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Predicting Severity of Foreign Body Injuries in Children in Upper Airways: An Approach Based on Regression Trees

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Listed:
  • Paola Berchialla
  • Silvia Snidero
  • Alexandru Stancu
  • Cecilia Scarinzi
  • Roberto Corradetti
  • Dario Gregori
  • the ESFBI Study Group

Abstract

The entry of a small item into the upper airways is one of the leading causes of injuries in children up to 14 years old. The aim of this study is to characterize types of objects causing choking along with the features of the children involved in the accident and compare results with current standards. The European Survey on Foreign Bodies Injuries Study (ESFBI) collected data on foreign body injuries from 19 European countries. The data from ESFBI were selected according to the ICD‐9‐CM codes 933 (foreign body in the pharynx and larynx) and 934 (foreign body in the trachea, bronchi, and lungs). Both a classification tree and a linear discriminant analysis (LDA) have been set up to predict the probability that an injured child experiences a hospitalization. The classification tree provides flowchart‐type decision rules and allows for analyzing the impact of the item features, the children characteristics, and the circumstances of the accidents on the severity of the foreign body injuries. Results showed that children younger than 3.5 who are involved in an accident have a high probability to experience a hospitalization.

Suggested Citation

  • Paola Berchialla & Silvia Snidero & Alexandru Stancu & Cecilia Scarinzi & Roberto Corradetti & Dario Gregori & the ESFBI Study Group, 2007. "Predicting Severity of Foreign Body Injuries in Children in Upper Airways: An Approach Based on Regression Trees," Risk Analysis, John Wiley & Sons, vol. 27(5), pages 1255-1263, October.
    • Handle: RePEc:wly:riskan:v:27:y:2007:i:5:p:1255-1263
    DOI: 10.1111/j.1539-6924.2007.00971.x
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    References listed on IDEAS

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    1. Barbara J. McNeil & James A. Hanley, 1984. "Statistical Approaches to the Analysis of Receiver Operating Characteristic (ROC) Curves," Medical Decision Making, , vol. 4(2), pages 137-150, June.
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    Cited by:

    1. Jinxian Weng & Qiang Meng & David Z. W. Wang, 2013. "Tree‐Based Logistic Regression Approach for Work Zone Casualty Risk Assessment," Risk Analysis, John Wiley & Sons, vol. 33(3), pages 493-504, March.

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