EPIDEMIOLOGY AND DISEASE BURDEN OF DERMATOMYOSITIS IN GERMANY - FIRST EXPERIENCES WITH THE HEALTH DATA LAB IN A NATIONWIDE RETROSPECTIVE CLAIMS DATA ANALYSIS
Author(s)
Lena Hasemann, MA1, Darja Baban, MSc1, Min-Jean Hsieh, MBA, MD2, Niklas Hegemann, MSc, PhD2, Martin Wieczorek, PhD2, Saskia Trescher, MSc, PhD2, Daniel Gensorowsky, MSc, PhD1, Agnes Kisser, PhD2, Udo Schneider, MD3, Cord H. Sunderkoetter, MD4, Carolina Schwedhelm, MPH, MSc, PhD2.
1Vandage GmbH, Bielefeld, Germany, 2Pfizer Pharma GmbH, Berlin, Germany, 3Department of Rheumatology and Clinical Immunology, Immanuel Hospital Berlin, Berlin, Germany, 4Department and Outpatient Clinic of Dermatology and Venereology, University Hospital Halle (Saale), Berlin, Germany.
1Vandage GmbH, Bielefeld, Germany, 2Pfizer Pharma GmbH, Berlin, Germany, 3Department of Rheumatology and Clinical Immunology, Immanuel Hospital Berlin, Berlin, Germany, 4Department and Outpatient Clinic of Dermatology and Venereology, University Hospital Halle (Saale), Berlin, Germany.
OBJECTIVES: Dermatomyositis (DM) is a rare autoimmune disease associated with increased mortality, substantial morbidity, and healthcare burden. The recently established German Health Data Lab (HDL; German: Forschungsdatenzentrum Gesundheit [FDZ]) enables access to nationwide statutory health insurance (SHI) data and represents a novel infrastructure for real-world evidence generation. This study aims to estimate the administrative incidence and prevalence of DM in Germany and to assess its disease burden among adults with incident DM compared with matched DM-free controls. In addition, the study explores the feasibility and potential of the HDL for rare disease research.
METHODS: This retrospective claims data study uses nationwide SHI data from the HDL (2009-2023; ~74.6 million individuals). Incident DM is defined as one inpatient or two confirmed outpatient diagnoses (ICD-10-GM: M33.1, M33.9) within three consecutive quarters, following a 4-year washout period. Adults with incident DM are matched 1:3 to DM-free controls. Outcomes include comorbidities, mortality, MACE, mean HCRU, and healthcare costs (expressed in 2023 euros). Feasibility was assessed via the HDL statistics portal and user experience with the HDL analysis environment was documented.
RESULTS: The HDL statistics portal identified 16,308 individuals with ICD-10-GM M33-Dermatomyositis-Polymyositis diagnoses in 2022. Based on published literature, approximately 45-50% are expected to represent DM, yielding estimates in line with prior reports and indicating sufficient sample size for rare disease analyses. Initial experience indicates strong analytical potential, while highlighting the importance of detailed knowledge of the HDL data structure and thorough methodological planning.
CONCLUSIONS: This study will provide comprehensive evidence on the burden, including mortality and cardiovascular risk, of DM in Germany. The HDL is a promising national data source for epidemiological and health economic research in rare diseases such as DM. Early feasibility supports its suitability and may guide future HDL-based analyses.
METHODS: This retrospective claims data study uses nationwide SHI data from the HDL (2009-2023; ~74.6 million individuals). Incident DM is defined as one inpatient or two confirmed outpatient diagnoses (ICD-10-GM: M33.1, M33.9) within three consecutive quarters, following a 4-year washout period. Adults with incident DM are matched 1:3 to DM-free controls. Outcomes include comorbidities, mortality, MACE, mean HCRU, and healthcare costs (expressed in 2023 euros). Feasibility was assessed via the HDL statistics portal and user experience with the HDL analysis environment was documented.
RESULTS: The HDL statistics portal identified 16,308 individuals with ICD-10-GM M33-Dermatomyositis-Polymyositis diagnoses in 2022. Based on published literature, approximately 45-50% are expected to represent DM, yielding estimates in line with prior reports and indicating sufficient sample size for rare disease analyses. Initial experience indicates strong analytical potential, while highlighting the importance of detailed knowledge of the HDL data structure and thorough methodological planning.
CONCLUSIONS: This study will provide comprehensive evidence on the burden, including mortality and cardiovascular risk, of DM in Germany. The HDL is a promising national data source for epidemiological and health economic research in rare diseases such as DM. Early feasibility supports its suitability and may guide future HDL-based analyses.
Conference/Value in Health Info
2026-11, ISPOR Europe 2026, Vienna, Austria
Value in Health, Volume 29, Issue 12S
Code
EPH75
Topic
Economic Evaluation, Epidemiology & Public Health, Real World Data & Information Systems
Disease
Musculoskeletal Disorders (Arthritis, Bone Disorders, Osteoporosis, Other Musculoskeletal)