Research progress on the pathogenesis and prediction of pneumoconiosis among coal miners.

Pneumoconiosis is the most common occupational disease among coal miners, which is a lung disease caused by long-term inhalation of coal dust and retention in the lungs. The early stage of this disease is highly insidious, and pulmonary fibrosis may occur in the middle and late stages, leading to an increase in patient pain index and mortality rate. Currently, there is a lack of effective treatment methods. The pathogenesis of pneumoconiosis is complex and has many influencing factors. Although the characteristics of coal dust have been considered the main cause of different mechanisms of pneumoconiosis, the effects of coal dust composition, particle size and shape, and coal dust concentration on the pathogenesis of pneumoconiosis have not been systematically elucidated. Meanwhile, considering the irreversibility of pneumoconiosis progression, early prediction for pneumoconiosis patients is particularly important. However, there is no early prediction standard for pneumoconiosis among coal miners. This review summarizes the relevant research on the pathogenesis and prediction of pneumoconiosis in coal miners in recent years. Firstly, the pathogenesis of coal worker pneumoconiosis and silicosis was discussed, and the impact of coal dust characteristics on pneumoconiosis was analyzed. Then, the early diagnostic methods for pneumoconiosis have been systematically introduced, with a focus on image collaborative computer-aided diagnosis analysis and biomarker detection. Finally, the challenge of early screening technology for miners with pneumoconiosis was proposed.

Effect of Coal Mining on Health Outcomes Between Male and Female Miners in Southern West Virginia: A Brief Report.

PURPOSE: There is evidence of an association between coal mining and an increased prevalence of respiratory and cardiovascular disease (CVD). Mining is significantly associated with elevated chronic CVD mortality rates. Research is limited and looks at the differences between specific health outcomes between male and female coal miners. The aim of this study was to compare the long-term health outcomes of male and female coal miners in southern West Virginia. METHODS: We used the Charleston Area Medical Center (CAMC) data registry to look at specific health outcomes of coal miners. We queried the data warehouse from September 1, 2016, to January 1, 2023, to identify any coal miners coming to CAMC for any treatment. We identified adult patients aged 18-90 years with at least one visit to a clinic in the CAMC system. FINDINGS: We identified (n=2,460) cases of coal miners, comprising of 2,280 males and 180 females. Overall, we found higher mortality rates as well as higher rates of ischemic heart disease, heart failure, cancer, and mental health disorders among male coal miners. CONCLUSIONS: Additional research is needed to further examine possible contributing factors that explain the differences in health outcomes between male and female coal miners. Clinicians and policymakers need to address health disparities and occupational hazards that impact the health outcomes of coal miners living in southern West Virginia.

Construction and validation of a musculoskeletal disease risk prediction model for underground coal miners.

