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Spatial variation and hot-spots of district level diarrhea incidences in Ghana: 2010–2014

BACKGROUND: Diarrhea is a public health menace, especially in developing countries. Knowledge of the biological and anthropogenic characteristics is abundant. However, little is known about its spatial patterns especially in developing countries like Ghana. This study aims to map and explore the spa...

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Detalles Bibliográficos
Autores principales: Osei, Frank Badu, Stein, Alfred
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5496362/
https://www.ncbi.nlm.nih.gov/pubmed/28673274
http://dx.doi.org/10.1186/s12889-017-4541-z
Descripción
Sumario:BACKGROUND: Diarrhea is a public health menace, especially in developing countries. Knowledge of the biological and anthropogenic characteristics is abundant. However, little is known about its spatial patterns especially in developing countries like Ghana. This study aims to map and explore the spatial variation and hot-spots of district level diarrhea incidences in Ghana. METHODS: Data on district level incidences of diarrhea from 2010 to 2014 were compiled together with population data. We mapped the relative risks using empirical Bayesian smoothing. The spatial scan statistics was used to detect and map spatial and space-time clusters. Logistic regression was used to explore the relationship between space-time clustering and urbanization strata, i.e. rural, peri-urban, and urban districts. RESULTS: We observed substantial variation in the spatial distribution of the relative risk. There was evidence of significant spatial clusters with most of the excess incidences being long-term with only a few being emerging clusters. Space-time clustering was found to be more likely to occur in peri-urban districts than in rural and urban districts. CONCLUSION: This study has revealed that the excess incidences of diarrhea is spatially clustered with peri-urban districts showing the greatest risk of space-time clustering. More attention should therefore be paid to diarrhea in peri-urban districts. These findings also prompt public health officials to integrate disease mapping and cluster analyses in developing location specific interventions for reducing diarrhea.