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Hamostaseologie
DOI: 10.1055/a-2276-4871
Original Article

Can German Health Insurance Claims Data Fill Information Gaps in Rare Chronic Diseases: Use Case of Haemophilia A

Vanessa Kratzer 
1   Institute for Medical Information Processing, Biometry, and Epidemiology - IBE, Ludwig Maximilian University of Munich, Munich, Germany
2   Comprehensive Cancer Center, CCC München LMU, Munich, Germany
,
Verena Rölz
1   Institute for Medical Information Processing, Biometry, and Epidemiology - IBE, Ludwig Maximilian University of Munich, Munich, Germany
3   Pettenkofer School of Public Health, Munich, Germany
,
Christoph Bidlingmaier
4   Kinderklinik und Kinderpoliklinik im Dr. von Haunerschen Kinderspital, Ludwig Maximilian University Hospital, Munich, Germany
,
Robert Klamroth
5   Vivantes Klinikum im Friedrichshain, Klinik für Innere Medizin Angiologie und Hämostaseologie, Berlin, Germany
,
Jochen Behringer
6   AOK Bayern - Die Gesundheitskasse Bereich Exzellenzzentrum Analytik u. Daten Fachbereich Datengovernance, AOK Bayern, München, Germany
,
Anja Schramm 
6   AOK Bayern - Die Gesundheitskasse Bereich Exzellenzzentrum Analytik u. Daten Fachbereich Datengovernance, AOK Bayern, München, Germany
,
Ulrich Mansmann 
1   Institute for Medical Information Processing, Biometry, and Epidemiology - IBE, Ludwig Maximilian University of Munich, Munich, Germany
,
Karin Berger 
1   Institute for Medical Information Processing, Biometry, and Epidemiology - IBE, Ludwig Maximilian University of Munich, Munich, Germany
7   Medizinische Klinik und Poliklinik III, Ludwig Maximilian University Hospital, Munich, Germany
› Author Affiliations Funding Research grants were received from ROCHE Pharma AG and Rudolf-Marx-Stiftung. The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
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Abstract

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.

Zusammenfassung

Abrechnungsdaten werden zunehmend diskutiert, um die Gesundheitsversorgung bei seltenen Erkrankungen (Ressourcenverbrauch, Outcomes und Kosten) zu evaluieren. Ziel dieser Analyse war es, am Beispiel der Hämophilie A (HA) anhand von Abrechnungsdaten der gesetzlichen Krankenversicherung (GKV) Evidenz für die oben genannten Informationen zu generieren.

Verwendet wurden Abrechnungsdaten (2017 bis 2019) der AOK Bayern - Die Gesundheitskasse. In die deskriptiven Analysen wurden Patienten mit den ICD-10-GM-Codes D66 und/oder D68.31 und HA-Medikation eingeschlossen. Die Schweregrade wurden nach der Einnahme von HA-Medikation klassifiziert.

Insgesamt wurden 257 Patienten identifiziert: leichte HA, 104 Patienten (Durchschnittsalter 40,0 Jahre; SD 22,9); mittelschwere HA, 17 Patienten (51,2 Jahre; SD 24,5); schwere HA, 128 Patienten (34,2 Jahre; SD 18,5). Es gab 8 Patienten, die mit Inhibitoren kategorisiert wurden (37,8 Jahre; SD 29,6). Eine Psychotherapie wurde bei 28,8% (leicht) bis 32,8% (schwer) der Patienten dokumentiert. Eine Gelenkerkrankung wurde bei 46,2% (leicht) bis 61,7% (schwer) der Patienten dokumentiert. Die durchschnittlichen direkten Kosten/Patient/Jahr lagen bei Patienten mit leichter HA um das 1,34-fache, bei Patienten mit mittelschwerer HA um das 11-fache, bei Patienten mit schwerer HA um das 81-fache und bei Patienten mit Inhibitoren um das 223-fache über den durchschnittlichen jährlichen Ausgaben pro Versicherten der AOK Bayern (2019).

Die deutschen GKV-Daten liefern umfassende Informationen. Die Belastung der Patienten durch HA ist unabhängig vom Schweregrad der HA im Hinblick auf Gelenkerkrankungen und psychische Belastung erheblich. Die Kosten der HA-Versorgung für die Patienten sind hoch. Große Kostenspannen legen nahe, dass bei der Interpretation der Kosten die individuelle Situation des Patienten berücksichtigt werden muss. Die größte Einschränkung bei der Analyse der GKV-Daten für Hämophilie A war die fehlende Granularität der ICD Codes.

Keywords

haemophilia A - claims data - health economics - rare disease

Schlüsselwörter

Hämophilie A - Leistungsdaten - Gesundheitsökonomie - Seltene Krankheit

Ethical Approval and Consent to Participate

Cooperation agreement to comply with the provisions of Regulation (EU) 2016/679 (General Data Protection Regulation—‘DSGVO’) and that data are used only to the extent permitted by data protection law (78 SGB X).


Consent for Publication

Not applicable.


Availability of Data and Materials

Data are not publicly available but were made available to the authors via §75 SGB X proposal.


Competing Interests

U.M., K.B., V.K., V.R., A.S., and J.B. have no relevant financial or nonfinancial interests to disclose. C.B. received honoraria research grants or advisory boards: Bayer, Biotest, CSL Behring, NovoNordisk, Pfizer, Roche/Chugai, Sanofi, SOBI, Takeda. C.B. is co-principal investigator of the German Pediatric Hemophilia Research Database which is or was supported with research grants by most companies in the field of haemophilia. R.K. received honoraria, research grants, or advisory boards: Bayer, BioMarin, Biotest, CSL Behring, Grifols, NovoNordisk, Octapharma, Pfizer, Roche/Chugai, Sanofi, SOBI, and Takeda.


Authors' Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by V.K., V.R. The first draft of the manuscript was written by V.K., and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.




Publication History

Received: 04 October 2023

Accepted: 20 February 2024

Article published online:
01 July 2024

© 2024. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

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