potentially high benefit at a relatively low resource cost."
The processed data set contains information on approximately 370,000 patients with type 2 diabetes, whose data can be worked with over the time horizon of the last six years.
"We're doing basic research here on personalizing care," Prof. Laxy notes. "If we can show that we can predict disease risks well from routine data, that would certainly be an important building block to eventually do stratified personalized medicine or care. Someday!"
To the homepage of the Associate Professorship of Public Health und Prevention
Contact:
Prof. Dr. Michael Laxy
Associate Professorship of Public Health and Prevention
Georg-Brauchle-Ring 60/62
80992 München
phone: 089 289 24977
e-mail: michael.laxy(at)tum.de
Anna-Janina Stephan
Associate Professorship of Public Health and Prevention
Georg-Brauchle-Ring 60/62
80992 München
phone: 089 289 24984
e-mail: anna-janina.stephan(at)tum.de
Text: Romy Schwaiger
Photos: DZD/private