RESEARCH ARTICLE


Risk Factors Associated with Tuberculosis Among Men; A Study of South Africa



Muziwandile Nhlakanipho Mlondo1, *, Sileshi Fanta Melesse1, Henry G Mwambi1
1 School of Mathematics, Statistics and Computer Science, University of KwaZulu Natal, Berea, Durban, South Africa


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Creative Commons License
© 2022 Mlondo et al.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the School of Mathematics, Statistics and Computer Science, University of KwaZulu Natal, Berea, Durban, South Africa; Tel: 0849725211; E-mail: mlondo02@gmail.com


Abstract

Background:

One of the public health problems all over the world is tuberculosis. An important factor for human well-being is good health. Worldwide, there are more cases of men with tuberculosis than women. Therefore, identifying risk factors associated with tuberculosis among men is essential. This study uses a survey logistic regression model to identify risk factors associated with tuberculosis in South Africa using the 2016 South African Demographic Health Survey data.

Methods:

Based on the fact that tuberculosis status is a binary variable, logistic regression and survey logistics were used for analysis.

Results and Conclusion:

The findings using the survey logistic model are presented. The results suggest that a survey logistic model that accounts for complex sampling design is better than logistic regression. The findings from the study show that the risk factors associated with tuberculosis are: chronic disease, current age, region, race, number of times away from home, marital status, weight, smoking status, the interaction effect of chronic disease and age, and the interaction effect of smoking status and number of household members. These factors can be used to implement strategies for reducing the risk of having tuberculosis.

Keywords: Tuberculosis, Risk factors, Survey logistic regression, Binary response, Chronic disease, Men.