Can German Health Insurance Claims Data Fill Information Gaps in Rare Chronic Diseases: Use Case of Haemophilia A.
Vanessa KratzerVerena RölzChristoph BidlingmaierRobert KlamrothJochen BehringerAnja SchrammUlrich Robert MansmannKarin Berger-ThürmelPublished in: Hamostaseologie (2024)
Claims data are increasingly discussed to evaluate health care for rare diseases (resource consumption, outcomes and costs). Using haemophilia A (HA) as a use case, this analysis aimed to generate evidence for the aforementioned information using German Statutory Health Insurance (SHI) claims data. Claims data (2017-2019) from the German SHI 'AOK Bayern - Die Gesundheitskasse' were used. Patients with ICD-10-GM codes D66 and HA medication were included in descriptive analyses. Severity levels were categorized according to HA medication consumption. In total, 257 patients were identified: mild HA, 104 patients (mean age: 40.0 years; SD: 22.9); moderate HA, 17 patients, (51.2 years; SD: 24.5); severe HA, 128 patients, (34.2 years; SD: 18.5). There were eight patients categorized with inhibitors (37.8 years; SD: 29.6). Psychotherapy was reported among 28.8% (mild) to 32.8% (severe) of patients. Joint disease was documented for 46.2% (mild) to 61.7% (severe) of patients. Mean direct costs per patient per year were 1.34× for mild, 11× for moderate, 81× higher for severe HA patients and 223× higher for inhibitor patients than the mean annual expenditure per AOK Bayern insurant (2019). German SHI data provide comprehensive information. The patient burden in HA is significant with respect to joint disease and psychological stress regardless of the HA severity level. The cost of HA care for patients is high. Large cost ranges suggest that the individual situation of a patient must be considered when interpreting costs. The main limitation of SHI data analysis for HA was the lack of granularity of ICD codes.
Keyphrases
- end stage renal disease
- health insurance
- ejection fraction
- healthcare
- newly diagnosed
- chronic kidney disease
- prognostic factors
- peritoneal dialysis
- emergency department
- metabolic syndrome
- skeletal muscle
- insulin resistance
- patient reported outcomes
- depressive symptoms
- adipose tissue
- cross sectional
- artificial intelligence
- deep learning