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

Factors associated with self-rated health among mineworkers in Zambia: a cross-sectional study

Background

This study aims to examine miners’ working conditions and self-rated health status in copper mines in Zambia and to identify the conditions and factors necessary to improve the safety and health of mineworkers.

Methods

A cross-sectional study using a self-administered questionnaire was conducted anonymously among copper mineworkers in Zambia in 2015 and 2016. Five targeted mining companies among 33 were introduced by the Mineworkers’ Union of Zambia. Study participants were recruited at the waiting space for underground work, waiting rooms of company clinics/hospitals, and/or at training sessions, which were places permitted by the target companies to perform data collection via convenience sampling. Bivariate analyses (e.g., t tests, Kruskal-Wallis tests, chi-square tests, or Cochran-Armitage tests) and logistic regression analysis were used to analyze differences in demographic characteristics and to compare their working conditions, health conditions, safety management at the workplace, and training opportunities by employment status.

Results

In total, 338 responses were analyzed. Regular employees had better working conditions, including higher incomes (P = 0.001), more likely to be guaranteed sickness insurance by the company (P < 0.001), paid holidays (P = 0.094), and sick leave (P = 0.064), although the difference was not statistically significant. Mineworkers’ decreased self-rated health was determined by job category (adjusted odds ratio [AOR], 0.41; 95% confidence interval [CI], 0.21, 0.82; P = 0.012). Having experienced violence from the boss/manager (AOR, 0.54; 95% CI, 0.32, 0.91; P = 0.020) was negatively associated with better self-rated health in the crude odds ratio.

Conclusions

Among mineworkers in Zambia, nonunderground work and not having experienced violence from their boss/manager contributed to increased self-rated health. From the perspective of psychological safety and human security, the management of safety and the working environment, including human resource management and preventing harassment/violence, should be assured, especially for underground mineworkers.

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

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.

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

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.

Estimating the prevalence and spatial clusters of coal workers' pneumoconiosis cases using medicare claims data, 2011-2014

BACKGROUND

Workers employed in the coal mining sector are at increased risk of respiratory diseases, including coal workers' pneumoconiosis (CWP). We investigated the prevalence of CWP and its association with sociodemographic factors among Medicare beneficiaries.

METHODS

We used 5% Medicare Limited Data Set claims data from 2011 to 2014 to select Medicare beneficiaries with a diagnosis of ICD-9-CM 500 (CWP). We aggregated the data by county and limited our analysis to seven contiguous states: Illinois, Indiana, Kentucky, Ohio, Pennsylvania, Virginia, and West Virginia. We estimated county-level prevalence rates using total Medicare beneficiaries and miners as denominators and performed spatial hotspot analysis. We used negative binomial regression analysis to determine the association of county-wise sociodemographic factors with CWP.

RESULTS

There was significant spatial clustering of CWP cases in Kentucky, Virginia, and West Virginia. Spatial clusters of 210 and 605 CWP cases representing an estimated 4200 to 12 100 cases of Medicare beneficiaries with CWP were identified in the three states. Counties with higher poverty levels had a significantly elevated rate of CWP (adjusted rate ratios [RR]: 1.15; 95% CI, 1.12-1.18). There was a small but significant association of CWP with the county-wise catchment area. Rurality was associated with a more than three-fold elevated rate of CWP in the unadjusted analysis (RR: 3.28, 95% CI, 2.22-4.84). However, the rate declined to 1.45 (95% CI, 1.04-2.01) after adjusting for other factors in the analysis.

CONCLUSIONS

We found evidence of significant spatial clustering of CWP among Medicare beneficiaries living in the seven states of the USA.

The Prevalence of Chronic Diseases Among Current and Ex-Miners in the United States

OBJECTIVE

To determine and compare the prevalence and odds of chronic diseases among ex- and current miners.

METHODS

Fourteen-year pooled data from the National Health Interview Survey between 2004 and 2017 were analyzed. Ex- and current miners ages 18 to 64 years were defined based on employment status at the time of National Health Interview Survey (NHIS) interview. We calculated age-adjusted prevalence rates and odds ratios of association of chronic diseases. The analysis was adjusted for respondent's age, sex, race, marital status, poverty-income ratio, health insurance, and smoking status.

RESULTS

Ex-miners have significantly increased prevalence of most chronic diseases. The age-adjusted prevalence and the adjusted odds of heart disease, cancer, hypertension, diabetes, and psychological stress were significantly higher among ex-miners as compared with current miners.

CONCLUSIONS

Ex-miners have worse health outcomes that may persist for years after leaving the mining industry.