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

Main Article Content

Kabtamu Tolosie Gergiso
Markos Abiso Erango

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.

Keywords:
CD4 cell count, individual profile, linear mixed model, longitudinal model.

Article Details

How to Cite
Gergiso, K. T., & Erango, M. A. (2019). Evaluation of the Trend of CD4 Cell Count Over Time in Case of HIV/AIDS Patients under ART Follow-up. Journal of Scientific Research and Reports, 24(5), 1-8. https://doi.org/10.9734/jsrr/2019/v24i530167
Section
Original Research Article

References

Joint United Nations Programme on HIV/AIDS, & UNAIDS, D. Geneva; 2017.

Ismail S, MItike G, Hailemariam D. HIV/AIDS for the Ethiopian health center team. Ethiopia Public Health Training Initiative; 2002.

Ayele G, Tessema B, Amsalu A, Ferede G, Yismaw G. Prevalence and associated factors of treatment failure among HIV/AIDS patients on HAART attending University of Gondar Referral Hospital Northwest Ethiopia. BMC immunology. 2018;19(1):37.

Department of Health and Human Services. Panel on antiretroviral guidelines for adults and adolescents. Guidelines for the use of antiretroviral agents in adults and adolescents living with HIV. Department of Health and Human Services; 2018.
Available:http://www.aidsinfo.nih.gov/ContentFiles/AdultandAdolescentGL.pdf

Yiannoutsos CT, Johnson LF, Boulle A, Musick BS, Gsponer T, Balestre E, Law M, Shepherd BE, Egger M. Estimated mortality of adult HIV-infected patients starting treatment with combination antiretroviral therapy. Sex Transm Infect. 2012;88(2):i33-43.

Temesgen A, Gurmesa A, Getchew Y. Joint modeling of longitudinal CD4 count and time-to-death of HIV/TB co-infected patients: A case of Jimma University Specialized Hospital. Annals of Data Science. 2018;5(4):659-78.

HIV/AIDS for veterans and the public. U.S. Department of Veterans AFFAIRS.
Available:https://www.hiv.va.gov/patient/diagnosis/labs-CD4-count.asp

AIDS info. Guidelines for the use of antiretroviral agents in adults and adolescents with HIV.
Available:https://aidsinfo.nih.gov/contentfiles/lvguidelines/adultandadolescentgl.pdf

Panel on antiretroviral guidelines for adults and adolescents. Guidelines for the use of antiretroviral agents in HIV-1-infected adults and adolescents. Washington: Department of Health and Human Services. 2011;1-167.

Verbeke G, Molenberghs G. Linear mixed models for longitudinal data, Springer, New York; 2000.

Molenberghs G, Verbeke G. Models for discrete longitudinal data, Springer Series in Statistics, Springer New York; 2005.

Laird NM, Ware JH. Random-effects models for longitudinal data. Biometrics. 1982;38(4):963-74.

Weiss RE. Modeling longitudinal data. Springer Science & Business Media; 2005.