Predictors of Human Death by Road Traffic Crashes in Bahir Dar City, North Western Ethiopia; A Count Data Analysis Regression Model

Abstract

Road traffic crashes are a major socioeconomic and public health problem, affecting all people of the world and Ethiopia is a country with a very large number of traffic crashes and fatality rate. This study has major objective of assessing the predictors of road traffic accident in Bahir Dar city, Ethiopia and identifies factors that contribute to the occurrence of road traffic crashes that leads human death. Data regarding to the number of deaths per road traffic crash were obtained from Bahir Dar city administration traffic police office for a two year period from July 2015-June 2017. In this study we applied six count models namely Poisson, negative binomial, generalized Poisson, zero inflated Poisson, zero-inflated negative binomial and zero inflated generalized Poisson regression models. Based on different models comparison criteria, e.g. AIC, log likelihood and Vuong test ZIGP regression model provides more appropriate fit to the number of human death per road traffic crashes data considered in this study. Sex, age, driving under alcohol, fatigue, not give priority, days of weeks, road condition, overloading, over speeding, and type of accident were found to be statistically significant predictors of human death due to road traffic crash.