Volume 3, Issue 2, June 2018, Page: 34-42
Application of Longitudinal Measured CD4+ Count on HIV-Positive Patients Following Active Antiretroviral Therapy: A Case of Debre Berhan Referral Hospital
Shewayiref Geremew, Department of Statistics, Debre Tabor University, Debre Tabor, Ethiopia; Department of Statistics, Debre Berhan University, Debre Berhan, Ethiopia
Dejen Tesfaw, Department of Statistics, Addis Ababa University, Addis Ababa, Ethiopia
Tibebu Getiye, Department of Statistics, Ethiopian Civil Service University, Addis Ababa, Ethiopia; Department of Statistics, Debre Berhan University, Debre Berhan, Ethiopia
Received: Jul. 9, 2018;       Accepted: Jul. 23, 2018;       Published: Aug. 24, 2018
DOI: 10.11648/j.bsi.20180302.15      View  483      Downloads  24
Abstract
The measurement of the CD4+ count is the predictor of evolution to AIDS, in ART. Studying the way of the CD4+ count over time provides an insight to the disease evolution. The main objective of this study was to apply statistical analysis on longitudinally measured CD4+ Cell counts of HIV-positive patients under ART. The study population consists of 647 HIV+ patients who were 16 years old or older and who were under ART follow up from 2012 to 2017 in Debre Berhan Referral Hospital, Debre Berhan, Ethiopia. The data were from the patients' chart. All patients who have initiated to ART and measured their CD4+ cell counts at least two times, including the baseline and those who started the first line ART regimen class was included in the study population. Data were explored using basic descriptive statistics and individual and mean profile plots. The methods of LMM and GLMM were used. The mean profile of CD4+ count revealed that there is an improvement in the duration of treatment in a linear pattern. From the GLMM covariates duration of treatment, sex, BMI, baseline CD4, regimen class, duration by age, duration by baseline CD4 and duration by regimen class significantly determines the change in CD4+ count overtime at 5% level of significance. There is the duration of treatment effect on the current CD4+ count. The study result suggests that HIV+ patients attending in ART improve their CD4+ count.
Keywords
ART, CD4 Count, GLMM, HIV/AIDS, Longitudinal Data Analysis, LMM
To cite this article
Shewayiref Geremew, Dejen Tesfaw, Tibebu Getiye, Application of Longitudinal Measured CD4+ Count on HIV-Positive Patients Following Active Antiretroviral Therapy: A Case of Debre Berhan Referral Hospital, Biomedical Statistics and Informatics. Vol. 3, No. 2, 2018, pp. 34-42. doi: 10.11648/j.bsi.20180302.15
Copyright
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Reference
[1]
Ugandan Antiretroviral Treatment and Care Guidelines for Adults, Adolescents, and Children. 2nd Edition, January, 2008.
[2]
Verbeke, G. and Molenberghs, G., (2008), Linear Mixed Models for Longitudinal Data. Springer Series in Statistics. New-York: Springer.
[3]
Lemma et al., (2016), Predictors of CD4 count over time among HIV patients initiated ART in Felege Hiwot Referral Hospital, northwest Ethiopia: multilevel analysis.
[4]
Tekle G, Kassahun W, Gurmessa A., (2016), Statistical Analysis of CD4+ Cell Counts progression of HIV- posi tive Patients enrolled in Antiretroviral Therapy at Hossana District Queen Elleni Mohamad Memorial Hospital, South Ethiopia. Biom Biostat Int J 3 (1): 00057.
[5]
Yakubu, Toyibu, Vincent K Dedu, and Patrick Owiredu Bampoh., (2016), Factors Affecting CD4 Count Response in HIV Patients within 12 Months of Treatment: A Case Study of Tamale Teaching Hospital. American Journal of Medical and Biological Research 4, no. 4, pp. 78-83.
[6]
Kebede MM, Zegeye DT, Zeleke BM, (2014), Predictors of CD4 Count Changes after Initiation of Antiretroviral Treatment in University of Gondar Hospital, Gondar, Ethiopia. Clin Res HIV/AIDS 1 (2): 1008.
[7]
Lemma et al., (2016), Predictors of CD4 count over time among HIV patients initiated ART in Felege Hiwot Referral Hospital, northwest Ethiopia: multilevel analysis.
[8]
Viviane, D., Fink, L. V., Benita, Y., Robert, S. H., Harrigan, R. and Julio, S. G. M, (2009), Association between HIV-1 RNA Level and CD4+ Cell Count among untreated HIV-infected individuals, American Journal of Public Health Vol., (99) 51.
[9]
Laird NM, Ware JH., (1982), Random-effects models for longitudinal data. Biometrics. 38, PP., 963-974.
[10]
G, Kassahun W, Gurmessa A, (2016), Statistical Analysis of CD4+ Cell Counts progression of HIV-1-positive Patients enrolled in Antiretroviral Therapy at Hossana District Queen Elleni Mohamad Memorial Hospital, South Ethiopia. Biom Biostat Int J 3 (1): 00057.
[11]
Debre Berhan Hospital annual report, 2016. [Accessed 16 February 2017].
[12]
DE Gruttola and X. M. TU, (1994), Modeling progression of CD4-lymphocyte count and its relationship to survival time, Biometrics, vol. 50, no. 4, pp. 1003-1014.
[13]
Diggle PJ, Heagerty P, Liang K-Y., (2002), Analysis of Longitudinal Data. 2nd ed. Oxford, United Kingdom: Oxford Uni versity Press.
[14]
Hoover DR, Graham NMG, Chen B, Taylor JMG, Phair J, Zhou SYJ, Munoz A., (1992), Effect of CD4+ cell count mea surement variability on staging HIV-1 infection. Journal of Acquired Immune Deficiency Syndrome. Vol. 5, pp. 794-802.
[15]
Langford SE, Ananworanich J and Cooper DA., (2007), Predictors of disease progression in HIV infection: a review. AIDS Res Ther., pp. 4:11.
[16]
Macro International Inc., (2008), Ethiopia Atlas of Key Demographic and Health Indicators, 2005. Calverton: Macro In ternational, 2008, pp. 24.
[17]
Mellors JW., Munoz A., Giorgi JV. (1997), Plasma viral load and CD4+ lymphocytes as prognostic markers of HIV-1 in- fection. Ann. Intern. Med, 126 (12):946-954.
[18]
Nelson K and Williams CM., (2007) Infectious Disease Epidemiology. Theory and Practice. 2nd ed.
[19]
Rizopoulos D, Verbeke G, Molenberhs G., (2008), Shared parameter models under random effects misspecification. Biome trika. 95, pp. 63-74.
[20]
World Health Organization (WHO), (2012), World AIDS Day 2012: Closing in on global HIV targets. In.
Browse journals by subject