Purpose. This research study on automobile collision in Virginia amongst fifteen to nineteen (15-19) year olds looked into the trend analysis over a five (5) year period of 2000 to 2004. Trend analysis is usually done for aggregates of all injuries—either intentional or unintentional injuries, or both. The primary objective of this research study was to examine the trend in hospitalization rates and mortality rates for males and females independently. It further looked into the trend, if any, in hospitalization rates, mortality rates, and case-fatality rates, for both males and females combined. The different Tables illustrate the extent and the impact of automobile collision in terms of demographics and characteristics of hospitalizations, types of hospitalizations, hospitalization rates, mortality rates and case-fatality rates among this age group.Methods: An investigation was carried out in a case control manner of 2353 cases using data from the Virginia Department of Health-Division of Injury Prevention & Violence on automobile collision amongst 15-19 year olds, from 2000 to 2004, a (5) five year period. Hospitalization data were obtained from Virginia Health Information, coded in line with International Classification of Diseases, 9th revision (ICD-9); external cause of injury (E)-codes. Mortalityldeath rates and case fatality rates were calculated using U.S. Census Bureau, Census 2000 for Virginia's population data. Frequency distribution analysis was done with SPSS 14.0, data entry using M.S. Excel, while rate ratio and confidence intervals for hospitalization rates, mortality rates were calculated. Linear trend was analyzed for hospitalization rates, mortality rates and case-fatality rates, using Chi square statistics test for significance. Geographical Information System (GIs) methods were used to display counties in Virginia.Results: Out of 2353 cases of automobile collision in Virginia, amongst 15-19 year olds, from 2000 to 2004, the demographic did not changed much. Males were fairly distributed over the five year period, while automobile collision characteristics showed that 2142 cases (91%) were more likely to be hospitalized on an emergency basis, with males having a higher percentage, fifty-nine (59%) percent, and forty (40%)percent for females. (Table 1 & 2). The hospitalization rates were higher for males than females, with rate ratio (RR>1) greater than one over the five years of study (Table 3). Mortality rates showed increase rates for males, over the five year of study (RR>1.5) (Table 4). Test for linear trend in hospitalization rates (Chi. Sq.=14.127, p-value ≤ 0.001) were significant for both males and females. Mortality rates test for trend were also significant for both males and females. (Chi Sq. = 377.0, p-value ≤ 0.001). Case-fatality rates trend test were significant for both males and females. (Chi sq. = 11.580, p value ≤ 0.001). The trend in hospitalization, mortality and case-fatality rates, showed a decrease over the five year of study.Conclusion: Given the impact of injuries in ,the U.S., mainly automobile collisions, it is socially beneficial to continue research, intervention and prevention programs in this area, particularly directed and targeted to this population - Healthy People 2010 objectives.

The driving risk of novice teenagers is the highest during the initial period after licensure but decreases rapidly. This dissertation develops recurrent-event change-point models to detect the time when driving risk decreases significantly for novice teenager drivers. The dissertation consists of three major parts: the first part applies recurrent-event change-point models with identical change-points for all subjects; the second part proposes models to allow change-points to vary among drivers by a hierarchical Bayesian finite mixture model; the third part develops a non-parametric Bayesian model with a Dirichlet process prior. In the first part, two recurrent-event change-point models to detect the time of change in driving risks are developed. The models are based on a non-homogeneous Poisson process with piecewise constant intensity functions. It is shown that the change-points only occur at the event times and the maximum likelihood estimators are consistent. The proposed models are applied to the Naturalistic Teenage Driving Study, which continuously recorded textit{in situ} driving behaviour of 42 novice teenage drivers for the first 18 months after licensure using sophisticated in-vehicle instrumentation. The results indicate that crash and near-crash rate decreases significantly after 73 hours of independent driving after licensure. The models in part one assume identical change-points for all drivers. However, several studies showed that different patterns of risk change over time might exist among the teenagers, which implies that the change-points might not be identical among drivers. In the second part, change-points are allowed to vary among drivers by a hierarchical Bayesian finite mixture model, considering that clusters exist among the teenagers. The prior for mixture proportions is a Dirichlet distribution and a Markov chain Monte Carlo algorithm is developed to sample from the posterior distributions. DIC is used to determine the best number of clusters. Based on the simulation study, the model gives fine results under different scenarios. For the Naturalist Teenage Driving Study data, three clusters exist among the teenagers: the change-points are 52.30, 108.99 and 150.20 hours of driving after first licensure correspondingly for the three clusters; the intensity rates increase for the first cluster while decrease for other two clusters; the change-point of the first cluster is the earliest and the average intensity rate is the highest. In the second part, model selection is conducted to determine the number of clusters. An alternative is the Bayesian non-parametric approach. In the third part, a Dirichlet process Mixture Model is proposed, where the change-points are assigned a Dirichlet process prior. A Markov chain Monte Carlo algorithm is developed to sample from the posterior distributions. Automatic clustering is expected based on change-points without specifying the number of latent clusters. Based on the Dirichlet process mixture model, three clusters exist among the teenage drivers for the Naturalistic Teenage Driving Study. The change-points of the three clusters are 96.31, 163.83, and 279.19 hours. The results provide critical information for safety education, safety countermeasure development, and Graduated Driver Licensing policy making. / Ph. D.