Risk factors for assaultive reinjury and death following a nonfatal firearm assault injury: A population-based retrospective cohort study
Abstract
Individuals with a firearm injury are at high risk of subsequent firearm victimization, but characteristics associated with sustaining recurrent firearm injuries are not well understood. In this retrospective cohort study, we sought to quantify the hazards of sustaining subsequent assaultive firearm injuries among people with an initial firearm assault injury and to identify characteristics associated with recurrent victimization. Using hospital discharge, emergency department, and mortality records, we identified and followed all individuals aged ≥15 years with a nonfatal firearm assault injury resulting in an emergency department visit or hospital admission in California, 2005-2013. We model transitions from one injury to the next and from injury to death, accounting for event history, covariates, and competing risks using multistate models. 29,156 people had an index nonfatal firearm assault injury. Among individuals with 1 such injury, 3.1% had additional nonfatal firearm assault injuries and 1.0% subsequently died from firearm homicide. Among individuals with 2+ nonfatal firearm assaults, 2.0% died from firearm homicide. The estimated transition probability for 1 to 2+ nonfatal injuries reached 10% by 8.5 years post-index injury. The rate of subsequent nonfatal firearm assault injury was highest among men (hazard ratio [HR]: 3.87; 95% confidence interval [CI]: 2.63-5.69) and Blacks (vs. whites) (HR: 2.69; 95% CI: 1.99-3.64). Identification of additional risk markers will…
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The comprehensive and representative sample may account for the higher recurrent injury observed in this study compared to others reporting recurrent firearm injury ranging from one to 10% across a variety of clinical and geographic settings and time periods.
“This may account for the higher recurrent injury observed in this study compared to others reporting recurrent firearm injury ranging from one to 10% across a variety of clinical and geographic settings and time periods.15,18,33-44”
From Incidence of and Factors Associated With Recurrent Firearm Injury Among Patients Presenting to St. Louis Trauma Centers, 2010 to 2019 (2023) · cites this paper as [18]
Racial disparities are evident in the study cohort.
“Racial disparities are evident in our cohort, as observed in other local and national studies.”
From Incidence of and Factors Associated With Recurrent Firearm Injury Among Patients Presenting to St. Louis Trauma Centers, 2010 to 2019 (2023) · cites this paper as [18]
Among the four studies that conducted a survival analysis, cumulative incidence for recurrent firearm injuries ranged from approximately 2% after five years to 16% after 10 years.
“Among the four studies that conducted a survival analysis, cumulative incidence for recurrent firearm injuries ranged from approximately 2% after five years to 16% after 10 years.18,35,42,44”
From Incidence of and Factors Associated With Recurrent Firearm Injury Among Patients Presenting to St. Louis Trauma Centers, 2010 to 2019 (2023) · cites this paper as [18]
Advanced computational techniques, such as ML and natural language processing, are promising ways to address diagnostic classification problems where issues with ICD coding persist.
“Advanced computational techniques, such as ML and natural language processing (NLP), are promising ways to address diagnostic classification problems where issues with ICD coding persist.11,15”
From Machine learning classification of new firearm injury encounters in the St Louis region: 2010-2020 (2024) · cites this paper as [11]
ML has been used to classify new firearm injury encounters by our team and by one other published study.
“Machine learning (ML) has also been used to classify new firearm injury encounters by our team, and we are aware of one other published study that has also used ML to classify new firearm injury encounters.11,12”
From Machine learning classification of new firearm injury encounters in the St Louis region: 2010-2020 (2024) · cites this paper as [11]
The results of the primary outcome align with the work of Pear et al., who also reported a high AUC using an ML model for classifying firearm injury visit type.
“The results of our primary outcome align with the work of Pear et al., who also reported a high AUC using an ML model for classifying firearm injury visit type.11”
From Machine learning classification of new firearm injury encounters in the St Louis region: 2010-2020 (2024) · cites this paper as [11]
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- Authors
- Pear, V. A., McCort, C. D., Kravitz-Wirtz, N., Shev, A. B., Rowhani-Rahbar, A., & Wintemute, G. J.
- Affiliations
- University of California System (US)
- Corresponding author
- Pear, Veronica A.
- Venue
- Preventive Medicine
- Publisher
- Elsevier BV
- Published
- 2020-10
- Volume
- 139
- Pages
- 106198
- ISSN
- 0091-7435 (ISSN-L)
- OpenAlex
- W3040803118
- Cited by
- 42
- References
- 25
- Citations by year
- 2026: 6 · 2025: 3 · 2024: 3 · 2023: 6 · 2022: 23 · 2020: 1
- FWCI
- 4.15
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Preventive Medicine (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Heising-Simons Foundation
- California Wellness Foundation
- Topics
- Social Sciences › Health › Gun Ownership and Violence Research
- Social Sciences › Sociology and Political Science › Homicide, Infanticide, and Child Abuse
- Social Sciences › Sociology and Political Science › Crime Patterns and Interventions
- Keywords
- injury recurrence, firearm homicide, multi-state models, competing risks, uninsured status, racial disparities, sex differences, California
- MeSH terms
- Reinjuries, Firearms, Humans, Male, Retrospective Studies, Risk Factors, Wounds, Gunshot, Cohort Studies, Crime Victims
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