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

BACKGROUND: 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 ele...

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Autores principales: Tegegne, Awoke Seyoum, Ndlovu, Principal, Zewotir, Temesgen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5922030/
https://www.ncbi.nlm.nih.gov/pubmed/29703155
http://dx.doi.org/10.1186/s12879-018-3108-7
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author Tegegne, Awoke Seyoum
Ndlovu, Principal
Zewotir, Temesgen
author_facet Tegegne, Awoke Seyoum
Ndlovu, Principal
Zewotir, Temesgen
author_sort Tegegne, Awoke Seyoum
collection PubMed
description BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.
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spelling pubmed-59220302018-05-07 Determinants of CD4 cell count change and time-to default from HAART; a comparison of separate and joint models Tegegne, Awoke Seyoum Ndlovu, Principal Zewotir, Temesgen BMC Infect Dis Research Article BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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. BioMed Central 2018-04-27 /pmc/articles/PMC5922030/ /pubmed/29703155 http://dx.doi.org/10.1186/s12879-018-3108-7 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Tegegne, Awoke Seyoum
Ndlovu, Principal
Zewotir, Temesgen
Determinants of CD4 cell count change and time-to default from HAART; a comparison of separate and joint models
title Determinants of CD4 cell count change and time-to default from HAART; a comparison of separate and joint models
title_full Determinants of CD4 cell count change and time-to default from HAART; a comparison of separate and joint models
title_fullStr Determinants of CD4 cell count change and time-to default from HAART; a comparison of separate and joint models
title_full_unstemmed Determinants of CD4 cell count change and time-to default from HAART; a comparison of separate and joint models
title_short Determinants of CD4 cell count change and time-to default from HAART; a comparison of separate and joint models
title_sort determinants of cd4 cell count change and time-to default from haart; a comparison of separate and joint models
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5922030/
https://www.ncbi.nlm.nih.gov/pubmed/29703155
http://dx.doi.org/10.1186/s12879-018-3108-7
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