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TB is rated as one of the world’s deadliest diseases and South Africa ranks 9th out of the 22 countries with hardest hit of TB. Although many pieces of research have been carried out on this subject, this paper steps further by inculcating past knowledge into the model, using Bayesian approach with informative prior. Bayesian statistics approach is getting popular in data analyses. But, most applications of Bayesian inference technique are limited to situations of non-informative prior, where there is no solid external information about the distribution of the parameter of interest. The main aim of this study is to profile people living with TB in South Africa. In this paper, identical regression models are fitted for classical and Bayesian approach both with non-informative and informative prior, using South Africa General Household Survey (GHS) data for the year 2014. For the Bayesian model with informative prior, South Africa General Household Survey dataset for the year 2011 to 2013 are used to set up priors for the model 2014. [ABSTRACT FROM AUTHOR]

Introduction: Health Care Workers (HCWs) are among the highest risk groups for contracting tuberculosis (TB), which is ranked the third most common occupational health disease in South Africa. Little is known about the true extent of the burden of TB among South African HCWs and current surveillance approaches are inadequate. The study aimed to determine the feasibility of using postal and web-based surveys accessed through registries of registered professionals to estimate the prevalence of TB among HCWs in South Africa. Materials and methods: We conducted a cross sectional survey on a sample of professional nurses and doctors (general practitioners) registered on the Medpages database platform; a subscription based registry for practising health care professionals. The survey included professionals who were actively involved in the clinical management of patients, either in public or private health care facilities. The paper based survey, including pre-paid return envelopes, was distributed via the post office and web-based surveys were distributed via e-mail through a hyperlink. Descriptive statistics were used to summarize the data and the Chi-square test to determine associations between categorical variables. Active TB was defined as any history of TB Results: Out of a total of 3,400 health care professionals contacted, 596 (18%) responses were received: 401 (67%) web-based and 195 (33%) postal. A significantly higher percentage of complete forms were from postal compared to web-based (97% [189/195] versus 87% [348/401], p<0.001). Younger (<60 years) professionals were more likely to use the web-based compared to postal (87% [236/270] versus 71% [134/189], p<0.001). Overall, the prevalence of active TB infection was 8.7%, (95%CI: 6.3%–11.7%) and there was no difference observed between doctors and nurses (10.8% [18/167] versus 7.5% [22/292], p = 0.236). Conclusion: This novel approach demonstrated the feasibility of using an existing registry of professionals to conduct surveys to estimate the prevalence of TB. Our findings showed a high TB prevalence; however the estimate might have been biased by the low response rate. Further research to optimise our approach could lead to a viable option in improving surveillance among health care professionals. [ABSTRACT FROM AUTHOR]