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Background <p>Pathological routine diagnostics generate extensive datasets, yet their potential for spatial epidemiological analyses—for instance for studying disease distributions, environmental exposures, or healthcare structures—has so far remained largely untapped.</p> Objective <p>With REDPath (Spatial Epidemiological Data Analysis of Pathology Data), a&#xa0;web-based tool to unlock this data source has been developed. Its goal is to visualize oncological disease burden and healthcare provision across different geographic levels, thereby supporting data-driven prevention and resource allocation strategies.</p> Materials and methods <p>The basis consists of 41,707&#xa0;oncological diagnoses (ICD-10: C00-C97, 2019–2025) from the Institute of Pathology, University Medical Center Mainz, supplemented by demographic and environmental context variables. REDPath was programmed in C++ and Python; visualizations were generated with Leaflet and statistical analyses were performed in R using the <i>lme4</i> and <i>CARBayes</i> packages. Data were processed on two levels (individual/aggregated) to ensure data protection and differentiated access rights.</p> Results <p>REDPath comprises three modules: (1)&#xa0;descriptive analyses for interactive visualization of disease distributions; (2)&#xa0;statistical models to examine spatial relationships and autocorrelations; and (3)&#xa0;a&#xa0;health services module currently in development, visualizing submitting institutions.</p> Conclusion <p>REDPath enables spatial epidemiological analysis of routine pathology data—in a&#xa0;user-friendly and accessible manner without statistical expertise. Its modular structure allows for the integration of additional disease entities and data sources, positioning the tool as an interface between pathology and epidemiology, with direct relevance for evidence-based healthcare planning.</p>

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Räumlich-epidemiologische Datenanalyse von Pathologiedaten (REDPath)

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月10日

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  • Stephanie Strobl,
  • Matthias Martin Gaida

摘要

Background

Pathological routine diagnostics generate extensive datasets, yet their potential for spatial epidemiological analyses—for instance for studying disease distributions, environmental exposures, or healthcare structures—has so far remained largely untapped.

Objective

With REDPath (Spatial Epidemiological Data Analysis of Pathology Data), a web-based tool to unlock this data source has been developed. Its goal is to visualize oncological disease burden and healthcare provision across different geographic levels, thereby supporting data-driven prevention and resource allocation strategies.

Materials and methods

The basis consists of 41,707 oncological diagnoses (ICD-10: C00-C97, 2019–2025) from the Institute of Pathology, University Medical Center Mainz, supplemented by demographic and environmental context variables. REDPath was programmed in C++ and Python; visualizations were generated with Leaflet and statistical analyses were performed in R using the lme4 and CARBayes packages. Data were processed on two levels (individual/aggregated) to ensure data protection and differentiated access rights.

Results

REDPath comprises three modules: (1) descriptive analyses for interactive visualization of disease distributions; (2) statistical models to examine spatial relationships and autocorrelations; and (3) a health services module currently in development, visualizing submitting institutions.

Conclusion

REDPath enables spatial epidemiological analysis of routine pathology data—in a user-friendly and accessible manner without statistical expertise. Its modular structure allows for the integration of additional disease entities and data sources, positioning the tool as an interface between pathology and epidemiology, with direct relevance for evidence-based healthcare planning.