Occupational hazards pose a major public health challenge that seriously endangers the health of the working population. As a statutory key component of occupational health work, occupational health risk assessment urgently requires more attention. Currently, commonly used occupational health risk assessment methods exhibit notable deficiencies such as insufficient comprehensive assessment capabilities and limited applicability. The absence of assessment methods that accurately align with occupational health practices has constrained the depth and breadth of occupational health risk assessment in China, resulting in inadequate risk identification and significant deficiencies in social service provision, decision-making support, and health protection. Meanwhile, under the current assessment system, the occupational health big data obtained from risk assessments, occupational health examinations, biological monitoring, and other sources suffer from issues such as fragmented resources and low utilization rates. As a result, existing systems are unable to effectively support critical operational needs, including accurate risk early warning, timely risk response, and refined risk management. This study systematically reviewed the common occupational health risk assessment methods and the current status of occupational health risk assessment and big data applications in China, identified existing shortcomings, proposed a comprehensive risk assessment and risk prediction-early warning scheme based on the application of occupational health big data. The scheme is expected to provide theoretical foundations and methodological references for advancing comprehensive risk assessment of occupational health through big data analytics, and to enhance the level of occupational health management, thereby safeguarding worker health and promoting sustainable socioeconomic development.

Objective To analyze the influenza-like illness (ILI) data in Ordos City from 2017 to 2023 and conduct nucleic acid detection of the virus to understand the local influenza epidemic situation, and to provide a reliable basis for influenza prevention and control in the city. Methods Real-time quantitative polymerase chain reaction (qPCR) was used to identify virus subtypes in ILI throat swab samples. Comparisons of positive rates were conducted using the chi-square test, with a significance level of α=0.05. Results From 2017 to 2023, a total of 3,283,434 outpatient and emergency visits were recorded at the Ordos City Central Hospital, including 74,159 ILI cases, with an ILI proportion of 2.26%. The majority of ILI cases (74.43%) occurred in children aged 0~14 years old. The overall positive rate of influenza virus nucleic acid detection was 10.87%, with the highest proportion being subtype A (seasonal H3) at 43.03%. The highest detection rate was observed in the 5~14 years age group, with statistically significant differences in positive rates across age groups (χ²=155.638, P<0.001). Influenza peaks occurred mainly from November to March of the following year. From January to April, three types of influenza were prevalent alternately or mixed, while from October to December, subtype A (seasonal H3) predominated. Positive rates varied significantly across months (χ²=250.923, P<0.001). The temporal trends of ILI proportions and PCR-positive rates were consistent. Conclusion Influenza in Ordos City exhibits distinct seasonal and age distribution characteristics, with alternating or mixed circulation of three virus types. Continued efforts are needed to strengthen influenza surveillance, especially the prevention and control of influenza in infants and adolescents.

Background The prevalence of chronic diseases among the Chinese occupational population is rising steadily, with hypertension and diabetes becoming important health concerns. Occupational health examinations (OHE) provide stable population coverage, standardized protocols, and fixed follow-up intervals, offering a robust data foundation for risk assessment. However, most existing hypertension prediction studies rely on cross-sectional data and mainly focus on clinic onset, failing to capture the dynamic progression and cumulation of individual risk. Objective To construct a machine learning-based risk prediction model for blood pressure abnormality in occupational populations, providing a reference for health risk stratification and targeted health interventions. Methods Longitudinal data from 2020 to 2023 were extracted from the occupational health examination database of an institution in Shanghai. After excluding individuals with hypertension in any of the first three years, 3710 workers who participated 4 consecutive years of OHE were included. Six categories of blood pressure-related indicators from 2020 to 2022 were selected, including basic information, lifestyles, occupational exposures, cardiovascular indicators, routine blood tests, and biochemical markers, comprising 30 variables in total. For feature engineering, continuous variables were processed to reflect three-year dynamic characteristics: level (mean), variability (variance), and temporal trend (slope). Categorical variables were incorporated using 2022 values. Least absolute shrinkage and selection operator regression was applied to identify variables for inclusion in the machine learning models. Five models (decision tree, logistic regression, random forest, support vector machine, and XGBoost) were employed to predict blood pressure abnormality risk in 2023. And then sensitive analysis was conducted. The optimal model was selected based on the area under the curve (AUC) of receiver operating characteristic. Feature importance and SHAP analyses were applied to interpret the final model. Results Feature engineering transformed 17 continuous variables into 51 secondary variables, combined with 13 baseline variables, resulted in 24 variables screened out by LASSO regression. The logistic regression model achieved the best performance. Feature importance analysis indicated that the dynamic trajectory of blood pressure played a central role in the model, complemented by other biochemical and lifestyle indicators. SHAP analysis further showed that the model’s ability to not only identify risks of high-normal blood pressure and hypertension but also quantify the specific contribution of individual examination indicators to predicted risk, supporting risk-stratified population management in occupational health management. Conclusion Based on an existing occupational health examination database and routinely collected data, this study has developed a risk prediction model for blood pressure abnormalities in occupational populations. By incorporating dynamic change characteristics from longitudinal examinations, the study demonstrates the feasibility of using routine health data for early risk identification and stratified management. However, its applicability is limited by the availability of repeated examination data and the focus on specific occupational groups, requiring further validation across broader populations and diverse clinic scenarios.

