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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.

Background: Diabetes Mellitus (DM) is a chronic, progressive disease characterized by elevated levels of blood glucose. Despite the fact that most international association/organization gave attention toward diabetes control and prevention by healthy professional, still diabetes and its complication such as cardio vascular, blood vessels, eyes, kidneys and nerves become a major cause of premature death and disability across the world. The overall aim of this study is assessing fasting blood sugar variation over time and its determinant among diabetic patients. Methods: Data were obtained from Adama Hospital Medical College diabetic patients who have been active in the follow-up treatment from September 1, 2018 to August 30, 2019. The data consists of basic demographic and clinical characteristics of 312 DM patients were selected using simple random sampling techniques and of whom 177 were males and the rest 135 were females. The linear mixed effect model for longitudinal data analysis was used by taking the correlation between Fasting Blood Sugar (FBS) level of patients into account. Linear mixed model, random intercept and slope models were used for feting the data. Results: The results from the linear mixed model with unstructured co-variance structure showed that for one-month change in time decreases log FBS level by 0.0111267 mg/dl. While a unit increase in Body Mass Index (BMI) of a patient on treatment, the log FBS level was increased by 0.0434 mg/dl. Similarly, a unit increase in Diastolic Blood Pressure (DBP), the log FBS level was increased by 0.0004749 mg/dl. Being tertiary and secondary level of education decreases logFBS level by 0.0058844 and 0.0055161 respectively compared with patients with no education.Conclusion: Age, Educational status, Dietary type, Drug type, History of hypertension, BMI, DBP, Time, interaction effect of Age, history of hypertension, Dietary type, other comorbidity at baseline with time were the significant determinant for the change in mean FBS level of the diabetes patients over time. Based on the findings of our study and WHO recommendation, maintaining of healthy body weight, by taking healthy diet along with lower blood glucose level is essential to control blood sugar in body and to prevent long term complication.

Objective: The aim of this study was to evaluate the change in fasting blood sugar (FBS) over time and its determinants in diabetic patients.Methods: A longitudinal data analysis retrospective-based study was considered with a sample of 312 patients, and the linear mixed effect model was applied.Results: Based on the linear mixed model, the 3-month change in time decreases the average FBS level by 0.0111. An increase of one unit of body mass index (BMI) increases the FBS level by 0.0434. Similarly, an increase in blood pressure (DBP) per unit increased the average log FBS level by 0.0005. Secondary and higher education levels lower log FBS levels by 99.41% and 99.45%, respectively, compared with noneducated individuals. Conclusion:The study showed that hypertension history, type of diet, age, status of education, type of drug, body mass index, diastolic blood pressure, and time were statistically significant factors.Implications: According to the study, eating a healthy diet, maintaining a healthy body weight, and a low blood sugar level are essential to controlling blood sugar and preventing long-term complications. The government should build an educational institution proportional to the population and open programs to increase awareness about the prevention mechanism of diabetes in communities.diabetes mellitus, fasting blood sugar, linear mixed model, longitudinal data analysis | INTRODUCTIONDiabetes mellitus (DM) is a lifelong disease that affects human health whether the pancreas does not produce enough insulin (the hormone that controls blood sugar), or the body cannot use insulin effectively. 1 DM is a chronic and progressive disease with an increase in blood sugar levels. 2 Diabetes can affect many parts of the body, including blood vessels, eyes, the heart, kidneys, and nerves. This also increases the overall risk of dying. [1][2][3][4] The typical signs of diabetes disease include inflated urine, water thirst, persistent hunger, loss of weight, changes in vision, and fatigue. 5 Diabetic diseases are divided into three main categories according to their origin and cause: gestational diabetes, Type 2 diabetes, and Type 1 diabetes. 6 When the body does not produce the necessary insulin, Type 1 diabetes develops, on the other hand, Type 1 diabetes develops when the body produces an elevated level of blood sugar or sugar due to errors in insulin synthesis or inadequate insulin. 6 Unlike the above two, gestational diabetes is a disease of

Background Agriculture is a critical source of food and income, making it a key component of initiatives aimed at reducing poverty and ensuring food security across the globe. It is the backbone of Ethiopia's economy, contributing significantly to the country's financial development. The sector earns 88.8 percent of trade profit and contributes 36.7 percent of GDP. The purpose of this paper was to identify the homogeneous and heterogeneous effects of agricultural inputs on crop productivity of the three-grain crop types in Ethiopia. Method The central statistical agency (CSA) provided the data for this study, which covered the entire country from 1990 to 2012 Ethiopian Calendar (E.C). Crop productivity, which is assessed in kilograms per hectare for cereal, pulse, and oil crops, was utilized as the response variable. For three-grain crop types from 1990 to 2012 E.C, the study used the pooled mean group estimate method, which allows for long-run homogeneity effects across cross-sections as well as short-run heterogeneity. Results In the long run, the study found that a one percent increase in fertilizer consumption resulted in a 2.686 percent increase in grain crop productivity in Ethiopia, while a one percent increase in improved seed per hectare and land size, resulted in a 48.31 percent and 10.58 percent increase in grain crop productivity per crop category respectively. Short-run productivity for grain crops increased by 30.29 percent as the amount of improved seed value at one period lag value of commercial farm holders is increased by one percent. In the same way, when the arable land at the first difference is increased by one percent then the productivity of grain crops increased by 40.61 percent. Conclusion The findings of this research showed that in the long run, fertilizer consumption, amount of improved seed use, and arable land area size had homogeneous significant contributions, while in the short run, agricultural inputs like the use of pesticides and improved seed use at first lagged value had heterogeneous significant contributions to grain crop productivity improvement across all cross-sectional units.