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

Overview of attention for article published in BMC Infectious Diseases, April 2018
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Title
Determinants of CD4 cell count change and time-to default from HAART; a comparison of separate and joint models
Published in
BMC Infectious Diseases, April 2018
DOI 10.1186/s12879-018-3108-7
Pubmed ID
Authors

Awoke Seyoum Tegegne, Principal Ndlovu, Temesgen Zewotir

Abstract

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. 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. Longitudinal 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. Both 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. Certain 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.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 62 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 62 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 15%
Student > Master 9 15%
Lecturer 7 11%
Researcher 6 10%
Student > Bachelor 4 6%
Other 8 13%
Unknown 19 31%
Readers by discipline Count As %
Nursing and Health Professions 14 23%
Medicine and Dentistry 13 21%
Pharmacology, Toxicology and Pharmaceutical Science 2 3%
Mathematics 2 3%
Social Sciences 2 3%
Other 6 10%
Unknown 23 37%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 May 2018.
All research outputs
#11,451,418
of 12,881,328 outputs
Outputs from BMC Infectious Diseases
#4,084
of 4,769 outputs
Outputs of similar age
#234,458
of 269,361 outputs
Outputs of similar age from BMC Infectious Diseases
#1
of 1 outputs
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