Objective: To understand the prevalence among underground coal miners of musculoskeletal disorders (MSDs), analyze the risk factors affecting MSDs, and develop and validate a risk prediction model for the development of MSDs. Materials and methods: MSD questionnaires were used to investigate the prevalence of work-related musculoskeletal disorders among 860 underground coal miners in Xinjiang. The Chinese versions of the Effort-Reward Imbalance Questionnaire (ERI), the Burnout Scale (MBI), and the Self-Rating Depression Inventory (SDS) were used to investigate the occupational mental health status of underground coal miners. The R4.1.3 software cart installation package was applied to randomly divide the study subjects into a 1:1 training set and validation set, screen independent predictors using single- and multi-factor regression analysis, and draw personalized nomogram graph prediction models based on regression coefficients. Subject work characteristic (ROC) curves, calibration (Calibrate) curves, and decision curves (DCA) were used to analyze the predictive value of each variable on MSDs and the net benefit. Results: (1) The prevalence of MSDs was 55.3%, 51.2%, and 41.9% since joining the workforce, in the past year, and in the past week, respectively; the highest prevalence was in the lower back (45.8% vs. 38.8% vs. 33.7%) and the lowest prevalence was in the hips and buttocks (13.3% vs. 11.4% vs. 9.1%) under different periods. (2) Underground coal miners: the mean total scores of occupational stress, burnout, and depression were 1.55 ± 0.64, 51.52 ± 11.53, and 13.83 ± 14.27, respectively. (3) Univariate regression revealed a higher prevalence of MSDs in those older than 45 years (49.5%), length of service > 15 years (56.4%), annual income <$60,000 (79.1%), and moderate burnout (43.2%). (4) Binary logistic regression showed that the prevalence of MSDs was higher for those with 5-20 years of service (OR = 0.295, 95% CI: 0.169-0.513), >20 years of service (OR = 0.845, 95% CI: 0.529-1.350), annual income ≥$60,000 (OR = 1.742, 95% CI: 1.100-2.759), and severe burnout (OR = 0.284, 95% CI: 0.109-0.739), and that these were independent predictors of the occurrence of MSDs among workers in underground coal mine operations (p < 0.05). (5) The areas under the ROC curve for the training and validation sets were 0.665 (95% CI: 0.615-0.716) and 0.630 (95% CI: 0.578-0.682), respectively, indicating that the model has good predictive ability; the calibration plots showed good agreement between the predicted and actual prevalence of the model; and the DCA curves suggested that the predictive value of this nomogram model for MSDs was good. Conclusion: The prevalence of MSDs among workers working underground in coal mines was high, and the constructed nomogram showed good discriminatory ability and optimal accuracy.

Correcting Coal Miner Respirator Total Inward Leakage Values for Respiratory Tract Deposition.

The great majority of workplace respirator efficacy studies have measured total inward leakage (TIL) for particulate contaminants. One of the first such studies, designated the Harris study, was conducted in the early 1970s in US underground coal mines. As in other particle-based studies, inside-the-facepiece dust sampling was continuously conducted across the inhalation and exhalation phases of the breathing cycle, although unlike in other studies, respirable dust cyclones were used in air sampling. Because exhaled air was partially depleted of dust particles due to deposition in the respiratory tract, the measured time-averaged dust concentration inside the facepiece underestimated the time-averaged dust concentration inspired into the facepiece. In turn, the reported TIL values underestimated the true TIL values experienced, which is to say, overestimated respirator efficacy. This paper describes a method to correct the Harris study's reported TIL values for respiratory tract deposition while accounting for particle size-selective sampling by the cyclone devices. Given the estimated coal mine particle size distribution outside the respirator, it is shown that the reported TIL values should be increased by the multiplicative factor 1.69. This paper also discusses the assigned protection factor (APF) of five for the quartermask respirator class and shows that 4/5 quartermasks in the Harris study did not meet the criterion for complying with this APF value when using the corrected TIL values.

Polycyclic aromatic hydrocarbons exposure was associated with microRNA differential expression and neurotransmitter changes: a cross-sectional study in coal miners.

Exposure to polycyclic aromatic hydrocarbons (PAHs) may cause neurobehavioral changes. This study aimed to explore the underlying mechanism of PAH neurotoxicity in coal miners. Urinary PAH metabolites, neurotransmitters, and oxidative stress biomarkers of 652 coal miners were examined. Subjects were divided into high and low-exposure groups based on the median of total urinary PAH metabolites. Differentially expressed miRNAs were screened from 5 samples in the low-exposure group (≤ 4.88 µmol/mol Cr) and 5 samples in the high-exposure group (> 4.88 µmol/mol Cr) using microarray technology, followed by bioinformatics analysis of the potential molecular functions of miRNA target genes. Reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) was used to validate differentially expressed miRNAs. Restricted cubic splines (RCS) were applied to assess the possible dose-response relationships. Compared to the low PAH exposure group, the high-exposure group had higher levels of 5-hydroxytryptamine (5-HT), epinephrine (E), and acetylcholine (ACh), and lower levels of acetylcholinesterase (AChE). 1-OHP had a dose-response relationship with malondialdehyde (MDA), dopamine (DA), 5-HT, and AChE (P for overall associations < 0.05). There were 19 differentially expressed microRNAs in microarray analysis, significantly enriched in the cell membrane, molecular binding to regulate transcription, and several signaling pathways such as PI3K-Akt. And in the validation stage, miR-885-5p, miR-20a-5p, and let-7i-3p showed differences in the low and high-exposure groups (P < 0.05). Changes in neurotransmitters and microRNA expression levels among the coal miners were associated with PAH exposure. Their biological functions are mainly related to the transcriptional regulation of nervous system diseases or signaling pathways of disorders. These findings provide new insights for future research of PAH neurotoxicity.

