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Objective: The availability of complete and accurate crash injury data is critical to prevention and intervention efforts. Relying solely on hospital discharge data or police crash reports may result in a biased undercount of injuries. Linking hospital data with crash reports may allow for a more robust identification of injuries and an understanding of which populations may be missed in an analysis of one source. We used the New Jersey Safety and Health Outcomes (NJ-SHO) data warehouse to examine the share of the entire crash-injured population identified in each of the two data sources, overall and by age, race/ethnicity, sex, injury severity, and road user type., Methods: We utilized 2016-2017 data from the NJ-SHO warehouse. We identified crash-involved individuals in hospital discharge data by applying the ICD-10-CM external cause of injury matrix. Among crash-involved individuals, we identified those with injury- or pain-related diagnosis codes as being injured. We also identified crash-involved individuals via crash report data and identified injuries using the KABCO scale. We jointly examined the two sources; injuries in the hospital discharge data were documented as being related to the same crash as injuries found in the crash report data if the date of the crash report preceded the date of hospital admission by no more than two days., Results: In total, there were 262,338 crash-involved individuals with a documented injury in the hospital discharge data or on the crash report during the study period; 168,874 had an injury according to hospital discharge data, and 164,158 had an injury in crash report data. Only 70,694 (26.9%) had an injury in both sources. We observed differences by age, race/ethnicity, injury severity, and road user type: hospital discharge data captured a larger share of those ages 65+, those who were Black or Hispanic, those with higher severity injuries, and those who were bicyclists or motorcyclists., Conclusions: Each data source in isolation captures approximately two-thirds of the entire crash-injured population; one source alone misses approximately one-third of injured individuals. Each source undercounts people in certain groups, so relying on one source alone may not allow for tailored prevention and intervention efforts.

Objective: Our objective was to describe child passenger restraint use in police reported crashes by key child and driver characteristics., Methods: We used data from 2017-2019 police reported crashes in New Jersey to identify child passengers who: (1) were less than 13 years of age, (2) were in an identified seating location in the first, second, or third vehicle row, and (3) had a known restraint status at the time of the crash. We described prevalence of child restraint use by key child and driver characteristics (child: age, sex, seating position, and crash-reported injury status; driver: age, sex, restraint use, evidenced alcohol use, and crash fault). We included 108,780 crash-involved child passengers in our analytic sample., Results: A small proportion of child passengers were unrestrained at the time of the crash. Most child passengers <2 years were restrained in a rear-facing restraint (59.7%). However, a sizeable proportion were either forward-facing (26.7%) or belted (11.3%). Use of booster seats among passengers age 5 to 8 years was limited. We observed few fatalities, with most children noted to have no apparent injury (89.8%). Among children with serious, minor, and possible injuries, the greatest proportion of injured children were restrained by the vehicle belt. Regarding driver characteristics, slightly more than half of child passengers riding in vehicles driven by drivers aged 21-34 years were restrained in either rear- or forward-facing restraints (53.0%), whereas the majority of children riding with younger (<21 years) or older (>34 years) drivers were restrained with the vehicle belt. Among unrestrained drivers, drivers with evidence of alcohol use, and drivers at fault for the crash, a larger proportion of children were unrestrained compared to drivers who were restrained, had no evidence of alcohol use, and were not at fault., Conclusions: While most child passengers were restrained at the time a crash, optimal age-based restraint use was inconsistent, particularly for the youngest child passengers. A sizeable proportion of drivers in this study failed to adhere to best practice recommendations for child restraint use and New Jersey child passenger restraint laws. This was particularly true for drivers who engaged in unsafe driving behaviors, for whom a larger proportion child passengers were unrestrained at the time of the crash. Across all injury categories, the greatest proportion of injured children were restrained by the vehicle belt rather than a child restraint system, suggesting a continued need to understand specific patterns of injuries to inform possible mitigation efforts. Future work to identify intervention opportunities for optimal child restraint practices for drivers is essential to enhancing child passenger safety.

Objective: Racial and ethnic disparities and/or inequities have been documented in traffic safety research. However, race/ethnicity data are often not captured in population-level traffic safety databases, limiting the field's ability to comprehensively study racial/ethnic differences in transportation outcomes, as well as our ability to mitigate them. To overcome this limitation, we explored the utility of estimating race and ethnicity for drivers in the New Jersey Safety and Health Outcomes (NJ-SHO) data warehouse using the Bayesian Improved Surname Geocoding (BISG) algorithm. In addition, we summarize important recommendations established to guide researchers developing and implementing racial and ethnic disparity research., Methods: We applied BISG to estimate population-level race/ethnicity for New Jersey drivers in 2017 and evaluated the concordance between reported values available in integrated administrative sources (e.g., hospital records) and BISG probability distributions using an area under the receiver operator curve (AUC) within each race/ethnicity category. Overall AUC was calculated by weighting each AUC value by the population count in each reported category. In an exemplar analysis using 2017 crash data, we conducted an analysis of average monthly police-reported crash rates in 2017 by race/ethnicity using the NJ-SHO and BISG sets of race/ethnicity values to compare their outputs., Results: We found excellent or outstanding concordance (AUC ≥0.86) between reported race/ethnicity and BISG probabilities for White, Hispanic, Black, and Asian/Pacific Islander drivers. We found poor concordance for American Indian/Alaskan Native drivers (AUC= 0.65), and concordance was no better than random assignment for Multiracial drivers (AUC = 0.52). Among White, Hispanic, Asian/Pacific Islander, and American Indian/Alaskan native drivers, monthly crash rates calculated using both NJ-SHO reported race/ethnicity values and BISG probabilities were similar. Monthly crash rates differed by 11% for Black drivers, and by more than 200% for Multiracial drivers., Conclusion: Findings of excellent or outstanding concordance between and mostly similar crash rates derived from reported race/ethnicity and BISG probabilities for White, Hispanic, Black, and Asian/Pacific Islander drivers (98.9% of all drivers in this sample) demonstrate the potential utility of BISG in enabling research on transportation disparities and inequities. Concordance between race/ethnicity values were not acceptable for American Indian/Alaskan Native and Multiracial drivers, which is similar to previous applications and evaluations of BISG. Future work is needed to determine the extent to which BISG may be applied to traffic safety contexts.