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A Multilevel Analysis of the Associated and Determining Factors of TB among Adults in South Africa: Results from National Income Dynamics Surveys 2008 to 2017

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  • Hilda Dhlakama

    (Department of Statistics, University of Johannesburg, Johannesburg 2028, South Africa
    School of Mathematics, Statistics and Computer Sciences, University of KwaZulu-Natal, Durban 4041, South Africa)

  • Siaka Lougue

    (School of Mathematics, Statistics and Computer Sciences, University of KwaZulu-Natal, Durban 4041, South Africa)

  • Henry Godwell Mwambi

    (School of Mathematics, Statistics and Computer Sciences, University of KwaZulu-Natal, Durban 4041, South Africa)

  • Ropo Ebenezer Ogunsakin

    (Discipline of Public Health Medicine, School of Nursing and Public Health, University of KwaZulu-Natal, Durban 4041, South Africa)

Abstract

TB is preventable and treatable but remains the leading cause of death in South Africa. The deaths due to TB have declined, but in 2017, around 322,000 new cases were reported in the country. The need to eradicate the disease through research is increasing. This study used population-based National Income Dynamics Survey data (Wave 1 to Wave 5) from 2008 to 2017. By determining the simultaneous multilevel and individual-level predictors of TB, this research examined the factors associated with TB-diagnosed individuals and to what extent the factors vary across such individuals belonging to the same province in South Africa for the five waves. Multilevel logistic regression models were fitted using frequentist and Bayesian techniques, and the results were presented as odds ratios with statistical significance set at p < 0.05. The results obtained from the two approaches were compared and discussed. Findings reveal that the TB factors that prevailed consistently from wave 1 to wave 5 were marital status, age, gender, education, smoking, suffering from other diseases, and consultation with a health practitioner. Also, over the years, the single males aged 30–44 years suffering from other diseases with no education were highly associated with TB between 2008 and 2017. The methodological findings were that the frequentist and Bayesian models resulted in the same TB factors. Both models showed that some form of variation in TB infections is due to the different provinces these individuals belonged. Variation in TB patients within the same province over the waves was minimal. We conclude that demographic and behavioural factors also drive TB infections in South Africa. This research supports the existing findings that controlling the social determinants of health will help eradicate TB.

Suggested Citation

  • Hilda Dhlakama & Siaka Lougue & Henry Godwell Mwambi & Ropo Ebenezer Ogunsakin, 2022. "A Multilevel Analysis of the Associated and Determining Factors of TB among Adults in South Africa: Results from National Income Dynamics Surveys 2008 to 2017," IJERPH, MDPI, vol. 19(17), pages 1-17, August.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:17:p:10611-:d:897507
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    References listed on IDEAS

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    1. Oluwatobi Blessing Ojo & Siaka Lougue & Woldegebriel Assefa Woldegerima, 2017. "Bayesian generalized linear mixed modeling of Tuberculosis using informative priors," PLOS ONE, Public Library of Science, vol. 12(3), pages 1-14, March.
    2. Bonnie N Young & Adrian Rendón & Adrian Rosas-Taraco & Jack Baker & Meghan Healy & Jessica M Gross & Jeffrey Long & Marcos Burgos & Keith L Hunley, 2014. "The Effects of Socioeconomic Status, Clinical Factors, and Genetic Ancestry on Pulmonary Tuberculosis Disease in Northeastern Mexico," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-8, April.
    3. Yach, Derek, 1988. "Tuberculosis in the Western Cape health region of South Africa," Social Science & Medicine, Elsevier, vol. 27(7), pages 683-689, January.
    4. Harling, Guy & Ehrlich, Rodney & Myer, Landon, 2008. "The social epidemiology of tuberculosis in South Africa: A multilevel analysis," Social Science & Medicine, Elsevier, vol. 66(2), pages 492-505, January.
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