46 Vol.6.Issue.3.2018 (July-Sept.) ©KY PUBLICATIONS SPATIAL AREAL DATA ANALYSIS WITH APPLICATION TO VARIOUS TUBERCULOSIS OUTCOMES IN KENYA Erick Okuto School of Mathematics & Actuarial Science, Jaramogi Oginga Odinga University of Science & Technology, Bondo-Usenge Road, P.O Box 210-40601, Bondo, Kenya * Erick Okuto (erickokuto@gmail.com ) ABSTRACT Tuberculosis is second only to HIV as the greater killer worldwide due to a single infectious agent. Improving the treatment outcome of tuberculosis is part of the Millennium Development Goals. Given the infectious nature of tuberculosis, its distribution and treatment outcomes should consider spatial patterning. Information on the distribution of tuberculosis treatment outcomes in Kenya is scarce, yet treatment outcome is an important indicator of tuberculosis management. Spatial analysis tools can be used to characterize spatial patterns of these treatment outcomes, thereby identifying areas at risk of the given outcomes. This study presents a Bayesian model for analysing spatial distribution of tuberculosis treatment outcomes in Kenya. Data was obtained from the national tuberculosis registers from January 2014 to March 2014 with incorporation of data from the Kenya Demographic and Health Survey 2014 and Census 2009. Treatment outcomes were categorized as cured, dead, defaulted, failure and treatment complete. Exploratory data analysis was done to estimate the proportions of the various covariates, and tests for global and local spatial auto correlation done to assess the relationship of the various outcomes per county. Covariates were selected using purposeful selection of variables, and variables with a significant univariate test were selected as candidates for the multivariate analysis. Augmentation of the linear predictors with a set of spatially correlated random effects was done, using conditional autoregressive prior distributions, specified by a set univariate full conditional distributions. Inference was based on obtaining the posterior distribution, of the different TB treatment outcomes, using the Integrated Nested Laplace Approximation Methodology (INLA) as a way of BULLETIN OF MATHEMATICS AND STATISTICS RESEARCH A Peer Reviewed International Research Journal http://www.bomsr.com RESEARCH ARTICLE Email:editorbomsr@gmail.com