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Updated: Feb 11, 2026

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Using matrix frame to present road traffic injury pattern.

Chien-Hsing Wang1,2, Wan-Hua Hsieh3, Fu-Wen Liang4

  • 1Division of Plastic Surgery, Department of Surgery and Trauma Center, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan.

Injury Epidemiology
|April 24, 2018
PubMed
Summary
This summary is machine-generated.

Road traffic injuries (RTIs) involving motorcycle riders in collisions with cars were most common. This analysis used a matrix frame to detail RTI patterns by victim and counterpart transport modes.

Keywords:
EpidemiologyPattern of injuryRoad traffic injuriesTransport accidents

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Area of Science:

  • Public Health
  • Epidemiology
  • Trauma Research

Background:

  • Road traffic injuries (RTIs) are a major public health concern.
  • Existing studies often focus solely on the victim's mode of transport.
  • A gap exists in understanding crash dynamics involving multiple transport modes.

Purpose of the Study:

  • To present the patterns of road traffic injuries (RTIs) using a matrix frame.
  • To analyze RTI data based on both the victim's and their counterpart's mode of transport.
  • To identify high-risk crash scenarios for targeted interventions.

Main Methods:

  • Utilized a matrix frame to categorize RTIs based on International Classification of Diseases, Tenth Revision (ICD-10) codes.
  • Analyzed data from 2727 patients admitted to Hualien Tzu Chi Hospital, Taiwan (2013-2016).
  • Categorized crashes by victim's transport (row margin) and counterpart's transport (column margin) within an 80-cell grid.

Main Results:

  • The most frequent RTI scenario was motorcycle riders in collision with cars, pick-up trucks, or vans (ICD-10 code V23, 27.0%).
  • Other significant crash types included motorcycle riders colliding with fixed objects (V27, 12.5%) and non-collision transport accidents (V28, 12.2%).
  • RTI patterns varied significantly based on patient sex and age.

Conclusions:

  • A matrix frame provides a clear visualization of complex RTI patterns.
  • This method effectively highlights priority crash types for injury prevention efforts.
  • Understanding crash dynamics involving different transport modes is crucial for public health strategies.