Modeling injury severity among motor vehicle occupants using a safe system–aligned, population-based framework: evidence from Ohio crash data (2017–2023)

BACKGROUND: Motor vehicle crashes remain a leading cause of serious injury and death in the United States. Although individual crash risk factors are well documented, less is known about how multiple risk factors co-occur across system domains to influence injury severity. This study applies a Safe System framework to estimate the population-level probability of suspected serious injury (SSI) or fatality associated with combinations of crash-related factors spanning the domains of People, Vehicle, Road, and Speed using statewide Ohio crash data from 2017 to 2023. METHODS: Crash records from the Ohio Department of Public Safety were analyzed using multivariable generalized linear models. Person-, vehicle-, and crash-level variables were classified into four Safe System domains. Regression models were used to estimate adjusted odds and predicted probabilities of SSI or fatality associated with individual risk factors and combinations of co-occurring factors. Marginal effects were calculated to quantify changes in predicted risk across varying risk profiles. RESULTS: Behavioral factors, including driver impairment and lack of restraint use, were associated with the largest increases in predicted probability of serious or fatal injury. Vehicle factors, such as older model year, and roadway characteristics, including roadway departure and curved alignments, also contributed significantly to injury risk. The highest predicted probabilities of severe outcomes occurred when multiple risk factors were present simultaneously across Safe System domains, demonstrating the compounding nature of injury risk within real-world crash environments. CONCLUSIONS: Serious and fatal crash outcomes are driven by interactions among risk factors across People, Vehicle, Road, and Speed domains. Consistent with Safe System principles, the most severe outcomes arise when layers of protection fail simultaneously. By quantifying conditional probabilities for specific combinations of risk factors, this study provides a practical, scalable, and data-driven framework for identifying system vulnerabilities and prioritizing integrated countermeasures. Strategies that simultaneously address behavioral risks, modernize the vehicle fleet, improve roadway design, and manage travel speeds are likely to produce greater reductions in SSI and fatalities than single-domain approaches. These findings support a shift toward coordinated, multi-domain safety programs and equitable vehicle and infrastructure investments as the foundation for future transportation safety and Vision Zero efforts.