Abstract:
Malaria is a serious health threat in the World, mostly in Africa, where it has been estimated that 90% of the world's cases occur. It is the major cause of health problems in Ethiopia, accounting for more than thousands of cases and deaths occurring annually. The risks of morbidity and mortality associated with malaria incidences are characterized by spatial variations across the country. The main objective of this study was to analyze spatial patterns of malaria distribution in Western Wollega Zone, Oromia Region, Ethiopia. Malaria incidence data for 2014 from all health centers of the zone was obtained from Gimbi Hospital, population size obtained from Central Statistical Agency and meteorological data were obtained from Gimbi Agricultural Bureau. The statistical methods used in this study include global and local measures of spatial autocorrelation as well as spatial autoregressive model. The results of the study indicated that malaria incidence varies according to geographical location, with eco-climatic condition and showed significant positive spatial autocorrelation. Significant local clustering of malaria incidence occurred between pairs of neighboring Woredas. Global Moran‟s I, Geary‟s C and Moran scatter plot are used in determining distribution of malaria incidence whereas the local Moran‟s I and Local Ord and Getis‟ Gi* statistic were used in identifying areas of hot spot and cold spot for giving strong care to monitor and reduce malaria incidence distribution. The values for Global Moran‟s I showed that the presence of significant malaria incidence clustering in Western Wollega Zone and in fifteen woredas significant malaria incidence clustering of similar values were observed by using cluster map while only in five woredas significant malaria incidence clustering of dissimilar values was observed. Malaria incidence was higher in the eastern part of the zone and lower in the northern part of the zone. The results of spatial lag model indicated that there were a statistically significant effect between malaria incidence and meteorological variables such as rainfall, maximum temperature minimum temperature, middle land and low land area.
