A longitudinally measured disease progression-marker data analysis of HIV/TB co-infected   patients taking HAART drugs in a public Hospital, Ethiopia

Abstract

Background The applications of HAART drugs play a great role in decreasing the incidence of TB and TB related death in persons living with HIV/AIDS. However, after HAART initiation, a considerable number of the TB infection and TB-HIV damages are still at a shocking level especially we could understand that the addition of TB to this damage made things “Mumps on the Goiter”. Methods A 407 co-infection patients data under HAART follow up and diagnosed for TB were analyzed using linear mixed model to explain the effects of HAART treatment for the improvement of patient’s CD4 count. The appropriate fixed and random effect model was selected using AIC, BIC, and log-likelihood values. Results Out of a total of 407 HIV/TB co-infected patients, 196 (48.16%) were females. When we look at the baseline functional status of patients, 24.57% were able to do their day to day activities like farming, harvesting, office work and others, 62.16% were ambulatory and 13.27% were bedridden for all visit times. Based on linear mixed model, the study results reveal that, duration of treatment, age, marital status, functional status, ART_Regimen, education and base line CD4 + count were among the significant predictors of linear mixed effect model at 5% significance level for the change of CD4 + counts over time. Conclusions Linear mixed effect model with random intercept and random slope model, and an autoregressive of order 1 (AR (1)) covariance structure have been used to model this correlated data type and a random variables effect. Age, marital status, functional status, ART_Regimen, education and base line CD4 + count were identified as a potential risk factor for the progression of CD4 + count of the patients. Patients who have a good baseline CD4 count had a positive significant effect for the improvement of CD4 counts of the patients over time.