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Clustering of HIV Patients in Ethiopia

BACKGROUND: Among the many worldwide health problems, HIV/AIDS has caused severe health problems in several countries. The problem is also widely seen in Ethiopia. The general objective of the study is to cluster HIV patients and to find out the factors that mostly affect the prevalence of HIV withi...

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Autores principales: Biressaw, Wondimu, Tilaye, Habtamu, Melese, Dessie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8164663/
https://www.ncbi.nlm.nih.gov/pubmed/34079385
http://dx.doi.org/10.2147/HIV.S301510
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author Biressaw, Wondimu
Tilaye, Habtamu
Melese, Dessie
author_facet Biressaw, Wondimu
Tilaye, Habtamu
Melese, Dessie
author_sort Biressaw, Wondimu
collection PubMed
description BACKGROUND: Among the many worldwide health problems, HIV/AIDS has caused severe health problems in several countries. The problem is also widely seen in Ethiopia. The general objective of the study is to cluster HIV patients and to find out the factors that mostly affect the prevalence of HIV within a group (cluster) and between groups (clusters) of HIV patients. METHODS: The study is made based on the 2016 Ethiopian Demographic Health Survey (EDHS) which was collected by the Central Statistical Agency (CSA) of Ethiopia, and the survey collected a total of 26,753 samples, of which 14,785 were women and 11,968 were men and the age group was between 15 and 49 years for both. Binary logistic regression, principal component analysis, cluster analysis, and ANOVA were applied to analyze the data. RESULTS: The result from binary logistic regression reveals that 15 factors such as ever heard of AIDS, region, water not available for at least a day in the last 2 weeks, has a radio, family members wash their hands, location of the source of water, everything completed to water to make it harmless to drink, food cooked in the house/separate house/outside, has a mobile telephone, has a table, type of place of residence, highest education level attained, current marital status, sex of household members, and age of household members are all significant factors that affect HIV status. CONCLUSION: Using these significant variables, 12 principal components are identified which describe 78% of the variation in the data. The result of HIV patients are clustered into 3 clusters and determine the status of HIV levels. Mainly, cluster 2 accounts for 50% of HIV patients whereas cluster 3 and 1 accounts for 40% and 10%, respectively.
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spelling pubmed-81646632021-06-01 Clustering of HIV Patients in Ethiopia Biressaw, Wondimu Tilaye, Habtamu Melese, Dessie HIV AIDS (Auckl) Original Research BACKGROUND: Among the many worldwide health problems, HIV/AIDS has caused severe health problems in several countries. The problem is also widely seen in Ethiopia. The general objective of the study is to cluster HIV patients and to find out the factors that mostly affect the prevalence of HIV within a group (cluster) and between groups (clusters) of HIV patients. METHODS: The study is made based on the 2016 Ethiopian Demographic Health Survey (EDHS) which was collected by the Central Statistical Agency (CSA) of Ethiopia, and the survey collected a total of 26,753 samples, of which 14,785 were women and 11,968 were men and the age group was between 15 and 49 years for both. Binary logistic regression, principal component analysis, cluster analysis, and ANOVA were applied to analyze the data. RESULTS: The result from binary logistic regression reveals that 15 factors such as ever heard of AIDS, region, water not available for at least a day in the last 2 weeks, has a radio, family members wash their hands, location of the source of water, everything completed to water to make it harmless to drink, food cooked in the house/separate house/outside, has a mobile telephone, has a table, type of place of residence, highest education level attained, current marital status, sex of household members, and age of household members are all significant factors that affect HIV status. CONCLUSION: Using these significant variables, 12 principal components are identified which describe 78% of the variation in the data. The result of HIV patients are clustered into 3 clusters and determine the status of HIV levels. Mainly, cluster 2 accounts for 50% of HIV patients whereas cluster 3 and 1 accounts for 40% and 10%, respectively. Dove 2021-05-25 /pmc/articles/PMC8164663/ /pubmed/34079385 http://dx.doi.org/10.2147/HIV.S301510 Text en © 2021 Biressaw et al. https://creativecommons.org/licenses/by-nc/3.0/This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/ (https://creativecommons.org/licenses/by-nc/3.0/) ). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php).
spellingShingle Original Research
Biressaw, Wondimu
Tilaye, Habtamu
Melese, Dessie
Clustering of HIV Patients in Ethiopia
title Clustering of HIV Patients in Ethiopia
title_full Clustering of HIV Patients in Ethiopia
title_fullStr Clustering of HIV Patients in Ethiopia
title_full_unstemmed Clustering of HIV Patients in Ethiopia
title_short Clustering of HIV Patients in Ethiopia
title_sort clustering of hiv patients in ethiopia
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8164663/
https://www.ncbi.nlm.nih.gov/pubmed/34079385
http://dx.doi.org/10.2147/HIV.S301510
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