AMERIKA
Using machine learning, study developed models to predict high-risk gun dealers
Results can help target enforcement to disrupt flow of firearms used in crimes
American Society of Criminology
Federally licensed firearm dealers are an important target of regulatory and enforcement efforts aimed at reducing the supply of firearms diverted to the illicit market, but the extent of dealers’ involvement in the illegal diversion of firearms is hard to measure.
In a new study using machine learning, researchers examined firearm transaction and crime gun recovery records from California from 2010 to 2021 to identify dealers selling the highest number and largest proportion of guns recovered in crimes within a year of sale, a well-established indicator of possible illegal activity by dealers or traffickers. The prediction models have the potential to support targeted enforcement, helping disrupt the flow of firearms to offenders.
The study, by researchers at the University of California (UC), Davis, appears in Criminology & Public Policy, a publication of the American Society of Criminology.
“Although most gun offenders do not get their firearms directly from licensed dealers and most dealers abide by laws, even a few negligent or corrupt dealers can contribute significantly to the supply of firearms used in crimes,” explains Hannah Laqueur, associate professor of emergency medicine at UC Davis, who led the study.
Firearms dealers can facilitate the diversion of guns to the criminal market through practices such as selling to straw purchasers or failing to conduct required background checks. Prior studies have shown that enforcement actions and lawsuits targeting law-evading dealers can deter these behaviors and reduce the flow of firearms into criminal markets.
In this study, researchers used machine learning techniques to develop two prediction models. The first classifies dealer-years in the top 5% of one-year crime gun sales volume (the number of sales of guns recovered in crimes within a year of sale); the second identifies dealer years in the top 5% based on the fraction of sales recovered within a year. Both models had strong discriminative performance, with the first model particularly effective at identifying the highest-risk dealers.
The models generally outperformed simpler regression and rule-based approaches, underscoring the value of data-adaptive models for prediction. Key predictors included prior-year crime gun sales, the average age of purchasers, the proportion of “cheap” handgun sales, and the local gun robbery and assault rate.
Many of the dealers with the highest predicted probabilities not only sold a large volume of guns with very short “time-to-crime” but also consistently sold crime guns over multiple years. This suggests that a relatively small group of dealers could be targeted for enforcement, offering the potential for outsized impact. More consistent and targeted inspections of high-risk dealers, along with citations or license revocations, could strengthen deterrence and promote compliance, helping reduce the supply of guns to offenders.
“Our findings show how machine learning techniques, combined with California’s comprehensive firearm transaction and crime gun recovery data, could help identify potentially high-risk retailers,” says Laqueur. “This type of identification can improve the efficiency and effectiveness of inspections and enforcement efforts aimed at interdicting negligent or corrupt dealers.”
Among the study’s limitations, the authors note that dealers selling many short time-to-crime guns may not have violated the law and conversely, non-compliant dealers may not be reflected in short time-to-crime statistics. They also point out that because California is a state with stringent gun laws and extensive dealer regulations, the number of negligent or law-evading dealers in the study may be lower than in states with fewer regulations. However, while the study’s findings are specific to California, consistency of risk factors across different jurisdictions and regulatory contexts suggests that the models could inform approaches in other states, the authors say.
The study was supported by the National Collaborative on Gun Violence Research.
Journal
Criminology & Public Policy
Article Title
Identifying high-risk firearms dealers: A machine learning study of rapidly diverted firearm sales in California
Article Publication Date
28-Jan-2025\
Standardizing provider assessments reveals important information about gun and opioid access for US veterans at risk of suicide
Study of nearly 39,000 health records is the first to examine access to firearms and opioids, and completion of related interventions, among veterans at risk for suicide receiving care at the VA
University of Pennsylvania School of Medicine
PHILADELPHIA—Standardizing an assessment process currently used by doctors during care discussions with veterans at risk for suicide in other context could shed more light on the risks related to firearms and opioids.
The findings from researchers at the Perelman School of Medicine at the University of Pennsylvania were reported today in JAMA Network Open. They found that fewer veterans reported having access to firearms than expected—either because some didn't mention it to their doctor, it wasn't recorded by the provider, or because the true prevalence is lower among this high-risk group.
The research was led by Gabriela Khazanov, PhD, a research associate with the Penn Center for Mental Health and research psychologist with the Corporal Michael J. Crescenz VA Medical Center (VA).
“Veterans have high rates of firearm ownership but may not always share this with their provider as part of suicide safety planning,” Khazanov said. “Some may worry, incorrectly, that their firearms would be confiscated, or their care would be impacted in some way, highlighting the importance of explaining the rationale for these discussions and describing any potential consequences, however unlikely, of firearm disclosure.”
Among veterans, firearm injury accounts for 72 percent of suicides and poisoning, typically by overdose, accounts for 8 percent of suicides among veterans, respectively, with suicides due to opioid overdose nearly tripling over the last 20 years.
The team reviewed health records of 38,454 veterans at risk for suicide receiving suicide safety plans in the VA—a brief, evidence-based intervention that helps patients identify strategies to prevent or de-escalate suicidal crises. One-third of veterans with access to firearms reported storing at least one firearm insecurely and only one-third of veterans with access to opioids accepted naloxone, an overdose-reversing drug. In addition, researchers found that only 5 percent of veterans included reported having access to opioids.
Doctors did report addressing firearm safety with 98 percent of veterans with access to firearms and discussing overdose risks with 79 percent of veterans with access to opioids.
“Veterans may have underreported access to opioids when discussing higher suicide risk due to concerns that their access to prescribed opioids would be limited,” Khazanov said.
Researchers noted that while the study’s relative size was a strength, further study and discussion is necessary on socioeconomic and demographic differences when leading these discussions.
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Penn Medicine is one of the world’s leading academic medical centers, dedicated to the related missions of medical education, biomedical research, excellence in patient care, and community service. The organization consists of the University of Pennsylvania Health System and Penn’s Raymond and Ruth Perelman School of Medicine, founded in 1765 as the nation’s first medical school.
The Perelman School of Medicine is consistently among the nation's top recipients of funding from the National Institutes of Health, with $550 million awarded in the 2022 fiscal year. Home to a proud history of “firsts” in medicine, Penn Medicine teams have pioneered discoveries and innovations that have shaped modern medicine, including recent breakthroughs such as CAR T cell therapy for cancer and the mRNA technology used in COVID-19 vaccines.
The University of Pennsylvania Health System’s patient care facilities stretch from the Susquehanna River in Pennsylvania to the New Jersey shore. These include the Hospital of the University of Pennsylvania, Penn Presbyterian Medical Center, Chester County Hospital, Lancaster General Health, Penn Medicine Princeton Health, and Pennsylvania Hospital—the nation’s first hospital, founded in 1751. Additional facilities and enterprises include GSPP Rehabilitation, Penn Medicine at Home, Lancaster Behavioral Health Hospital, and Princeton House Behavioral Health, among others.
Penn Medicine is an $11.1 billion enterprise powered by more than 49,000 talented faculty and staff.
Journal
JAMA Network Open
Article Title
Access to Firearms and Opioids Among Veterans at Risk for Suicide
Article Publication Date
28-Jan-2025
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