Abstract
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.