Interpreting MSHA citations through the lens of occupational health and safety management systems: investigating their impact on mine injuries and illnesses 2003-2010

Predicting occupational injury causal factors using text-based analytics: A systematic review

Workplace accidents can cause a catastrophic loss to the company including human injuries and fatalities. Occupational injury reports may provide a detailed description of how the incidents occurred. Thus, the narrative is a useful information to extract, classify and analyze occupational injury. This study provides a systematic review of text mining and Natural Language Processing (NLP) applications to extract text narratives from occupational injury reports. A systematic search was conducted through multiple databases including Scopus, PubMed, and Science Direct. Only original studies that examined the application of machine and deep learning-based Natural Language Processing models for occupational injury analysis were incorporated in this study. A total of 27, out of 210 articles were reviewed in this study by adopting the Preferred Reporting Items for Systematic Review (PRISMA). This review highlighted that various machine and deep learning-based NLP models such as K-means, Naïve Bayes, Support Vector Machine, Decision Tree, and K-Nearest Neighbors were applied to predict occupational injury. On top of these models, deep neural networks are also included in classifying the type of accidents and identifying the causal factors. However, there is a paucity in using the deep learning models in extracting the occupational injury reports. This is due to these techniques are pretty much very recent and making inroads into decision-making in occupational safety and health as a whole. Despite that, this paper believed that there is a huge and promising potential to explore the application of NLP and text-based analytics in this occupational injury research field. Therefore, the improvement of data balancing techniques and the development of an automated decision-making support system for occupational injury by applying the deep learning-based NLP models are the recommendations given for future research.

Occupational Health Hazards: Employer, Employee, and Labour Union Concerns

This review paper examines the extent of employer, worker, and labour union concerns to occupational health hazard exposure, as a function of previously reported and investigated complaints. Consequently, an online literature search was conducted, encompassing publicly available reports resulting from investigations, regulatory inspection, and enforcement activities conducted by relevant government structures from South Africa, the United Kingdom, and the United States. Of the three countries' government structures, the United States' exposure investigative activities conducted by the National Institute for Occupational Safety and Health returned literature search results aligned to the study design, in the form of health hazard evaluation reports reposited on its online database. The main initiators of investigated exposure cases were employers, workers, and unions at 86% of the analysed health hazard evaluation reports conducted between 2000 and 2020. In the synthesised literature, concerns to exposure from chemical and physical hazards were substantiated by occupational hygiene measurement outcomes confirming excessive exposures above regulated health and safety standards in general. Recommendations to abate the confirmed excessive exposures were made in all cases, highlighting the scientific value of occupational hygiene measurements as a basis for exposure control, informing risk and hazard perception. Conclusively, all stakeholders at the workplace should have adequate risk perception to trigger abatement measures.

Imminent danger:: Characterizing uncertainty in critically hazardous mining situations

■ Mineworkers are routinely tasked with making critically important decisions about whether or not a hazard presents an imminent danger. Researchers from the U.S. National Institute for Occupational Safety and Health collected formative data to investigate mine safety professional perspectives on workplace examinations, which revealed a potential gap in how mineworkers are assessing risk. During interviews, participants revealed not having a systematic methodology for mineworkers to use to determine if a hazard is considered imminent danger. In this paper, we identify and describe three distinct categories of imminent danger complexity and discuss potential steps that could lead to improved identification of imminent danger situations. Finally, we identify potential practices to incorporate into risk management efforts, including feedback, communication and specialized training, to increase awareness of imminent danger situations.

Examining Factors that Influence the Existence of Heinrich's Safety Triangle Using Site-Specific H&S Data from More than 25,000 Establishments

In the 1930s, Heinrich established one of the most prominent and enduring accident prevention theories when he concluded that high severity occupational safety and health (OSH) incidents are preceded by numerous lower severity incidents and near misses. Seventy-five years of theory expansion/interpretation includes two fundamental tenets: (1) the ratio of lower to higher severity incidents exists in the form of a "safety-triangle" and (2) similar causes underlie both high and low severity events. Although used extensively to inform public policy and establishment-level health and safety priorities, recent research challenges the validity of the two tenets. This study explored the validity of the first tenet, the existence of the safety triangle. The advantage of the current study is the use of a detailed, establishment-specific data set that evaluated over 25,000 establishments over a 13-year time period, allowing three specific questions to be explored: (1) Are an increased number of lower severity incidents at an establishment significantly associated with the probability of a fatal event over time? (2) At the establishment level, do the effects of OSH incidents on the probability of a fatality over time decrease as the degree of severity decreases-thereby taking the form of a triangle? and (3) Do distinct methods for delineating incidents by severity affect the existence of the safety triangle form? The answer to all three questions was yes with the triangle form being dependent upon how severity was delineated. The implications of these findings in regard to Heinrich's theory and OSH policy and management are discussed.

Evaluating hearing loss risks in the mining industry through MSHA citations

A new noise regulation for the mining industry became effective in 2000, providing a consistent regulatory requirement for both coal and non-coal mining divisions. The new rule required mines to implement hearing conservation programs, including a system of continuous noise monitoring, provision of hearing protection devices, audiometric testing, hearing loss training, and record keeping. The goal of this study was to assess hearing conservation program compliance, and excessive noise exposure and hearing loss risks for both coal and non-coal mining divisions through evaluating MSHA citations. We analyzed 13,446 MSHA citations from 2000-2014 pertinent to 30 CFR Part 62. Descriptive statistics were generated and comparisons were made among mines of different commodities. In addition, one-way ANOVA on ranks was conducted to estimate the correlation between excess risks and establishment size. Results showed that 25.6% of coal mines and 14.7% of non-coal mines were cited at least once during this period of time. Larger numbers of noncompliance were seen in stone, sand, and gravel mines (SSG). Results also suggested inadequate efforts in both audiometric testing and minimizing risk after excessive noise exposure. Finally, establishment size of mine was correlated with the increasing risk of noncompliance. We anticipate that this study can guide resource allocation for preventing noise-induced hearing loss, and help improve risk management in mining.