Spatial Clustering of Esophageal Cancer in Central Oromia, Ethiopia: A Spatial Autocorrelation and Scan Statistics Analysis

Abstract

Summary of data management process SaTScan version 10.3.3 was used for cluster identification and estimation of magnitude of risks within the clusters; GeoDa 1.22.01.21 was used for spatial autocorrelation and determining neighborhood relationship; and Quantum Geographic Information System (QGIS) desktop version 3.12 for spatial description and visualization. The R software version 4.3.3. Was used for farther descriptive (Choropleth map), inferential and spatial inequality. The detail descriptions of the data used were summarized hereunder: 1. Socio-demographic and clinical characteristic’s data: data_PLOS_One = esophageal ca.i.xlsx 2. District- and kebele-level maps by case rates and count’s data: 2.1. Kebele level: data_PLOS_One => study_area_new_new_final.shp 2.2. Woreda/distirict level: data_PLOS_One => study_area_district_56.shp 3. Spatial inequality’s data: 3.1. Kebele level: data_PLOS_One => shape.file.k.xlsx 3.2. Woreda/ district level: data_PLOS_One => shape.file.w.xlsx 4. Spatial Autocorrelation’s data 4.1. Woreda level Moran’s I (Global & LISA test), by GeoDa: data_PLOS_ONE => study_area_district_56.gel 4.2. Kebele level Moran’s I (Global & LISA test), By GeoDa: data_PLOS_ONE => study_area_new_new_final.gel 5. Scan statistic (SaTScan) data 5.1. Kebele level: data_PLOS_One => SaTScan data and output => Kebele level => shape.file.k.xlsx 5.2. District/ woreda Level: data_PLOS_One => SaTScan data and output => Woreda level =>shape.file.w.56.xlsx 5.3. Outputs: 5.3.1. Low rate kebele data_PLOS_One => SaTScan data and output => Kebele level => low rate kebele 5.3.2. High rate kebele data_PLOS_One => SaTScan data and output => Kebele level => High rate kebele 5.3.3. Low rate district/ woreda data_PLOS_One => SaTScan data and output => Woreda level => low rate woreda 5.3.4. High rate district/ woreda data_PLOS_One => SaTScan data and output => Woreda level => High rate woreda 6. R Markdown showing the output of the analyzed data data_PLOS_One => PLOS ONE EC.Rmd