Accident analysis of mining industry in the United States e A retrospective study for 36 years

Elham Rahimi; Younes Shekarian; Naser Shekarian; Pedram Roghanchi

doi:10.46873/2300-3960.1345

Accident analysis of mining industry in the United States e A retrospective study for 36 years

Elham Rahimi
, Younes Shekarian
, Naser Shekarian
, Pedram Roghanchi

Mining Engineering

Research output: Contribution to journal › Article › peer-review

14 Scopus citations

Abstract

A retrospective study on accident analysis of the United States mines for 36 years was achieved using statistical analysis on the MSHA’s accident databases between 1983 and 2018. A regression model of generalized estimation equation (GEE) was used for unbalanced panel data that provided 95,812 observations for 19,924 mine-ID-year in aggregate, coal, metal, and nonmetal mines. The contributions of various parameters, including mine type, injured body part, days lost, age, and experience on the rate of accidents and injuries were investigated across the commodity types. The results showed coal miners in the East region are at a higher risk of an accident. The results of regression analysis show that mine-tenured workers have a vital role in accident frequencies. Analysis of the injured body part on the injury rate indicates that the upper body injuries are the most significant among all mine types. Also, the fatality rate is significant in aggregate and coal mines in comparison with metal and nonmetal mines.

Original language	English
Article number	3
Pages (from-to)	27-44
Number of pages	18
Journal	Journal of Sustainable Mining
Volume	21
Issue number	1
DOIs	https://doi.org/10.46873/2300-3960.1345
State	Published - 2022

Bibliographical note

Publisher Copyright:
© Central Mining Institute, Katowice, Poland.

Funding

The authors would like to thank the Mine Safety and Health Administration for providing the accident/injury/employee databases and also express immeasurable appreciation to the National Institute for Occupational Safety and Health (NIOSH) for funding this study [75D30119C06390]. Expressed thanks to Dr. Pierre Mousset-Jones and Dr. Arvin Ebrahimkhanlou for all precise technical and editorial comments related to this paper. This research was funded by National Institute for Occupational Safety and Health (NIOSH), grant number 75D30119C06390.

Funders	Funder number
Mine Safety and Health Administration
National Institute for Occupational Safety and Health	75D30119C06390
National Institute for Occupational Safety and Health

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Accident
Generalized Estimation Equation (GEE)
Mines
Statistical analysis

ASJC Scopus subject areas

Environmental Engineering
Renewable Energy, Sustainability and the Environment
Geotechnical Engineering and Engineering Geology
Pollution
Geology

Access to Document

10.46873/2300-3960.1345

Cite this

@article{82df3166a12e46478e760f690567bc99,

title = "Accident analysis of mining industry in the United States e A retrospective study for 36 years",

abstract = "A retrospective study on accident analysis of the United States mines for 36 years was achieved using statistical analysis on the MSHA{\textquoteright}s accident databases between 1983 and 2018. A regression model of generalized estimation equation (GEE) was used for unbalanced panel data that provided 95,812 observations for 19,924 mine-ID-year in aggregate, coal, metal, and nonmetal mines. The contributions of various parameters, including mine type, injured body part, days lost, age, and experience on the rate of accidents and injuries were investigated across the commodity types. The results showed coal miners in the East region are at a higher risk of an accident. The results of regression analysis show that mine-tenured workers have a vital role in accident frequencies. Analysis of the injured body part on the injury rate indicates that the upper body injuries are the most significant among all mine types. Also, the fatality rate is significant in aggregate and coal mines in comparison with metal and nonmetal mines.",

keywords = "Accident, Generalized Estimation Equation (GEE), Mines, Statistical analysis",

author = "Elham Rahimi and Younes Shekarian and Naser Shekarian and Pedram Roghanchi",

note = "Publisher Copyright: {\textcopyright} Central Mining Institute, Katowice, Poland.",

year = "2022",

doi = "10.46873/2300-3960.1345",

language = "English",

volume = "21",

pages = "27--44",

journal = "Journal of Sustainable Mining",

issn = "2543-4950",

publisher = "Central Mining Institute",

number = "1",

}

TY - JOUR

T1 - Accident analysis of mining industry in the United States e A retrospective study for 36 years

AU - Rahimi, Elham

AU - Shekarian, Younes

AU - Shekarian, Naser

AU - Roghanchi, Pedram

N1 - Publisher Copyright: © Central Mining Institute, Katowice, Poland.

PY - 2022

Y1 - 2022

N2 - A retrospective study on accident analysis of the United States mines for 36 years was achieved using statistical analysis on the MSHA’s accident databases between 1983 and 2018. A regression model of generalized estimation equation (GEE) was used for unbalanced panel data that provided 95,812 observations for 19,924 mine-ID-year in aggregate, coal, metal, and nonmetal mines. The contributions of various parameters, including mine type, injured body part, days lost, age, and experience on the rate of accidents and injuries were investigated across the commodity types. The results showed coal miners in the East region are at a higher risk of an accident. The results of regression analysis show that mine-tenured workers have a vital role in accident frequencies. Analysis of the injured body part on the injury rate indicates that the upper body injuries are the most significant among all mine types. Also, the fatality rate is significant in aggregate and coal mines in comparison with metal and nonmetal mines.

AB - A retrospective study on accident analysis of the United States mines for 36 years was achieved using statistical analysis on the MSHA’s accident databases between 1983 and 2018. A regression model of generalized estimation equation (GEE) was used for unbalanced panel data that provided 95,812 observations for 19,924 mine-ID-year in aggregate, coal, metal, and nonmetal mines. The contributions of various parameters, including mine type, injured body part, days lost, age, and experience on the rate of accidents and injuries were investigated across the commodity types. The results showed coal miners in the East region are at a higher risk of an accident. The results of regression analysis show that mine-tenured workers have a vital role in accident frequencies. Analysis of the injured body part on the injury rate indicates that the upper body injuries are the most significant among all mine types. Also, the fatality rate is significant in aggregate and coal mines in comparison with metal and nonmetal mines.

KW - Accident

KW - Generalized Estimation Equation (GEE)

KW - Mines

KW - Statistical analysis

UR - https://www.scopus.com/pages/publications/85126645908

UR - https://www.scopus.com/pages/publications/85126645908#tab=citedBy

U2 - 10.46873/2300-3960.1345

DO - 10.46873/2300-3960.1345

M3 - Article

AN - SCOPUS:85126645908

SN - 2543-4950

VL - 21

SP - 27

EP - 44

JO - Journal of Sustainable Mining

JF - Journal of Sustainable Mining

IS - 1

M1 - 3

ER -

Accident analysis of mining industry in the United States e A retrospective study for 36 years

Abstract

Bibliographical note

Funding

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this