Citation

Yasmin S, Eluru N, Pinjari AR. Anal. Meth. Accid. Res. 2015; 7: 1-15.

Copyright

(Copyright © 2015, Elsevier Publishing)

DOI

10.1016/j.amar.2015.03.001

PMID

unavailable

Abstract

In the United States, safety researchers have focused on examining fatal crashes (involving at least one fatally injured vehicle occupant) by using Fatality Analysis Reporting System (FARS) dataset. FARS database compiles crashes if at least one person involved in the crash dies within 30 consecutive days from the time of crash along with the exact timeline of the fatal occurrence. Previous studies using FARS dataset offer many useful insights on what factors affect crash related fatality, particularly in the context of fatal vs. non-fatal injury categorization. However, there is one aspect of fatal crashes that has received scarce attention in the traditional safety analysis. The studies that dichotomize crashes into fatal vs. non-fatal groups assume that all fatal crashes in the FARS dataset are similar. Keeping all else same, a fatal crash that results in an immediate fatality is clearly much more severe than another crash that leads to fatality after several days. Our study contributes to continuing research on fatal crashes. Specifically, rather than homogenizing all fatal crashes as the same, our study analyzes the fatal injury from a new perspective by examining fatality as a continuous spectrum based on survival time ranging from dying within 30 days of crash to dying instantly (as reported in the FARS data). The fatality continuum is represented as a discrete ordered dependent variable and analyzed using the mixed generalized ordered logit (MGOL) model. By doing so, we expect to provide a more accurate estimation of critical crash attributes that contribute to death. In modeling the discretized fatality timeline, the Emergency Medical Service (EMS) response time variable is an important determinant. However, it is possible that the EMS response time and fatality timeline are influenced by the same set of observed and unobserved factors, generating endogeneity in the outcome variable of interest. Hence, we propose to estimate a two equation model that comprises of a regression equation for EMS response time and MGOL for fatality continuum with residuals from the EMS model to correct for endogeneity bias on the effect of exogenous factors on the timeline of death. Such research attempts are useful in determining what factors affect the time between crash occurrence and time of death so that safety measures can be implemented to prolong survival. The model estimates are augmented by conducting elasticity analysis to highlight the important factors affecting time-to-death process.

Language: en