Abstract
The global mining industry has recorded significant declines in accident and injury rates attributed to the advancement in technology, increased enforcement, and safety consciousness. A goal of the mining industry is to achieve zero injury and occupational illness on all mine sites, prompting increased research into ways to further reduce mine accidents. A machine learning technique known as multiclass logistic regression is applied on a 10-year injury dataset from the Mine Safety and Health Administration (MSHA) to determine a miner’s susceptibility to a class of injury and to help identify significant risk factors associated with different classes of injury. The data is aggregated based on injury classification to provide statistically relevant results. The analysis identifies specific risk factors that influence a mine worker’s susceptibility to a given class of injury, i.e., non-fatal with no days lost or restricted activity, non-fatal with days lost and/or days of restricted work activity, and fatal and total permanent or partial permanent disability. These factors include miner’s age, mine type (coal vs. non-coal), experience on the current job (years), shift start time, employment type (operator vs. contractor), mining district, and type of accident. The results of the analysis indicate that a miner’s experience on the job, i.e., the number of years worked in a current job, is a significant risk to injury occurrence, even for those with decades of total mining experience. We further show the differences and similarities between the surface and underground mine incidents.
Similar content being viewed by others
Change history
02 December 2020
A Correction to this paper has been published: https://doi.org/10.1007/s42461-020-00355-x
References
Onder M, Onder S, Adiguzel E (2014) Applying hierarchical loglinear models to nonfatal underground coal mine accidents for safety management. Int J Occup Saf Ergon 20:239–248
Karra VK (2005) Analysis of non-fatal and fatal injury rates for mine operator and contractor employees and the influence of work location. J Saf Res 36:413–421
Friedman LS, Almberg KS, Cohen RA (2019) Injuries associated with long working hours among employees in the US mining industry: risk factors and adverse outcomes. Occup Environ Med 76:389–395
Cullen ET, Camm T, Jenkins M, Mallett L (2006) Getting to zero: the human side of mining. Information Circular 9484. National Institute for Occupational Safety and Health (NIOSH), Spokane Research Laboratory, Spokane, WA
MSHA (2020) MSHA reports fatal mining accidents dropped in 2019. In: Min Eng. https://me.smenet.org/webContent.cfm?webarticleid=2956. Accessed 20 Apr 2020
Nieto A, Duerksen A (2008) The effects of mine safety legislation on mining technology in the USA. Int J Min Miner Process Eng 1:95–103
NIOSH (2016) Section 8 Coding Manual. https://www.cdc.gov/niosh/mining/UserFiles/data/codes.pdf. Accessed 19 Aug 2020
Grosan C, Abraham A (2011) Machine learning. Intell Syst Ref Libr. https://doi.org/10.1007/978-3-642-21004-4_10
Carranza EJM, Hale M (2001) Logistic regression for geologically constrained mapping of gold potential, Baguio district, Philippines. Explor Min Geol 10:165–175
Palei SK, Das SK (2009) Logistic regression model for prediction of roof fall risks in bord and pillar workings in coal mines: an approach. Saf Sci 47:88–96
Peng CYJ, Lee KL, Ingersoll GM (2002) An introduction to logistic regression analysis and reporting. J Educ Res 96:3–14
Bennett JD, Passmore DL (1985) Multinomial logit analysis of injury severity in U.S. underground bituminous coal mines, 1975-1982. Accid Anal Prev 17:399–408
Muzaffar S, Cummings K, Hobbs G, Allison P, Kreiss K (2013) Factors associated with fatal mining injuries among contractors and operators. J Occup Environ Med 55:1337–1344
Maiti J, Bhattacherjee A (2001) Predicting accident susceptibility: a logistic regression analysis of underground coal mine workers. J South Afr Inst Min Metall 101:203–208
Ajith MM, Ghosh AK, Jansz J (2020) Risk factors for the number of sustained injuries in artisanal and small-scale mining operation. Saf Health Work 11:50–60
Chau N, Mur JM, Benamghar L, Siegfried C, Dangelzer JL, Français M, Jacquin R, Sourdot A (2004) Relationships between certain individual characteristics and occupational injuries for various jobs in the construction industry: a case-control study. Am J Ind Med 45:84–92
Zhang KI, Hassan M (2019) Injury severity analysis of nighttime work zone crashes. ICTIS 2019 - 5th Int Conf Transp Inf Saf 1301–1308
Robin P (2014) Use on multinomial logistic regression in work zone crash analysis for Missouri work zones. MS Thesis. Missouri University of Science and Technology
Akboga Kale Ö, Baradan S (2020) Identifying factors that contribute to severity of construction injuries using logistic regression model. Tek Dergi. https://doi.org/10.18400/tekderg.470633
MSHA (2018) Mine Injury and Worktime, Yearly Report. https://arlweb.msha.gov/Stats/Part50/WQ/2018/MIWQReportCY2018.pdf. Accessed 22 Apr 2020
SPSS Software (2020) IBM SPSS software. https://www.ibm.com/analytics/spss-statistics-software. Accessed 22 Apr 2020
Stockburger DW (1996) Introductory Statistics: Concepts, Models, and Applications. Missouri State University
Heumann C, Schomaker M, Shalabh (2017) Introduction to statistics and data analysis: With exercises, solutions and applications in R. Springer Int Publ. https://doi.org/10.1007/978-3-319-46162-5
Minitab (2020) Multicollinearity in regression - Minitab. https://support.minitab.com/en-us/minitab/18/help-and-how-to/modeling-statistics/regression/supporting-topics/model-assumptions/multicollinearity-in-regression/. Accessed 22 Apr 2020
Hosmer DW, Lemeshow S (2000) Applied Logistic Regression, 2nd ed. John Wiley & Sons
Smith S, Pegula S (2020) Fatal occupational injuries to older workers. Mon Labor Rev. https://doi.org/10.21916/mlr.2020.2
Nowrouzi-Kia B, Sharma B, Dignard C, Kerekes Z, Dumond J, Li A, Larivière M (2017) Systematic review: lost-time injuries in the US mining industry. Occup Med (Chic Ill) 67:442–447
Funding
We thank the National Institute for Occupational Safety and Health (NIOSH) for funding this research under grant 0000HCCR-2019-36404.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interest
The authors declare that they have no conflict of interest.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
The original online version of this article was revised to correct the presentation of the tables.
Rights and permissions
About this article
Cite this article
Amoako, R., Buaba, J. & Brickey, A. Identifying Risk Factors from MSHA Accidents and Injury Data Using Logistic Regression. Mining, Metallurgy & Exploration 38, 509–527 (2021). https://doi.org/10.1007/s42461-020-00347-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s42461-020-00347-x