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Estimating district HIV prevalence in Zambia using small-area estimation methods (SAE)

BACKGROUND: The HIV/AIDS pandemic has had a very devastating impact at a global level, with the Eastern and Southern African region being the hardest hit. The considerable geographical variation in the pandemic means varying impact of the disease in different settings, requiring differentiated inter...

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Autores principales: Mweemba, Chris, Hangoma, Peter, Fwemba, Isaac, Mutale, Wilbroad, Masiye, Felix
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8858531/
https://www.ncbi.nlm.nih.gov/pubmed/35183216
http://dx.doi.org/10.1186/s12963-022-00286-3
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author Mweemba, Chris
Hangoma, Peter
Fwemba, Isaac
Mutale, Wilbroad
Masiye, Felix
author_facet Mweemba, Chris
Hangoma, Peter
Fwemba, Isaac
Mutale, Wilbroad
Masiye, Felix
author_sort Mweemba, Chris
collection PubMed
description BACKGROUND: The HIV/AIDS pandemic has had a very devastating impact at a global level, with the Eastern and Southern African region being the hardest hit. The considerable geographical variation in the pandemic means varying impact of the disease in different settings, requiring differentiated interventions. While information on the prevalence of HIV at regional and national levels is readily available, the burden of the disease at smaller area levels, where health services are organized and delivered, is not well documented. This affects the targeting of HIV resources. There is need, therefore, for studies to estimate HIV prevalence at appropriate levels to improve HIV-related planning and resource allocation. METHODS: We estimated the district-level prevalence of HIV using Small-Area Estimation (SAE) technique by utilizing the 2016 Zambia Population-Based HIV Impact Assessment Survey (ZAMPHIA) data and auxiliary data from the 2010 Zambian Census of Population and Housing and the HIV sentinel surveillance data from selected antenatal care clinics (ANC). SAE models were fitted in R Programming to ascertain the best HIV predicting model. We then used the Fay–Herriot (FH) model to obtain weighted, more precise and reliable HIV prevalence for all the districts. RESULTS: The results revealed variations in the district HIV prevalence in Zambia, with the prevalence ranging from as low as 4.2% to as high as 23.5%. Approximately 32% of the districts (n = 24) had HIV prevalence above the national average, with one district having almost twice as much prevalence as the national level. Some rural districts have very high HIV prevalence rates. CONCLUSIONS: HIV prevalence in Zambian is highest in districts located near international borders, along the main transit routes and adjacent to other districts with very high prevalence. The variations in the burden of HIV across districts in Zambia point to the need for a differentiated approach in HIV programming within the country. HIV resources need to be prioritized toward districts with high population mobility. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12963-022-00286-3.
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spelling pubmed-88585312022-02-23 Estimating district HIV prevalence in Zambia using small-area estimation methods (SAE) Mweemba, Chris Hangoma, Peter Fwemba, Isaac Mutale, Wilbroad Masiye, Felix Popul Health Metr Research BACKGROUND: The HIV/AIDS pandemic has had a very devastating impact at a global level, with the Eastern and Southern African region being the hardest hit. The considerable geographical variation in the pandemic means varying impact of the disease in different settings, requiring differentiated interventions. While information on the prevalence of HIV at regional and national levels is readily available, the burden of the disease at smaller area levels, where health services are organized and delivered, is not well documented. This affects the targeting of HIV resources. There is need, therefore, for studies to estimate HIV prevalence at appropriate levels to improve HIV-related planning and resource allocation. METHODS: We estimated the district-level prevalence of HIV using Small-Area Estimation (SAE) technique by utilizing the 2016 Zambia Population-Based HIV Impact Assessment Survey (ZAMPHIA) data and auxiliary data from the 2010 Zambian Census of Population and Housing and the HIV sentinel surveillance data from selected antenatal care clinics (ANC). SAE models were fitted in R Programming to ascertain the best HIV predicting model. We then used the Fay–Herriot (FH) model to obtain weighted, more precise and reliable HIV prevalence for all the districts. RESULTS: The results revealed variations in the district HIV prevalence in Zambia, with the prevalence ranging from as low as 4.2% to as high as 23.5%. Approximately 32% of the districts (n = 24) had HIV prevalence above the national average, with one district having almost twice as much prevalence as the national level. Some rural districts have very high HIV prevalence rates. CONCLUSIONS: HIV prevalence in Zambian is highest in districts located near international borders, along the main transit routes and adjacent to other districts with very high prevalence. The variations in the burden of HIV across districts in Zambia point to the need for a differentiated approach in HIV programming within the country. HIV resources need to be prioritized toward districts with high population mobility. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12963-022-00286-3. BioMed Central 2022-02-19 /pmc/articles/PMC8858531/ /pubmed/35183216 http://dx.doi.org/10.1186/s12963-022-00286-3 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Mweemba, Chris
Hangoma, Peter
Fwemba, Isaac
Mutale, Wilbroad
Masiye, Felix
Estimating district HIV prevalence in Zambia using small-area estimation methods (SAE)
title Estimating district HIV prevalence in Zambia using small-area estimation methods (SAE)
title_full Estimating district HIV prevalence in Zambia using small-area estimation methods (SAE)
title_fullStr Estimating district HIV prevalence in Zambia using small-area estimation methods (SAE)
title_full_unstemmed Estimating district HIV prevalence in Zambia using small-area estimation methods (SAE)
title_short Estimating district HIV prevalence in Zambia using small-area estimation methods (SAE)
title_sort estimating district hiv prevalence in zambia using small-area estimation methods (sae)
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8858531/
https://www.ncbi.nlm.nih.gov/pubmed/35183216
http://dx.doi.org/10.1186/s12963-022-00286-3
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