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 These findings support a shift from siloed, behavior-focused injury prevention strategies toward integrated, system-level approaches consistent with the Safe System framework. Estimating conditional injury risk across combinations of People, Vehicle, Road, and Speed factors provides actionable evidence to inform transportation safety programs, Vision Zero initiatives, and policy interventions aimed at reducing serious injuries and fatalities.