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Structured Topic Modeling of Child Fatal Injury Narratives Using Machine Learning with County-Level Demographic

Gia E Barboza-Salerno1, Karla J Shockley McCarthy2, Taylor R Harrington3

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This summary is machine-generated.

Analyzing child fatality reports using machine learning revealed 11 key themes. Sociodemographic factors influenced injury types, highlighting needs for targeted prevention strategies for child safety.

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child maltreatmentfatalitieshomicidepolicypreventionsubstance abuse

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Area of Science:

  • Public Health
  • Computational Linguistics
  • Sociology

Background:

  • Child fatal injuries lack systematic classification methods for intervention.
  • Unstructured text narratives in death reports hinder analysis.
  • Existing data lacks sociodemographic context for child fatalities.

Purpose of the Study:

  • To develop and apply a machine learning model for classifying child death circumstances.
  • To integrate sociodemographic data with child fatality reports for enhanced analysis.
  • To identify trends and risk factors associated with child fatalities.

Main Methods:

  • Analysis of 453 child fatality reports (2016-2023) from Pennsylvania.
  • Integration of American Community Survey (ACS) data for county-level sociodemographics.
  • Application of Natural Language Processing (NLP) and Structured Topic Modeling (STM).

Main Results:

  • Identified 11 distinct themes in child fatality reports, including Substance Misuse (13.4%) and Sleep-Related Deaths (12%).
  • Observed temporal shifts: Homicide due to Parental Neglect decreased, while Substance Misuse increased.
  • Poverty and non-White populations correlated with higher rates of Firearm-Related Injury and Severe Traumatic Injury.

Conclusions:

  • Computational text analysis with sociodemographic data provides actionable insights into child deaths.
  • Machine learning models can strengthen fatality surveillance systems.
  • Findings can inform targeted prevention strategies to improve child safety.