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Introduction: Claims data in health service research offers potential for diverse analysis due to its large sample size, potential for longitudinal tracking, and lack of selection bias. This study aims to describe the Deutsche Analysedatenbank für Evaluation und Versorgungsforschung (DADB) database and explore how different extrapolation strategies affect representativeness relative to the German statutory health insurance (SHI) population based on available demographic measures and morbidity groups.

Methods: The DADB includes data from approximately 4.4 million insured individuals (4% of the German SHI population in 2023), covering inpatient and outpatient diagnoses, procedural codes, and cost data. To ehance representativeness, adjustments for age, gender, and morbidity – based on the Risikostrukturausgleich (RSA) – were applied. The impact on confidence intervals is also discussed.

Results: Compared to the overall SHI population, the DADB includes a slightly younger demographic. Frequencies of morbidity groups are similar between the datasets, though slightly lower in the DADB.

These differences diminish after demographic and morbidity-based adjustments.

Conclusion: The age and gender-adjusted DADB demonstrates good representativeness for the German SHI population. When significant discrepancies persist, further risk adjustment is advised. he DADB offers a comprehensive, longitudinal view of patient healthcare utilization, including the capacity to construct medication treatment lines.

The authors declare that they have no competing interests.

The authors declare that an ethics committee vote is not required.

The contribution has already been published: [1]

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