Risk prediction and optimizing outcomes to 1-year after firearm injury among children using emergency services in the US
Reducing firearm injuries and related mortality in children is a critical public health priority. We will build three unique national cohorts of children injured by firearms and served by different aspects of the US emergency care system (emergency medical services, emergency departments, and trauma centers). Using these cohorts, novel analytic methods, and predictors at the patient-, home-, incident-, and county-levels, we will develop risk prediction models to: identify children at risk of firearm injuries (primary injury prevention); children at risk of adverse outcomes to one year after initially surviving a firearm injury (secondary injury prevention); and ED and hospital factors associated with optimal survival after firearm injury (tertiary injury prevention).
This project will build three national cohorts of children requiring emergency services over a 10-year period to evaluate primary, secondary, and tertiary firearm injury prevention. It will use existing research infrastructure, data science methods, the ability to track children to one year, novel analytics, and an interdisciplinary team to address this critical public health need. Importance: Injury remains the leading cause of death in children, with firearms resulting in the highest mortality, surgical intervention, critical care, and cost compared to all other mechanisms of injury. However, there are many challenges to firearm injury prevention in children. Risk prediction tools are sparse, firearm injury recidivism and death are concerns among children who initially survive such an injury, and processes of care to optimize survival are under-investigated. There is a compelling need for research in these areas to facilitate targeted interventions to reduce firearm injury and mortality among at-risk children.
Objectives: The proposal has 3 specific aims: Specific Aim 1: Using three national cohorts of children 0 to 17 years requiring emergency services, employ machine learning and geospatial analysis to develop and validate risk prediction models for firearm injury and short-term mortality using individual-, home-, incident-, and county-level factors. Specific Aim 2. Among children discharged alive following a firearm injury, measure injury recidivism, healthcare utilization, and mortality to 1-year and develop risk prediction models to identify children at-risk of adverse outcomes in the year after an index firearm event. Specific Aim 3: For children presenting to an ED after a firearm injury, identify ED and hospital characteristics independently associated with in-hospital, 30-day, and 1-year survival, and quantify the additional lives saved through feasible shifts in the location of initial emergency care. Study Design & Setting: The project will build 3 cohorts of children using emergency services from 1/1/2012 to 12/31/2021 in 1,217 EDs in 8 states (ED cohort), 832 trauma centers in 50 states (Trauma Center cohort), and 5,461 EMS agencies in 28 states (EMS cohort). We will link six different data sources to the cohorts to capture longitudinal outcomes and 110 predictor variables at the patient-, incident-, home-, and county-level.
Participants: Children 0–17 years using emergency services, including: 40.9 million in the ED cohort (n = 35,240 with firearm injuries), 620,007 in the Trauma Center cohort (n = 22,847 with firearm injuries), and 6.8 million in the EMS cohort (n = 14,314 with firearm injuries).
Outcome measures: We will evaluate models for firearm injury, adverse events to 1-year among children initially surviving a firearm injury, and short- and long-term survival following a firearm injury.