Application of Longitudinal Count Data Models to Progression of CD4 Count: A Case of Debre Markos Referral Hospital
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Date
2017-06
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Addis Abeba university
Abstract
Even though the world is ghting HIV disease in unity and patients are getting antiretroviral
therapy treatment, HIV disease continues to be a serious health issue for parts of the world and
large number of AIDS related deaths are being registered every year. A number of studies have
been conducted to assess factors related with the progression of the disease using surrogate end-
points like CD4 cell count. The main objective of this study was to make use of appropriate
statistical models to analyze CD4 cell counts data and identify associated risk factors a ecting
the CD4 cell progression of patients under ART tra etment in Debre Markos Re eral Hospital. In
this longitudinal retrospective cohort based study, data was collected from 445 HIV patients reg-
istered for ART treatment between September, 2005 and August, 2014 in the Hospital. Poisson,
Poisson-Gamma, Poisson-Normal, and Poisson-Gamma-Normal models were applied to account
for overdispersion and correlation in the data. Poisson-Gamma-Normal model with random in-
tercept was selected as a best model to t the data based on di erent model selection criteria.
The ndings of the study revealed that time in months, sex of patients, baseline WHO stage
and baseline CD4 cell count were found to be signi cant factors for progression of HIV patients'
CD4 cell count. Patients who started ART at higher baseline CD4 counts evolved higher than
those who started at lower CD4 counts. Therefore, patients should start ART treatment early to
increase their CD4 cell count progression.
Keywords: CD4 count, Longitudinal data analysis, Poisson-Normal Model, Poisson-Gamma-
Normal model, Antiretroviral therapy (ART), HIV/AIDS
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Keywords
CD4 Count, Longitudinal Data Analysis, Poisson-Normal Model, Poisson-Gamma- Normal Model, Antiretroviral Therapy (ART), HIV/AIDS