Hurdle negative binomial model for motor vehicle crash injuries in Namibia select="/dri:document/dri:meta/dri:pageMeta/dri:metadata[@element='title']/node()"/>

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Show simple item record Shiyuka, Ndilimeke 2018-05-23T06:59:08Z 2018-05-23T06:59:08Z 2018
dc.description A thesis submitted in partial fulfillment of the requirements for the Degree of Master of Science in Applied Statistics and Demography en_US
dc.description.abstract Injuries caused by Motor Vehicle crashes were ranked 10th among the leading causes of death and 9th among the leading cause of disability worldwide (World Health Organization, 2013). In developing countries 90% of disabilities are caused by road traffic crashes. In Namibia, The Motor Vehicle Accident Fund spends approximately N$ 22,6 million monthly towards medical expenses of people injured in crashes (Tjihenuna, 2015), causing a concern, to the victims, their families and affects the country’s economy at large. Efforts have been put in place by stakeholders to reduce road crashes, injuries and fatalities, despite these efforts the trend of crashes keeps on increasing yearly. The objectives of the study were to explore generalised linear models to establish risk factors associated with road traffic injuries in Namibia and develop strategies to guide policy on reduction of road traffic injuries. The study was based on a quantitative cross sectional research design for all road crash injuries recorded from 2011 -2016 of secondary data from the MVA Fund database (21869), with number of injured persons per crash as the dependent variable. Using the MASS and pscl packages in R version 3.3.2, six Generalised linear were explored: Poisson, Negative binomial, Zero inflated Poisson, Zero- inflated Negative Binomial, the Hurdle Poisson and the Hurdle negative binomial. The Akaike Information Criterion (AIC) and the Vuong’s test showed that the Hurdle Negative Binomial was the best. The probability of road traffic injuries (RTI) was inferred by the following variables: the crash type with vehicle to vehicle (OR=0.5, p<0.001), Cause of crash with driver behaviour (OR=0.1, p<0.001), fatalities with deaths (OR= -1.8, p<0.001) and Time of crash with peak time (OR=0.3, p<0.001) being higher probability. The intensity of RTI was inferred by: Months of crashes with holiday month (RR=0.2, p<0.001) Day of crash with Weekend (RR =0.1, p<0.001) Region with Northern regions (RR= 0.3, p<0.001), Type of crash with vehicle by vehicle (RR =1.6, p<0,001), Crash cause with driver behaviour (RR= 0.2,p<0.001), fatalities with deaths (RR = 1.0, p<0.001) number of vehicles with single vehicle (RR= -0.2, p<0.001) having higher probabilities when compared to others. This thesis suggests that emphasis should be placed on driver behaviour, since it is evident that a higher risk of injuries presents itself among crashes that occurred as a result of driver behaviour. Additionally, campaigns should focus more on school holiday months and weekends, due to the fact that a large number of crashes occur more on weekends and school holiday months. en_US
dc.language.iso en en_US
dc.publisher University of Namibia en_US
dc.subject Motor vehicle crash en_US
dc.subject Injuries en_US
dc.subject Binomial model en_US
dc.subject.lcsh Accidents, Statistics
dc.subject.lcsh Wounds and injuries, Statistics
dc.title Hurdle negative binomial model for motor vehicle crash injuries in Namibia en_US
dc.type Thesis en_US

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