Conference abstract
Geospatial variation of confirmed malaria incidence and related environmental characteristics in sub-districts of Upper East Region
Pan African Medical Journal - Conference Proceedings. 2017:3(82).27
Oct 2017.
doi: 10.11604/pamj-cp.2017.3.82.235
Archived on: 27 Oct 2017
Contact the corresponding author
Keywords: Malaria, spatial analysis, spatial clustering
Oral presentation
Geospatial variation of confirmed malaria incidence and related environmental characteristics in sub-districts of Upper East Region
Ernest Akyereko1,&, Donne Ameme1, Patrick Lamptey2, Edwin Andrew Afari1, Kofi Mensah Nyarko3, Ernest Kenu1
1Ghana Field Epidemiology and Laboratory Training Programme, Accra, Ghana, 2Ghana Health Service, Ghana, 3Namibia FELTP, Namibia
&Corresponding author
Ernest Akyereko, Ghana Field Epidemiology and Laboratory Training Programme, Accra, Ghana
Introduction: malaria is a life-threatening disease caused by parasites that are transmitted to people through the bites of infected female anopheles mosquitoes. The disease is endemic in African countries such as Ghana, and children bears the highest consequence. Even though malaria believe to have associated with environmental conditions and for that matter the risk of malaria incidence may not be the same across a geographic location. Past studies in Upper East region of Ghana on malaria did not show the relationship between spatial cluster of malaria and its related factors it’s related. Spatial analysis of patterns of these cases could lead to a deeper understanding of the underlying causes and potential prediction of high risk areas. We mapped and generated high risk areas of Malaria in sub-districts of Upper East region of Ghana.
Methods: the study was a retrospective cross-sectional study in which spatial tools in Quantum Geographic Information System (QGIS) and Geoda was used to perform risk mapping using 5-year secondary data on malaria incidence in the sub-districts of Upper East region from January 1, 2012 to December 31, 2016, as well its relationship with rainfall, temperature (maximum and minimum), humidity and sunshine hours.
Results: a total of 2464943 cases (both suspected and confirm) were reported within the 5-year period with 71.12% laboratory confirmation. Prevalence of malaria varies from district to district. There was statistically significant evidence of spatial clustering into high and low rate clusters. The study found 25 clusters (p = 0.024) and 61 not significant clusters of meningitis with 4 High-high risk areas.
Conclusion: the results indicated that temperature, humidity and rainfall varies comparing areas of high risk and low risk. These findings show that public health resource allocations should focus on the areas with the highest malaria risk in Upper East region.
