ArticleHIV/AIDS (Auckland, N.Z.)2021
Clustering of HIV Patients in Ethiopia.
Article in HIV/AIDS (Auckland, N.Z.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 7 citations in OpenAlex.
- Perceived stigma and the role of BMI on perceived HIV-related stigma among people living with HIV/AIDS in Southeast Ethiopia.Frontiers in psychiatry · 2025Article
- Mapping the spatial disparities of HIV prevalence in Ethiopian zones using the generalized additive model.Scientific reports · 2024Article
- Use of unsupervised machine learning to characterise HIV predictors in sub-Saharan Africa.BMC infectious diseases · 2023Article
- COVID-19 Burden on HIV Patients Attending Antiretroviral Therapy in Addis Ababa, Ethiopia: A Multicenter Cross-Sectional Study.Frontiers in medicine · 2022Article
- Poor adherence to highly active antiretroviral therapy and associated factors among people living with HIV in Eastern Ethiopia.SAGE open medicine · 2022Article
- Prevalence of HIV/AIDS Infection among Sexually Active Women in Ethiopia: Further Analysis of 2016 EDHS.AIDS research and treatment · 2022Article
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAmong 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.
methodsThe 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.
resultsThe 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.
conclusionUsing 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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