Evaluating the effectiveness of mine safety enforcement actions in forecasting the lost-days rate at specific worksites

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

The safety of mining in the United States has improved significantly over the past few decades, although it remains one of the more dangerous occupations. Following the Sago mine disaster in January 2006, federal legislation (The Mine Improvement and New Emergency Response [MINER] Act of 2006) tightened regulations and sought to strengthen the authority and safety-inspection practices of the Mine Safety and Health Administration (MSHA). While penalties and inspection frequency have increased, understanding of what types of inspection findings are most indicative of serious future incidents is limited. The most effective safety management and oversight could be accomplished by a thorough understanding of what types of infractions or safety inspection findings are most indicative of serious future personnel injuries. However, given the large number of potentially different and unique inspection findings, varied mine characteristics, and types of specific safety incidents, this question is complex in terms of the large number of potentially relevant input parameters. New regulations rely on increasing the frequency and severity of infraction penalties to encourage mining operations to improve worker safety, but without the knowledge of which specific infractions may truly be signaling a dangerous work environment. This paper seeks to inform the question: What types of inspection findings are most indicative of serious future incidents for specific types of mining operations? This analysis utilizes publicly available MSHA databases of cited infractions and reportable incidents. These inspection results are used to train machine learning Classification and Regression Tree (CART) and Random Forest (RF) models that divide the groups of mines into peer groups based on their recent infractions and other defining characteristics with the aim of predicting whether or not a fatal or serious disabling injury is more likely to occur in the following 12-month period. With these characteristics available, additional scrutiny may be appropriately directed at those mining operations at greatest risk of experiencing a worker fatality or disabling injury in the near future. Increased oversight and attention on these mines where workers are at greatest risk may more effectively reduce the likelihood of worker deaths and injuries than increased penalties and inspection frequency alone.

Original languageEnglish (US)
Article number041002
JournalASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering
Volume2
Issue number4
DOIs
StatePublished - Dec 1 2016

Fingerprint

incident
Inspection
penalty
worker
mine worker
regulation
peer group
health
work environment
disaster
personnel
occupation
legislation
act
Health
death
regression
management
Disasters
learning

All Science Journal Classification (ASJC) codes

  • Mechanical Engineering
  • Safety, Risk, Reliability and Quality
  • Safety Research

Cite this

@article{87e2e45430544ffe9fc3bc0d28573ddc,
title = "Evaluating the effectiveness of mine safety enforcement actions in forecasting the lost-days rate at specific worksites",
abstract = "The safety of mining in the United States has improved significantly over the past few decades, although it remains one of the more dangerous occupations. Following the Sago mine disaster in January 2006, federal legislation (The Mine Improvement and New Emergency Response [MINER] Act of 2006) tightened regulations and sought to strengthen the authority and safety-inspection practices of the Mine Safety and Health Administration (MSHA). While penalties and inspection frequency have increased, understanding of what types of inspection findings are most indicative of serious future incidents is limited. The most effective safety management and oversight could be accomplished by a thorough understanding of what types of infractions or safety inspection findings are most indicative of serious future personnel injuries. However, given the large number of potentially different and unique inspection findings, varied mine characteristics, and types of specific safety incidents, this question is complex in terms of the large number of potentially relevant input parameters. New regulations rely on increasing the frequency and severity of infraction penalties to encourage mining operations to improve worker safety, but without the knowledge of which specific infractions may truly be signaling a dangerous work environment. This paper seeks to inform the question: What types of inspection findings are most indicative of serious future incidents for specific types of mining operations? This analysis utilizes publicly available MSHA databases of cited infractions and reportable incidents. These inspection results are used to train machine learning Classification and Regression Tree (CART) and Random Forest (RF) models that divide the groups of mines into peer groups based on their recent infractions and other defining characteristics with the aim of predicting whether or not a fatal or serious disabling injury is more likely to occur in the following 12-month period. With these characteristics available, additional scrutiny may be appropriately directed at those mining operations at greatest risk of experiencing a worker fatality or disabling injury in the near future. Increased oversight and attention on these mines where workers are at greatest risk may more effectively reduce the likelihood of worker deaths and injuries than increased penalties and inspection frequency alone.",
author = "Gernand, {Jeremy Michael}",
year = "2016",
month = "12",
day = "1",
doi = "10.1115/1.4032929",
language = "English (US)",
volume = "2",
journal = "ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering",
issn = "2332-9017",
publisher = "American Society of Mechanical Engineers(ASME)",
number = "4",

