| dc.description.abstract | World Health Organization reports that about 1.24 million people die annually on the world’s roads, with 20–50 million sustaining non-fatal injuries. More than 85% (1.05 million) of the global deaths due to injuries occur in the developing world.
Statistical techniques known and applied to model these scenarios are limited to basic statistics such as linear and Poisson regression that do not account for over dispersion. Further, Road Traffic Accident data violates assumptions that standard Poisson regression models is based. Appropriate extensions of this model, even though available, are rarely used by most applied statisticians.
Data was collected from Zambia Police, Traffic Section on accidents that occurred on the Great North Road (GNR) highway between Lusaka and Kapiri-Mposhi in Zambia from January 1, 2010 to December 31, 2016. Results from standard Poisson regression were compared to those obtained using the Negative Binomial (NB), Zero-Truncated Negative Binomial (ZTNB) and the Zero-Truncated Poisson (ZTP) regression models. Diagnostic tests were used to determine the best fit model. The data was analysed using STATA software, version 14.0 SE (Stata Corporation, College Station, TX, USA).
A total of 1, 023 Road Traffic Accidents (RTAs) were analysed in which 1, 212 people died. Of these deaths, 82 (7%) were Juveniles and 1, 130 (93%) were adults. Cause of accident such as pedestrians crossing the road accounted for 30% (310/1,023) while 29% (295/1,023) were as a result of driver’s excessive speed. The study revealed that driving in the early hours of the day as compared to driving in the night had a significant increase in the incidence rate of death from RTAs, Incident Rate Ratio (IRR) of 2.1, (95% CI={1.01-4.41}), p-value=0.048, further, public transport as compared to private transport had an increased incidence of death from RTAs (IRR=5.65, 95% CI={2.97-10.73}), p-value<0.0001.
There is a reduced incidence of dying if one is using a private vehicle as compared to a public vehicle or a truck. Driving in the early hours of the day (between 1AM and 7AM) had an increased incidence of death from RTAs. This study suggests that when dealing with counts in which there are a few zeros observed such as in serious and fatal RTAs, ZTNB fits the data well as compared to other models.
Key words: Road Traffic Accidents, Poisson, Zero Truncated Poisson, Negative Binomial, Zero Truncated Negative Binomial, Number of deaths. | en |
