Evaluation of the Trend of CD4 Cell Count Over Time in Case of HIV/AIDS Patients under ART Follow-up

Abstract

Background: Globally 36.7 million people living with HIV, 1.8 million new HIV infection, and 1 million AIDS-related deaths in 2016.Patient mortality was high during the first 6 months after therapy for all patient subgroups and exceeded 40 per 100 patient years among patients who started treatment at low CD4 count. The aim this study was to evaluate the trend of CD4 cell count over time and to determine the progress of patient characteristics measured at baseline on CD4 cell count of HIV-infected patients who were under ART treatment in Arba Minch Hospital. 
 Methods: This study was retrospective follow up study using data extracted from medical records, patient interviews, and laboratory work-up. The study was employed among 550 adult patients that were selected by simple random sampling. The continuous outcome variable CD4 cell count has measured at months 0, 6, 12, 18, and 24. Longitudinal data analysis were used because the set of measurements on one patient tend to be correlated, measurements on the same patient close in time tend to be more highly correlated than measurements far apart in time, and the variability of longitudinal data often changes with time and the data handled through linear mixed effect models.
 Result: The fitted result of the linear mixed model showed that linear visit time effect and the baseline characteristics education status, condom, tobacco, degree of Disclosure, and weight effects had significant effect on CD4 measurements. Also, the interaction age with linear visit time effect had significant effect on the evolution of CD4 cell count. However, no significant difference between sex, WHO stage, and marital status groups.
 Conclusion: This study find that the CD4 cell count of HIV/AIDS patients is significantly determined by the visit time, education status, condom, tobacco, degree of Disclosure, and weight effects of patients.

Joint Modeling of Longitudinal Outcome and Competing Risks: Application to HIV/AIDS Data

“…This may be because people with higher education are less likely to become infected with HIV a result consistent with the Gergiso and Erango study. 33 …”