}

TY - JOUR

T1 - Evaluating the effectiveness of mine safety enforcement actions in forecasting the lost-days rate at specific worksites

AU - Gernand, Jeremy Michael

PY - 2016/12/1

Y1 - 2016/12/1

N2 - The safety of mining in the United States has improved significantly over the past few decades, although it remains one of the more dangerous occupations. Following the Sago mine disaster in January 2006, federal legislation (The Mine Improvement and New Emergency Response [MINER] Act of 2006) tightened regulations and sought to strengthen the authority and safety-inspection practices of the Mine Safety and Health Administration (MSHA). While penalties and inspection frequency have increased, understanding of what types of inspection findings are most indicative of serious future incidents is limited. The most effective safety management and oversight could be accomplished by a thorough understanding of what types of infractions or safety inspection findings are most indicative of serious future personnel injuries. However, given the large number of potentially different and unique inspection findings, varied mine characteristics, and types of specific safety incidents, this question is complex in terms of the large number of potentially relevant input parameters. New regulations rely on increasing the frequency and severity of infraction penalties to encourage mining operations to improve worker safety, but without the knowledge of which specific infractions may truly be signaling a dangerous work environment. This paper seeks to inform the question: What types of inspection findings are most indicative of serious future incidents for specific types of mining operations? This analysis utilizes publicly available MSHA databases of cited infractions and reportable incidents. These inspection results are used to train machine learning Classification and Regression Tree (CART) and Random Forest (RF) models that divide the groups of mines into peer groups based on their recent infractions and other defining characteristics with the aim of predicting whether or not a fatal or serious disabling injury is more likely to occur in the following 12-month period. With these characteristics available, additional scrutiny may be appropriately directed at those mining operations at greatest risk of experiencing a worker fatality or disabling injury in the near future. Increased oversight and attention on these mines where workers are at greatest risk may more effectively reduce the likelihood of worker deaths and injuries than increased penalties and inspection frequency alone.

AB - The safety of mining in the United States has improved significantly over the past few decades, although it remains one of the more dangerous occupations. Following the Sago mine disaster in January 2006, federal legislation (The Mine Improvement and New Emergency Response [MINER] Act of 2006) tightened regulations and sought to strengthen the authority and safety-inspection practices of the Mine Safety and Health Administration (MSHA). While penalties and inspection frequency have increased, understanding of what types of inspection findings are most indicative of serious future incidents is limited. The most effective safety management and oversight could be accomplished by a thorough understanding of what types of infractions or safety inspection findings are most indicative of serious future personnel injuries. However, given the large number of potentially different and unique inspection findings, varied mine characteristics, and types of specific safety incidents, this question is complex in terms of the large number of potentially relevant input parameters. New regulations rely on increasing the frequency and severity of infraction penalties to encourage mining operations to improve worker safety, but without the knowledge of which specific infractions may truly be signaling a dangerous work environment. This paper seeks to inform the question: What types of inspection findings are most indicative of serious future incidents for specific types of mining operations? This analysis utilizes publicly available MSHA databases of cited infractions and reportable incidents. These inspection results are used to train machine learning Classification and Regression Tree (CART) and Random Forest (RF) models that divide the groups of mines into peer groups based on their recent infractions and other defining characteristics with the aim of predicting whether or not a fatal or serious disabling injury is more likely to occur in the following 12-month period. With these characteristics available, additional scrutiny may be appropriately directed at those mining operations at greatest risk of experiencing a worker fatality or disabling injury in the near future. Increased oversight and attention on these mines where workers are at greatest risk may more effectively reduce the likelihood of worker deaths and injuries than increased penalties and inspection frequency alone.

UR - http://www.scopus.com/inward/record.url?scp=85046234498&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85046234498&partnerID=8YFLogxK

U2 - 10.1115/1.4032929

DO - 10.1115/1.4032929

M3 - Article

AN - SCOPUS:85046234498

VL - 2

JO - ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering

JF - ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering

SN - 2332-9017

IS - 4

M1 - 041002

ER -