Clustering of HIV Patients in Ethiopia

Abstract

Wondimu Biressaw,1 Habtamu Tilaye,2 Dessie Melese2 1Benishangul-Gumuz, Wombera Sineor Secondary and Preparatory School, Benishangul, Ethiopia; 2University of Gondar, College of Natural and Computational Science, Departmentof Statistics, Gondar, EthiopiaCorrespondence: Dessie MeleseDepartment of Statistics, College Natural and Computational Science, University of Gondar, P.O. Box 196, Gondar, EthiopiaEmail [email protected]: 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.Keywords: Ethiopian Demographic Health Survey; EDHS, cluster analysis, principal component analysis, HIV patients

Keywords

• ethiopian demographic health survey
• cluster analysis
• principal component analysis
• hiv patients