Latent Factors Affecting Smoking Cessation Among TB and HIV Patients: Bayesian Structural Equation Modeling Approach

Structural Equation Model (SEM) is an advanced multivariate statistical tool for modeling latent variables and to control measurement errors.Bayesian SEM (BSEM) gives better estimates of latent variable compared to classical SEM.To our knowledge the use of BSEM approach to identify the significant latent constructs influencing quit smoking in tuberculosis (TB) and human immunodeficiency virus (HIV) patients was not studied so far.The aim of this study is to identify latent variables influencing quitting smoking in TB and HIV patients using BSEM.The data used for the study consist of 160 patients (80 TB and 80 HIV) randomised to receive smoking cessation intervention under clinical trial.The smoking status was measured after one month of intervention.The latent variables 'reasons for smoking' (measured by the variables work tension, family tension and pleasure while smoking) and 'intensity of smoking' (measured by the variables Fagerstrom score, smoking type, number of times smoking per day and smoking duration) and the information on socio-economic characteristics were considered for analyses.This study elucidates the importance of applying BSEM to assess smoking cessation in TB and HIV patients.BSEM gave the estimates indicating the latent variable 'intensity of smoking' had negative effect on quit smoking.