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Spatial distribution and characteristics of HIV clusters in Ethiopia

OBJECTIVES: Ethiopia’s HIV prevalence has decreased by 75% in the past 20 years with the implementation of antiretroviral therapy, but HIV transmission continues in high‐risk clusters. Identifying the spatial and temporal trends, and epidemiologic correlates, of these clusters can lead to targeted i...

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Autores principales: Ying, Roger, Fekadu, Lelisa, Schackman, Bruce R., Verguet, Stéphane
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7079229/
https://www.ncbi.nlm.nih.gov/pubmed/31808592
http://dx.doi.org/10.1111/tmi.13356
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author Ying, Roger
Fekadu, Lelisa
Schackman, Bruce R.
Verguet, Stéphane
author_facet Ying, Roger
Fekadu, Lelisa
Schackman, Bruce R.
Verguet, Stéphane
author_sort Ying, Roger
collection PubMed
description OBJECTIVES: Ethiopia’s HIV prevalence has decreased by 75% in the past 20 years with the implementation of antiretroviral therapy, but HIV transmission continues in high‐risk clusters. Identifying the spatial and temporal trends, and epidemiologic correlates, of these clusters can lead to targeted interventions. METHODS: We used biomarker and survey data from the 2005, 2011 and 2016 Ethiopia Demographic and Health Surveys (DHS). The spatial‐temporal distribution of HIV was estimated using the Kulldorff spatial scan statistic, a likelihood‐based method for determining clustering. Significant clusters (P < 0.05) were identified and compared based on HIV risk factors to non‐cluster areas. RESULTS: In 2005, 2011 and 2016, respectively, 219, 568 and 408 individuals tested positive for HIV. Four HIV clusters were identified, representing 17% of the total population and 43% of all HIV cases. The clusters were centred around Addis Ababa (1), Afar (2), Dire Dawa (3) and Gambella (4). Cluster 1 had higher rates of unsafe injections (4.9% vs. 2.2%, P < 0.001) and transactional sex (6.0% vs. 1.6%, P < 0.001) than non‐cluster regions, but more male circumcision (98.5% vs. 91.3%, P < 0.001). Cluster 2 had higher levels of transactional sex (4.9% vs. 1.6%, P < 0.01), but lower levels of unsafe injections (0.8% vs. 2.2%, P < 0.01). Cluster 3 had fewer individuals with> 1 sexual partner (0% vs. 1.7%, P < 0.001) and more male circumcision (100% vs. 91.3%, P < 0.001). Cluster 4 had less male circumcision (59.1% vs. 91.3%, P < 0.01). CONCLUSIONS: In Ethiopia, geographic HIV clusters are driven by different risk factors. Decreasing the HIV burden requires targeted interventions.
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spelling pubmed-70792292020-03-19 Spatial distribution and characteristics of HIV clusters in Ethiopia Ying, Roger Fekadu, Lelisa Schackman, Bruce R. Verguet, Stéphane Trop Med Int Health Original Research Papers OBJECTIVES: Ethiopia’s HIV prevalence has decreased by 75% in the past 20 years with the implementation of antiretroviral therapy, but HIV transmission continues in high‐risk clusters. Identifying the spatial and temporal trends, and epidemiologic correlates, of these clusters can lead to targeted interventions. METHODS: We used biomarker and survey data from the 2005, 2011 and 2016 Ethiopia Demographic and Health Surveys (DHS). The spatial‐temporal distribution of HIV was estimated using the Kulldorff spatial scan statistic, a likelihood‐based method for determining clustering. Significant clusters (P < 0.05) were identified and compared based on HIV risk factors to non‐cluster areas. RESULTS: In 2005, 2011 and 2016, respectively, 219, 568 and 408 individuals tested positive for HIV. Four HIV clusters were identified, representing 17% of the total population and 43% of all HIV cases. The clusters were centred around Addis Ababa (1), Afar (2), Dire Dawa (3) and Gambella (4). Cluster 1 had higher rates of unsafe injections (4.9% vs. 2.2%, P < 0.001) and transactional sex (6.0% vs. 1.6%, P < 0.001) than non‐cluster regions, but more male circumcision (98.5% vs. 91.3%, P < 0.001). Cluster 2 had higher levels of transactional sex (4.9% vs. 1.6%, P < 0.01), but lower levels of unsafe injections (0.8% vs. 2.2%, P < 0.01). Cluster 3 had fewer individuals with> 1 sexual partner (0% vs. 1.7%, P < 0.001) and more male circumcision (100% vs. 91.3%, P < 0.001). Cluster 4 had less male circumcision (59.1% vs. 91.3%, P < 0.01). CONCLUSIONS: In Ethiopia, geographic HIV clusters are driven by different risk factors. Decreasing the HIV burden requires targeted interventions. John Wiley and Sons Inc. 2020-01-12 2020-03 /pmc/articles/PMC7079229/ /pubmed/31808592 http://dx.doi.org/10.1111/tmi.13356 Text en © 2019 The Authors. Tropical Medicine & International Health Published by John Wiley & Sons Ltd. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Research Papers
Ying, Roger
Fekadu, Lelisa
Schackman, Bruce R.
Verguet, Stéphane
Spatial distribution and characteristics of HIV clusters in Ethiopia
title Spatial distribution and characteristics of HIV clusters in Ethiopia
title_full Spatial distribution and characteristics of HIV clusters in Ethiopia
title_fullStr Spatial distribution and characteristics of HIV clusters in Ethiopia
title_full_unstemmed Spatial distribution and characteristics of HIV clusters in Ethiopia
title_short Spatial distribution and characteristics of HIV clusters in Ethiopia
title_sort spatial distribution and characteristics of hiv clusters in ethiopia
topic Original Research Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7079229/
https://www.ncbi.nlm.nih.gov/pubmed/31808592
http://dx.doi.org/10.1111/tmi.13356
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