Racial and Ethnic Disparities in Non-Fatal Self-Inflicted Firearm Injuries Among Adults in the United States, 2000-2021

Wed, 17 Jun 2026 00:00:00 +0000

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.

NEISS-FISS | ISSUES | The Ohio State University

Racial and Ethnic Disparities in Non-Fatal Self-Inflicted Firearm Injuries Among Adults in the United States, 2000-2021

Plain-Language Summary