Project Abstract Although analyses of crash data have advanced our understanding of motor vehicle crashes, the leading cause of death for US teens, the majority of studies have been plagued by an inability to account for “driving exposure”—that is, the extent to which drivers actually drive and thus are “at risk” for a crash. This means that researchers cannot calculate crash risk estimates that appropriately account for differences in time “at risk” between driver groups, for drivers subject to different traffic safety policies, or within individual drivers over time. The traffic safety field also does not yet have high-quality methods to estimate teen drivers’ population-level frequency of engagement in (i.e., their “exposure” to) high-risk driving behaviors such as driving at nighttime and seat belt nonuse. As a result, we are limited in our ability to identify specific high-risk populations or public health priorities for crash and injury mitigation. Thus, there is an urgent need to develop new, logistically and economically feasible methods that overcome these two critical gaps. The overall objective of this project is to extend and broaden the use of quasi-induced exposure (QIE) methods—a traffic safety method whose use has thus far been limited—to accomplish two Specific Aims. Aim 1 will establish a novel application of QIE to capture population-level frequency of (i.e., exposure to) behaviors that heighten crash risk (at night; with peer passengers; driving pre-license or while suspended) or crash injury (less safe vehicles; seat belt nonuse). To do so, we will conduct in-depth analyses of the New Jersey Traffic Safety Outcomes (NJ-TSO) data warehouse—a unique statewide data source of linked driver licensing and crash data. The project will capitalize on several rarely available and valuable data elements within the NJ-TSO—including exact date of and age at licensure, geocoded residential address, and race/ethnicity—to estimate and compare frequencies of engagement: (1) among demographic groups (e.g., by license age, sex, race/ethnicity); (2) by residential neighborhood; and (3) within drivers over calendar time, with increasing driving experience, and as they transition from intermediate (i.e., restricted) to unrestricted licensure. Aim 2 will develop a QIE-based approach to directly adjust comparisons of crash rates for driving exposure in population-based studies. We will then apply this method to determine whether observed increases in crash rates among teen drivers as they transition between licensing phases can be accounted for by underlying changes in driving exposure—providing important insight on the need for additional intervention. The proposed project will overcome current barriers in identifying high-risk populations and estimating valid crash rate ratios by establishing an innovative, vitally important method to do so that is cost-effective and highly generalizable. In addition, by using this approach to conduct novel analys...