title: The added value of the new ESAW/Eurostat variables in accident analysis in the mining and quarrying industry.
abstract: BACKGROUND
  This paper presents a study on the causes and circumstances of occupational accidents in the Portuguese Mining and Quarrying (M&Q) activity sector.
  
  
  METHODS
  To this effect, it uses national accident data (2001-2003), giving particular attention to the newly implemented harmonized ESAW/Eurostat variables. The study begins with a broad description of the M&Q sector and identifies its "typical accident." A series of accident pyramids are used to discriminate between causal patterns of fatal and non-fatal accidents. The causation mechanisms of the "typical accidents" (fatal and non-fatal) are established by examining the statistical dependency between pairs of modalities of two key variables: the Contact and the Deviation.
  
  
  RESULTS
  The results obtained by this method revealed certain details of the cause-effect mechanism that were unknown before, which also facilitate prioritizing preventive strategies. The study highlights the benefits of the new Eurostat variables for the production of accident statistics.
  
  
  CONCLUSIONS
  The consistent adoption of harmonized classifications has a potentially positive impact on several communities: researchers will find it easier to compare their studies; the national authorities can plan more target-oriented preventive strategies; and the individual organizations may use this instrument to facilitate benchmarking of their performance indicators.
authors.affiliation: Safety, Reliability and Maintenance Group of CENTEC, Technical University of Lisbon, Instituto Superior Técnico, Av. Rovisco Pais, 1049-001 Lisboa, Portugal. mcjacinto@mar.ist.utl.mt
authors.forename: Celeste
authors.initials: C
authors.lastname: Jacinto
authors.forename: C Guedes
authors.initials: CG
authors.lastname: Soares
mesh_terms: D000062:Accidents, Occupational / Q000401:mortality / Q000706:statistics & numerical data*; D019985:Benchmarking; D015984:Causality; D003627:Data Interpretation, Statistical; D005060:Europe; D006801:Humans; D007221:Industry / Q000706:statistics & numerical data; D008906:Mining / Q000706:statistics & numerical data*; D011174:Portugal