Determinants of CD4 cell count change and time-to default from HAART; a comparison of separate and joint models

Awoke Seyoum Tegegne,Temesgen Zewotir,Principal Ndlovu

doi:10.1186/s12879-018-3108-7

Awoke Seyoum Tegegne, Temesgen Zewotir + Show 1 more

Open Access

https://doi.org/10.1186/s12879-018-3108-7

Copy DOI

Abstract

BackgroundHIV has the most serious effects in Sub-Saharan African countries as compared to countries in other parts of the world. As part of these countries, Ethiopia has been affected significantly by the disease, and the burden of the disease has become worst in the Amhara Region, one of the eleven regions of the country. Being a defaulter or dropout of HIV patients from the treatment plays a significant role in treatment failure. The current research was conducted with the objective of comparing the performance of the joint and the separate modelling approaches in determining important factors that affect HIV patients’ longitudinal CD4 cell count change and time to default from treatment.MethodsLongitudinal data was obtained from the records of 792 HIV adult patients at Felege-Hiwot Teaching and Specialized Hospital in Ethiopia. Two alternative approaches, namely separate and joint modeling data analyses, were conducted in the current study. Joint modeling was conducted for an analysis of the change of CD4 cell count and the time to default in the treatment. In the joint model, a generalized linear mixed effects model and Weibul survival sub-models were combined together for the repetitive measures of the CD4 cell count change and the number of follow-ups in which patients wait in the treatment. Finally, the two models were linked through their shared unobserved random effects using a shared parameter model.ResultsBoth separate and joint modeling approach revealed a consistent result. However, the joint modeling approach was more parsimonious and fitted the given data well as compared to the separate one. Age, baseline CD4 cell count, marital status, sex, ownership of cell phone, adherence to HAART, disclosure of the disease and the number of follow-ups were important predictors for both the fluctuation of CD4 cell count and the time-to default from treatment. The inclusion of patient-specific variations in the analyses of the two outcomes improved the model significantly.ConclusionCertain groups of patients were identified in the current investigation. The groups already identified had high fluctuation in the number of CD4 cell count and defaulted from HAART without any convincing reasons. Such patients need high intervention to adhere to the prescribed medication.

Highlights

Human Immune Deficiency Virus (HIV) has the most serious effects in Sub-Saharan African countries as compared to countries in other parts of the world
The graph shows that at each visiting time, the variance was greater than the mean which suggested that the distribution of the Classification Determinant Four (CD4) cell count change was over dispersed, and could be either be negative Binomial or quasi-Poisson
The results of the current study indicate that the joint model was in favor of separate models for determining the predictors of CD4 cell count change and time to default

Summary

Introduction

HIV has the most serious effects in Sub-Saharan African countries as compared to countries in other parts of the world. As part of these countries, Ethiopia has been affected significantly by the disease, and the burden of the disease has become worst in the Amhara Region, one of the eleven regions of the country. The current research was conducted with the objective of comparing the performance of the joint and the separate modelling approaches in determining important factors that affect HIV patients’ longitudinal CD4 cell count change and time to default from treatment. As other regions in Ethiopia, the Amhara region, the study area, is highly affected by the disease [5]. Another study reported that there is a positive correlation between baseline CD4 cell count change and the CD4 cell count after the commencement of HAART [13, 14]

Methods

Results

Conclusion