Occupational health risk assessment methods in China: A scoping review

Occupational Exposure to Silica Dust and Silicosis Risk in Chinese Noncoal Mines: Qualitative and Quantitative Risk Assessment

BACKGROUND

Despite increasing awareness, silica dust-induced silicosis still contributes to the huge disease burden in China. Worryingly, recent silica dust exposure levels and silicosis risk in Chinese noncoal mines remain unclear.

OBJECTIVE

We aimed to determine recent silica dust exposure levels and assess the risk of silicosis in Chinese noncoal mines.

METHODS

Between May and December 2020, we conducted a retrospective cohort study on 3 noncoal mines and 1 public hospital to establish, using multivariable Cox regression analyses, prediction formulas of the silicosis cumulative hazard ratio (H) and incidence (I) and a cross-sectional study on 155 noncoal mines in 10 Chinese provinces to determine the prevalence of silica dust exposure (PDE), free silica content, and total dust and respirable dust concentrations. The qualitative risk of silicosis was assessed using the International Mining and Metals Commission's risk-rating table and the occupational hazard risk index; the quantitative risk was assessed using prediction formulas.

RESULTS

Kaplan-Meier survival analysis revealed significant differences in the silicosis probability between silica dust-exposed male and female miners (log-rank test χ21=7.52, P=.01). A total of 126 noncoal mines, with 29,835 miners and 4623 dust samples, were included; 13,037 (43.7%) miners were exposed to silica dust, of which 12,952 (99.3%) were male. The median PDE, free silica content, total dust concentration, and respirable dust concentration were 61.6%, 27.6%, 1.30 mg/m3, and 0.58 mg/m3, respectively, indicating that miners in nonmetal, nonferrous metal, small, and open-pit mines suffer high-level exposure to silica dust. Comprehensive qualitative risk assessment showed noncoal miners had a medium risk of silicosis, and the risks caused by total silica dust and respirable silica dust exposure were high and medium, respectively. When predicting H and I over the next 10, 20, and 30 years, we assumed that the miner gender was male. Under exposure to current total silica dust concentrations, median I10, I20, and I30 would be 6.8%, 25.1%, and 49.9%, respectively. Under exposure to current respirable silica dust concentrations, median I10, I20, and I30 would be 6.8%, 27.7%, and 57.4%, respectively. These findings showed that miners in nonmetal, nonferrous metal, small, and open-pit mines have a higher I and higher qualitative silicosis risk.

CONCLUSIONS

Chinese noncoal miners, especially those in nonmetal, nonferrous metal, small, and open-pit mines, still suffer high-level exposure to silica dust and a medium-level risk of silicosis. Data of both total silica dust and respirable silica dust are vital for occupational health risk assessment in order to devise effective control measures to reduce noncoal mine silica dust levels, improve miners' working environment, and reduce the risk of silicosis.

The prediction of occupational health risks of n-Hexane in small and micro enterprises within China's printing industry using five occupational health risk assessment models

Background

Chronic n-Hexane poisoning is prevalent among workers in small and micro printing industries in China. Despite this, there is limited research on occupational health risk assessment in these sectors. Conducting comprehensive risk assessments at key positions and proposing effective countermeasures are essential.

Methods

Data were collected from 84 key positions across 32 small and micro-sized printing enterprises. Air samples were tested for n-Hexane exposure levels in accordance with Chinese standards. Five risk assessment models were employed: COSHH, EPA, MOM, ICMM, and Technical Guide GBZ/T 289-2017 of China. The consistency of results across these models was analyzed.

Results

Workers in 84 job positions were categorized into four exposure groups, with exposure to n-Hexane for 8-10 h daily, 5-6 days weekly. Most positions operated with low automation levels (96.9% in printing, 5.9% in oil blending, and 42.9% in pasting), while others were manual. Localized ventilation rates were notably low in oil blending (23.5%), cleaning (14.3%), and pasting (9.5%) groups. n-Hexane concentrations exceeded Chinese occupational limits in 15.6% of printing, 17.7% of oil blending, and 21.4% of cleaning groups. Risk assessment models identified over 60% of work groups as high risk. Significant differences (p < 0.05) were found among the seven risk assessment methods. Consistency analysis revealed moderate agreement between the Chinese synthesis index and exposure index methods (k = 0.571, p < 0.01).

Conclusion

The Chinese synthesis and exposure index methods from Technical Guide GBZ/T 289-2017 are practical and reliable for assessing n-Hexane exposure risks in small and micro printing enterprises. Cleaning and printing roles were found to be at the highest risk for n-Hexane exposure. These findings provide valuable insights for targeted risk management strategies to protect workers' health in the industry.

The application of metaverse in occupational health

Background

The metaverse, as a new digital interactive platform, is garnering significant attention and exploration across industries due to technological advancements and societal digital transformation. In occupational health, there is immense potential for leveraging the metaverse to enhance work environments and occupational health management. It offers companies more efficient and intelligent solutions for occupational health management while providing employees with safer and more comfortable work environments.

