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Firearm Injury Risk Prediction Among Children Transported by 9-1-1 Emergency Medical Services: A Machine Learning

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Machine learning models can predict firearm injury risk in children transported by ambulance using demographic data and ZIP code. This tool aids in targeting injury prevention resources to at-risk youth.

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Area of Science:

  • Public Health
  • Epidemiology
  • Data Science in Medicine

Background:

  • Firearm injuries represent a significant public health concern among children and adolescents.
  • Predictive tools are needed to identify high-risk pediatric populations for targeted interventions.

Purpose of the Study:

  • To develop and validate machine learning models for predicting firearm injury risk in pediatric patients transported by ambulance.
  • To utilize demographic information and home ZIP code data for risk stratification.

Main Methods:

  • Analysis of ambulance transport records for children aged 0-17 years across 47 states (2014-2022).
  • Inclusion of 96 predictors, including demographics and neighborhood data from 5 sources.
  • Development of separate, high-specificity machine learning models for preadolescent (0-10 years) and adolescent (11-17 years) cohorts.

Main Results:

  • Over 6.19 million children were included, with 21,625 (0.35%) experiencing firearm injuries.
  • The model for preadolescents achieved 95.1% specificity (AUC 0.761), and for adolescents, 94.8% specificity (AUC 0.818).
  • Key predictors varied by age group, highlighting distinct risk factors for firearm injury.

Conclusions:

  • Basic demographic data and neighborhood characteristics effectively identify pediatric patients at elevated risk for firearm injury.
  • These predictive models can inform the allocation of injury prevention resources and guide targeted interventions for at-risk youth.