, gastric cancer and socioeconomic factors in an urban area: Evaluating the strengths and limitations of spatial analysis.

OBJECTIVE

Spatial epidemiology provides a valuable opportunity to utilize routinely collected clinical data for analyzing disease distribution. This study evaluates the potential of such data to model the geographical distribution of () colonization and gastric cancer in Mainz, Germany. We aimed to assess the feasibility of spatial statistical analyses of clinical routine data, identify factors influencing colonization, and investigate whether colonization and gastric cancer share common spatial patterns and risk factors relevant for prevention strategies.

METHODS

Data on colonization was extracted from routine gastric biopsy reports (2008-2019), while gastric cancer cases were derived from local cancer registry data (2019-2022). Geospatial data and socioeconomic variables were integrated into generalized linear mixed-effects models (GLMMs) to explore their associations with the diseases. The Moran's I statistic was used to assess spatial autocorrelation.

RESULTS

Among 19,727 biopsies analyzed, 24.7 % were -positive, with colonization varying widely across districts (10.7 %-38.9 %). Significant associations were found with unemployment rates, household size, and foreign or immigrant background populations. In contrast, the GLMM for gastric cancer revealed no significant predictors, likely due to low case numbers (108 cases) and limited variation across districts. Nonetheless, the observed association between and gastric cancer aligns with established literature.

CONCLUSIONS

This study demonstrates the potential of routine data in spatial epidemiology for identifying at-risk populations. While challenges remain, particularly for rarer diseases, this approach provides valuable insights into disease distributions and can support targeted prevention strategies.