DOI: 10.3886/icpsr39169
title: Firearm Purchase Behavior and Subsequent Adverse Events, United States, 1985-2018
title: Archival Version

description: The primary purpose of this project was to develop and test a new large-scale approach to threat assessment that relies on objective data regarding firearm purchases. Specifically, to analyze firearm transaction records in California to better understand the firearm purchasing patterns of mass shooters and perpetrators of firearm-related crimes and to build risk prediction models to help identify individuals who might be at extreme risk for committing such crimes in the future.
The objectives of the work included:

Objective 1: To determine whether there were unusual pre-event firearm purchasing patterns among known mass shooters with a record of purchase in California (1985-2018), as compared to the general population of registered firearm purchasers in the state of California.
Objective 2. To determine whether perpetrators of homicide, robbery, or aggravated assault in California with a record of purchase (1985-2018) have distinct pre-event firearm purchasing patterns as compared to the general population of registered firearm purchasers in the state.
Objective 3: To use machine learning methods to forecast who is at an elevated risk of involvement in a mass shooting based on features generated from firearm transaction records, along with criminal history records, and other key individual and community characteristics available in the administrative data.
Objective 4: To use the same machine learning methods and predictor variables to forecast who is at elevated risk of involvement in other serious firearm violence (homicide, robbery, or aggravated assault with a firearm).
description: <strong>Mass shooter analyses</strong>

A. Case-control analysis:
Researchers conducted a case-control analysis with a study population of a total of 22 individuals from California who perpetrated an attack between 1996 and 2018 and had a record of transaction in the state's DOJ Dealer Record of Sale database. Researchers used risk-set sampling to select controls (individuals with purchasing records in DROS who did not perpetrate a mass shooting) at a ratio of 1:15, and matched cases and controls based on gender and age, and compared purchasing behaviors and other relevant risk factors using conditional logistic regression.
 B. Mixed Model examining legal and illegal transactions:
In the second analysis of mass shooters, the sample comprised all identified California mass and active shooters between 1985 and 2018 (n = 55), irrespective of whether they had a record of transaction in California's Dealer Record of Sale (DROS) database. Researchers implemented a mixed model to evaluate factors associated with firearms acquired in close temporal proximity to the attack, including firearms acquired through unauthorized means. Unauthorized acquisitions included theft, acquisition through an unlicensed dealer, home manufacture, and straw purchasing.

<strong>Perpetrators of other firearm-related crime and major violent crime</strong>

 A. Machine Learning Analysis:
Criminal history information, legal handgun purchasing trends, and purchaser demographics were used as input features to predict time to arrest. Features in the model were included as one of four types: constant, the time since an event, characteristics of the most recent purchase or arrest, and lifetime purchase and arrest characteristics. Criminal history features included the cumulative number of times a purchaser had been arrested at a given date for felonies and for misdemeanors, the years since the purchaser was last arrested; using 58 categories of crime defined by the California department of justice, which category of crime did the most recent arrest corresponds to; and indicators denoting if a purchaser has ever been arrested for each of the 58 crime categories. All criminal history features were time dependent. Handgun purchasing trends were captured with features including the total number of handguns purchased at a given date, years since last legal handgun purchase, and characteristics of the last legal handgun purchase such as the type of handgun (single shot, semi-automatic, revolver, derringer or other); caliber, categorized into small, medium, and large; median cost of a handgun from a manufacturer, categorized into two groups; the type of retailer (licensed dealer, private party, pawn shop, or other; and if the handgun was purchased at a gun show. Finally, demographic features included the purchasers age, race, and gender. Researchers used a gradient boosting machine (GBM) with a Cox proportional hazards loss function to predict risk of arrest for violent crime. 
 B. Case-control Analysis:
For the case-control analyses, individuals entered the cohort at the time of their first purchase and are considered at risk until December 31, 2021, their death from any cause, or if they could no longer be identified as a resident of California. To ensure that researchers had complete legal handgun purchasing records for individuals from the age at which they were legally eligible to purchase (age 21), individuals were enrolled based on age, over a twenty-four-year period (1996-2020): those with a record of purchase who were age 21 in 1996, individuals aged 21-22 in 1997, individuals aged 21-23 in 1997, and so on, up to individuals aged 21-45 in 2020. Though this approach sacrifices the study of older purchasers, given our study focus on interpersonal violence and the well-established finding that criminal risk peaks in the early to mid-twenties and declines significantly with age, are primary interest was in younger individuals. Importantly, they did not enroll or match on age at time of entry into the study population. That is, an individual who was 21 in 1996 could have, for example, purchased their first handgun in 2010 and entered the cohort at age 35. Researchers used incidence density sampling to select 10 controls from DROS who were still at-risk at the time of the case's arrest. Under this sampling approach, controls may be randomly selected as controls more than once, and a person selected as control may later become a case the odds ratio provides an estimate of the rate ratio for the full cohort. Cases and controls were matched on gender for statistical efficiency. Also, the criminal history arrest data was included. These variables included arrests for a violent crime involving a firearm (pre-purchase), violent crime not involving a firearm, non-violent misuse of a firearm, property crime, drug or alcohol related crime, and other crime.
description: For the study of mass shooters, researchers did not have sufficient sample size to implement a machine learning approach or a multivariate model. Instead, they compared mass shooters to non-mass shooter purchasers using univariate analyses, and used incidence density sampling to match mass and active shooters to other purchasers and compared the two groups using conditional logistic regressions.
description: Datasets: 
DS1: Firearm Purchase Behavior and Subsequent Adverse Events, United States, 1985-2018
description: mixed mode