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Clinics register based HIV prevalence in Jimma zone, Ethiopia: applications of likelihood and Bayesian approaches

BACKGROUND: The distribution of HIV is not uniform in Ethiopia with some regions recording higher prevalence than others. However, reported regional HIV prevalence estimates mask the heterogeneity of the epidemic within regions. The main purpose of this study was to assess the district differences i...

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Autores principales: Bedaso, Nemso Geda, Debusho, Legesse Kassa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8944036/
https://www.ncbi.nlm.nih.gov/pubmed/35331136
http://dx.doi.org/10.1186/s12879-021-06965-0
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author Bedaso, Nemso Geda
Debusho, Legesse Kassa
author_facet Bedaso, Nemso Geda
Debusho, Legesse Kassa
author_sort Bedaso, Nemso Geda
collection PubMed
description BACKGROUND: The distribution of HIV is not uniform in Ethiopia with some regions recording higher prevalence than others. However, reported regional HIV prevalence estimates mask the heterogeneity of the epidemic within regions. The main purpose of this study was to assess the district differences in HIV prevalence and other factors that affect the prevalence of HIV infection in Jimma zone, Oromia region of Ethiopia. We aimed to identify districts which had higher or lower than zone average HIV prevalence. Such in-depth analysis of HIV data at district level may help to develop effective strategies to reduce the HIV transmission rate. METHODS: Data collected from 8440 patients who were tested for HIV status in government clinics at the 22 Districts between September 2018 to August 2019 in Jimma zone were used for the analyses. A generalized linear mixed effects model with district random effects was applied to assess the factors associated with HIV infection and the best linear unbiased prediction was used to identify districts that had higher or lower HIV infection. Both likelihood and Bayesian methods were considered. RESULTS: The statistical test on district random effects variance suggested the need for district random effects in all the models. The results from applying both methods on full data show that the odds of HIV infection are significantly associated with covariates considered in this study. Disaggregation of prevalence by gender also highlighted the persistent features of the HIV epidemic in Jimma zone. After controlling for covariates effects, the results from both techniques revealed that there was heterogeneity in HIV infection prevalence among districts within Jimma zone, where some of them had higher and some had lower HIV infection prevalence compared to the zone average HIV infection prevalence. CONCLUSIONS: The study recommends government to give attention to those districts which had higher HIV infection and to conduct further research to improve their intervention strategies. Further, related to those districts which had lower infection, it would be advantageous to identify reasons for their performance and may apply them to overcome HIV infection among residents in those districts which had higher HIV infection. The approach used in this study can also help to assess the effect of interventions introduced by the authorities to control the epidemic and it can easily be extended to assess the regions HIV infection rate relative to the rate at the national level, or zones HIV infection rate relative to the rate at a region level. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12879-021-06965-0.
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spelling pubmed-89440362022-03-25 Clinics register based HIV prevalence in Jimma zone, Ethiopia: applications of likelihood and Bayesian approaches Bedaso, Nemso Geda Debusho, Legesse Kassa BMC Infect Dis Research BACKGROUND: The distribution of HIV is not uniform in Ethiopia with some regions recording higher prevalence than others. However, reported regional HIV prevalence estimates mask the heterogeneity of the epidemic within regions. The main purpose of this study was to assess the district differences in HIV prevalence and other factors that affect the prevalence of HIV infection in Jimma zone, Oromia region of Ethiopia. We aimed to identify districts which had higher or lower than zone average HIV prevalence. Such in-depth analysis of HIV data at district level may help to develop effective strategies to reduce the HIV transmission rate. METHODS: Data collected from 8440 patients who were tested for HIV status in government clinics at the 22 Districts between September 2018 to August 2019 in Jimma zone were used for the analyses. A generalized linear mixed effects model with district random effects was applied to assess the factors associated with HIV infection and the best linear unbiased prediction was used to identify districts that had higher or lower HIV infection. Both likelihood and Bayesian methods were considered. RESULTS: The statistical test on district random effects variance suggested the need for district random effects in all the models. The results from applying both methods on full data show that the odds of HIV infection are significantly associated with covariates considered in this study. Disaggregation of prevalence by gender also highlighted the persistent features of the HIV epidemic in Jimma zone. After controlling for covariates effects, the results from both techniques revealed that there was heterogeneity in HIV infection prevalence among districts within Jimma zone, where some of them had higher and some had lower HIV infection prevalence compared to the zone average HIV infection prevalence. CONCLUSIONS: The study recommends government to give attention to those districts which had higher HIV infection and to conduct further research to improve their intervention strategies. Further, related to those districts which had lower infection, it would be advantageous to identify reasons for their performance and may apply them to overcome HIV infection among residents in those districts which had higher HIV infection. The approach used in this study can also help to assess the effect of interventions introduced by the authorities to control the epidemic and it can easily be extended to assess the regions HIV infection rate relative to the rate at the national level, or zones HIV infection rate relative to the rate at a region level. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12879-021-06965-0. BioMed Central 2022-03-24 /pmc/articles/PMC8944036/ /pubmed/35331136 http://dx.doi.org/10.1186/s12879-021-06965-0 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
Bedaso, Nemso Geda
Debusho, Legesse Kassa
Clinics register based HIV prevalence in Jimma zone, Ethiopia: applications of likelihood and Bayesian approaches
title Clinics register based HIV prevalence in Jimma zone, Ethiopia: applications of likelihood and Bayesian approaches
title_full Clinics register based HIV prevalence in Jimma zone, Ethiopia: applications of likelihood and Bayesian approaches
title_fullStr Clinics register based HIV prevalence in Jimma zone, Ethiopia: applications of likelihood and Bayesian approaches
title_full_unstemmed Clinics register based HIV prevalence in Jimma zone, Ethiopia: applications of likelihood and Bayesian approaches
title_short Clinics register based HIV prevalence in Jimma zone, Ethiopia: applications of likelihood and Bayesian approaches
title_sort clinics register based hiv prevalence in jimma zone, ethiopia: applications of likelihood and bayesian approaches
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8944036/
https://www.ncbi.nlm.nih.gov/pubmed/35331136
http://dx.doi.org/10.1186/s12879-021-06965-0
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