Firearm violence and associated factors among young adults presenting to emergency departments in three cities: Baseline results from Project SPARK

Objective

Recent shifts in U.S. violence dynamics call for updated violence epidemiology among general emergency department (ED) samples of young adults. Using baseline data from a multi-site longitudinal study of firearm violence prediction, we describe violence rates and associated factors.

Methods

Staff approached age 18–24 entrants to Level-1 trauma centers in three cities (Flint, Seattle, Philadelphia; 7/2021–5/2023). Consenting participants completed a survey including validated measures of violence experience, firearm-related behaviors, substance use, mental health symptoms, peer/parental/familial behaviors, community violence, and attitudes/norms. We described the sample and examined factors associated with firearm assault (victimization/aggression, including threats).

Results

Across sites, 1506 participants enrolled (41.7. % Black; 33.6 % White; 61.4. % female). Half of participants self-reported past-six-month violent victimization and/or aggression; non-partner violence, and violent victimization were most common. Over half of participants self-reported high-risk substance use, and over half screened positive for post-traumatic stress disorder, depression, and/or anxiety. About 14.4 % self-reported past-six-month firearm assault, and 5.9 % self-reported firearm violence (excluding threats). Adjusted analysis showed community violence exposure was most strongly associated with firearm assault; each one-point-increase corresponded to a 13.7 % (95 %CI: 10.4 %–16.9 %) absolute increase in firearm assault prevalence. Drug misuse, mental health symptoms, firearm carrying, retaliatory attitudes, prosocial attitudes, and family conflict were also associated with firearm assault.

Conclusions

Violence, including firearm assault, is common among young adults entering urban EDs, and is associated with several psychosocial factors. High rates of substance use and mental health symptoms underscore this as a high-need population. Leveraging this information could help tailor interventions and optimize resource allocation.