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. 2019 Mar 11;18(1):67.
doi: 10.1186/s12936-019-2709-y.

Geostatistical analysis and mapping of malaria risk in children under 5 using point-referenced prevalence data in Ghana

Robert Yankson  1 , Evelyn Arthur Anto  1 , Michael Give Chipeta  2
Affiliations

Geostatistical analysis and mapping of malaria risk in children under 5 using point-referenced prevalence data in Ghana

Robert Yankson et al. Malar J. .

Abstract

Background: Malaria remains a major challenge in sub-Saharan Africa and Ghana is not an exception. Effective malaria transmission control requires evidence-based targeting and utilization of resources. Disease risk mapping provides an effective and efficient tool for monitoring transmission and control efforts. The aim of this study is to analyse and map malaria risk in children under 5 years old, with the ultimate goal of identifying areas where control efforts can be targeted.

Methods: Data collected from the 2016 Ghana demographic and health survey was analyzed. Binomial logistic regression was applied to examine the determinants of malaria risk among children. Model-based geostatistical methods were applied to analyze, predict and map malaria prevalence.

Results: There is a significant association of malaria prevalence with area of residence (rural/urban), age, indoor residual spray use, social economic status and mother's education level. Overall, parasitaemia prevalence among children under 5 years old for the year 2016 is low albeit characterized by "hotspots" in specific areas.

Conclusion: The risk maps indicate the spatial heterogeneity of malaria prevalence. The high resolution maps can serve as an effective tool in the identification of locations that require targeted interventions by programme implementers; this is key and relevant for reducing malaria burden in Ghana.

Keywords: Exceedance probability; Geostatistics; Hotspot; Malaria; Mapping.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
The 10 administrative regions covering the study area for Ghana demographic health survey
Fig. 2
Fig. 2
Model validation plot, the solid line is the variogram based on the residuals from a non-spatial model (empirical semi variogram). The dashed lines are the 95% confidence intervals generated under the fitted spatial model
Fig. 3
Fig. 3
Malaria prevalence predictions among children aged under 5 years in Ghana
Fig. 4
Fig. 4
Map showing areas where transmission is above or below 20% threshold. Exceedance (≥ 20%) and non-exceedance (>20%) probabilities in Ghana in 2016
See this image and copyright information in PMC

References

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