Browsing by Author "Tefera, Worku (PhD)"

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    Assessment of Willingness to Pay for Improved Sanitation in Slum Area of Addis Ababa
    (Addis Ababa University, 2016-06) Kefale, Higu; Tefera, Worku (PhD)
    Background: Understanding demand for improved sanitation in the local context is critical if sanitation facilities are to be continually used. The drivers of demand for sanitation are different for different slum areas, so context specific study is better to know the real factors and demand for program design and implementation. People‟s willingness to invest a part of their meager resource in maintaining the sanitation facility is taken as an indication that they value the service and are therefore committed to keep it in good working order. Objective: To measure level of willingness to pay and identify factors affecting willingness to pay for improved sanitation in slum areas of Addis Ababa. Methods: This willingness to pay assessment has been conducted based on cross sectional study quantitative design. The study has been conducted in slum area of Addis Ababa. Dichotomous Choice Contingent Valuation Method (DC-CVM) i.e. double bound have been used for the assessment of willingness to pay. The analyses have been done using logistic and ordinal logistic regression models. Result: Totally 404 households were interviewed from March to April study period. Approximately 88% of respondents are willing to pay less than 1000 birr for hand washing facility installation and 7% are willing to pay in between range from 1000 birr to 1500 birr and only 5% are willing to pay more than 1500 birr. For communal toilet renovation, 94.6% are willing to pay less than 600 birr, 5.2% are willing to pay from 600 to 1132 birr and none of them are willing to pay more than 1132 birr in 1 year.The mean willingness to pay for communal toilet was 212 birr per year and the maximum mean willingness to pay for hand washing facility installation was 508.5 birr. Households who were unsatisfied on existing sanitation were more likely to pay for improved sanitation (AOR 2.85, CI 1.05-7.72). Conclusion: The study has found the community was willing to pay 2.12% of their disposable income for improved sanitation. Among the factors that affect willingness to pay , past sanitation expenditure amount, knowledge on health effect of poor sanitation, knowledge on type of sanitation, attitude on sanitation, number of households sharing the toilet, monthly saving and number of years lived in the present house significantly correlates with amount of willingness to pay.
  • No Thumbnail Available
    Item
    Predicting the Pattern of Under-Five Mortality in Ethiopia Using Data Mining Technology: The Case of Butajira Rural Health Program
    (Addis Ababa University, 2012-06) Tekabe, Be’emnetu; Jemaneh, Getachew (PhD); Tefera, Worku (PhD)
    Introduction: The under-five deaths in Ethiopia represent 48% of all mortality. More than half of the under-five deaths occurred during the first year of life, and 53% of these before 2 months of age. Data mining is a collection of techniques for efficient automated discovery of previously unknown, valid, novel, useful and understandable patterns in large databases. Objective: The main objective of this study is to explore the potential applicability of data mining to predict the determinants, levels and pattern of under-five mortality in Ethiopia, particularly for the Butajira rural health program sites. This can greatly support for policy makers, planners, and healthcare providers working on the control of under-five children mortality in Ethiopia. Methods and Material: The methodology used for this research was a hybrid six-step Cios Knowledge Discovery Process. The required data was collected from Butajira rural health program database covering the period 1987-2008. The researcher used two popular data mining algorithms (C4.5 J48 Decision Trees and Naïve Bayes Classifier) to develop the predictive model using a larger dataset (11,600 cases). The researcher also used a 10-fold cross validation and 90% split test mode for data mining methods of the two predictive models for performance comparison purposes. Results: The results indicated that the decision tree (J48 algorithm) is the best predictor with pruned parameter of the tree of 90% split test mode; it has 97.49% accuracy on the holdout dataset (this predictive accuracy is better than any reported in the literature), Naïve Bayes Classifier came out to be the second with supervised discretization has 96.67% accuracy. Conclusion: The results from this study were very capable and confirmed the belief that applying data mining techniques could indeed support a predictive model building task that predicts the pattern of under-five mortality in Ethiopia; particularly for Butajira rural health program sites are possible. In the future, more classification studies by using a possible large amount of Butajira rural health program demographic and surveillance sites dataset records with epidemiological information and employing other classification algorithms, tools and techniques could yield better results.