Purchaser, firearm, and retailer characteristics associated with crime gun recovery: a longitudinal analysis of firearms sold in California from 1996 to 2021.

BACKGROUND: Firearm violence is a major cause of death and injury in the United States. Tracking the movement of firearms from legal purchase to use in crimes can help inform prevention of firearm injuries and deaths. The last state-wide studies analyzing crime gun recoveries used data from over 20 years ago; thus, an update is needed. METHODS: We used data for 5,247,348 handgun and 2,868,713 long gun transactions and law enforcement recoveries from California crime gun recovery (2010-2021) and California's Dealer Records of Sales records. Covariates included characteristics of dealership sales, firearms and their transactions, and purchaser's demographic characteristics, purchasing history, criminal history (from firearm purchaser criminal history records), and neighborhood socioeconomic status. Analyses for handguns and long guns was conducted separately. In multivariable analysis, we included correlates into a Cox proportional hazard model accounting for left truncation and clustering between the same firearm, purchaser, dealerships, and geographic location. Covariates that remained significant (P < 0.05) were retained. For handguns, we evaluated associations of violent and weapons crimes separately. In supplementary analyses, we examined interactions by purchasers' race and ethnicity. RESULTS: In total, 38,441 handguns (0.80%) and 6,806 long guns (0.24%) were recovered in crimes. A firearm dealer's sales volume, percent of transactions that were denials, pawns, pawn redemptions, and firearms that became crime guns were each positively associated with firearm recovery in crime. Handguns that were inexpensive, larger caliber, and that had been reported lost or stolen were positively associated with recovery in crimes. Purchaser characteristics associated with crime gun recovery included: being younger, female, Black, Hispanic, Native American or Pacific Islander, or other race/ethnicity (vs white), having previous arrests, living in close proximity to the firearm dealership, and living in a more socially vulnerable census tract. Associations with race and ethnicity were modified by previous infraction-only arrests. CONCLUSIONS: This study confirms that many previously studied correlates of firearm recovery are still relevant today. We were able to expand on previous research by examining novel associations including purchasers' criminal history and previous firearm transaction history. These results provide evidence that can be used to disrupt firearm use in crimes.

Trends and Sources of Crime Guns in California: 2010-2021.

Firearm-related interpersonal violence is a leading cause of death and injury in cities across the United States, and understanding the movement of firearms from on-the-books sales to criminal end-user is critical to the formulation of gun violence prevention policy. In this study, we assemble a unique dataset that combines records for over 380,000 crime guns recovered by law enforcement in California (2010-2021), and more than 126,000 guns reported stolen, linked to in-state legal handgun transactions (1996-2021), to describe local and statewide crime gun trends and investigate several potentially important sources of guns to criminals, including privately manufactured firearms (PMFs), theft, and "dirty" dealers. We document a dramatic increase over the decade in firearms recovered shortly after purchase (7% were recovered within a year in 2010, up to 33% in 2021). This corresponds with a substantial rise in handgun purchasing over the decade, suggesting some fraction of newly and legally acquired firearms are likely diverted from the legal market for criminal use. We document the rapid growth of PMFs over the past 2-3 years and find theft plays some, though possibly diminishing, role as a crime gun source. Finally, we find evidence that some retailers contribute disproportionately to the supply of crime guns, though there appear to be fewer problematic dealers now than there were a decade ago. Overall, our study points to temporal shifts in the dynamics of criminal firearms commerce as well as significant city variation in the channels by which criminals acquire crime guns.

Crime and Building Rehabilitation or Demolition: A Dose-Response Analysis.

