No abstract

This paper is a country case study for the Universal Health Coverage Collection, organized by WHO. Abebe Alebachew and colleagues illustrate progress towards UHC and its monitoring and evaluation in Ethiopia. Please see later in the article for the Editors' Summary

Background: Continued investment, especially from domestic financing, is needed for Ethiopia to achieve universal health coverage and a sustainable health system over time. Understanding costs of providing health services will assist the government to mobilize adequate resources for health, and to understand future costs of changes in quality of care, service provision scope, and potential decline in external resources. This study assessed costs per unit of service output, "unit costs", for government primary hospitals and health centers, and disease-specific services within each facility. Methods: Quantitative and qualitative data were collected from 25 primary hospitals and 47 health centers across eight of the eleven regions of Ethiopia for 2013/14, and 2014/15 and 2015/16 but only for primary hospitals, and supplemented by other related health and financial institutions records. A top-down costing approach was used to estimate unit costs for each facility by departmentinpatient, outpatient, maternal and child health, and delivery. A mixed-method approach was used for the disease-specific unit costs exempt from fees.

Women have always faced a number of disadvantageous gaps in the labour market; the status of women at the labour markets throughout the world has not substantially narrowed gender gaps in the workplace. Many women in developing countries are domestic workers or informal factory workers, while others are unpaid workers in family enterprises and family farms. Agriculture is the primary sector of women's employment; Sub-Saharan Africa is among regions with the highest proportion of women employment in the agriculture sector. This research was conducted on 274 sampled households with the objective to determine the factors associated with women's employment status and to examine whether the estimated parameters for logistic regression model adopting Bayesian and maximum likelihood estimation approaches are similar or not. The research revealed that about 144 (52.6%) of sampled women were unemployed that is, they were not involved in any activity for earning during the data collection. The inferential analysis using both Bayesian and Maximum likelihood estimation schemes indicated that, pregnancy, age, education level, husband/partner occupation, marital status, family size, training opportunity and a child less than 5 years old had statistically significant (p < 0.05) effect on employment status of women. The maximum likelihood estimates and Bayesian estimates with non-informative prior do not have considerable difference.

Instructors’ publication (IP) is one of the major activity in higher education institutes. Currently, IP faced problem both high prevalence and severity in Ethiopia public universities. Even if the problem is common to both developed and developing countries, about 352 (73.9 %) of the instructors employed by public universities in Ethiopia have been affected by a lack of scholarly publications. Since the outcomes from IP factors are mostly discrete variable; they are often modelled using advanced count regression models. The purpose of this study was to model the appropriate count regression model that efficiently fit the IP data and further to identify the key risk factors contributing significantly to IP in public Universities in Ethiopia. The data were collected between November 2015 through November 2016 from selected thirteen (13) public universities in Ethiopia through both questionnaires and interview. The cross-sectional study design was employed using IP data. A simple random sampling technique was applied to the population of Ethiopia public universities to obtain a sample of 13 universities or 476 individual instructors were selected. The average age of the 476 participants was found to be 30 years with 31(6.5%) being females and 445(93.5%) being males. The count outcomes obtained were modelled using count regression models which included Zero-Inflated Negative Binomial (ZINB), Zero-Inflated Poisson (ZIP) and Poisson Hurdle regression models. To compare the performance and the efficiency of the listed count regression models concerning the IP data, the various model selection methods such as the Vuong Statistic (V) and Akaike’s Information Criterion (AIC) were used. The ZINB count regression model concerning the values of the Vuong Statistic and AIC was selected as the most appropriate and efficient count regression model for modelling IP data. Based on the ZINB model the variables age, experience, average work-load, association member and motivation to work were statistically significant risk factors contributing to IP in Ethiopia public universities.