RESEARCH ARTICLE


Semi-parametric Model to Study the Risk Factors of Tuberculosis among Adult men in South Africa



Muziwandile Nhlakanipho Mlondo1, *, Sileshi Fanta Melesse1, Henry G. Mwambi1
1 School of Mathematics, Statistics, and Computer Science, University of KwaZulu Natal, Pietermaritzburg Campus, Private Bag X01, Scottsville, 3209, 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, Pietermaritzburg Campus, Private Bag X01, Scottsville, 3209, South Africa; Email: mlondo02@gmail.com


Abstract

Background:

Understanding the relationship between tuberculosis and the risk factors of tuberculosis is vital to be able to address them. Even though tuberculosis is curable and preventable, it remains a public threat, especially in low- and middle-income countries. There are more cases of men infected with tuberculosis compared to women.

Methods:

This study determines the risk factors that influence TB infection among adult men in South Africa. The Generalized Additive Mixed Models that incorporate a nonparametric smooth additive function were used to analyze the 2016 South African Demographic Health Survey. The Generalized Additive Model and Generalized Additive Mixed Model were compared to find the most suitable model.

Results:

The finding reveals that the significant determinates associated with tuberculosis were: region, primary education compared to secondary, race, health, weight, and wealth index, which were modeled parametrically. The conclusion is that only the interaction effect of age and number of times away from home were significant in variables modeled non-parametrically.

Conclusion:

In conclusion, the South African government needs to intervene on men living in Western Cape, Eastern Cape, Northern Cape, Free State, and men with poor health - to reduce South Africa’s infections. The government should also implement programs that will teach and discourage lower body mass index. Thus, targeting the factors that have a positive significant effect among adult men can help to reduce the risk of having tuberculosis.

Keywords: Tuberculosis, Semi-parametric models, Risk factors among men, Organs, Lungs, Brain.