Modeling the Longtiudnal Change of Viral Load of HIV Positive Patients on Antiretroviral Therapy

Abstract

HIV/AIDS continues to be a major public health concern and cause of death in the world. Even- though WHO recommended viral load testing as the preferred monitoring approach to diagnose and confirm ARV treatment failure, but in most cases, factors influencing the trend of viral load were not well identified. The main objective of this study was to modeling the change of viral load and identifying its associated factors among HIV positive patients. In this retrospective longitudinal data analysis, data was collected from 287 HIV positive patients registered for ART between January 2017 and June 2019 in Zewditu hospital and unstructured covariance structure was parsimonious for the data. linear mixed model with different random effect were applied to the data. Linear mixed model with random intercept and slope were selected as a best model to fit the data based on different model selection criteria. The findings of the study revealed that there was a decrement over time in the log VL of patients with HIV on ART. Furthermore, time, baseline CD4 count, WHO clinical stage, functional status of the patient, adherence, smoking status, initial ART Regimen and Time interaction with adherence and WHO stage were found to be significant predictors of log VL evolution. Therefore, Patients should take ART regimens with good adherence to decrease their viral load over time.