Systematic review: Lost-time injuries in the US mining industry

Nowrouzi‐Kia, Behdin; Sharma, Bhanu; Dignard, Caroline; Kerekes, Zsuzsanna; Dumond, Jennifer; Li, A; Larivière, Michel

doi:10.1093/occmed/kqx077

Cited by 19 publications

(12 citation statements)

References 26 publications

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“…Working in mines exposes miners to a dangerous and toxic work environment, with most exposures leading to chronic diseases of the cardiac, respiratory, and renal systems 7‐11 . Earlier studies have documented increased occurrence of cancer, 12‐14 diabetes, 15,16 and accidental injuries 17,18 among miners. In the United States, there has been a substantial decline in occupational injuries in the mining industry since the passage of the 1969 Federal Coal Mine Health and Safety Act 19 .…”

Section: Introductionmentioning

confidence: 99%

Mortality among workers employed in the mining industry in the United States: A 29‐year analysis of the National Health Interview Survey—Linked Mortality File, 1986‐2014

Adeyemi

2020

American J Industrial Med

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Background Working in the mining industry increases the risk of chronic diseases and mortality. We investigated overall and cause‐specific mortality rates among workers employed in the mining sector in the United States. Methods We pooled 29 years of National Health Interview Survey (NHIS) public‐use data from 1986 to 2014, with mortality follow‐up until 31 December 2015. We grouped respondents into the mining and nonmining sectors based on the responses given at the time of the NHIS interview. We compared the overall and cause‐specific mortality rates using standardized mortality ratios (SMR) and 95% confidence interval (CI) adjusted for the competing cause of death. Results From 1986 to 2014, an estimated 14 million deaths were recorded among subjects eligible for mortality follow‐up. Of these, an estimated 50,000 deaths occurred among those working in the mining sector. A significantly higher overall mortality (SMR = 1.26, 95% CI: 1.17‐1.36), and mortality from heart diseases (adjusted SMR = 1.56, 95% CI: 1.31‐1.83), cancer (adjusted SMR = 1.30, 95% CI: 1.14‐1.48) and unintentional injuries (adjusted SMR = 1.41, 95%CI: 1.03‐1.85) were observed among those employed in the mining sector. When the analyses were restricted to men, only the SMRs for heart disease and cancer remained statistically significant. No elevated SMR for deaths from chronic lower respiratory disease was observed in the study. Conclusion Workers employed in the mining sector have a significantly increased total death rate and death rates from heart disease, cancer, and unintentional injuries.

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Section: Introductionmentioning

confidence: 99%

Mortality among workers employed in the mining industry in the United States: A 29‐year analysis of the National Health Interview Survey—Linked Mortality File, 1986‐2014

Adeyemi

2020

American J Industrial Med

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“…Although there is regulatory oversight within the U.S. mining industry to require near miss reporting, it is well known that underreporting occurs in the industry [Nowrouzi-Kia et al 2017]. However, many mine sites, including those who participated in the study, do have informal and formal near miss reporting procedures and forms that encourage workers to complete any near miss observed, prevented, or fixed on site.…”

Section: Worker Hands Performancementioning

confidence: 99%

Assessing the impact of safety climate constructs on worker performance in the mining industry.

Haas¹,

Hoebbel²,

Yorio³

2020

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This document is in the public domain and may be freely copied or reprinted. Disclaimer Mention of any company or product does not constitute endorsement by the National Institute for Occupational Safety and Health (NIOSH). In addition, citations to websites external to NIOSH do not constitute NIOSH endorsement of the sponsoring organizations or their programs or products. Furthermore, NIOSH is not responsible for the content of these websites. All web addresses referenced in this document were accessible as of the publication date.

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“…Occupational injuries are a significant problem in the mining industry [ 1 ]. The Mine Safety and Health Administration (MSHA) reported that there were a total of 16,394 non-fatal lost time injuries in the US from 2015 to 2018 [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Predictive Modeling for Occupational Safety Outcomes and Days Away from Work Analysis in Mining Operations

Yedla

Kakhki

Jannesari

2020

IJERPH

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Mining is known to be one of the most hazardous occupations in the world. Many serious accidents have occurred worldwide over the years in mining. Although there have been efforts to create a safer work environment for miners, the number of accidents occurring at the mining sites is still significant. Machine learning techniques and predictive analytics are becoming one of the leading resources to create safer work environments in the manufacturing and construction industries. These techniques are leveraged to generate actionable insights to improve decision-making. A large amount of mining safety-related data are available, and machine learning algorithms can be used to analyze the data. The use of machine learning techniques can significantly benefit the mining industry. Decision tree, random forest, and artificial neural networks were implemented to analyze the outcomes of mining accidents. These machine learning models were also used to predict days away from work. An accidents dataset provided by the Mine Safety and Health Administration was used to train the models. The models were trained separately on tabular data and narratives. The use of a synthetic data augmentation technique using word embedding was also investigated to tackle the data imbalance problem. Performance of all the models was compared with the performance of the traditional logistic regression model. The results show that models trained on narratives performed better than the models trained on structured/tabular data in predicting the outcome of the accident. The higher predictive power of the models trained on narratives led to the conclusion that the narratives have additional information relevant to the outcome of injury compared to the tabular entries. The models trained on tabular data had a lower mean squared error compared to the models trained on narratives while predicting the days away from work. The results highlight the importance of predictors, like shift start time, accident time, and mining experience in predicting the days away from work. It was found that the F1 score of all the underrepresented classes except one improved after the use of the data augmentation technique. This approach gave greater insight into the factors influencing the outcome of the accident and days away from work.

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Systematic review: Lost-time injuries in the US mining industry

Cited by 19 publications

References 26 publications

Mortality among workers employed in the mining industry in the United States: A 29‐year analysis of the National Health Interview Survey—Linked Mortality File, 1986‐2014

Mortality among workers employed in the mining industry in the United States: A 29‐year analysis of the National Health Interview Survey—Linked Mortality File, 1986‐2014

Assessing the impact of safety climate constructs on worker performance in the mining industry.

Predictive Modeling for Occupational Safety Outcomes and Days Away from Work Analysis in Mining Operations

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