scholarly journals Spatial analysis of hypothyroidism and ground water pH in an Urban Area of Kerala using the geographic information system

2020 ◽  
Vol 64 (3) ◽  
pp. 300
Author(s):  
MathewJoseph Valamparampil ◽  
Sara Varghese ◽  
Ananth Mohan ◽  
Rajesh Reghunath ◽  
AL Achu ◽  
...  
Keyword(s):  
Information System ◽  
Geographic Information System ◽  
Spatial Analysis ◽  
Ground Water ◽  
Urban Area ◽  
Geographic Information ◽  
Water Ph

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

2020 ◽  
Vol 17 (21) ◽  
pp. 7824
Author(s):  
Jaehyeong Cho ◽  
Seng Chan You ◽  
Seongwon Lee ◽  
DongSu Park ◽  
Bumhee Park ◽  
...  
Keyword(s):  
United States ◽  
Information System ◽  
Geographic Information System ◽  
Spatial Analysis ◽  
Health Outcomes ◽  
Open Source ◽  
Geographical Distribution ◽  
The United States ◽  
Geographic Information ◽  
Common Data Model

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.

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