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Application of Epidemiological Geographic Information System: An Open-Source Spatial Analysis Tool Based on the OMOP Common Data Model

Author

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  • Jaehyeong Cho

    (Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon 16499, Korea
    These authors contributed equally to this work.)

  • Seng Chan You

    (Department of Biomedical Informatics, Ajou University School of Medicine, Suwon 16499, Korea
    These authors contributed equally to this work.)

  • Seongwon Lee

    (Department of Biomedical Informatics, Ajou University School of Medicine, Suwon 16499, Korea)

  • DongSu Park

    (Department of Biomedical Informatics, Ajou University School of Medicine, Suwon 16499, Korea)

  • Bumhee Park

    (Department of Biomedical Informatics, Ajou University School of Medicine, Suwon 16499, Korea
    Office of Biostatistics, Ajou Research Institute for Innovative Medicine, Ajou University Medical Center, Suwon 16499, Korea)

  • George Hripcsak

    (Department of Biomedical Informatics, Columbia University Medical Center, New York, NY 10032, USA
    Medical Informatics Services, New York-Presbyterian Hospital, New York, NY 10032, USA)

  • Rae Woong Park

    (Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon 16499, Korea
    Department of Biomedical Informatics, Ajou University School of Medicine, Suwon 16499, Korea)

Abstract

Background: Spatial epidemiology is used to evaluate geographical variations and disparities in health outcomes; however, constructing geographic statistical models requires a labor-intensive process that limits the overall utility. We developed an open-source software for spatial epidemiological analysis and demonstrated its applicability and quality. Methods: Based on standardized geocode and observational health data, the Application of Epidemiological Geographic Information System (AEGIS) provides two spatial analysis methods: disease mapping and detecting clustered medical conditions and outcomes. The AEGIS assesses the geographical distribution of incidences and health outcomes in Korea and the United States, specifically incidence of cancers and their mortality rates, endemic malarial areas, and heart diseases (only the United States). Results: The AEGIS-generated spatial distribution of incident cancer in Korea was consistent with previous reports. The incidence of liver cancer in women with the highest Moran’s I (0.44; p < 0.001) was 17.4 (10.3–26.9). The malarial endemic cluster was identified in Paju-si, Korea ( p < 0.001). When the AEGIS was applied to the database of the United States, a heart disease cluster was appropriately identified ( p < 0.001). Conclusions: As an open-source, cross-country, spatial analytics solution, AEGIS may globally assess the differences in geographical distribution of health outcomes through the use of standardized geocode and observational health databases.

Suggested Citation

  • Jaehyeong Cho & Seng Chan You & Seongwon Lee & DongSu Park & Bumhee Park & George Hripcsak & Rae Woong Park, 2020. "Application of Epidemiological Geographic Information System: An Open-Source Spatial Analysis Tool Based on the OMOP Common Data Model," IJERPH, MDPI, vol. 17(21), pages 1-14, October.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:21:p:7824-:d:434837
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    References listed on IDEAS

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    1. Lindgren, Finn & Rue, Håvard, 2015. "Bayesian Spatial Modelling with R-INLA," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i19).
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    1. Sharifah Saffinas Syed Soffian & Azmawati Mohammed Nawi & Rozita Hod & Huan-Keat Chan & Muhammad Radzi Abu Hassan, 2021. "Area-Level Determinants in Colorectal Cancer Spatial Clustering Studies: A Systematic Review," IJERPH, MDPI, vol. 18(19), pages 1-20, October.

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