Statistical Analysis of Spatial Distribution of Malaria in Ilu Aba Bor Zone, southwest Ethiopia

Abstract

Introduction: Malaria is the major public health problem, widespread throughout the tropical and subtropical regions, including parts of Africa, Asia and America. According to the World Health Organization report an estimated 219 million cases of malaria occurred worldwide in 2017.The WHO African Region takes largest burden of malaria morbidity, with 200 million cases (92%) in 2017. In Ethiopia 68 percent of population lives in malarious areas, and 75 percent of country’s landmass is favorable for malaria transmission. Objectives: The main objective of this study was to find spatial distribution of malaria and to identify variables that are associated with malaria distribution in Ilu Aba Bor Zone, southwest Ethiopia. Methods: The study has been conducted in Ilu Aba Bor zone of entire districts and the data is basically secondary which is obtained from Ilu Aba Bor zone health office and Ethiopian metrological agency. Spatial distribution of malaria was identified using global and local measures of spatial autocorrelation. After spatial pattern is identified, the counts of malaria case have been analyzed with covariates like average annual maximum temperature, average annual minimum temperature, average annual rainfall, percentage of highland area, percentage of midland area and percentage of lowland area. The author of this study extended the application performed using generalized linear model to generalized linear mixed model by adding random effect and correlation structure to account spatial dependence in the model. Results: The value of global and local measures of spatial autocorrelation shows that malaria varies according to geographical location and shows significant positive spatial autocorrelation. The results of negative binomial regression model with spatial dependence shows that statistically significant relationship between malaria case counts and independent variables (rainfall, maximum temperature, midland area and lowland area). Conclusions: There is evidence of significant malaria clustering in Ilu Aba Bor zone, southwest Ethiopia. Significant hot spots clusters were identified in three woredas and cold spots of malaria clusters were identified in eight woredas. Clustering of dissimilar value identified in three woreda. There is significant relationship between malaria and covariates (rainfall, maximum temperature, midland area and lowland area).