Geo-additive Bayesian model and its application to spatial analysis of malaria problem in Ethiopia

International Conference on Medical Physics

August 03-05, 2015 Birmingham, UK

Dawit G Ayele

Posters-Accepted Abstracts: J Nucl Med Radiat Ther

Abstract :

Background: Malaria is a major public health issue in Ethiopia in terms of both morbidity and mortality, with about 4-5 million Ethiopians affected each year. Because malaria is a serious problem and has severe health and economic burden, it is important to apply methods that will help understand influencing factors. Therefore, this study considers modelling of the dependence of malaria cases on spatial determinants and socio-economic, demographic and geographic variables. Methods: The analysis carried out in this work exploits the household cluster malaria survey which was conducted from December 2006 to January 2007. A total of 224 clusters of about 25 households were selected each from the Amhara, Oromiya and Southern Nation Nationalities and People (SNNP) regions of Ethiopia. Semi-parametric regression models are used. The spatial analysis is based on a geo-additive model using Kebele as the geographic units of study. Results: The results showed that households in the SNNP region were found to be at more risk than Amhara and Oromiya regions. Households which have toilet facilities, clean drinking water, and a greater number of rooms and mosquito nets in the rooms have less chance of having household members testing positive for malaria rapid diagnosis test. Moreover, from this study, it can be suggested that incorporating spatial variability is necessary for understanding and devising the most appropriate strategies to reduce the risk of malaria. Conclusions: In this paper, semi-parametric models were used to model the effects of both socio-economic, demographic and geographic covariates and spatial effects on malaria distribution in Ethiopia. The results obtained from the proposed model suggest a strong positive association between malaria rapid diagnosis test and socio-economic, demographic and geographic factors. From the spatial effects, important spatial patterns of malaria were identified.