Analysis of the severity of occupational injuries in the mining industry using a Bayesian network

A fuzzy Bayesian network DEMATEL model for predicting safety behavior

Objectives. Safety behavior significantly affects safety performance in the workplace. This study aimed to develop a Bayesian network (BN) model for managing and improving safety behavior. Methods. This study was carried out in the chemical industries in Iran. The data were gathered by a questionnaire consisting of 13 variables including organization safety priority, systems design, safety communication, safety education, work strategy, human-system interaction, mental workload, environmental distractions, work pressure, fatigue, sleepiness, safety knowledge and locus of control. The BN structure was created using the fuzzy decision-making trial, evaluation laboratory method and expert opinions. Belief updating was used to determine variables with the strongest effect on safety behavior. Results. Locus of control, organization safety priority and safety knowledge were the best predictors of safety behavior. Moreover, it was found that improving organization safety priority and safety knowledge is the best intervention strategy to improve safety behavior significantly. Conclusions. BN is a powerful tool that can model causal relationships among variables. Improving organization safety priority and safety knowledge can lead to the maximum possible level of safety behavior.

Retrospective assessment of the association between noise exposure and nonfatal and fatal injury rates among miners in the United States from 1983 to 2014

BACKGROUND

Mining is a significant economic force in the United States but has historically had among the highest nonfatal injury rates across all industries. Several factors, including workplace hazards and psychosocial stressors, may increase injury and fatality risk. Mining is one of the noisiest industries; however, the association between injury risk and noise exposure has not been evaluated in this industry. In this ecological study, we assessed the association between noise exposure and nonfatal and fatal occupational injury rates among miners.

METHODS

Federal US mining accident, injury, and illness data sets from 1983 to 2014 were combined with federal quarterly mining employment and production reports to quantify annual industry rates of nonfatal injuries and fatalities. An existing job-exposure matrix for occupational noise was used to estimate annual industry time-weighted average (TWA, dBA) exposures. Negative binomial models were used to assess relationships between noise, hearing conservation program (HCP) regulation changes in 2000, year, and mine type with incidence rates of injuries and fatalities.

RESULTS

Noise, HCP regulation changes, and mine type were each independently associated with nonfatal injuries and fatalities. In multivariate analysis, each doubling (5 dB increase) of TWA was associated with 1.08 (95% confidence interval: 1.05, 1.11) and 1.48 (1.23, 1.78) times higher rate of nonfatal injuries and fatalities, respectively. HCP regulation changes were associated with 0.61 (0.54, 0.70) and 0.49 (0.34, 0.71) times lower nonfatal injury and fatality rates, respectively.

CONCLUSION

Noise may be a significant independent risk factor for injuries and fatalities in mining.

Contributing effects of individual characteristics, behavioural and job-related factors on occurrence of mining-related injuries: A systematic review

BACKGROUND

Occupational health and safety (OHS) is a complex system due to its three components, namely human, technological and organizational factors. The interplay between the three systems causes workplace accidents and, subsequently, injuries. The body of research currently available demonstrates a disparity in the focus on contributors that cause mining-related injuries beyond the presence of hazards.

OBJECTIVE

The aim of this paper was to systematically review and synthesise peer-reviewed published studies that have investigated whether certain individual characteristics, behavioural factors and job-related factors predict mining-related injuries.

METHODS

Databases were searched and peer-reviewed publications from 2004 to 2020 were retrieved and analysed. Only 24 from 3073 identified articles were retained for review and synthesis following careful screening. Most identified studies were either cross-sectional or case-control studies, and they were rated as moderate-to-good quality.

RESULTS

The review results showed that there is a diverging view in relation to risk factors that cause mining-related injuries. Some publications suggested that old age, male miners, married miners, less educated miners, less experienced miners, alcohol and drug usage, poor working conditions, poor management or supervision, job dissatisfaction and job stress predict injury events while other studies found contradictory relationships or insignificant statistical associations.

CONCLUSIONS

Despite the fact that studied risk factors have been well-established in other industries, there is a significant gap in mining that needs further examination. It is imperative that health and safety intervention strategies are devised and implemented for vulnerable groups.