Analyzing the influencing factors of sleep quality of coal miners in a company in Shanxi Province / 中国职业医学

{L-End}Objective To investigate the current status of sleep quality and its influencing factors among coal miners in a company in Shanxi Province. {L-End}Methods A total of 1 047 coal miners from a coal mine company in Shanxi Province were selected as the study subjects by convenient sampling method. The occupational stress, occupational burnout and sleep quality of the study subjects were investigated using Occupational Stress Core Scale, Maslach Burnout Inventory-General Survey and Pittsburg Sleep Quality Index Scale. {L-End}Results The detection rates of occupational stress, occupational burnout, sleep disorder were 58.9%, 59.1% and 57.9%, respectively. The result of multivariate logistic regression analysis showed that education level, alcohol consumption, work shift, duration of dust-exposure, phase of respiratory symptoms, self-assessment of health, occupational stress and occupational burnout were independent influencing factors of sleep disorders in the coal miners (all P<0.05). Among them, the risk of sleep disorders in drinkers was higher than that in non-drinkers (P<0.05); the risk of sleep disorders was higher in miners working in a rotating work shift with two shifts than in those with three shifts (P<0.05); the higher the education level, the longer the duration of dust-exposure, the more serious the phase of respiratory symptoms, the worse the self-assessment of health, the higher the degree of occupational stress and the higher the degree of occupational burnout, the higher the risk of sleep disorders (all P<0.05). {L-End}Conclusion The incidence of sleep disorders in coal miners in this company is relatively high. Occupational stress, occupational burnout, education level, alcohol consumption, work shift, duration of dust-exposure, respiratory symptoms and health status are risk factors for sleep disorders in coal miners.

Influencing mechanism of coal miners' safety compliance: A chain mediating model.

The influence that job insecurity has on employees' safety psychology and behavior has been identified in many empirical studies, but few of these examine the influencing mechanism of job insecurity on coal miners' safety behaviors. In the de-overcapacity circumstances of coal production in China, using the strength model of self-control and conservation of resources theories, a chain mediating model was constructed to determine the relationships between job insecurity, emotional exhaustion, mind wandering, and safety compliance among coal miners. Data were collected from 447 coal miners working in three coal mines of Henan Pingdingshan Coal Industry Group. It was found that job insecurity negatively affected safety compliance, and emotional exhaustion and mind wandering played a chain mediating effect in the relationship between job insecurity and miners' safety compliance, along three specific paths. This study helps advance the understanding of the internal mechanisms of coal miners' job insecurity and how this affects individual safety performance. It also provides empirical evidence that managers can use effectively intervene in coal miners' safety performance.

Association of psychological symptoms with job burnout and occupational stress among coal miners in Xinjiang, China: A cross-sectional study.

