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

Jaehyeong Cho, Seng Chan You, Seongwon Lee, DongSu Park, Bumhee Park, George Hripcsak and Rae Woong Park
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Jaehyeong Cho: Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon 16499, Korea
Seng Chan You: Department of Biomedical Informatics, Ajou University School of Medicine, Suwon 16499, Korea
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
George Hripcsak: Department of Biomedical Informatics, Columbia University Medical Center, New York, NY 10032, USA
Rae Woong Park: Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon 16499, Korea

IJERPH, 2020, vol. 17, issue 21, 1-14

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.

Keywords: spatial epidemiology; disease clustering; geographical information system (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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