Citation

Betz ME, Boggs JM, Goss FR. JAMA Netw. Open 2022; 5(7): e2223758.

Copyright

(Copyright © 2022, American Medical Association)

DOI

10.1001/jamanetworkopen.2022.23758

PMID

35816308

Abstract

Suicide remains a leading cause of death in the United States, with half of all suicide deaths by firearm. Reducing firearm access during times of suicide risk is a recommended approach for suicide prevention, but identification of those at risk remains difficult. Many who die by firearm suicide may not self-disclose or seek help from a medical professional. Laqueur et al1 look instead to the question of whether firearm suicide risk might be predicted--and those at risk helped--at or around the time of firearm purchase. Using California's database of nearly 5 million handgun purchases, they applied machine learning techniques to predict subsequent firearm suicide deaths among purchasers. They found that firearm suicide occurred within a year for only 0.066% of handgun transactions, but 40% of suicides were among those with the highest risk score. Factors associated with subsequent firearm suicide death included older age at first firearm purchase, month of purchase, and shorter distance between home and point of firearm sale.

Machine learning models can provide insights where clinical trials or meta-analyses may be lacking. Laqueur et al1 chose a supervised machine learning approach using a random forest model that has previously been applied in prior risk prediction of suicide or suicide behavior.2 Benefits of such models are that they provide both classification and regression, helping identify those at risk while also showing important variables.3 The broad number of predictor variables here, including transaction data and purchaser demographic characteristics and geocoding, provide a rich number of features to evaluate. However, transaction history or death records may not provide the full story or context, and adding other data sources--mental health disorders, substance abuse, hospitalizations, or natural language processing of clinical notes--could prove valuable and improve risk prediction.4 Lastly, consideration of time frame and risk of suicide may be relevant to the type of intervention recommended...

Language: en