The relationship between occupational stress, musculoskeletal disorders and the mental health of coal miners: The interaction between BDNF gene, TPH2 gene polymorphism and the environment

Xue, Lin; Jiang, Ting; Sun, Xuemei; Yong, Xianting; Ma, Xiaofan; Liu, Jiwen

doi:10.1016/j.jpsychires.2020.12.061

Cited by 23 publications

(16 citation statements)

References 21 publications

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“…Animal behavior experiments showed that CEBS mice exhibited significant anxiety and depression-like behavior after 4 weeks of environmental simulation. This was consistent with previous research on the mental health of coal miners that anxiety and depression are common and more pronounced than in the rest of the population ( Considine et al, 2017 ; Li et al, 2021 ). However, the CEBS model only reflected the significant influence of the coal mine workplace environment on negative emotions, further indicating that environmental stress is an important factor to induce the cognitive dysfunction related to unsafe behavior.…”

Section: Discussionsupporting

confidence: 93%

“…Relationships, family environment, income level, work characteristics, physical condition, and many other factors may all affect employees’ mental statuses ( Probst and Brubaker, 2001 ; Adler et al, 2006 ; Beseler and Stallones, 2010 ; Deng et al, 2017 ), and the hostile workplace environment is a marked feature that distinguishes coal miners from other workers ( Considine et al, 2017 ). The high strength and physical labor, high temperature, high humidity, noise, dust, and other harsh workplace conditions led to coal miners’ suffering from a variety of occupational diseases ( Liu and Liu, 2020 ; Li et al, 2021 ), and seriously causing anxiety and depression symptoms ( Liu et al, 2014 ). These negative emotions are more likely to cause unsafe behavior among coal miners ( Yu et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

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The Impacts of Workplace Environment on Coal Miners’ Emotion and Cognition Depicted in a Mouse Model

Wang

et al. 2022

Front. Behav. Neurosci.

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Most coal mine accidents are caused by the unsafe behavior of employees. Previous studies have shown that there is a significant connection among the working environment, the psychological state of employees, and unsafe behaviors. However, the internal biological mechanism has not been revealed. To explore the physiological and psychological alterations of coal mine workers and the underlying mechanisms that cause unsafe behaviors, the current study established a novel coal mine environment biological simulation (CEBS) model in mice. This model recreated the underground workplace environment facts in coal mines such as temperature, humidity, and noise, and mice were employed to receive these conditioning stresses according to the 8-h work. Animal behavior tests were performed to evaluate the evolution of the mental state including anxiety and depression, as well as the abilities of learning and memory during the 4-week environmental simulation. CEBS mice showed the adaptation process of anxiety from occurrence to stability in the process of environmental simulation, and also suffered from severe depression compared to the control mice. In addition, impaired spatial memory was also implicated in mice after 4-week CEBS. The behavior results of CEBS mice were consistent with the previous psychological investigation of coal workers. In summary, a novel mouse model was established in this study to depict the occurrence of negative emotions and impaired cognition in coal miners by simulating the underground workplace environment, which provided a basis for further exploring the biological mechanism of miners’ unsafe behavior.

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Section: Discussionsupporting

confidence: 93%

Section: Introductionmentioning

confidence: 99%

The Impacts of Workplace Environment on Coal Miners’ Emotion and Cognition Depicted in a Mouse Model

Wang

et al. 2022

Front. Behav. Neurosci.

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“…A cross-sectional study in Xinjiang showed that 38.27% of factory workers and miners had mental health problems 29. And a study revealed that 633 out of 1675 coal miners (37.8%) suffered from mental disorders between August 2018 and June 2019 30. In this study, we assumed a 30% prevalence of mental health problem to obtain the maximum required sample size.…”

Section: Methodsmentioning

confidence: 98%

“… 29 And a study revealed that 633 out of 1675 coal miners (37.8%) suffered from mental disorders between August 2018 and June 2019. 30 In this study, we assumed a 30% prevalence of mental health problem to obtain the maximum required sample size. which would calculate a sample size of 934, taking into account non-response and a 20% loss of questionnaires, which would require approximately 1168 people.…”

Section: Methodsmentioning

confidence: 99%

Development and validation of a nomogram for predicting the risk of mental health problems of factory workers and miners

