Trial-based evaluations: The implementation of health care innovations and new preventive measures requires robust evidence for their costs and benefits - preferably from prospective, randomized, controlled study designs.  In cooperation with partners from health care and public health practice, we design, implement and evaluate the effectiveness and cost-effectiveness of various interventions.

Examples include the evaluation of DALIG (digital intervention for the prevention of type 2 diabetes), ACHT (structured aftercare for bariatric surgery), D-CLIP (lifestyle prevention of type 2 diabetes), INDEPENDENT (integrated care for diabetes and depression), and ADDITION (intensified treatment for type 2 diabetes). 

Model-based evaluations:  Robust ex ante estimates are important for decision makers to assess the long-term health and economic impact of interventions. In various projects, we are working on the further development of simulation models and carry out model-based evaluations of health policy measures.

Methodological contributions include the development and parameterization of a simulation model for type 2 diabetes for the US context and the cross-validation of a public health simulation model for non-communicable diseases. 

Examples of applied modeling studies are the health economic evaluation of intensified diabetes management in the UK, of life-style-based diabetes prevention in the US, or of taxation of sugar-sweetened beverages in Germany.

Quasi-experimental evaluations: Many measures in the area of care, prevention and public health cannot be evaluated using prospective, randomized and controlled study designs. Another focus of our research is therefore impact evaluation using quasi-experimental designs together with secondary data.

Examples include analyses of the effect of hypertension screening on cardiovascular mortality, of COVID-19 measures on air quality in Munich, of disease management programs for type 2 diabetes on mortality and of bariatric surgery on medical utilization.