prevCOV – Improving the prevention and treatment of Long COVID and Post-VAC

>Federal Ministry of Health (BMG)

Statistical AnalysesDisease incidence

prevCOV is a BMG-funded project that aims to develop new approaches to the care of patients with long COVID and post-VAC by identifying factors that influence the risk of disease development and progression.

The expected outcomes of prevCOV are 1) a systematic literature review of risk and prognostic factors, 2) an epidemiological description of the situation of Long COVID in Germany as well as healthcare utilization and costs incurred, and 3) a joint modeling of risk and prognostic factors combining previously known and unknown factors (including COVID vaccinations).

To achieve its objectives, prevCOV uses a complex design that combines the analysis of secondary and primary data. In order to describe the Long COVID situation and to identify new risk and predictive factors as well as therapeutic approaches in Germany, the data of all persons with statutory health insurance (FDZ) will be used. The COVID vaccination is also such a factor, therefor prevCOV will link vaccination data at a population level with health insurance data for the first time in Germany in order to finally determine the relevance of the factors for Long COVID in Germany. In addition, primary data on symptoms and therapies will be collected by means of a quantitative survey.

To combine the data from the different sources, PMV will apply a complex data linkage in prevCOV, which was developed in a previous study (RiCO) of the partners.

Duration: 2024–2027
Project partners: Paul-Ehrlich-Institut (PEI) | PMV forschungsgruppe, Universität Köln | Abteilung für Medizinische Informatik, Biometrie und Epidemiologie (AMIB) der Ruhr-Universität Bochum
Methods: Statistical Analyses
Topics: Disease incidence, Population health
Data: Data linkage, Data preparation, Claims data, Data infrastructure