Modeling the longtiudnal change of viral load of HIV positive patients on antiretroviral therapy
HIV/AIDS continues to be a major public health concern and cause of death in the world. Even though WHO recommended viral load testing as the preferred monitoring approach to diagnose and confirm ARV treatment failure, but in most cases, factors influencing the trend of viral load were not well iden...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2021-01-01
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| Series: | Cogent Medicine |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/2331205X.2021.2008607 |
| _version_ | 1811227613427924992 |
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| author | Dawit Getachew Aragaw Eshetie Dessie Melese Chekole |
| author_facet | Dawit Getachew Aragaw Eshetie Dessie Melese Chekole |
| author_sort | Dawit Getachew |
| collection | DOAJ |
| description | HIV/AIDS continues to be a major public health concern and cause of death in the world. Even though WHO recommended viral load testing as the preferred monitoring approach to diagnose and confirm ARV treatment failure, but in most cases, factors influencing the trend of viral load were not well identified. The main objective of this study was to modeling the change of viral load and identifying its associated factors among HIV positive patients. In this retrospective longitudinal data analysis, data was collected from 287 HIV positive patients registered for ART between January 2017 and June 2019 in Zewditu hospital and unstructured covariance structure was parsimonious for the data. Linear mixed model with different random effect were applied to the data. Linear mixed model with random intercept and slope were selected as a best model to fit the data based on different model selection criteria. The findings of the study revealed that there was a decrement over time in the log VL of patients with HIV on ART. Furthermore, time, baseline CD4 count, WHO clinical stage, functional status of the patient, adherence, smoking status, initial ART Regimen and time interaction with adherence and WHO stage were found to be significant predictors of log VL evolution. Linear mixed model with random intercept and slope were selected to fit the data based on different information criteria. There was a significant variation in log VL of patients at baseline and through ART treatment time. Therefore, patients should take ART regimens with good adherence to decrease their viral load over time. |
| first_indexed | 2024-04-12T09:45:00Z |
| format | Article |
| id | doaj.art-0db2503514684a9c9707fe8ab12b3ed9 |
| institution | Directory Open Access Journal |
| issn | 2331-205X |
| language | English |
| last_indexed | 2024-04-12T09:45:00Z |
| publishDate | 2021-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Cogent Medicine |
| spelling | doaj.art-0db2503514684a9c9707fe8ab12b3ed92022-12-22T03:37:58ZengTaylor & Francis GroupCogent Medicine2331-205X2021-01-018110.1080/2331205X.2021.20086072008607Modeling the longtiudnal change of viral load of HIV positive patients on antiretroviral therapyDawit Getachew0Aragaw Eshetie1Dessie Melese Chekole2College of Natural and Computational Science, Aksum UniversityCollege of Natural and Computational Science, University of GondarCollege of Natural and Computational Science, University of GondarHIV/AIDS continues to be a major public health concern and cause of death in the world. Even though WHO recommended viral load testing as the preferred monitoring approach to diagnose and confirm ARV treatment failure, but in most cases, factors influencing the trend of viral load were not well identified. The main objective of this study was to modeling the change of viral load and identifying its associated factors among HIV positive patients. In this retrospective longitudinal data analysis, data was collected from 287 HIV positive patients registered for ART between January 2017 and June 2019 in Zewditu hospital and unstructured covariance structure was parsimonious for the data. Linear mixed model with different random effect were applied to the data. Linear mixed model with random intercept and slope were selected as a best model to fit the data based on different model selection criteria. The findings of the study revealed that there was a decrement over time in the log VL of patients with HIV on ART. Furthermore, time, baseline CD4 count, WHO clinical stage, functional status of the patient, adherence, smoking status, initial ART Regimen and time interaction with adherence and WHO stage were found to be significant predictors of log VL evolution. Linear mixed model with random intercept and slope were selected to fit the data based on different information criteria. There was a significant variation in log VL of patients at baseline and through ART treatment time. Therefore, patients should take ART regimens with good adherence to decrease their viral load over time.http://dx.doi.org/10.1080/2331205X.2021.2008607hiv/aidsviral loadunstructured covariance structurelinear mixed model |
| spellingShingle | Dawit Getachew Aragaw Eshetie Dessie Melese Chekole Modeling the longtiudnal change of viral load of HIV positive patients on antiretroviral therapy Cogent Medicine hiv/aids viral load unstructured covariance structure linear mixed model |
| title | Modeling the longtiudnal change of viral load of HIV positive patients on antiretroviral therapy |
| title_full | Modeling the longtiudnal change of viral load of HIV positive patients on antiretroviral therapy |
| title_fullStr | Modeling the longtiudnal change of viral load of HIV positive patients on antiretroviral therapy |
| title_full_unstemmed | Modeling the longtiudnal change of viral load of HIV positive patients on antiretroviral therapy |
| title_short | Modeling the longtiudnal change of viral load of HIV positive patients on antiretroviral therapy |
| title_sort | modeling the longtiudnal change of viral load of hiv positive patients on antiretroviral therapy |
| topic | hiv/aids viral load unstructured covariance structure linear mixed model |
| url | http://dx.doi.org/10.1080/2331205X.2021.2008607 |
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