The study on Quality of life in the cities of both developing and developed countries is gaining interest from a variety of disciplines and is becoming an important tool for policy evaluation, rating of cities, urban planning and management. Cities are the center of economy, politics, commerce and other activities, so it is necessary to analyze the conditions that contribute to the quality of urban life. This study is on urban quality of life of the residents in Hawassa city and its main purpose is to identify the factors that may affect the quality of life of Hawassa residents. For the study a cross sectional data from 570 heads of household which were selected based on stratified random sampling by making the seven sub cities in Hawassa as stratum was collected. Statistical methods such as descriptive statistics, factor analysis and binary logistic regression are used to analyze the data in the study. The principal component analysis revealed that six factors (dimensions) of quality of life were extracted from twenty subjective attributes and all of the factor scores are positively and significantly related to quality of life. Factor analysis also extracts six factors using fifteen objective attributes. Housing, length of residence, economic status, distance from educational center and religious place all have statistically significant impact on people's quality of life in Hawassa. But access to public service is not significant predictor of quality of life of the residents in Hawassa. Housing, economic condition, environment, neighborhood safety and security, social connectedness and quality of public service are identified as dimensions of subjective quality of life of the residents in Hawassa. The paper also conclude that socioeconomic affairs, access to public service, access to education, housing, access to religious place and length of residency are found to be the dimensions of the objective quality of life of the residents in Hawassa.

9013 under-five children were included for study. Descriptive Statistics and POM were employed identify socio-economic, demographic, and proximate factors of underweight to children among regions of Ethiopia. SAS version 9.4 was used. About 25.3% of children in Ethiopia were underweighted. POM showed that sex, residence, size at birth, age group, incidence of diarrhea, educational level, breastfeeding status, mothers’ employment status, and birth order were found to be significant factors of underweight. Improvement of education for caring child in appropriate age needed. Personal hygiene be improved to prevent exposures to diarrhea and environmental sanitation.

The purpose of this study is to assess the employee turnover rate and cause in the case of Moret and Jiru Wereda. In light of this objective the study adopted quantitative and qualitative method of research approaches to test a series research hypothesis and subjective analysis respectively. Specifically, the study used survey of questionnaire analysis of Moret and Jiru Wereda. 326 respondents were selected based on stratified random sampling method. Data was then analyzed on quantitative basis using binary logistic regression and descriptive statistics; however, qualitative basis was used to subjective data. The result of this paper indicates that among the respondents 39.9 % of them do not want to quit their current job but majority of them (60.1%) said they seriously consider quitting their job very often. And furthermore the logistic regression found that Age of respondent, income of the respondents, experience of the respondents, educational level, satisfaction and incentive to be significantly related with turnover intension of the respondents and other factors such as gender, working hour and stress are found to be insignificant predictors of the turnover intension of the employees.

Background Trachoma is a serious health problem in the world's poorest countries, such as Ethiopia. The WHO aims to eliminate trachoma by 2030 through implementation of annual mass drug administration and other strategies. With almost eight years left, is Ethiopia on track to reach the goal? This research article attempts to model the time to stop MDA in one of the nations with the highest prevalence oftrachoma in the world. Objectives Evaluation of predictive analytic models (Cox proportional-hazards model and the random survival forest) to model the time to stop trachoma mass drug administration in persistence districts of Ethiopia and identify factors that accelerate or decelerate time to stop trachoma mass drug administration Materials and Methods We propose survival and machine learning models to predict the time needed to stop trachoma MDA in Ethiopia using secondary data from the Tropical Data Platform and the Trachoma Elimination Monitor Form. The impact of average mass drug administration coverage, improved latrine coverage, access to improved water, delay in MDA intervention, TF prevalence in thefirst Trachoma Impact Survey (TIS1),and MDA omission were also assessed. Results The result shows that the probability of districts reaching the 5% threshold varies by region, and there are also discrepancies between districts that have delayed MDA and those that have not. We also note the significant effects of MDA coverage, latrine coverage, access to water supply, initial TIS score, and MDAomission on current TF score. Ourmodel also predicts that under the existing scenarios, there are districts that will not meet the 2030 goal of eliminating trachoma. Conclusions In order to stop trachoma MDA or eliminate the infection efficiently and effectively, it is crucial to identify the appropriate efficacy of drug, quality of MDA coverage, frequency, timing and number of rounds of MDA. Additionally, increase environmental and hygienic conditions may accelerate progress towards 2030 goals.