gms | German Medical Science

Gesundheit – gemeinsam. Kooperationstagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie (GMDS), Deutschen Gesellschaft für Sozialmedizin und Prävention (DGSMP), Deutschen Gesellschaft für Epidemiologie (DGEpi), Deutschen Gesellschaft für Medizinische Soziologie (DGMS) und der Deutschen Gesellschaft für Public Health (DGPH)

08.09. - 13.09.2024, Dresden

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Exploring the potential of German claims data to identify incident lung cancer patients

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  • Josephine Kanbach - Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany
  • Nikolaj Rischke - Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany
  • Sabine Luttmann - Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany
  • Ulrike Haug - Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany; Faculty of Human and Health Sciences, University of Bremen, Bremen, Germany

Gesundheit – gemeinsam. Kooperationstagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie (GMDS), Deutschen Gesellschaft für Sozialmedizin und Prävention (DGSMP), Deutschen Gesellschaft für Epidemiologie (DGEpi), Deutschen Gesellschaft für Medizinische Soziologie (DGMS) und der Deutschen Gesellschaft für Public Health (DGPH). Dresden, 08.-13.09.2024. Düsseldorf: German Medical Science GMS Publishing House; 2024. DocAbstr. 903

doi: 10.3205/24gmds824, urn:nbn:de:0183-24gmds8245

Published: September 6, 2024

© 2024 Kanbach et al.
This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 License. See license information at http://creativecommons.org/licenses/by/4.0/.

Outline

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Background: Real-world healthcare databases offer immense potential for cancer research, but the valid identification of cancer patients is crucial for the suitability of a database in this regard. We aimed to assess the plausibility of an algorithm to identify incident lung cancer (LC) patients in German claims data.

Methods: Using the German Pharmacoepidemiological Research Database (GePaRD; claims data from ~20% of the German population) we applied a previously developed algorithm which identifies incident LC patients and classifies them into advanced and non-advanced. We calculated age-standardized (ASIRs) and age-specific incidence rates per 100,000 for the years 2013-2018. Further, we assessed the ASIRs stratified by the deprivation index of the district of residence and determined age-standardized five-year absolute and relative survival. We stratified all analyses by sex.

Results: Overall, we identified ~9,500-10,500 incident LC patients per year. In 2018, (N=10,625, mean age: 69.2 years) the proportion diagnosed at an advanced stage was 71.4% and the ASIRs of LC per 100,000 were 45 per 100,000 in men (9% lower than in 2013) and 27 per 100,000 persons in women (similar to 2013). ASIRs were lowest in persons living in areas with a low deprivation index. Age-standardized five-year absolute and relative survival rates, respectively, were 31% and 32% in women and 27% and 34% in men.

Conclusion: The algorithm we applied to identify incident LC patients in German claims data yielded plausible results, supporting its validity.

The authors declare that they have no competing interests.

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