Bayesian Parametric Modeling of Time to Tuberculosis Co-Infection of HIV/AIDS Patients Under Antiretroviral Therapy Treatment at Jimma University Medical Center, Ethiopia

Abstract

Background: Tuberculosis is the most common opportunistic infection among HIV/AIDS patients, including those following Antiretroviral Therapy treatment. The risk of Tuberculosis infection is higher in people living with HIV/AIDS than in people who are free from HIV/AIDS. Many studies focused on prevalence and determinants of Tuberculosis in HIV/AIDS patients without taking into account the censoring aspects of the time to event data. Therefore, this study was undertaken with aim to model time to Tuberculosis co-infection of HIV/AIDS patients following Antiretroviral Therapy treatment using Bayesian parametric survival models.Methods: A data of a retrospective cohort of HIV/AIDS patients under Antiretroviral Therapy treatment follow-up from January 2016 to December 2020 until Tuberculosis was clinically diagnosed or until the end of the study was collected from Antiretroviral Therapy treatment center of Jimma University Medical Center, Ethiopia. In order to identify the risk factors which have association with Tuberculosis co-infection survival time, Bayesian parametric Accelerated failure time survival models were fitted to the data using Integrated Nested Laplace Approximation methodology.Results: About 26.37% of the study subjects had been co-infected with tuberculosis during the study period. Among the parametric Accelerated failure time models, the Bayesian log-logistic Accelerated failure time model was found to be the best fitting model for the data.Conclusions: Tuberculosis co-infection survival time was significantly associated with place of residence, smoking, drinking alcohol, family size, WHO clinical stages, functional status, CD4 count, BMI and hemoglobin level. The finding of this study provide timely information on the risk factors associated with TB co-infection survival time for healthy policy makers and planners.