Development of a new cancer atlas in Lower Saxony using Bayesian smoothing methods

25gmds013 10.3205/25gmds013 urn:nbn:de:0183-25gmds0139 Meeting Abstract Development of a new cancer atlas in Lower Saxony using Bayesian smoothing methods Blohm Blohm Kolja K

OFFIS - Institut für Informatik, Oldenburg, Germany

author Beckhaus Beckhaus Julia J

OFFIS - Institut für Informatik, Oldenburg, Germany Carl von Ossietzky Universität Oldenburg, Fakultät VI – Medizin und Gesundheitswissenschaften, Department für Versorgungsforschung, Abteilung für Assistenzsysteme und Medizintechnik, Oldenburg, Germany

author Specht Specht Sebastian S

OFFIS - Institut für Informatik, Oldenburg, Germany

author Urbschat Urbschat Iris I

Epidemiologisches Krebsregister Niedersachsen (EKN), Registerstelle, Oldenburg, Germany

author Vohmann Vohmann Claudia C

Epidemiologisches Krebsregister Niedersachsen (EKN), Registerstelle, Oldenburg, Germany Klinische Landesauswertungsstelle Niedersachsen (KLast), Oldenburg, Germany

author Lüpkes Lüpkes Christian C

OFFIS - Institut für Informatik, Oldenburg, Germany

author Hübner Hübner Joachim J

Klinische Landesauswertungsstelle Niedersachsen (KLast), Oldenburg, Germany

author Kieschke Kieschke Joachim J

Epidemiologisches Krebsregister Niedersachsen (EKN), Registerstelle, Oldenburg, Germany

author Hein Hein Andreas A

author German Medical Science GMS Publishing House

Düsseldorf

610 20251103 engl This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 License. Dieser Artikel ist ein Open-Access-Artikel und steht unter den Lizenzbedingungen der Creative Commons Attribution 4.0 License (Namensnennung). M0631 013 Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie 70. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS) V: Cancer epidemiology Jena 20250907 20250911 Abstr. 128 TextIntroduction: Lower Saxony comprises nine metropolitan areas and numerous sparsely populated regions. Approximately 75% of the population live in small town (<50,000 inhabitants), suburban, and rural areas . Cancer burden can vary over small geographical areas, given different distribution of risk factors . By displaying cancer incidence information spatially, spatial patterns can be better identified . However, when comparing small areas, local dependencies need to be considered. For this reason, small area disease mapping with Bayesian spatial smoothing was introduced as a methodological solution . Using a Bayesian Conditional Autoregressive (CAR) modeling approach, a new cancer atlas has been developed at the level of regional units (RUs) with a population ranging from 5,000 to over 530,000 inhabitants.State of the art: In recent years, Bayesian spatial models have been used to model cancer incidence at the small area level. Two examples of cancer atlases have been developed in Germany. Both, the “GEKID Kreisatlas” and the Cancer atlas Schleswig-Holstein deployed the most prominent approach of Besag, York, and Mollié (BYM) . More recently, the Australian Cancer Atlas and the Dutch cancer atlas by IKNL used CAR models with the Leroux prior . In each implementation, neighboring areas were defined by Queens adjacency matrices.Concept: Incidence data for 2013–2022 were retrieved from the Epidemiological Cancer Registry Lower Saxony (EKN), covering 366 regional units. Analyses included sex- and age-specific cancer data for among others overall cancer (ICD-10 C00–C97, excluding C44) and specific entities such as lung (C33+C34), breast (C50), prostate (C61), and mesothelioma (C45). Standardized incidence ratios (SIRs) were calculated by comparing observed versus expected cancer cases, with expected cases constructed through indirect standardization to minimize sampling variations .Following the more recent approaches, we used Bayesian spatial models with the Leroux prior to estimate the incidence of cancer . Markov Chain Monte Carlo methods produced posterior, smoothed SIRs for each RU. The median smoothed SIR and corresponding 95% credible intervals were reported to account for uncertainty. All analyses were performed in R version 4.4.2 using packages CARBayes (version: 6.1.1), sf (version: 1.0-19) and shiny (version: 1.10.0). RUs were created through aggregation of municipal VG250 data from the Federal Agency for Cartography and Geodesy (BKG).Implementation: The development process and adjustment of statistical analyses were supported by a visualization in R Shiny. However, the interactive web version targeting the public is currently under development using several web techniques such as React and Leaflet. Future versions of the cancer atlas Lower-Saxony plan to include survival measures. The atlas will be updated annually, to improve research on regional cancer burden.Lessons learned: Bayesian spatial models with the Leroux prior demonstrated robustness in mapping cancer incidence and visualized relevant spatial clusters. 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