Machine learning analysis of handgun transactions to predict firearm suicide risk

Machine learning analysis of handgun transactions to predict firearm suicide risk

  • by Laqueur, HS, Smirniotis, C, McCort, C, Wintemute, GJ

Quick Summary

  • This study suggests that handgun transaction information routinely collected in California can be used to create predictive algorithms of firearm suicide risk and inform targeted interventions.

Abstract

QUESTION

Can handgun purchasing records, coupled with machine learning techniques, be used to forecast firearm suicide risk?

FINDINGS

In this prognostic study of nearly 2 million individuals with handgun transaction records, among transactions classified in the riskiest 5%, close to 40% were associated with a purchaser who died by firearm suicide within 1 year. Among the small number of transactions with a random forest score of 0.95 and above, more than two-thirds were affiliated with a purchaser who died by firearm suicide within 1 year (24 of 35).

MEANING

This study suggests that passively collected administrative data on handgun transactions may be used to inform targeted interventions based on risk stratification.

Citation

Laqueur HS, Smirniotis C, McCort C, Wintemute GJ. Machine learning analysis of handgun transactions to predict firearm suicide risk. JAMA Network Open. 2022 Jul 11;5(7):e2221041.

Learn More

Primary Category

Using Data to Predict Violence

Secondary Categories

Risk and Protective Factors Firearm Access and Risk Suicide and Self Harm

Tags

Research