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Citation Information

Type Thesis or Dissertation - Master Thesis
Title A Bayesian Approach To The Spatial Analysis Of Tuberculosis Treatment Outcomes In Kenya
Author(s)
Publication (Day/Month/Year) 2015
URL http://erepository.uonbi.ac.ke/bitstream/handle/11295/94513/Mwangia, Elizabeth N_tuberculosis​treatment outcomes in Kenya .pdf?sequence=1
Abstract
Introduction: 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 examined the spatial distribution of the tuberculosis treatment outcomes across the counties in Kenya. Objective: To model the spatial distribution of tuberculosis treatment outcomes using Bayesian techniques. Methods: Study area was Kenya, a country in the East Africa region. Secondary 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 approximating the posterior marginals as proposed by Besag et al.

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