Liu

Yan

et al. 2022

BMJ Open

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ObjectiveA nomogram for predicting the risk of mental health problems was established in a population of factory workers and miners, in order to quickly calculate the probability of a worker suffering from mental health problems.MethodsA cross-sectional survey of 7500 factory workers and miners in Urumqi was conducted by means of an electronic questionnaire using cluster sampling method. Participants were randomly assigned to the training group (70%) and the validation group (30%). Questionnaire-based survey was conducted to collect information. A least absolute shrinkage and selection operator (LASSO) regression model was used to screen the predictors related to the risk of mental health problems of the training group. Multivariate logistic regression analysis was applied to construct the prediction model. Calibration plots and receiver operating characteristic-derived area under the curve (AUC) were used for model validation. Decision curve analysis was applied to calculate the net benefit of the screening model.ResultsA total of 7118 participants met the inclusion criteria and the data were randomly divided into a training group (n=4955) and a validation group (n=2163) in a ratio of 3:1. A total of 23 characteristics were included in this study and LASSO regression selected 12 characteristics such as education, professional title, age, Chinese Maslach Burnout Inventory, effort–reward imbalance, asbestos dust, hypertension, diabetes, working hours per day, working years, marital status and work schedule as predictors for the construction of the nomogram. In the validation group, the Brier score was 0.176, the calibration slope was 0.970 and the calibration curve of nomogram showed a good fit. The AUC of training group and verification group were 0.785 and 0.784, respectively.ConclusionThe nomogram combining these 12 characteristics can be used to predict the risk of suffering mental health problems, providing a useful tool for quickly and accurately screening the risk of mental health problems.

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“…Bayesian network is a probability graph model proposed by Judea Pearl in 1985, which is an uncertainty processing model that simulates causality in the human reasoning process, and its topological structure is a directed acyclic graph. Each node in the directed acyclic graph represents a random variable, and the directed edge represents the conditional dependence between random variables [ 13 ]. Bayesian network model can clearly and intuitively express the interdependence between variables, which is more convenient for us to identify the probability causal dependence and conditional probability independence.…”

Section: Introductionmentioning

confidence: 99%

The relationship between vitamin D level and second acid-fast bacilli (AFB) smear-positive during treatment for TB patients was inferred by Bayesian network

Zhang

Xia

et al. 2022

PLoS ONE

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Background Vitamin D is related to human immunity, so we used Bayesian network model to analyze and infer the relationship between vitamin D level and the acid-fast bacilli (AFB) smear-positive after two months treatment among pulmonary tuberculosis (TB) patients. Methods This is a cross-sectional study. 731 TB patients whose vitamin D level were detected and medical records were collected from December 2019 to December 2020 in XinJiang of China. Logistic regression was used to analyze the influencing factors of second AFB smear-positive. Bayesian network was used to further analyze the causal relationship among vitamin D level and the second AFB smear-positive. Results Baseline AFB smear-positive (OR = 6.481, 95%CI: 1.604~26.184), combined cavity (OR = 3.204, 95%CI: 1.586~6.472), full supervision (OR = 8.173, 95%CI:1.536~43.492) and full management (OR = 6.231, 95%CI:1.031~37.636) were not only the risk factors and can also be considered as the reasons for second AFB smear-positive in TB patients (Ensemnle > 0.5). There was no causal relationship between vitamin D level and second AFB smear-positive (Ensemnle = 0.0709). Conclusions The risk factors of second AFB smear-positive were baseline AFB smear-positive, combined cavity, full supervision and full management. The vitamin D level in TB patients was not considered as one of the reasons for the AFB smear-positive.

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The relationship between occupational stress, musculoskeletal disorders and the mental health of coal miners: The interaction between BDNF gene, TPH2 gene polymorphism and the environment

Cited by 23 publications

References 21 publications

The Impacts of Workplace Environment on Coal Miners’ Emotion and Cognition Depicted in a Mouse Model

The Impacts of Workplace Environment on Coal Miners’ Emotion and Cognition Depicted in a Mouse Model

Development and validation of a nomogram for predicting the risk of mental health problems of factory workers and miners

The relationship between vitamin D level and second acid-fast bacilli (AFB) smear-positive during treatment for TB patients was inferred by Bayesian network

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