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Determining factors associated with cholera disease in Ethiopia using Bayesian hierarchical modeling

BACKGROUND: Cholera is a diarrheal disease caused by infection of the intestine with the gram-negative bacteria Vibrio cholera. It is caused by the ingestion of food or water and infected all age groups. This study aimed at identifying risk factors associated with cholera disease in Ethiopia using t...

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Autores principales: Letta, Tsigereda Tilahun, Belay, Denekew Bitew, Ali, Endale Alemayehu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9487065/
https://www.ncbi.nlm.nih.gov/pubmed/36123680
http://dx.doi.org/10.1186/s12889-022-14153-1
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author Letta, Tsigereda Tilahun
Belay, Denekew Bitew
Ali, Endale Alemayehu
author_facet Letta, Tsigereda Tilahun
Belay, Denekew Bitew
Ali, Endale Alemayehu
author_sort Letta, Tsigereda Tilahun
collection PubMed
description BACKGROUND: Cholera is a diarrheal disease caused by infection of the intestine with the gram-negative bacteria Vibrio cholera. It is caused by the ingestion of food or water and infected all age groups. This study aimed at identifying risk factors associated with cholera disease in Ethiopia using the Bayesian hierarchical model. METHODS: The study was conducted in Ethiopia across regions and this study used secondary data obtained from the Ethiopian public health institute. Latent Gaussian models were used in this study; which is a group of models that contains most statistical models used in practice. The posterior marginal distribution of the Latent Gaussian models with different priors is determined by R-Integrated Nested Laplace Approximation. RESULTS: There were 2790 cholera patients in Ethiopia across the regions. There were 81.61% of patients are survived from cholera outbreak disease and the rest 18.39% have died. There was 39% variation across the region in Ethiopia. Latent Gaussian models including random and fixed effects with standard priors were the best model to fit the data based on deviance. The odds of surviving from cholera outbreak disease for inpatient status are 0.609 times less than the outpatient status. CONCLUSIONS: The authors conclude that the fitted latent Gaussian models indicate the predictor variables; admission status, aged between 15 and 44, another sick person in a family, dehydration status, oral rehydration salt, intravenous, and antibiotics were significantly associated with cholera outbreak disease. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-022-14153-1.
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spelling pubmed-94870652022-09-21 Determining factors associated with cholera disease in Ethiopia using Bayesian hierarchical modeling Letta, Tsigereda Tilahun Belay, Denekew Bitew Ali, Endale Alemayehu BMC Public Health Research BACKGROUND: Cholera is a diarrheal disease caused by infection of the intestine with the gram-negative bacteria Vibrio cholera. It is caused by the ingestion of food or water and infected all age groups. This study aimed at identifying risk factors associated with cholera disease in Ethiopia using the Bayesian hierarchical model. METHODS: The study was conducted in Ethiopia across regions and this study used secondary data obtained from the Ethiopian public health institute. Latent Gaussian models were used in this study; which is a group of models that contains most statistical models used in practice. The posterior marginal distribution of the Latent Gaussian models with different priors is determined by R-Integrated Nested Laplace Approximation. RESULTS: There were 2790 cholera patients in Ethiopia across the regions. There were 81.61% of patients are survived from cholera outbreak disease and the rest 18.39% have died. There was 39% variation across the region in Ethiopia. Latent Gaussian models including random and fixed effects with standard priors were the best model to fit the data based on deviance. The odds of surviving from cholera outbreak disease for inpatient status are 0.609 times less than the outpatient status. CONCLUSIONS: The authors conclude that the fitted latent Gaussian models indicate the predictor variables; admission status, aged between 15 and 44, another sick person in a family, dehydration status, oral rehydration salt, intravenous, and antibiotics were significantly associated with cholera outbreak disease. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-022-14153-1. BioMed Central 2022-09-20 /pmc/articles/PMC9487065/ /pubmed/36123680 http://dx.doi.org/10.1186/s12889-022-14153-1 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
Letta, Tsigereda Tilahun
Belay, Denekew Bitew
Ali, Endale Alemayehu
Determining factors associated with cholera disease in Ethiopia using Bayesian hierarchical modeling
title Determining factors associated with cholera disease in Ethiopia using Bayesian hierarchical modeling
title_full Determining factors associated with cholera disease in Ethiopia using Bayesian hierarchical modeling
title_fullStr Determining factors associated with cholera disease in Ethiopia using Bayesian hierarchical modeling
title_full_unstemmed Determining factors associated with cholera disease in Ethiopia using Bayesian hierarchical modeling
title_short Determining factors associated with cholera disease in Ethiopia using Bayesian hierarchical modeling
title_sort determining factors associated with cholera disease in ethiopia using bayesian hierarchical modeling
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9487065/
https://www.ncbi.nlm.nih.gov/pubmed/36123680
http://dx.doi.org/10.1186/s12889-022-14153-1
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