Objective: The study aimed to investigate the influencing factors of psychological symptoms in relation to job burnout and occupational stress among coal miners in Xinjiang, so as to provide data support for enterprises in an effort to help them identify internal psychological risk factors and improve the mental health of coal miners. Methods: A cross-sectional study was carried out. A total of 12 coal mines were selected using the stratified cluster random sampling method and 4,109 coal miners were investigated by means of online electronic questionnaires. The Symptoms Check List-90 (SCL-90), Chinese Maslach Burnout Inventory (CMBI), and Job Demand-Control (JDC) model were respectively used to measure the status of psychological symptoms, job burnout, and occupational stress among coal miners. The mediation analysis was performed through structural equation modeling (SEM) by using Analysis of Moment Structure (AMOS). Results: The prevalence of psychological symptoms was higher in the occupational stress group than in the non-occupational stress group, and increased with job burnout (P < 0.05). The multivariate logistic regression analysis results showed that mild (OR = 1.401, 95% CL: 1.165, 1.685), moderate (OR = 2.190, 95% CL: 1.795, 2.672), or severe levels of burnout (OR = 6.102, 95% CL: 3.481, 10.694) and occupational stress (OR = 1.462, 95% CL: 1.272, 1.679) were risk factors for psychological symptoms in coal miners. The results of structural equation modeling indicated that occupational stress (ß = 0.11, P = 0.002) and job burnout (ß = 0.46, P = 0.002) had significant positive direct effects on psychological symptoms, and job burnout was an intermediate variable between occupational stress and psychological symptoms. Conclusion: High levels of job burnout and occupational stress were risk factors for psychological symptoms. Both occupational stress and job burnout had direct effects on psychological symptoms, and occupational stress could also have an indirect effect on coal miners' psychological symptoms through the intermediate variable of job burnout.

Predicting the risk of nodular thyroid disease in coal miners based on different machine learning models.

Background: Nodular thyroid disease is by far the most common thyroid disease and is closely associated with the development of thyroid cancer. Coal miners with chronic coal dust exposure are at higher risk of developing nodular thyroid disease. There are few studies that use machine learning models to predict the occurrence of nodular thyroid disease in coal miners. The aim of this study was to predict the high risk of nodular thyroid disease in coal miners based on five different Machine learning (ML) models. Methods: This is a retrospective clinical study in which 1,708 coal miners who were examined at the Huaihe Energy Occupational Disease Control Hospital in Anhui Province in April 2021 were selected and their clinical physical examination data, including general information, laboratory tests and imaging findings, were collected. A synthetic minority oversampling technique (SMOTE) was used for sample balancing, and the data set was randomly split into a training and Test dataset in a ratio of 8:2. Lasso regression and correlation heat map were used to screen the predictors of the models, and five ML models, including Extreme Gradient Augmentation (XGBoost), Logistic Classification (LR), Gaussian Parsimonious Bayesian Classification (GNB), Neural Network Classification (MLP), and Complementary Parsimonious Bayesian Classification (CNB) for their predictive efficacy, and the model with the highest AUC was selected as the optimal model for predicting the occurrence of nodular thyroid disease in coal miners. Result: Lasso regression analysis showed Age, H-DLC, HCT, MCH, PLT, and GGT as predictor variables for the ML models; in addition, heat maps showed no significant correlation between the six variables. In the prediction of nodular thyroid disease, the AUC results of the five ML models, XGBoost (0.892), LR (0.577), GNB (0.603), MLP (0.601), and CNB (0.543), with the XGBoost model having the largest AUC, the model can be applied in clinical practice. Conclusion: In this research, all five ML models were found to predict the risk of nodular thyroid disease in coal miners, with the XGBoost model having the best overall predictive performance. The model can assist clinicians in quickly and accurately predicting the occurrence of nodular thyroid disease in coal miners, and in adopting individualized clinical prevention and treatment strategies.

Defining the Primary Work Stress Factors of Chinese Coal Miners-A Mixed-Methods Study.

