Strobl Stephanie, Moirano Giovenale
Institute of Pathology of the University Medical Center Mainz, Johannes Gutenberg-University, Mainz, Germany.
Department of Medical Sciences, University of Turin and CPO-Piemonte, Torino, Italy.
Prev Med Rep. 2025 Jun 13;56:103138. doi: 10.1016/j.pmedr.2025.103138. eCollection 2025 Aug.
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.
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.
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.
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.
空间流行病学提供了一个利用常规收集的临床数据来分析疾病分布的宝贵机会。本研究评估了此类数据用于模拟德国美因茨幽门螺杆菌（Hp）定植和胃癌地理分布的潜力。我们旨在评估临床常规数据进行空间统计分析的可行性，确定影响Hp定植的因素，并调查Hp定植与胃癌是否具有共同的空间模式以及与预防策略相关的风险因素。
从常规胃活检报告（2008 - 2019年）中提取Hp定植数据，而胃癌病例来自当地癌症登记数据（2019 - 2022年）。将地理空间数据和社会经济变量纳入广义线性混合效应模型（GLMMs）以探索它们与疾病的关联。使用莫兰指数（Moran's I）统计量来评估空间自相关性。
在分析的19727份活检中，24.7%为Hp阳性，各地区的Hp定植差异很大（10.7% - 38.9%）。发现与失业率、家庭规模以及外国或移民背景人群存在显著关联。相比之下，胃癌的GLMM未显示出显著的预测因素，可能是由于病例数较少（108例）且各地区差异有限。尽管如此，观察到的Hp与胃癌之间的关联与现有文献一致。
本研究证明了常规数据在空间流行病学中用于识别高危人群的潜力。尽管挑战依然存在，尤其是对于罕见疾病，但这种方法为疾病分布提供了有价值的见解，并可支持有针对性的预防策略。
