Abstract
Background: Tuberculosis (TB) is regarded as an international pandemic, ranked among the top ten leading causes of mortality and morbidity in Sub-Saharan Africa. In South Africa, it is the leading cause of death especially among mine workers. Despite being deadly, TB is curable if diagnosed early and initiated into treatment. Studies on TB prevalence and risk factors among mine workers have mostly used retrospective clinical record review and secondary data analysis of already existing data. There are very limited studies that used primary data collection methods and used the Acid-Fast Basili (AFB) test for TB to determine the prevalence and identify risk factors of TB among miners.
Aim: The study aimed to investigate the prevalence and risk factors associated with TB among gold miners in West Rand Region in Gauteng, South Africa.
Objectives: To estimate the prevalence of TB among gold miners in the West Rand Region in Gauteng, South Africa, to determine the association between demographic and socioeconomic risk factors of TB among gold miners in the West Rand Region in Gauteng, South Africa, and lastly to determine the association between clinic risk factors of TB among gold miners in the West Rand Region in Gauteng, South Africa.
Methodology: A cross-sectional study design with a structured questionnaire that collected demographic, socio-economic and clinical variables was used. An intensity sampling was used to select the 300 employees from the gold mine workers, of which
25 individuals received positive test results and 275 individuals tested negative. Statistical software STATA 16.1 (State Corporation, College Station, TX, USA) was used to analyse the data, with descriptive statistics summarizing the data as frequencies and percentages.
A structured questionnaire was used to collect data and a sample of phlegm or sputum collected for AFB (Acid Fast Bacilli). Bivariate and multivariable logistic regression models were used to determine risk factors and reported unadjusted and adjusted Odds Ratios, 95% confidence intervals and p-values to demonstrate statistical significance.