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Spatial analysis of factors associated with HIV infection in Malawi: indicators for effective prevention
BACKGROUND: The objective of this study was to model the predictors of HIV prevalence in Malawi through a complex sample logistic regression and spatial mapping approach using the national Demographic and Health Survey datasets. METHODS: We conducted a secondary data analysis using the 2015–2016 Mal...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7382788/ https://www.ncbi.nlm.nih.gov/pubmed/32711500 http://dx.doi.org/10.1186/s12889-020-09278-0 |
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author | Nutor, Jerry John Duah, Henry Ofori Agbadi, Pascal Duodu, Precious Adade Gondwe, Kaboni W. |
author_facet | Nutor, Jerry John Duah, Henry Ofori Agbadi, Pascal Duodu, Precious Adade Gondwe, Kaboni W. |
author_sort | Nutor, Jerry John |
collection | PubMed |
description | BACKGROUND: The objective of this study was to model the predictors of HIV prevalence in Malawi through a complex sample logistic regression and spatial mapping approach using the national Demographic and Health Survey datasets. METHODS: We conducted a secondary data analysis using the 2015–2016 Malawi Demographic and Health Survey and AIDS Indicator Survey. The analysis was performed in three stages while incorporating population survey sampling weights to: i) interpolate HIV data, ii) identify the spatial clusters with the high prevalence of HIV infection, and iii) perform a multivariate complex sample logistic regression. RESULTS: In all, 14,779 participants were included in the analysis with an overall HIV prevalence of 9% (7.0% in males and 10.8% in females). The highest prevalence was found in the southern region of Malawi (13.2%), and the spatial interpolation revealed that the HIV epidemic is worse at the south-eastern part of Malawi. The districts in the high HIV prevalent zone of Malawi are Thyolo, Zomba, Mulanje, Phalombe and Blantyre. In central and northern region, the district HIV prevalence map identified Lilongwe in the central region and Karonga in the northern region as districts that equally deserve attention. People residing in urban areas had a 2.2 times greater risk of being HIV-positive compared to their counterparts in the rural areas (AOR = 2.16; 95%CI = 1.57–2.97). Other independent predictors of HIV prevalence were gender, age, marital status, number of lifetime sexual partners, extramarital partners, the region of residence, condom use, history of STI in the last 12 months, and household wealth index. Disaggregated analysis showed in-depth sociodemographic regional variations in HIV prevalence. CONCLUSION: These findings identify high-risk populations and regions to be targeted for Pre-Exposure Prophylaxis (PrEP) campaigns, HIV testing, treatment and education to decrease incidence, morbidity, and mortality related to HIV infection in Malawi. |
format | Online Article Text |
id | pubmed-7382788 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-73827882020-07-27 Spatial analysis of factors associated with HIV infection in Malawi: indicators for effective prevention Nutor, Jerry John Duah, Henry Ofori Agbadi, Pascal Duodu, Precious Adade Gondwe, Kaboni W. BMC Public Health Research Article BACKGROUND: The objective of this study was to model the predictors of HIV prevalence in Malawi through a complex sample logistic regression and spatial mapping approach using the national Demographic and Health Survey datasets. METHODS: We conducted a secondary data analysis using the 2015–2016 Malawi Demographic and Health Survey and AIDS Indicator Survey. The analysis was performed in three stages while incorporating population survey sampling weights to: i) interpolate HIV data, ii) identify the spatial clusters with the high prevalence of HIV infection, and iii) perform a multivariate complex sample logistic regression. RESULTS: In all, 14,779 participants were included in the analysis with an overall HIV prevalence of 9% (7.0% in males and 10.8% in females). The highest prevalence was found in the southern region of Malawi (13.2%), and the spatial interpolation revealed that the HIV epidemic is worse at the south-eastern part of Malawi. The districts in the high HIV prevalent zone of Malawi are Thyolo, Zomba, Mulanje, Phalombe and Blantyre. In central and northern region, the district HIV prevalence map identified Lilongwe in the central region and Karonga in the northern region as districts that equally deserve attention. People residing in urban areas had a 2.2 times greater risk of being HIV-positive compared to their counterparts in the rural areas (AOR = 2.16; 95%CI = 1.57–2.97). Other independent predictors of HIV prevalence were gender, age, marital status, number of lifetime sexual partners, extramarital partners, the region of residence, condom use, history of STI in the last 12 months, and household wealth index. Disaggregated analysis showed in-depth sociodemographic regional variations in HIV prevalence. CONCLUSION: These findings identify high-risk populations and regions to be targeted for Pre-Exposure Prophylaxis (PrEP) campaigns, HIV testing, treatment and education to decrease incidence, morbidity, and mortality related to HIV infection in Malawi. BioMed Central 2020-07-25 /pmc/articles/PMC7382788/ /pubmed/32711500 http://dx.doi.org/10.1186/s12889-020-09278-0 Text en © The Author(s) 2020 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/. The Creative Commons Public Domain Dedication waiver (http://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 Article Nutor, Jerry John Duah, Henry Ofori Agbadi, Pascal Duodu, Precious Adade Gondwe, Kaboni W. Spatial analysis of factors associated with HIV infection in Malawi: indicators for effective prevention |
title | Spatial analysis of factors associated with HIV infection in Malawi: indicators for effective prevention |
title_full | Spatial analysis of factors associated with HIV infection in Malawi: indicators for effective prevention |
title_fullStr | Spatial analysis of factors associated with HIV infection in Malawi: indicators for effective prevention |
title_full_unstemmed | Spatial analysis of factors associated with HIV infection in Malawi: indicators for effective prevention |
title_short | Spatial analysis of factors associated with HIV infection in Malawi: indicators for effective prevention |
title_sort | spatial analysis of factors associated with hiv infection in malawi: indicators for effective prevention |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7382788/ https://www.ncbi.nlm.nih.gov/pubmed/32711500 http://dx.doi.org/10.1186/s12889-020-09278-0 |
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