Background Childhood under-nutrition is a major global health problem. Although the rate of under-nutrition in Ethiopia has declined in the last decade, but it still remains being the major causes of morbidity and mortality of children under-five years. The problem is even worse in rural areas. The prevalence of underweight among rural children was 25% compared with 13% among urban children. To alleviate this problem, it is necessary to determine the magnitude and determinants of underweight. The study models non-Gaussian data analysis to identify risk factors associated with underweight among under-five children in rural Ethiopia. Methodology The data source for this study was secondary data, which was retrieved from EDHS 2016 database. It was analyzed using two model families; one with marginal models (GEE and ALR) in which responses are modeled and marginalized overall other responses, and the other is random effects model (GLMM) which is useful when the interest of the analyst lies in the individual’s response profiles as well as to evaluate within and between regional variations of underweight. Result From fitting non-Gaussian data analysis to identify risk factors associated with underweight among under five children in rural Ethiopia, the independent variable which have significant effect on underweight were:—Age of child, birth interval, mothers education, fathers education, wealth index, diarrhea in last two weeks, fever in last two weeks are significant and also father’s work status shows that difference in significance among the category. Conclusion Child age, preceding birth interval, mother’s education, household’s wealth index, fever, diarrhea, father’s education and father’s work status were associated with child underweight. Furthermore, there is both within and between regional heterogeneity of underweight among children in rural Ethiopia. Therefore, rigorous community-based interventions (such as uplifting mother’s education by providing formal education and preventing infectious diseases that cause diarrhea and fever) should be developed and executed throughout the country to improve this grave situation of underweight prevalence in rural areas of Ethiopia.

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Background Mortality rate under the age of five is the proportion of deaths of children below the age of 5 years out of 1000 live births. It is related with the living standard of a population, and it is taken as one of the health and socioeconomic status deterioration index. Mortality rate under the age of five also indicates a poor quality life standards of a population. It is very significantly high in Sub-Saharan African countries. Ethiopia is one of these Sub-Saharan African countries where mortality rate under the age five is high. This research work aims to identify the determinants and associated factors of under-five mortality in Ethiopia. Methods The data for this paper were gathered from the EDHS 2016, collected by CSA. In this study, count family models such as Poisson, negative binomial, zero-inflated Poisson and zero-inflated negative binomial regression were applied for analyzing the data. Each of these count models were compared with different statistical tests like log-likelihood ratio test, Akaike information criteria, mean absolute difference, Vuong test and observed versus predicted probability plot. Results The study revealed that as mothers’ age at first birth increased by one unit, the average number of under-five mortality rate decreased by 2.69%. In the same way the number of under-five mortality of Afar, Benishangul Gumuz and Dire Dawa were 1.3446, 1.6429 and 1.3320 times more likely to Tigray respectively. The risk of under-five mortality for primary and secondary education level of the mother was 28.31 and 40.96% less likely than to mothers who have no education respectively. Conclusion From the result we found that, there were overabundance zeros and broad heterogeneity in the non-zero outcomes. Zero-inflated negative binomial regression model was found to best fit the data, and from the regression model, age of mothers at first birth, mother’s education level, place of residence and region were statistically significant factors of under-five mortality per mother.

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Background: Preeclampsia is a hypertensive disorder of pregnancy that affects 2-8% of pregnant women. It is the major cause of maternal and perinatal morbidity and mortality worldwide. The purpose of this study was to identify factors associated with hypertension measurements and time-to-onset of preeclampsia among pregnant women attending antenatal care service at Arerti Primary Hospital. Methodology: A retrospective longitudinal study design was employed on a total of 201 pregnant women attending the antenatal clinic of Arerti Primary Hospital between September 2018 and June 2019. A closed-form sample size formula for estimating the effect of the longitudinal data on time-to-event was used. To analyze our data we employed descriptive method, linear mixed effect model, Cox-PH model and joint models for longitudinal and survival outcomes.Relevantdemographicandclinicalcovariateswereincludedinsubmodels. Results: This study revealed that baseline age, visiting times, weight, diabetes, history of PE and parity had significantly associated with mean change in the BP measurements. From the Cox model result, age, weight, history of PE and marital status were associated with a significant hazard of developing preeclampsia. The univariate joint models reveal that the each longitudinal BP measurements are significantly associated with hazard of developing preeclampsia. Form the bi-ariate joint model; only DBP is significantly associated with risk of developing PE. Conclusion: As the result obtained in this study, we summarized that, age, weight, history of PE and marital status had a significant effect on time to developing preeclampsia. Furthermore, due to significance of association between the longitudinal BP measurements and time to onset of preeclampsia, joint model analysis was suggested as it incorporates all information simultaneously and provides valid and efficient inferences over separate models analysis.