Exposure to dust and respiratory health among Australian miners

Rumchev, Krassi; Hoang, Dong Van; Lee, Andy H.

doi:10.1007/s00420-022-01922-z

Cited by 4 publications

(3 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compared to the current study, the PMC (located about 5km from the tanzanite mines) will belong to the low exposure group residing > 1.5km from the mines’ waste. A study report among non-smokers coal workers (both underground and surface) in USA showed the prevalence of obstructive lung disease of 7.7% which went up to 16.4% among those with CWP [28] while another report among miners in Western Australia reported a prevalence of 6.3% [29].…”

Section: Discussionmentioning

confidence: 99%

Prevalence and determinants of evidence of silicosis and impaired lung function, among small scale tanzanite miners and the peri-mining community in northern Tanzania

Mbuya,

Mboya,

Semvua

et al. 2023

Preprint

View full text Add to dashboard Cite

Among mining communities in Tanzania, the limited data available suggests the prevalence of silicosis, obstructive lung diseases (OLD) and restrictive lung disease (RLD) to be around 1.6%, 1.9% and 8.8% respectively. Our study therefore aimed to determine the prevalence and factors associated with evidence of silicosis and ILF among tanzanite mining community in northern Tanzania.We conducted a cross-sectional study, involving 330 randomly selected miners and 330 conveniently selected non-mine workers from the peri-mining community (PMC) in Mererani mines, northern Tanzania. Evidence of silicosis was defined based on study participants’ history of exposure to mining dust and digital chest radiological findings with reference to the 2011 ILO classification of pneumoconiosis. Impaired lung function was determined by spirometry using American Thoracic Society (ATS)/European Respiratory Society (ERS) recommended system 3. Association between evidence of silicosis/impaired lung function and presumed risk factors were determined using binary logistic regression analyses. The study found that 99/330 (30.0%) of miners had evidence of silicosis, of whom 97.0% had accelerated silicosis. Among miners and community members, 75 (11.4%) had ILF, of whom 30 (4.5%) had COPD, 9 (1.4%) had asthma, 29 (4.4%) had restrictive lung disease and 7 (1.1%) had mixed pattern of both obstructive and restrictive lung disease. We found that having a daily income of more than USD 4.3 was associated with lower odds of silicosis (aOR 0.57, 95% CI 0.37-0.89, p<0.05) while ILF was associated with being a miner (aOR 2.06, CI=1.38-3.07, p<0.001).We found a concerningly high prevalence of evidence of silicosis despite short durations of exposure among small scale tanzanite miners. Immediate dust control measures including deployment of wet drilling, wearing of personal protective equipment (PPE) and regular monitoring of dust exposure need to be enforced by the OSHA (Tanzania).

show abstract

Section: Discussionmentioning

confidence: 99%

Prevalence and determinants of evidence of silicosis and impaired lung function, among small scale tanzanite miners and the peri-mining community in northern Tanzania

Mbuya,

Mboya,

Semvua

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Hayumbu et al ( 14 ) further concluded that the mining companies had substandard dust monitoring programs, which may increase miners’ risk of non-malignant disease. A retrospective analysis conducted in Australia by Rumchev et al ( 18 ) investigated the association between exposure to inhalable and respirable dust and respiratory health problems among mine workers between 2001 and 2012. The study found a decline in exposure to inhalable and respirable dust over the 12 years despite workers exhibiting high prevalence of phlegm and cough.…”

Section: Introductionmentioning

confidence: 99%

Analysis of respirable dust exposure data collected at a Zambian copper mine between 2017 and 2022