BACKGROUND: Studies have indicated that coal miners in China have higher levels of perceived job stress. However, few studies have investigated the work stress structure of coal miners. OBJECTIVE: Our study focused on the work stress of coal miners in China, with a primary aim to determine the work stress structure of coal miners in China using a mixed-methods approach. METHODS: Semi-structured interviews were performed with thirty-three people (team leaders and frontline coal miners) conducted with participants from various state-owned large- and medium-sized coal mines in China. Grounded theory was used to construct an initial model for the concept of coal miners' work stress. Using the results of this initial survey and findings in the existing literature, we then constructed a preliminary questionnaire regarding coal miners' work stress and administered the questionnaire to 900 coal miners in the Shaanxi, Henan, Inner Mongolia, and Gansu provinces. RESULTS: The results show that the work stress structure for coal miners differs from that for other occupational types in China, due to differences in the Chinese culture and foreign cultural influences. We revised our questionnaire based on these considerations and administered a new survey to the frontline production workers in coal mines. The preliminary questionnaires were revised and analyzed through exploratory and confirmatory factor analysis, resulting in a final formal model for work stress, which was supported by content and structural validity. CONCLUSION: In this research, we used the framework of grounded theory to conduct an empirical analysis of the structure model of coal miners' work stress. The findings support that the primary work stress factors of Chinese coal miners included the stress of the work environment, job responsibility, interpersonal relationships, career development, the family environment, and organizational systems. Coal enterprises should therefore always take these factors into consideration when developing and implementing safety management policies aimed at to improve the occupational health status of coal miners.

The Mediation Role of Safety Attitude in the Impact of Resilience on the Safety Behavior of Coal Miners in China.

Resilience can improve the adaptability of coal miners to high-hazard and high-stress environments. After facing setbacks or adversities, resilience can enable coal miners to recover from bad mental states and have an optimistic safety attitude and positive safety behaviors. However, how resilience affects safety behavior and the role of safety attitude in the relationship have not been clear. This study systematically reviewed previous research on resilience, safety attitude, and safety behavior. By recovering 639 valid questionnaires, the validity and reliability of the resilience scale, safety attitude scale, and safety behavior scale for coal miners were verified. Hierarchical regression analysis explored the relationships between resilience, safety attitude, and safety behavior. Studies have shown that resilience positively affects safety attitude and safe behavior. Safety attitude positively affects safety behaviors and plays a role as a partial mediator in the impact of resilience on safe behavior. The theoretical contribution is that the resilience of miners has a positive impact on safety behavior. Moreover, resilience can also act on safety behaviors through the partial intermediation of safety attitude. The practical contribution is that managers of coal mining companies can promote the resilience and safety attitude of coal miners to improve safety behaviors and prevent accidents.

Association of coal mine dust lung disease with Nodular thyroid disease in coal miners: A retrospective observational study in China.

Background: Coal dust is a major risk factor for the occupational health of coal miners, and underground workers with coal mine dust lung disease (Coal miners with coal mine dust lung disease (CMDLD) may have a higher risk of developing Nodular thyroid disease (NTD). The aim of this study was to investigate the relationship between coal mine dust lung disease and the development of Nodular thyroid disease in coal miners. Methods: This was a clinical retrospective observational study that included 955 male coal miners from 31 different coal mining companies in Huainan, Anhui Province, China, who were examined in April 2021 at the Huainan Occupational Disease Prevention and Control Hospital to collect all their clinical physical examination data, including general conditions, laboratory test indices and imaging indices. Based on the presence or absence of Nodular thyroid disease, 429 cases with Nodular thyroid disease were classified as the diseased group and 526 cases without Nodular thyroid disease were classified as the control group. Logistic regression was used to analyse the correlation between the occurrence of Nodular thyroid disease in coal miners, and further single- and multi-factor logistic regression was used to screen the risk exposure factors for Nodular thyroid disease in coal miners. Results: Age, coal mine dust lung disease (CMDLD), red blood cells (RBC), mean red blood cell volume (MCV), albumin (ALB), albumin/globulin (A/G), indirect bilirubin (IBIL), globulin (GLOB), total bilirubin (TBil) and myeloperoxidase (MPO) were associated with the development of Nodular thyroid disease in coal miners (p < 0.05) The results of univariate and multifactorial logistic regression analysis showed that CMDLD (OR:4.5,95%CI:2.79-7.51) had the highest OR and CMDLD was the strongest independent risk exposure factor for the development of Nodular thyroid disease in coal miners. Conclusions: There is a strong correlation between coal mine dust lung disease and Nodular thyroid disease in underground coal miners, and clinicians need to be highly aware of the high risk of NTD in coal miners with CMDLD and adopt individualized clinical prevention strategies.