Methods

A comprehensive literature search was conducted using PubMed, Web of Science, IEEE Xplore, and Google Scholar databases to identify relevant studies published between January 2015 and March 2024. The search terms included "metaverse," "virtual reality," "occupational health," "workplace safety," "job training," and "telemedicine." The selected articles were analyzed, and key findings were summarized narratively.

Results

The review summarizes the broad application prospects of metaverse technology in immersive training, occupational risk identification and assessment, and occupational disease monitoring and diagnosis. However, applying the metaverse in occupational health also faces challenges such as inadequate technical standards, data privacy issues, human health hazards, high costs, personnel training, and lagging regulations.

Conclusion

Metaverse offers new possibilities for addressing the numerous challenges faced in occupational health and has broad application prospects. In the future, collaborative efforts from multiple stakeholders will be necessary to promote the sustainable development of metaverse technology in occupational health and better protect workers' occupational health.

Sex disparities in physical activity domains and hypertension prevalence

BACKGROUND

This study aimed to examine the associations of leisure time physical activity (LTPA) and occupational physical activity (OPA) with the prevalence of hypertension, while exploring the sex disparities in these associations.

METHODS

A cross-sectional study was conducted using data from the Korea National Health and Nutrition Examination Survey between 2014 and 2019 (n = 26,534). Hypertension was defined as the use of antihypertensive drugs or systolic and diastolic blood pressure ≥ 140/90 mm Hg. Self-reported physical activity (PA), assessed by the global PA questionnaire, was categorized into three domains: total PA, LTPA and OPA. Each PA domain was classified based on METs-min/wk and intensity.

RESULTS

In a multivariable adjusted model, the odds ratio (OR) with 95% confidence intervals (CIs) for the prevalence of hypertension in the active versus inactive group, based on METs, was 0.92 (95% CI 0.85-0.99) for total PA, 0.90 (95% CI 0.83-0.98) for LTPA and 1.21 (95% CI 1.05-1.38) for OPA. Compared to the inactive group, moderate to vigorous intensity was associated with a lower odds of hypertension for total PA and LTPA (total PA: OR 0.95, 95% CI 0.89-1.00 and LTPA: OR 0.92, 95% CI 0.86-0.98), but a higher odd for OPA (OR 1.17, 95% CI 1.05-1.30). Subgroup analyses showed significant evidence of effect modification by sex on the associations of total PA and LTPA (METs and intensity) with hypertension prevalence (p-values for interaction < 0.01); the associations were generally stronger for women. OPA was associated with a higher prevalence of hypertension in women, but not in men (p-value for interaction > 0.05).

CONCLUSIONS

Higher levels of total PA and LTPA were associated with lower prevalence of hypertension in both men and women, with slightly stronger associations for women. However, higher OPA was associated with a higher prevalence of hypertension in women. These findings support the PA health paradox hypothesis and highlight the sex disparities in the association between OPA and hypertension prevalence.

Occupational health risk assessment of workplace solvents and noise in the electronics industry using three comprehensive risk assessment models

Background

Occupational hazards such as solvents and noise in the electronics industry are serious. Although various occupational health risk assessment models have been applied in the electronics industry, they have only been used to assess the risks of individual job positions. Few existing studies have focused on the total risk level of critical risk factors in enterprises.

Methods

Ten electronics enterprises were selected for this study. Information, air samples and physical factor measurements were collected from the selected enterprises through on-site investigation, and then the data were collated and samples were tested according to the requirements of Chinese standards. The Occupational Health Risk Classification and Assessment Model (referred to as the Classification Model), the Occupational Health Risk Grading and Assessment Model (referred to as the Grading Model), and the Occupational Disease Hazard Evaluation Model were used to assess the risks of the enterprises. The correlations and differences between the three models were analyzed, and the results of the models were validated by the average risk level of all of the hazard factors.

Results

Hazards with concentrations exceeding the Chinese occupational exposure limits (OELs) were methylene chloride, 1,2-dichloroethane, and noise. The exposure time of workers ranged from 1 to 11 h per day and the frequency of exposure ranged from 5 to 6 times per week. The risk ratios (RRs) of the Classification Model, the Grading Model and the Occupational Disease Hazard Evaluation Model were 0.70 ± 0.10, 0.34 ± 0.13, and 0.65 ± 0.21, respectively. The RRs for the three risk assessment models were statistically different (P &lt; 0.001), and there were no correlations between them (P &gt; 0.05). The average risk level of all of the hazard factors was 0.38 ± 0.18, which did not differ from the RRs of the Grading Model (P &gt; 0.05).

Conclusions

The hazards of organic solvents and noise in the electronics industry are not negligible. The Grading Model offers a good reflection of the actual risk level of the electronics industry and has strong practicability.