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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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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2022
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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. |
format | Online Article Text |
id | pubmed-9487065 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
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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