Factors associated with anti tuberculosis therapy (ATT) compliance, ATT outcomes and survival of patients with TB/HIV co-infection using generalized structural equation modeling (GSEM) - Yusof, Ruhana Che (2016)
Introduction: The number of TB/HIV co-infection reported in Malaysia is about eight percent of total HIV cases and about 5.9 percent of total notified TB cases. The proportion of the cases was decreased each year started in 2007. For more effective strategies of preventive, a model of TB/HIV should be developed especially compatible with Malaysia situation. Objectives: This study was proposed to model TB/HIV co-infection based on associated factors of anti TB treatment compliance and associated factors of TB treatment outcome and also predictor factors of mortality in TB/HIV co-infection and to assess the model using a new method known as a generalized structural equation model. Methodology: Retrospective cohort study had extracted out information such as socio-demographic, social and medical history, signs and symptoms at diagnosis and treatment from 284 medical records from 2005 to 2012 in two selected government hospitals. Multiple logistic regression was applied in two analyses; determination of associated factors in ATT compliance and associated factors in the ATT outcome. Meanwhile, Cox regression was used in determining predictor factors of mortality. All outcomes were combined together and latent variable of TB diagnosis was added to determine the TB/HIV co-infection model using generalized structural equation modeling. The mediating effect also was assessed in the model. Results: The model had identified three significant associated factors of ATT compliance (hepatitis, age of diagnosis TB and history of previous TB), five significant factors of success in TB treatment (CD4 count, area of residency, ATT compliance, ATT duration and received HAART) and five significant predictor factors of mortality (ATT outcome, MOT sexual, MOT IVDU, received HAART, ATT duration) included one time varying covariate variable (area of residency). The latent TB diagnosis variable was significantly measured by symptoms of cough, fever, night sweating, loss of weight and laboratory findings (chest x-ray, sputum AFB smear and culture). The combination of all outcomes of the analyses simultaneously by a new method gave a similar result as traditional methods with advances in increments of a latent variable added. ATT outcome was suspected to mediate the effect of ATT compliance to mortality. Conclusion: Development of generalized structural equations modeling to analyze simultaneously many outcomes of different distributions with the addition of latent variables at the same time can benefit many researchers to validate their models. However, the method was still in early development and has many limitations need to improve by the software developer.