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The purpose of the proposed work is to harness cutting-edge machine learning methods to optimize prediction of future firearm violence in youth ages 18-24 so that prevention resources can be allocated efficiently.
Full description
Firearm violence is a public health crisis in the United States, and new epidemiological data suggest we may have reached a new endemic level of firearm mortality in recent years. Youth are disproportionately affected by firearm violence, with those age 18-24 being demonstrably the highest risk group. This study will recruit 1,500 youth age 18-24 from urban emergency departments (EDs) in three broadly different locales-Flint, Philadelphia, and Seattle-and administer a baseline survey covering several domains of potential risk factors for future violence, and follow up with those youth at 6- and 12-months to ascertain the primary outcome-firearm violence involvement (as victim or perpetrator, including threats and sub-clinical injuries)-as well as the secondary outcomes: high-risk firearm behaviors, non-firearm violence, and violent injury. This work will generate new insights into the prediction of firearm violence, and will lay the ground for future research involving the development and testing of interventions for interpersonal firearm violence both by identifying potential high-leverage modifiable predictive factors, and by focusing on youth most in need of intervention.
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1,506 participants in 1 patient group
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Central trial contact
Amanda Ballestros, MPH
Data sourced from clinicaltrials.gov
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