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.

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.

Introductions: Cholera is a diarrheal disease caused by infection of the intestine with the gram-negative bacteria Vibrio cholera. According to updated global burden of cholera estimate 2018 in Ethiopia 68,805,272 populations are at risk of cholera with incidence rate of 4 per 1000 population and case fatality of 3.8% estimated annual number of cases 275,221.Methods: The main objective of this study is to identify the significant risk factors of dehydration status of cholera outbreak in Oromia regional state of Ethiopia. Ordinal logistic regression was used to model the data by incorporating the assumption behind this novel model. Results: The results of the study indicated that of the total 965 cholera patients, most of them 560(58%) were severely dehydrated by cholera. The overall goodness of model (p-valu=0.07) shows that the model fits the data well. Besides, the proportional odds assumption also revealed that the slop coefficients in the model are the same across dehydration status (p-value=0.094). For those have history of travel, the odds of severely dehydrated versus the combined some dehydrated and no dehydrated was exp(1.133804)=3.11 times higher than those have no history of travel (p-value<0.01). All the other factors like history of contact with other patients, other sick patients in the family, Intravenous and Antibiotics drugs are statistically significant with 5% level of significance to determine the status of dehydration. Conclusions: The ordinal logistic regression was fitted the data well and most of the included factors were significant for the dehydration status of cholera outbreak.

Introductions: Cholera is a diarrheal disease caused by infection of the intestine with the gram-negative bacteria Vibrio cholera. According to updated global burden of cholera estimate 2019 in Ethiopia 68,805,272 populations are at risk of cholera with incidence rate of 4 per 1000 population and case fatality of 3.8% estimated annual number of cases 275,221.Methods: The main objective of this study is to identify the significant risk factors of dehydration status of cholera outbreak in Oromia regional state of Ethiopia. Ordinal logistic regression was used to model the data by incorporating the assumption behind this novel model. Results: The results of the study indicated that of the total 965 cholera patients, most of them 560(58%) were severely dehydrated by cholera. The overall goodness of model (p-valu=0.07) shows that the model fits the data well. Besides, the proportional odds assumption also revealed that the slop coefficients in the model are the same across dehydration status (p-value=0.094). For those have history of travel, the odds of severely dehydrated versus the combined some dehydrated and no dehydrated was exp(1.133804)=3.11 times higher than those have no history of travel (p-value<0.01). All the other factors like history of contact with other patients, other sick patients in the family, Intravenous and Antibiotics drugs are statistically significant with 5% level of significance to determine the status of dehydration. Conclusions: The ordinal logistic regression was fitted the data well and most of the included factors were significant for the dehydration status of cholera outbreak.