Modeling of tuberculosis through structural equations and bayesian approach
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Date
2023-01
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CCSHAU, Hisar
Abstract
This study analyzed data on tuberculosis in India to identify latent variables and understand relationships between variables. A structural equation model (SEM) was used, but the initial model did not converge to a satisfactory solution. The model was revised and modified until it converged to an optimal solution with acceptable fit statistics. The Markov Chain Monte Carlo (MCMC) method was also used to identify change points in the number of notified cases of tuberculosis. The results showed an increase in TB cases in the 2000s, followed by two change points after 2010 when the government prioritized controlling the disease. However, the number of cases has continued to increase in recent years. The MCMC method and Gibbs sampler were found to be useful for analyzing epidemiological data with change points. The study also analyzed the prevalence of TB in India using data from the National Family Health Surveys from 2005-2006 and 2015-2016. The results showed that overall, the prevalence of TB did not significantly change between the two surveys. However, the gender gap in TB prevalence (difference in prevalence between males and females) did show a statistically significant decrease, mainly observed in rural areas and found to vary by religion and social group. The rural-urban gap in TB prevalence was most prominent among certain groups, including Muslims, individuals belonging to other religious groups, Scheduled Tribes, and those in the poorest wealth quintile. It is suggested that the decreasing trend in the gender gap may be due to an improvement in the socio-economic status of women and increased detection and reporting of TB cases among women.