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Modelling tuberculosis risk factors among adult men in South Africa.

dc.contributor.advisorMelesse, Sileshi Fanta.
dc.contributor.advisorMwambi, Henry Godwell.
dc.contributor.authorMlondo, Muziwandile Nhlakanipho.
dc.descriptionMasters Degree. University of KwaZulu-Natal, Pietermaritzburg.en_US
dc.description.abstractTuberculosis is among the major public health problems not only in South Africa but worldwide. Tuberculosis is an underlying cause of more than 1.5 million deaths each year worldwide, making it the world's top infectious killer. There are more cases for men than women. Such a heavy burden requires an understanding of the tuberculosis status of the people, especially among men, and associated risk factors. Therefore, this study uses some statistical methods that are suitable to estimate the effect of the risk factors associated with tuberculosis among adult men. The study used the 2016 South African Demographic and Health Survey data. The Generalized Linear Models, such as the binary logistic regression model that assumes a simple random sampling as a sampling method followed by survey logistics that incorporate the complex design by means of robust standard errors of estimates, were applied to the data. The findings revealed that models that account for complex design are more suitable than those that do not account for complexity. To account for variability between the primary sampling units generalized linear mixed model was then used. GLMMs accounts for correlation within clusters by means of random effects which also account for cluster to cluster heterogeneity. Further, a generalized additive mixed-effect model was used to fit nonlinear and non-normal data; the categorical variables were modeled parametrically and continuously by non-parametric models. The thesis also discussed limitations for each of these models. The findings from this study revealed that the risk factors of tuberculosis are: any chronic disease, current age, region, race, number of times away from home, marital status, weight, and interaction effect of chronic disease and age, the interaction effect of smoking status and number of household members.en_US
dc.subject.otherStatistical methods.en_US
dc.subject.otherGeneralized linear mixed models.en_US
dc.subject.otherGeneralized linear models.en_US
dc.subject.otherPublic health.en_US
dc.titleModelling tuberculosis risk factors among adult men in South Africa.en_US


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