New Publication: Racial and Ethnic Disparities in Non-Fatal Self-Inflicted Firearm Injuries

Abstract

Nonfatal self-inflicted firearm injuries represent an important and understudied public health concern in the United States. We analyzed nationally representative data from the National Electronic Injury Surveillance System–Firearm Injury Surveillance Study (NEISS-FISS), 2000–2021, to characterize nonfatal self-inflicted firearm injuries among U.S. adults aged 18–64 years. Emergency department (ED) narratives were combined using text mining and an elastic net (LASSO) classification model to augment structured surveillance data. Among 7,362 unweighted cases (representing approximately 260,602 ED visits, 58% were classified as probable unintentional discharges, whereas approximately 10% were probable suicide attempts. Among cases with complete race and ethnicity data, Non-Hispanic White (NHW; 69.2%), Non-Hispanic Black (NHB; 23.2%), and Hispanic (7.6%) individuals comprised the primary analytic groups. Narrative analysis substantially increased detection of contextual factors, particularly alcohol involvement among Hispanic patients (12.0% to 20.1%). NHB patients had the highest proportion of lower-extremity injuries (51%), while Hispanic patients had the highest prevalence of craniofacial injuries (26%). The LASSO model effectively distinguished terms associated with probable suicide attempts from unintentional firearm discharges based on narrative language. Findings highlight limitations of structured intent coding within NEISS-FISS and demonstrate the value of narrative for improving characterization of firearm injury circumstances, revealing distinct racial and ethnic group patterns, to inform more tailored prevention strategies.

Plain-Language Summary

This study analyzes nonfatal, self-inflicted firearm injuries in the United States over a 22-year period (2000-2021) using the National Electronic Injury Surveillance System (NEISS-FISS). Because structured database codes often fail to capture the context or intent behind a self-inflicted discharge, the research team used text mining and a machine learning (LASSO) classification model to analyze the unstructured clinical narratives written by emergency department staff.

The results show that while 58% of visits were likely unintentional discharges (accidents), 10% were probable suicide attempts. The narrative text mining revealed distinct, racially disparate patterns of injury: Non-Hispanic Black patients suffered the highest proportion of lower-extremity injuries (51%), while Hispanic patients had the highest rates of craniofacial injuries (26%) and a significant association with alcohol involvement (increasing from 12% in coded data to 20% in narrative data). This highlights the value of using natural language processing to analyze emergency department notes for better injury prevention and public health surveillance.