Thousands of buildings in Cleveland, Ohio were demolished or rehabilitated since the Great Recession in the 2000s. Recent evidence suggests removing vacant and decaying buildings reduces violent and firearm-involved crime. This study examines the dose-response relationship between demolitions, rehabilitations, and crime. We use Bayesian spatiotemporal models to estimate the association of interest for five types of crime outcomes: violent crimes, violent crimes involving a firearm, drug crimes, and crimes often associated with building vacancy. We estimate associations in quarterly time periods from 2012 through 2017 in 569 hexagons approximately the size of a neighborhood (2000 feet, approximately 610 m, in diameter), stratified by vacancy level. Across vacancy levels, the majority of our models do not identify statistically significant associations between demolition and rehabilitation dose and crime incidence. However, in some cases, we identify positive associations between demolition and crime. These associations generally appeared at higher levels of demolition (2 or 3 or more demolitions) in areas characterized by medium to high levels of vacancy. We also find that the presence of a property rehabilitation is associated with an increase in drug crimes in areas with medium levels of vacancy.

Effects of building demolitions on firearm violence in Detroit, Michigan.

Former industrial cities facing economic challenges and depopulation often experience high levels of firearm and other forms of violence. Within these cities, violent crime often clusters in neighborhoods affected by high levels of vacant and abandoned housing. This study estimates the effects of building demolition in Detroit, Michigan on the subsequent risk of violent crime using property-level data and longitudinal targeted maximum likelihood estimation. The primary outcome is violent Crime Index crimes (homicide, rape, robbery and aggravated assault). We estimate effects for this category of crimes as a whole and for the subset involving firearms. Drug and other lower-level crimes are included as secondary outcomes. We compare the risk of experiencing each crime type following building demolition in Census blocks and block groups to an estimate of the risk had there been no demolition in the 1-3 quarters prior in 2017. There were >2600 total demolitions in about 1700 blocks in 2017 in Detroit. Nearly all demolished buildings were sourced from tax foreclosures. Estimates suggest the risk for all crime types tested would have been statistically indistinguishable from the observed crime risk had demolitions in the prior 1-3 quarters of 2017 not occurred. Our results run counter to most previous research on this topic, which tends to show a protective effect of demolition on violent crime. Understanding why our results differ may provide important insights into the types of demolition programs with the greatest potential to reduce violent crime.

Machine Learning Analysis of Handgun Transactions to Predict Firearm Suicide Risk.

Importance: Evidence suggests that limiting access to firearms among individuals at high risk of suicide can be an effective means of suicide prevention, yet accurately identifying those at risk to intervene remains a key challenge. Firearm purchasing records may offer a large-scale and objective data source for the development of tools to predict firearm suicide risk. Objective: To test whether a statewide database of handgun transaction records, coupled with machine learning techniques, can be used to forecast firearm suicide risk. Design, Setting, and Participants: This prognostic study used the California database of 4 976 391 handgun transaction records from 1 951 006 individuals from January 1, 1996, to October 6, 2015. Transaction-level random forest classification was implemented to predict firearm suicide risk, and the relative predictive power of features in the algorithm was estimated via permutation importance. Analyses were performed from December 1, 2020, to May 19, 2022. Main Outcomes and Measures: The main outcome was firearm suicide within 1 year of a firearm transaction, derived from California death records (1996-2016). With the use of California's Dealer's Records of Sale (1996-2015), 41 handgun, transaction, purchaser, and community-level predictor variables were generated. Results: There are a total of 4 976 391 transactions in the California's Dealer's Record of Sale database representing 1 951 006 individuals (1 525 754 men [78.2% of individuals]; mean [SD] age, 43.4 [13.9] years). Firearm suicide within 1 year occurred in 0.07% of handgun transactions (3278 transactions among 2614 individuals). A total of 38.6% of observed firearm suicides were among transactions classified in the highest-risk ventile (379 of 983 transactions), with 95% specificity. Among the small number of transactions with a random forest score above 0.95, more than two-thirds (24 of 35 [68.6%]) were associated with a purchaser who died by firearm suicide within 1 year. Important features included known risk factors, such as older age at first purchase, and previously unreported predictors, including distance to firearms dealer and month of purchase. Conclusions and Relevance: This prognostic study presented the first large-scale machine learning analysis of individual-level handgun transaction records. The results suggested the potential utility of such records in identifying high-risk individuals to aid suicide prevention efforts. It also identified handgun, individual, and community characteristics that have strong predictive relationships with firearm suicide and may warrant further study.