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.

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Adverse pregnancy outcome is a complex outcome of pregnancy other than the normal live birth. It lead to serious health consequences to the mother or the baby. It also can be still major public health and socioeconomic status problems in developing countries where most pregnancies are unplanned, complications. There is disparity of adverse pregnancy outcomes rate from region to region in Ethiopia. Objectives: The main objectives of the study were to identify the important determinant of adverse pregnancy outcomes in Ethiopia. With this study the multilevel logistic regression models were used to explore the major risk factors and regional variations. Different stages of multilevel models like intercept model and slope model were employed to attain the goal of the study. The results indicated that out of 15683 reproductive age of women, 8412 (86.8%) not experiencing adverse pregnancy outcome while 1282 (13.2%) of women have experienced adverse pregnancy outcome at the time of the survey. From multilevel logistic regression, it was found that the random intercept model provided the best fit for the data under consideration. All the fitted models gave the same conclusion that, Age of mother, place of residence, antenatal care visit and delivery place, Parity, Education of mother, Marital status, Anemia level were found to be statistically significant. Conclusion:The random intercept multilevel model provided the best fit for the data under consideration. Furthermore, it is found that not having Antenatal care, residing in rural area, working occupational status, being anemic, increased educational level, never married, divorced, or separated marital status, being in age group of 15-24 or >35 years are associated with increased risk of adverse pregnancy outcome among reproductive age group women in Ethiopia.

Introduction: HIV is a virus that causes Acquired Immunodeficiency Syndrome (AIDS) by reducing a person's ability to fight the infection. It attacks an immune cell called the CD4 cell which is responsible for the body's immune response to infectious agents. Now a days anti retro viral therapy treatment is avail to elongate the life of patients. The treatment is given for patients to increase the CD4 counts of patients to keep the ability of body preventing the disease. Objectives: This study was aimed to identify the potential associated risk factors with CD4 counts of patients under ART treatment at public hospital in Ethiopia. The other was to fit linear mixed model by handling missing value of the data during follow up time. Method: To see the structure of the data, exploratory data analysis was conducted. Of the familiar variance structures, unstructured variance covariance is selected to be best and to fit the data under study, step-by-step procedure was passed to obtain best model. Results: The descriptive statistics directed that the progressive change in CD4 counts of females seems better than that of males. On the other hand, the output of the fitted model indicated that covariates significant with 5% level of significance is that baseline CD4, time, weight and interaction of Sex, baseline CD4 with time. Allowing the significance level to increase to 25% increases most covariates to be significant that help patients in a better awareness. Conclusion: With this result, full linear mixed with random intercept and slop is found to best model. There was high variability within patients over time and between patients and the interaction of time with covariates was also significant. Generally, the data was fitted by handling the missing value using multiple imputation technique.