The recognition of lung disease in coal workers: The role of Gough-Wentworth whole lung sections.

From the identification of a specific lung disease caused by coal dust exposure in miners in 1831 until the demonstration of the association of that exposure to risk of emphysema in 1984, there was continuous argument about the harmfulness of coal dust. Ill health in miners was attributed variously to tuberculosis, quartz exposure or cigarette smoking. An acceptance that coal dust was harmful only started with investigative radiology and pathology in the 1920s, and physiology in the 1950s. Most of the early investigations were in South Wales, the centre of the most important coal field in Great Britain. Among the investigators was Professor Jethro Gough who, with his technician James Wentworth, introduced a technique for making thick sections of whole, inflated lungs on paper backing. Here, we describe this method and its central role in understanding the relationships between coal dust exposure, pneumoconiosis, emphysema and lung dysfunction in miners.

Coal Miners and Lung Cancer: Can Mortality Studies Offer a Perspective on Rat Inhalation Studies of Poorly Soluble Low Toxicity Particles?

Inhalation studies involving laboratory rats exposed to poorly soluble particles (PSLTs), such as carbon black and titanium dioxide, among others, have led to the development of lung cancer in conditions characterized as lung overload. Lung overload has been described as a physiological state in which pulmonary clearance is impaired, particles are not effectively removed from the lungs and chronic inflammation develops, ultimately leading to tumor growth. Since lung tumors have not occurred under similar states of lung overload in other laboratory animal species, such as mice, hamsters and guinea pigs, the relevance of the rat as a model for human risk assessment has presented regulatory challenges. It has been suggested that coal workers' pneumoconiosis may reflect a human example of apparent "lung overload" of poorly soluble particles. In turn, studies of risk of lung cancer in coal miners may offer a valuable perspective for understanding the significance of rat inhalation studies of PSLTs on humans. This report addresses whether coal can be considered a PSLT based on its composition in contrast to carbon black and titanium dioxide. We also review cohort mortality studies and case-control studies of coal workers. We conclude that coal differs substantially from carbon black and titanium dioxide in its structure and composition. Carbon black, a manufactured product, is virtually pure carbon (upwards of 98%); TiO2 is also a manufactured product. Coal contains carcinogens such as crystalline silica, beryllium, cadmium and iron, among others; in addition, coal mining activities tend to occur in the presence of operating machinery in which diesel exhaust particles, a Type I Human carcinogen, may be present in the occupational environment. As a result of its composition and the environment in which coal mining occurs, it is scientifically inappropriate to consider coal a PSLT. Despite coal not being similar to carbon black or TiO2, through the use of a weight of evidence approach-considered the preferred method when evaluating disparate studies to assess risk- studies of coal-mine workers do not indicate a consistent increase in lung cancer risk. Slight elevations in SMR cannot lead to a reliable conclusion about an increased risk due to limitations in exposure assessment and control of inherent biases in case-control studies, most notably confounding and recall bias. In conclusion, the weight of the scientific literature suggests that coal mine dust is not a PSLT, and it does not increase lung cancer risk.

A New Strategy for Identification of Coal Miners With Abnormal Physical Signs Based on EN-mRMR.

