Bayesian Modelling of Tuberculosis Risk Factors in South Africa 2014


  • Hilda Dhlakama Department of Statistics, University of Johannesburg, P.O Box 524, Auckland Park, Johannesburg, 2006, South Africa
  • Siaka Lougue School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Private Bag X54001, Durban, 4000, South Africa



Tuberculosis, HIV, South Africa, National Income Dynamics Survey, Bayesian analysis, Frequentist Logistic Regression.


Background: Although the number of deaths has declined since 2007, Tuberculosis (TB) continues to be the number one cause of death in South Africa. To create a country free of TB, there is need for continued research to explore models that will provide the Department of Health with new interventions.

Aim: This study was aimed at identifying the risk factors of active self-reported TB prevalence for South Africa in 2014.

Methods: The Frequentist Logistic Regression (FLR) approach was applied on a sample of 19213 individuals taken from the National Income Dynamics Survey (NIDS) wave data. Bayesian analysis with non-informative priors were used to model Wave 1 to 3 data and elicitation of the obtained posterior density parameters by averaging done to obtain the informative priors used to model wave 4. The wave 4 results obtained under the two estimation approaches were compared as well as the results for non-informative and informative priors.

Results: The findings show that self-reported TB prevalence is higher than the reported 1%, Human Immuno Deficiency Virus (HIV) remains a major threat to TB and Eastern Cape is the province mostly affected by TB with Limpopo recording the least prevalence. Poor living conditions and lower socio-economic conditions continue to be drivers of TB whilst English illiteracy, lack of Secondary/Tertiary education, alcohol consumption, marital status, gender and age groups also influence TB progression to disease. FLR yielded similar results to Bayesian with non-informative priors whilst the results are more precise for informative priors.

Conclusion: This study identified individuals and communities at risk of developing active TB disease in South Africa.


WHO report (2011); available from: whosis/whostat/EN_WHS2011_Full.pdf?ua=1

TB Statistics for South Africa – National & provincial available from 7MG2pSTA.dpuf

WHO End TB Strategy 2015; available from http://www.who. int/tb/strategy/End_TB_Strategy.pdf?ua=1

TB Statistics for South Africa-national and provincial

Global TB strategy 2015; available at:

Cramm JM, Koolman X, Møller V, Nieboer AP. Socioeconomic status and self-reported tuberculosis: a multilevel analysis in a low-income township in the Eastern

Cape, South Africa. Journal of Public Health in Africa Research and Practise 2011; 2(2): 143-146. DOI:

Harling G, Ehrlich R, Myer L. The Social Epidemiology of Tuberculosis in South Africa: A Multilevel Analysis. Social Science & Medicine 2008; 66(2): 492-505. DOI:

Janssens J, Rieder H, An ecological analysis of incidence of tuberculosis and per capita gross domestic product. Eur Respir J 2008; 32(5): 1415-1416. DOI:

Mozolo T, Mwambi H, Zuma K. Bayesian approach in estimating risk determinants of infectious diseases. Presented at the 51st Annual South African Statistical Association (SASA) Conference, University of Pretoria, Pretoria, South Africa 27-31 October 2008.

Lesaffre E, Lawson B. Bayesian Biostatistics, John Wiley and Sons 2012. DOI:

Bolstad WM. Introduction to Bayesian Statistics, New jersey, USA, John Wiley and Sons 2007. DOI:

Mohammed OMM. Statistical methods for analysing complex survey data: An application to HIV/AIDS in Ethiopia. PhD, 2013.

Leibbrandt M, Woolard I, De Villiers L. Methodology: Report on NIDS Wave 1. Cape Town: University of Cape Town, 2009.

Akaike H. Information theory and an extension of the maximum likelihood principle. 1973. Proc. 2nd Inter. Symposium on Information Theory 267-281, Budapest.

Siddiqui AA, Siddiqui JS, Wasay M, Azam SI, Ahmed A. A dynamical study of risk facctors in intracerebral haemorrhage using the multivariate approach. International Journal of Statistics in Medical Research 2013; 2: 23-33. DOI:

Khaial FB, Bodalal Z, Elramli A, Elkhwsky F, Eltaguri A, Bendardaf R. A study of risk factors for breast cancer in a primary oncology clinic in Benghazi-Libya. Internation Journal of Statistics in Medical Research 2015; 4(1). DOI:

Ryan TP. Modern regression methods. New York, JohnWiley and Sons 2009. DOI:

Holmes CC, Caron F, Griffin JE, Stephens DA. Two-sample Bayesian Nonparametric Hypothesis Testing Bayesian Anal 2015; 10(2): 297-320. DOI:

Congdon P. Bayesian methods and Bayesian estimation, in Applied Bayesian Modelling, John Wiley & Sons, Ltd, Chichester, UK 2014. DOI:

Gelfand AE, Smith AFM. Sampling-based approaches to calculating marginal densities. Journal of the American Statistical Association 1990; 85(40): 398-409. DOI:




How to Cite

Dhlakama, H., & Lougue, S. (2017). Bayesian Modelling of Tuberculosis Risk Factors in South Africa 2014. International Journal of Statistics in Medical Research, 6(1), 34–48.



General Articles