Using Integrated Data to Examine Characteristics Related to Pedestrian Injuries

Harmon, Katherine J; Sandt, Laura; Hancock, Kari; Rodgman, Eric; Thomas, Libby · 2021 · ROSA P / Collaborative Sciences Center for Road Safety

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Summary

This study addresses the rising trend of pedestrian fatalities and serious injuries in the United States by examining characteristics associated with injury severity using integrated data. Motivated by the limitations of existing data sources—where police crash reports lack clinical detail and emergency department (ED) records lack crash context—the researchers aimed to link these datasets to provide a comprehensive view of pedestrian motor vehicle crashes. The study specifically sought to identify person, crash, environment, roadway, and vehicle factors associated with serious pedestrian injuries, defined by clinical metrics rather than police-assigned severity scores. The researchers analyzed five years of population-based data from North Carolina (October 1, 2010 – September 30, 2015). They linked police-reported pedestrian crash data with NC DETECT emergency department visit data using hierarchical deterministic methods. Approximately 50% of crash records were successfully linked, resulting in a study population of 6,923 injured pedestrians. The study employed descriptive epidemiologic analysis and multivariate logistic regression to examine predictors of serious injury. Variables included pedestrian demographics (age, gender, race, comorbidities), driver characteristics, crash circumstances (time, light, alcohol use, crash type), roadway features, and vehicle types. Injury severity was categorized using clinical diagnoses, including traumatic brain injuries, rather than the standard KABCO police scale. The findings identified several significant factors associated with increased pedestrian injury severity. Pedestrian age, gender, race/Hispanic ethnicity, and comorbidities were significant predictors. Driver characteristics, including age and gender, also influenced outcomes. Crash-related factors such as hour of day, suspected alcohol use by either the pedestrian or driver, ambient light levels, and specific crash types were linked to severity. Roadway characteristics, particularly intersection-relatedness, and vehicle type were also significant. The analysis distinguished between roadway and non-roadway collisions, revealing distinct risk profiles for each. Additionally, the integrated data allowed for a detailed classification of injury nature and location, highlighting the prevalence of specific injuries like traumatic brain injuries. The significance of this study lies in its demonstration that linking crash and health outcome data provides a more accurate and detailed understanding of pedestrian injuries than either source alone. By moving beyond police-assigned severity scores to clinical metrics, the research offers a more valid assessment of injury outcomes. The findings underscore the complex interplay of demographic, behavioral, and environmental factors in pedestrian safety. This approach supports the development of targeted safety interventions and highlights the need for continued data integration efforts to address the increasing morbidity and mortality among pedestrians, particularly as vehicle sizes and speeds continue to change.

Key finding

Pedestrian injury severity is significantly associated with a complex set of factors including pedestrian demographics, driver characteristics, crash circumstances, and environmental conditions, as determined through the integration of crash and clinical data.

Methodology

dataset

Sample size: 6923

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (6 acquisition events logged).

Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

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• demographic disparities
• vru crash typology
• incidence prevalence
• fatality injury trends

Information type

What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).

• Empirical Findings: crash risk outcomes