Spatial Pattern and Predictors of Malaria in Ethiopia: an Application of Autologistics Regression

Menber, Yamral

dc.contributor.author	Menber, Yamral
dc.date.accessioned	2021-07-27T12:21:18Z
dc.date.available	2021-07-27T12:21:18Z
dc.date.issued	2021-07-27
dc.identifier.uri	http://ir.bdu.edu.et/handle/123456789/12284
dc.description.abstract	Malaria is a serious health threat in the World, mostly in Africa, where it has been estimated that 94% 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 case are characterized by spatial variations across the county. The objective of this study was to analyze spatial patterns of malaria distribution in Ethiopia. About weighted sample 15239 of RDTs individual in Malaria rapid diagnosis data for Ethiopian Malaria Indicator Survey (2015) from all regions obtained from Central Statistical Agency and Ethiopia malaria indicator survey. The statistical methods used in this study include global and local measures of spatial autocorrelation and Autologistics spatial binary regression model. Global Moran’s I, and Moran scatter plot were used in determining distribution of malaria cases whereas the local Moran’s I statistic were used in identifying areas of hot spot and cold spot for giving strong care to monitor and reduce malaria distribution. The results of the study indicated that malaria rapid diagnosis test varies according to geographic allocation, with socio-economic, demographic and risk variables and showed significant positive spatial autocorrelation. Significant local clustering of malaria transmission occurred between pairs of neighboring regions. The values for Global Moran’s I 0.366475 showed that the presence of significant malaria transmission clustering in Ethiopia and the cluster outlier in show that seven regions and two city was significant malaria transmission clustering of similar values were observed by using cluster map while only two regions significant malaria incidence clustering of dissimilar values was observed. Malaria incidence was higher in the western part of the regions and lower in the southern part of the regions. The finding of Autologistics spatial model indicated that there were a statistically significant effect between malaria rapid test and socio-economic, demographic and risk variables such as gender, age, region, altitude, main source of drinking water, time taken to collect water, toilet facilities, availability of radio and television, main material of the room's wall, main material of the room's roof, main material of the room's floor, use of mosquito nets, place residence, spatial auto-covariate variable. Concerned body should be facilitated in highly clustered malaria transmission (hot spot) areas by giving special attention in affecting intervention and health services to the highly risk exposed regions and neighboring regions.	en_US
dc.language.iso	en_US	en_US
dc.subject	Statistics	en_US
dc.title	Spatial Pattern and Predictors of Malaria in Ethiopia: an Application of Autologistics Regression	en_US
dc.type	Thesis	en_US