BACKGROUND: Suicide is a leading cause of death in the United States with rates increasing over the past two decades. The rate of suicide is higher in rural areas, but a greater number of people in urban areas die by suicide; understanding risk factors for suicide in this context is critically important to public health. Additionally, while many studies have focused on individual-level risk factors, few studies have identified social or structural features associated with suicide or firearm suicide, especially among young people. METHODS: Study outcomes included total firearm suicide, total youth (age 10-29) firearm suicide, total suicide, and total youth suicide in Detroit, Michigan from 2012 through 2019. The predictors in this study included 58 census-tract level variables characterizing the physical features, residential stability, socioeconomic status, and demographics of neighborhoods in Detroit over the study period. We used random forest, extreme gradient boosting (XGBoost), and generalized linear mixed models to predict the four outcomes. RESULTS: We found that the tract-level variables used in all three modeling approaches performed poorly at predicting the suicide outcomes, with area under the curve values at times exceeding 0.60 but with extremely low sensitivity (ranging from 0.05 to 0.45). However, the percentage of parcels sold in arms-length transfers in the previous 5 years, the count of vacant lots per square mile, and the percentage of children aged three and older who were enrolled in preschool each demonstrated associations with at least two of the outcomes studied. CONCLUSIONS: Our findings suggest place-based factors at the tract level do not provide meaningful insight into the risk of suicide or firearm suicide among youth or the general population in Detroit, Michigan. Future practice and study should consider focusing on both larger and smaller areas, including city and individual-level factors. For example, studies might benefit from the use of both neighborhood and individual-level measures and their interactions to improve our understanding of place-based risk factors and suicide risk.

Gun-related crime continues to be an urgent public health and safety problem in cities across the US. A key question is: how are firearms diverted from the legal retail market into the hands of gun offenders? With close to 8 million legal firearm transaction records in California (2010-2020) linked to over 380,000 records of recovered crime guns (2010-2021), we employ supervised machine learning to predict which firearms are used in crimes shortly after purchase. Specifically, using random forest (RF) with stratified under-sampling, we predict any crime gun recovery within a year (0.2% of transactions) and violent crime gun recovery within a year (0.03% of transactions). We also identify the purchaser, firearm, and dealer characteristics most predictive of this short time-to-crime gun recovery using SHapley Additive exPlanations and mean decrease in accuracy variable importance measures. Overall, our models show good discrimination, and we are able to identify firearms at extreme risk for diversion into criminal hands. The test set AUC is 0.85 for both models. For the model predicting any recovery, a default threshold of 0.50 results in a sensitivity of 0.63 and a specificity of 0.88. Among transactions identified as extremely risky, e.g., transactions with a score of 0.98 and above, 74% (35/47 in the test data) are recovered within a year. The most important predictive features include purchaser age and caliber size. This study suggests the potential utility of transaction records combined with machine learning to identify firearms at the highest risk for diversion and criminal use soon after purchase.

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.

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.

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 Californias Dealer Records of Sales records. Covariates included characteristics of dealership sales, firearms and their transactions, and purchasers 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 dealers 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.