ArticleOrphanet journal of rare diseases2025
Epidemiology and economic burden of selected rare genetic diseases in Germany - a claims database study.
Article in Orphanet journal of rare diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Five-year Incidence Trends and the Status of Major Rare Diseases in 2023 in the 「Annual Report on the Rare Disease Patients in Korea」.Public health weekly report · 2026Article
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7 authors.
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Abstract
backgroundResearch on rare genetic diseases is challenging due to their low prevalence and complex clinical manifestations. Despite their individual rarity, the cumulative burden of these diseases is substantial. Larger claims databases could enable a quantification and characterization of patients with rare diseases and contribute to determining their specific healthcare needs. This study examines the prevalence, comorbidities, and annual direct costs of Huntington’s disease (HD), Beta-Thalassemia (BT), and Spinal Muscular Atrophy type 1 (SMA type 1) in Germany.
methodsUtilizing anonymized claims data from the InGef research database, we conducted a cross-sectional analysis for each disease from 2017 to 2023. Patients and comorbidities were identified based on ICD-10-GM-codes. Prevalence was extrapolated to the German population. Costs were analyzed as direct costs per patient and by sector incurred.
resultsPrevalences for the selected diseases were 6.65 (HD), 12.81 (BT), and 1.24 (SMA type 1) patients per 100,000 persons in Germany in 2023. The most frequent comorbidities identified are strongly related to the specific disease. For instance, in 2023, among the most common diagnoses were depression in HD patients (38.48%), dorsalgia in BT patients (37.03%), and acute upper respiratory infections in SMA type 1 patients (45.19%). Average total direct costs in 2023 amounted to 9,527.89 € for HD, 6,656.27 € for BT and 144,585.02 € for SMA type 1, respectively.
conclusionClaims data enable robust identification and characterization of rare disease populations. The findings on prevalences and comorbidities align with existing literature and support the use of such data for treatment planning and post-marketing studies.
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