Abstract
The purpose of this study was to assess the spatial distribution of malaria prevalence rates among selected rural part of woredas in SNNPR, Ethiopia. This work is based on data available from the 2011 malaria indicator survey (MIS 2011) of Ethiopian Public Health Institution. ESDA, Spatial regression model and Bayesian Spatial analysis were employed for data analysis. From ESDA, we found positive spatial autocorrelation in malaria prevalence rate. Relying on specification diagnostics and measures of fit; Spatial lag model was found to be the best model for modeling malaria prevalence rate data. The relationship between malaria prevalence and its risk factors was assessed using spatial models. The spatial models also showed an increase of malaria prevalence with a number of factors. From results, increase in the proportion of households sprayed in 12 months and the average altitude in the woreda estimated to decrease the average malaria prevalence. The result also demonstrated that increase in the House hold size of the district, proportion of households having access to piped water, proportion of households having access to radio, proportion of households having access to radio and Main construction material of the room’s wall are estimated to raise the average malaria prevalence rate. Finally, the study concluded that malaria is spatially clustered in space and the risk factors exhibit effect on the malaria prevalence in the study area. Based on the results of the study, We recommend for policy makers on the way to reduce malaria prevalence in the rural part of woreda of SNNPR using spatial information.
Highlights
Results for tests of Spatial autocorrelation in the malaria prevalence rate to determine the distribution pattern of malaria and its modeling of spatial Autoregressive model is presented in this chapter
Bayesian spatial analysis of the malaria prevalence rate was incorporated in this session to provide inference which included prior information
Significant coefficients for the variables implies that malaria prevalence rate in a given area depends on the change in explanatory variable in the same area controlling the effect raised due to spatial lag
Summary
Malaria is a mosquito-borne infectious disease of humans and other animals caused by protists of the genus Plasmodium which are introduced into the circulatory system by the bite from an infected female anopheles mosquito. A large number of malaria causing factors including the proximity to the vector breeding sites, the inadequate use of control measures, low income, illiteracy, land use and the house material play a big role [41]. He mentioned the multiplicity of malaria causing factors in rural areas as the main cause of its persistence as they are difficult to control at the same time [41]
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