Sifanu,

Kalebaila,

Hayumbu

et al. 2024

Front. Public Health

View full text Add to dashboard Cite

Mine workers are occupationally exposed to respirable dust, which can cause irreversible lung diseases and controlling exposure concentrations to as low as reasonably practicable is, therefore, essential. To implement exposure reduction strategies and adequately manage exposure to hazardous chemicals, exposure needs to be measured and recorded according to a standard exposure management plan. This study aimed to assess the available respirable dust exposure data measured between 2017 and 2022 in various work areas and job categories at two mining shafts and a concentrator plant of a Zambian copper mine. Additionally, the monitoring program implemented at the mine was assessed for appropriateness. Descriptive data analysis was used to create an exposure matrix comprising 253 exposure measurements. Approximately 5.6% of the measured concentrations exceeded the South African time-weighted average occupational exposure limit (TWA-OEL) of 3 mg/m3. The geometric means of respirable dust exposure for shaft A, shaft B and the concentrator plant were 0.44 mg/m3, 0.44 mg/m3, and 0.68 mg/m3, respectively. The work areas with the highest maximum exposure results were the tipping area (18.0 mg/m3) at shaft A, the sump and waste bin (8.05 mg/m3) at shaft B and the screening (10.8 mg/m3), secondary crusher (14.0 mg/m3), foreign ore bin (4.43 mg/m3), and tertiary crusher (5.07 mg/m3) areas at the concentrator plant. It was found that the monitoring strategy implemented at the mine was flawed and did not collect a sufficient number of samples in each area during each year to make rigorous statistical assessment possible. This study highlights the sampling strategy’s shortcomings and recommends adopting a standardized monitoring strategy, such as EN689, to improve the respirable dust monitoring program at the mine. Additionally, this strategy can be implemented across Zambia and, if implemented correctly, it has the potential to be improve exposure monitoring across the country as no standard sampling strategy is currently enforced by the Government.

show abstract

“…Dust refers to particles with a size of approximately 1-100 μm. Coal miners are frequently exposed to air filled with fibers and dust [5], making it one of the major threats to their health. It is well known that substantial occupational dust exposure can have adverse effects on the respiratory system.…”

Section: Introductionmentioning

confidence: 99%

Prediction and Validation of Mild Cognitive Impairment in Occupational Dust Exposure Population Based on Machine Learning

Cai

Zhang

Liu

et al. 2023

Preprint

View full text Add to dashboard Cite

Objective: Workers who are exposed to dust for extended periods of time may experience varying degrees of cognitive impairment. However, there is limited research on the associated risk factors. This study aims to identify key variables using machine learning algorithms (ML) and develop a model to predict the occurrence of mild cognitive impairment in miners. Methods: A total of 1938 miners were included in the study. Univariate analysis and multivariable logistic regression were employed to identify independent risk factors for cognitive impairment among miners. The dataset was randomly divided into a training set and a validation set in an 8:2 ratio, consisting of 1550 and 388 individuals, respectively. An additional group of 351 miners was collected as a test set for cognitive impairment assessment. Seven machine learning algorithms, including XGBoost, Logistic Regression, Random Forest, Complement Naive Bayes, Multi-Layer Perceptron, Support Vector Machine, and K-Nearest Neighbors, were used to establish a predictive model for mild cognitive impairment in the dust-exposed population, based on baseline characteristics of the workers. The predictive performance of the models was evaluated using the Area Under the Receiver Operating Characteristic Curve (AUC), and the XGBoost model was further explained using the SHapley Additive exPlanations (SHAP) package. Cognitive function assessments using rank sum tests were conducted to compare differences in cognitive domains between the mild cognitive impairment group and the normal group. Results: Univariate and multivariable logistic regression analyses revealed that education level, age, years of employment, SSRS (Self-Rating Scale for Stress), and HAMA (Hamilton Anxiety Rating Scale) were independent risk factors for cognitive impairment among dust-exposed workers. Comparative analysis of the performance of the seven machine learning algorithms demonstrated that XGBoost (training set: AUC=0.959, validation set: AUC=0.795) and Logistic Regression (training set: AUC=0.818, validation set: AUC=0.816) models exhibited superior predictive performance. Results from the test set showed that the AUC of the XGBoost model (AUC=0.913) outperformed the Logistic Regression model (AUC=0.778). Miners with mild cognitive impairment exhibited significant impairments (p<0.05) in visual spatial abilities, attention, abstract thinking, and memory areas. Conclusion: Education level, age, years of employment, SSRS, and HAMA were identified as independent risk factors for cognitive impairment among miners. Machine learning algorithms can be used to predict the risk of cognitive impairment in this population, with XGBoost algorithm showing the best performance. The developed model can guide the implementation of appropriate preventive measures for dust-exposed workers.

show abstract

Exposure to dust and respiratory health among Australian miners

Cited by 4 publications

References 44 publications

Prevalence and determinants of evidence of silicosis and impaired lung function, among small scale tanzanite miners and the peri-mining community in northern Tanzania

Prevalence and determinants of evidence of silicosis and impaired lung function, among small scale tanzanite miners and the peri-mining community in northern Tanzania

Analysis of respirable dust exposure data collected at a Zambian copper mine between 2017 and 2022

Prediction and Validation of Mild Cognitive Impairment in Occupational Dust Exposure Population Based on Machine Learning

Contact Info

Product

Resources

About