Coal miners' occupational health is a key part of production safety in the coal mine. Accurate identification of abnormal physical signs is the key to preventing occupational diseases and improving miners' working environment. There are many problems when evaluating the physical health status of miners manually, such as too many sign parameters, low diagnostic efficiency, missed diagnosis, and misdiagnosis. To solve these problems, the machine learning algorithm is used to identify miners with abnormal signs. We proposed a feature screening strategy of integrating elastic net (EN) and Max-Relevance and Min-Redundancy (mRMR) to establish the model to identify abnormal signs and obtain the key physical signs. First, the raw 21 physical signs were expanded to 25 by feature construction technology. Then, the EN was used to delete redundant physical signs. Finally, the mRMR combined with the support vector classification of intelligent optimization algorithm by Gravitational Search Algorithm (GSA-SVC) is applied to further simplify the rest of 12 relatively important physical signs and obtain the optimal model with data of six physical signs. At this time, the accuracy, precision, recall, specificity, G-mean, and MCC of the test set were 97.50%, 97.78%, 97.78%, 97.14%, 0.98, and 0.95. The experimental results show that the proposed strategy improves the model performance with the smallest features and realizes the accurate identification of abnormal coal miners. The conclusion could provide reference evidence for intelligent classification and assessment of occupational health in the early stage.

Risk Factors Identification of Unsafe Acts in Deep Coal Mine Workers Based on Grounded Theory and HFACS.

The risk factors affecting workers' unsafe acts were comprehensively identified by Human Factors Analysis and Classification System (HFACS) and grounded theory based on interview data and accident reports from deep coal mines. Firstly, we collected accident case and field interview data from deep coal mines issued by authoritative institutions. Then, the data were coded according to grounded theory to obtain relevant concepts and types. The HFACS model was used to classify the concepts and categories. Finally, the relationship between core and secondary categories was sorted out by applying a story plot. The results show that risk factors of unsafe acts of deep coal mine workers include environmental factors, organizational influence, unsafe supervision and unsafe state of miners, and the main manifestations of unsafe acts are errors and violations. Among them, the unsafe state of miners is the intermediate variable, and other factors indirectly affect risky actions of coal miners through unsafe sates. Resource management, organizational processes and failure to correct problems are the top three risk factors that occur more frequently in unsafe acts. The three most common types of unsafe act are unreasonable labor organization, failure to enforce rules, and inadequate technical specifications. By combining grounded theory and the HFACS framework to analyze data, risk factors for deep coal miners can be quickly identified, and more precise and comprehensive conceptual models of risk factors in unsafe acts of deep coal miners can be obtained.

Research on the Construction of a Comprehensive Evaluation Model of Psychological Factors in Coal Mine Workers' Safety: Investigation and Analysis of 1,500 Miners in East China.

With China's economic and social development entering a new era, the improvement of miners' living standards and safety production conditions in coal mine are bound to have a new impact on the safety needs of miners. In order to explore the structural changes of miners' safety demands in the new era, this research adopts the second-order confirmatory factor analysis method to investigate miners from six coal mining enterprises based on Koffka's cognitive psychology theory. Firstly, according to the interaction between the behavioral environment and the self-regulation of coal miners, six potential variables affecting miners' safety psychology, such as material satisfaction, non-skill internal causes, professionalism, emotional attribution, safety atmosphere, and organizational management, are selected. Then, each potential variable is subdivided into 3 observation variables, for a total of 18 observation variables, and a 3-tier comprehensive structural model of miners' safety psychology is constructed that takes into account both evaluation and path integration. The results showed that, affected by the interaction of various potential variables, the degree and intensity of the influence of each factor on miners' safety psychology were different. Among them, emotional attribution was the most significant factor affecting miners' safety psychology, while the influence of organizational management was slightly less important than emotional attribution. Organizational management had a positive impact on material satisfaction and non-skill internal factors. Occupational literacy, material satisfaction, and safety atmosphere had strong impacts on miners' safety psychology. But the impact of non-skill factors on miners' safety psychology was lower than other factors, which is different to previous studies on this aspect.

Influencing Factors, Formation Mechanism, and Pre-control Methods of Coal Miners' Unsafe Behavior: A Systematic Literature Review.

