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Finding causation in occupational fatalities: A latent class analysis.

Elena Farina1, Selene Bianco1, Antonella Bena1

  • 1Department of Epidemiology-ASL TO3, Grugliasco, Torino, Italy.

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|December 19, 2018
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Summary

Occupational injury investigations using the "Learning by mistakes" method revealed distinct patterns in fatal accidents. Latent Class Analysis (LCA) identified "Fall from height or vehicle rollover" as the most common injury dynamic factor.

Keywords:
classificationinjury investigationinjury preventionlatent class analysisoccupational fatalities

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Area of Science:

  • Occupational Safety and Health
  • Data Analysis
  • Injury Epidemiology

Background:

  • The "Learning by mistakes" method was developed in Italy for occupational injury investigations.
  • This method collects data on the genesis of injuries to understand accident causation.
  • Analyzing this data aims to identify recurring patterns in injury dynamics.

Purpose of the Study:

  • To analyze data collected using the "Learning by mistakes" method.
  • To identify patterns and common factors contributing to occupational injury dynamics.
  • To leverage statistical analysis for a deeper understanding of accident causes.

Main Methods:

  • Utilized data from 354 occupational fatalities in Italy's Piedmont region (2005-2014).
  • Considered 673 contributing factors to these fatalities.
  • Applied Latent Class Analysis (LCA) to uncover patterns among these factors.

Main Results:

  • An eight-class model was selected as the best fit for the data.
  • The most prevalent class, "Fall from height or vehicle rollover due to incorrect practice," accounted for 40.56% of factors.
  • Remaining factors were distributed across the other seven classes.

Conclusions:

  • The identified classes provide logical interpretations of injury causation.
  • Systematic application of Latent Class Analysis (LCA) can reveal hidden patterns in accident factors.
  • This approach offers insights beyond the analysis of individual fatal accidents.