A new study from the Violence Prevention Research Program (VPRP) at UC Davis suggests machine learning, a type of artificial intelligence, may help identify handgun purchasers who are at high risk of suicide. It also identified individual and community characteristics that are predictive of firearm suicide. The study was published in JAMA Network Open.

Previous research has shown the risk of suicide is particularly high immediately after purchase, suggesting that acquisition itself is an indicator of elevated suicide risk.

Risk factors identified by the algorithm to be predictive of firearm suicide included:

  • older age
  • first-time firearm purchaser
  • white race
  • living in close proximity to the gun dealer
  • purchasing a revolver

While limiting access to firearms among individuals at increased risk for suicide presents a critical opportunity to save lives, accurately identifying those at risk remains a key challenge. Our results suggest the potential utility of handgun records in identifying high-risk individuals to aid suicide prevention,” said Hannah S. Laqueur, an assistant professor in the Department of Emergency Medicine and lead author of the study.

In 2020, almost 48,000 Americans died by suicide, of which more than 24,000 were firearm suicides. Firearms are by far the most lethal method of suicide. Access to firearms has been identified as a major risk factor for suicideand is a potential focus for suicide prevention.

Methodology

To see if an algorithm could identify gun purchasers at risk of firearm suicide, the researchers looked at data from almost five million firearm transactions from the California Dealer Record of Sale database (DROS). The records, which spanned from 1996 to 2015, represented almost two million individuals. They also looked at firearm suicide data from California death records between 1996 and 2016.

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