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

Abstract

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.

Fig. 1

District map of Ghana showing the its neighboring countries; Cote d’Ivaire (left), Burkina Faso (top) and Togo (right)

Fig. 2

Spatial distributions of the empirical Bayesian smoothed estimates of the relative risks for 2010 (a), 2011 (b), 2012 (c), 2013 (d), 2014 (e), and cumulative estimates from 2010-2014 (f)

Fig. 3

Empirical and theoretical semi-variogram models for both adjusted and traditional estimator. a: Adjusted semi-variogram estimator and theoretical models (exponential, spherical, and Gaussian). b: Traditional semi-variogram estimator and theoretical models (exponential, spherical, and Gaussian). c: Variations between exponential semi-variogram models estimated from the traditional and adjusted estimators

Fig. 4

Spatial distribution of purely spatial (a) and space-time hot-spots (b) for diarrhea, 2010–2014