Exploratory GIS-based Mapping and Analysis of Chronic Kidney Disease Incidences in Gashua Town, Yobe State, Nigeria
Issue: 2023 - Volume 6 [Issue 1]
Modibbo Babagana-Kyari *
Department of Geography, Yobe State University, KM 7 Gujba Road, Damaturu, Yobe State, Nigeria.
*Author to whom correspondence should be addressed.
Northern Yobe State, particularly Gashua town, has been known as a hotspot of chronic kidney disease of unknown origin for decades. Little is known about the spatial epidemiology of the disease in the area. Therefore, this study is aimed at understanding the spatial dimension of the disease so as to ease the efforts of scouting the disease etiology, which is still unknown. To achieve the study aim, a community-centric approach was used in which a multi-stage purposive sampling method was employed to select the study area (Gashua town) in northern Yobe. The Yobe State Geographic Information Services (YOGIS)’s House Enumeration Area for the 2020 DLI 11.3 project was purposely adopted as spatial units for the data collection. Snow-balling sampling technique was used in surveying the disease incidences in the community. The survey lasted for three months, and 1,855 cases (including both morbidity and mortality incidences) were collected out of which 630 medically confirmed cases were sorted for the analysis. Android-based Kobotoolbox and QField app were used during the data collection exercise. Global Moran’s 1 index of Arcgis Pro 2.4 was used to analyze the spatial distribution patterns of the disease incidences using 250-meter hexagonal tessellation surfaces. Standard deviation and dot-size thematic data classification were used in analyzing the disease incidences. Findings reveal statistically significant CKD hotspots with a p-value (significant value) of 0.01 and a z-value (critical value) of 2.58, indicating a high degree of incidence clustering in locales such as Zango (29.68%), Sabon-Gari (27.14%), Lawan Fannami (19.2%) and Lawan Musa (12.86%), respectively. The CKD prevalence is largely among 15-75 year-old men. The 33% of the CKD victims’ households reported their incidences as “undefined kidney disease", while only 4% reported their cases as ‘Diabetic Kidney Disease’. Accordingly, 46.3% of the victims' households suspect drinking water as the potential risk factor for the disease. However, further investigation in the identified hotspots is recommended to understand people’s lifestyles, biomedical characteristics and drinking water quality. This study simply provides a bird's-eye view of community-based prevalence mapping of chronic kidney disease incidences (including morbidities and mortalities). Overall, findings derived from this study will guide subsequent investigations as it has highlighted the hotspots for the disease in the area.
Keywords: Kidney disease, clustering, spatial autocorrelation, CKDu, bade, disease mapping, tessellation, spatial epidemiology
How to Cite
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