Coal mine accidents are mainly caused by the unsafe behavior of workers. Studying workers' unsafe behaviors can help in regulating such behaviors and reducing the incidence of accidents. However, there is a dearth of systematic literature review in this area, which has hindered mine managers from fully understanding the unsafe behavior of workers. This study aims to address this research gap based on the literature retrieved from the Web of Science. First, a descriptive statistical analysis is conducted on the year, quantity, publications, and keywords of the literature. Second, the influencing factors, formation mechanism, and pre-control methods of coal miners' unsafe behavior are determined and discussed, and the research framework and future research directions of this study are proposed. The study results will help mine safety managers fully understand the influencing factors, formation mechanism, and pre-control methods of workers' unsafe behavior, and lay a theoretical foundation for the future research direction in this field.

Analysis of Histopathological Findings of Lung Carcinoma in Czech Black Coal Miners in Association with Coal Workers' Pneumoconiosis.

Coal miners with coal workers' pneumoconiosis (CWP, J60 according to ICD-10) were previously found to have a significantly higher risk of lung carcinoma compared to the general male population. The presented study aimed to analyze the (i) incidence of lung carcinoma in miners, (ii) histopathological findings in cohorts with and without CWP, and (iii) effect of smoking cessation on the histopathological profile. Analyzed cohorts consisted of miners with (n = 3476) and without (n = 6687) CWP. Data on personal and working history obtained from the medical records were combined with information on lung cancer from the Czech Oncological Register and histopathological findings. Statistical analysis was performed using non-parametric tests and the incidence risk ratio at the significance level of 5%. In 1992-2015, 180 miners (2.7%) without CWP and 169 (4.9%) with CWP, respectively, were diagnosed with lung carcinoma. The risk of lung cancer in miners with CWP was 1.82 (95% CI: 1.48-2.25) times higher than in those without CWP. Squamous cell carcinoma (37%) was the most common histopathological type, followed by adenocarcinoma (22%) and small cell carcinoma (21%). A statistically significant difference between the cohorts (p = 0.003) was found in the histopathological subtypes, with the incidence of small cell carcinoma being 2 times higher in miners without CWP than in those with CWP. Only a few individuals with lung carcinoma were non-smokers. The incidence of small cell carcinoma, which is strongly associated with smoking, is significantly higher in miners without CWP. Smoking constitutes the most important risk factor for developing lung carcinoma even in that cohort. However, CWP remains a very important risk factor.

Respiratory health of American Indian and Alaska Native coal miners participating in the Coal Workers' Health Surveillance Program, 2014-2019.

BACKGROUND: In 2014, a federal rule reduced occupational exposure limits to coal mine dust and expanded medical surveillance eligibility beyond underground miners to surface and contract coal miners. This expansion may have provided an opportunity for more American Indian and Alaska Native (AI/AN) coal miners to participate in screening, since many surface coal mines are located near AI/AN communities and may employ AI/AN miners. Therefore we sought to better understand the respiratory health of AI/AN coal miners by characterizing prevalence of coal workers' pneumoconiosis (CWP), progressive massive fibrosis (PMF), and abnormal lung function in this population. METHODS: Descriptive analysis of 1405 chest radiographs and 627 spirometry test results for AI/AN miners who participated in the Coal Workers' Health Surveillance Program (CWHSP) during 2014-2019 was conducted. RESULTS: Most AI/AN miners (0-25+ years of tenure) were western United States residents (82.3%) and active surface miners (76.9%) with no underground tenure. Among miners with at least 10 years of tenure, prevalence of CWP was 3.0%, and of PMF was 0.3%. Lung function abnormalities were seen in 9.0% with primarily restrictive patterns. CONCLUSIONS: The prevalence of CWP, PMF, and lung function abnormality among active and former AI/AN coal miners was higher than seen in a larger CWHSP study of active western miners working primarily underground with 10+ years of tenure. Interventions that eliminate or control coal mine dust exposure, identify miners with CWP early, and limit respiratory disease progression and complications remain vital for eliminating the preventable adverse health effects of coal mining. Comprehensive demographic data on the coal mining workforce are needed to improve CWHSP participation assessment.