Determinants and Spatial Modeling of Acute Respiratory Infections (ARI) Among Children Less Than Five Years in Kenya
American Journal of Theoretical and Applied Statistics
Volume 6, Issue 2, March 2017, Pages: 123-128
Received: Feb. 20, 2017; Accepted: Mar. 1, 2017; Published: Mar. 21, 2017
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Kinyua Ann Muthoni, Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Oscar Owino Ngesa, Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
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Bayesian disease mapping is a field of statistics that is used to model the spatial distribution of disease outcomes especially in application to studies in spatial biostatistics and also as a tool to help develop the required intervention strategies. In this study, we perform a spatial modeling of ARI among children less than five years in Kenya using data from the 2014 Kenya Demographic and Health Survey (KDHS). Four models were used in this study namely the logistic regression model, the normal unstructured heterogeneity random effects model, ICAR (Integrated Conditional Autoregressive) spatial random effects model and the convolution model. A full Bayesian approach was used and the models were implemented using the Winbugs software version 1.4. Model selection was based on the DIC value where the model with the lowest DIC value was considered to be the best. The convolution model was the best model in this case and was used to map ARI across the different counties in Kenya. The national prevalence was 47.3%. The prevalence was found to be highest in the counties in the western part of Kenya. From the analysis, it’s clear that ARI is still a menace that need to be controlled. Proper planning and allocation of resources need to be put in place by the county governments in order to curb the rising cases of ARI.
Prevalence, Spatial, Bayesian
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Kinyua Ann Muthoni, Oscar Owino Ngesa, Determinants and Spatial Modeling of Acute Respiratory Infections (ARI) Among Children Less Than Five Years in Kenya, American Journal of Theoretical and Applied Statistics. Vol. 6, No. 2, 2017, pp. 123-128. doi: 10.11648/j.ajtas.20170602.18
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