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. 2022 Aug 25;19(17):10611.
doi: 10.3390/ijerph191710611.

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

Hilda Dhlakama  1   2 , Siaka Lougue  2 , Henry Godwell Mwambi  2 , Ropo Ebenezer Ogunsakin  3
Affiliations

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

Hilda Dhlakama et al. Int J Environ Res Public Health. .

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.

Keywords: Bayesian analysis; National Income dynamics survey; South Africa; TB; generalized linear mixed-effects models; multilevel models.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure A1
Figure A1
Density plots for Bayesian MLM with non-informative priors.
Figure A2
Figure A2
BGR convergence plots for Bayesian MLM with non-informative priors.
Figure A3
Figure A3
Autocorrelation plots for Bayesian MLM with non-informative priors.
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