Bayesian Structural Equation Modeling to Identify Factors Influencing Tuberculosis Treatment Adherence

Vasantha, M and Ponnuraja, C and Muniyandi, M and Bhaskar, Adhin and Tamizhselvan, M and Muthu Vijayalakshmi, M and Venkatesan, P (2023) Bayesian Structural Equation Modeling to Identify Factors Influencing Tuberculosis Treatment Adherence. Bayesian Structural Equation Modeling to Identify Factors Influencing Tuberculosis Treatment Adherence, 6(4) (555691).

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Abstract

Adherence to anti-Tuberculosis (TB) treatment is vital for curing TB patients and avoids drug resistance TB. Structural Equation Model (SEM) is a powerful tool for modeling latent variables and to control measurement errors. Bayesian SEM (BSEM) gives better estimates of the latent variable compared to conventional frequentist approaches to SEM. To our knowledge the use of BSEM to identify the significant latent constructs influencing the TB patients’ adherence to anti-TB treatment was not studied so far. A total of 725 pulmonary TB patients who were registered with Directly Observed Treatment Short course (DOTS) at Government health facilities in Tiruvallur district, south India under National Tuberculosis Elimination Programme were used for this analysis. In this study, a model of adherence to anti-TB treatment of TB patients using BSEM was explored which used to identify the relationship between adherence to anti-TB treatment and the latent variables “socio-economic”, “habits”, “problems” (problems in taking treatment) and “DOT” (treatment related variables). The latent variables “DOT” treatment regularity under guided supervision. (0.199, p<0.005) and “problems” (-0.202 p<0.01) were significantly associated with adherence variable. The latent variable “DOT” had a positive effect while “problems” had a negative effect on adherence to anti-TB treatment. Hence reducing the patient’s specific problems might encourage

Affiliation:	ICMR-National Institute for Research in Tuberculosis
Item Type:	Article
URI:	http://eprints.nirt.res.in/id/eprint/2219

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