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LER60
Road traffic accidents are a major public health concern. In developing countries road traffic accidents are among the leading cause of death and injury. Ethiopia experiences the highest rate of such accidents in Sub-Saharan Africa. Out of all the accidents registered in Ethiopia, Addis Ababa accounts for 60% on average especially in Wolaita zone more 25% on average. The objective of this study is to identify factors that contribute to the occurrence of road traffic accidents leading to human injuries and death. For study purpose we employed ordinal logistic regression models to identify factors influencing traffic fatalities and injuries. Stratified sampling with proportional allocation and simple random sampling technique are used to select samples from recorded frame. The result of ordinal logistic regression analyses shows that drivers aged 18-30 years caused the largest number of accidents.
Bereket Tessema Zewude. 2016. \u201cStatistical Modeling on Determinants of Traffic Fatalities and Injuries in Wolaita Zone, Ethiopia\u201d. Global Journal of Human-Social Science - H: Interdisciplinary GJHSS-H Volume 16 (GJHSS Volume 16 Issue H4): .
Crossref Journal DOI 10.17406/GJHSS
Print ISSN 0975-587X
e-ISSN 2249-460X
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Total Score: 72
Country: Ethiopia
Subject: Global Journal of Human-Social Science - H: Interdisciplinary
Authors: Bereket Tessema Zewude, Kidus Meskele Ashine (PhD/Dr. count: 0)
View Count (all-time): 165
Total Views (Real + Logic): 3832
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Publish Date: 2016 11, Fri
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Road traffic accidents are a major public health concern. In developing countries road traffic accidents are among the leading cause of death and injury. Ethiopia experiences the highest rate of such accidents in Sub-Saharan Africa. Out of all the accidents registered in Ethiopia, Addis Ababa accounts for 60% on average especially in Wolaita zone more 25% on average. The objective of this study is to identify factors that contribute to the occurrence of road traffic accidents leading to human injuries and death. For study purpose we employed ordinal logistic regression models to identify factors influencing traffic fatalities and injuries. Stratified sampling with proportional allocation and simple random sampling technique are used to select samples from recorded frame. The result of ordinal logistic regression analyses shows that drivers aged 18-30 years caused the largest number of accidents.
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