As a primary approach to safeguarding workers' occupational health in China, the work-related injury insurance still has insufficient preventive measures for occupational groups. By reviewing the preventive measures embedded in Germany’s statutory accident insurance (Gesetzliche Unfallversicherung) and Japan’s workers’ accident compensation insurance, this study summarized their best practices and then proposed the following policy directions based on China’s specific context: strengthening the prevention-oriented institutional framework, increasing financial investment in occupational injury prevention, improving the incentive mechanisms of floating premium rate, and promoting data integration and intelligent applications in occupational health management. These measures are expected to shift China’s work-related injury insurance system from a compensation-oriented model to one that emphasizes comprehensive prevention and health promotion, thereby providing useful insights for better safeguarding the health of Chinese workers.

Background Long-term occupational health surveillance facilitates the identification of chronic health risks arising from occupational hazard exposures. However, under low-level exposure conditions in chemical enterprises, traditional workplace-centered risk assessment models are insufficient to fully explain the observed health effects among workers, highlighting the need for individual-focused health management strategies that focus on the worker. Objective To characterize long-term health trends among workers in a chemical enterprise and examine the limitations of workplace hazard monitoring and health surveillance under low-level multiple exposure scenarios, thereby providing a scientific basis for more comprehensive worker health protection measures. Methods We analyzed 972 occupational health examination records collected over a 15-year period (2010–2024) from a chemical enterprise, integrating these with enterprise hazard monitoring reports and workshop air monitoring of volatile organic compounds (VOCs). The annual counts and prevalences of abnormalities in liver function tests, metabolic syndrome-related indicators, and liver imaging were summarized. Temporal trends were evaluated using the Cochran-Armitage trend test. Time-related changes in abnormal health indicators among active workers were evaluated by generalized linear mixed-effects models (GLMMs), adjusting for age and sex. Individual longitudinal profiles were described for nine workers with continuous 15-year records. In addition, paired comparisons of health outcomes were conducted for 81 workers with both pre-employment and periodic examinations. Results Workplace hazard monitoring indicated that airborne concentrations of major occupational contaminants including VOCs, acids and bases, dust, and physical factors were below national occupational exposure limits. However, 62 VOC species—including chlorobenzene, chloromethane, and cyclohexane—were detected in workshop air, suggesting complex mixed organic solvent exposure. From 2010 to 2015, occupational health examination records were primarily based on physicians’ qualitative assessment, yielding generally low abnormality detection rates. During 2016–2024, data collection was complete, and the prevalences of abnormalities in liver function, metabolic syndrome-related indicators, and liver imaging among active workers remained relatively high. Trend analysis indicated that only the abnormality rates of total bilirubin (TBIL) and hypertension showed statistically significant temporal trends; other indicators exhibited no significant linear changes despite high prevalences. After adjusting for age and sex, the GLMMs showed that the abnormality rates of aspartate aminotransferase (AST), gamma-glutamyl transferase (GGT), TBIL, and hypertension decreased over time, whereas those of total cholesterol, triglycerides, and fatty liver increased. In a subgroup of 9 workers with continuous 15-year follow-up data, the cumulative number of abnormal health indicators increased in several individuals after 2016. Among the 81 workers with both pre-employment and periodic records, abnormalities in total cholesterol, triglycerides, body mass index, and fatty liver were significantly more prevalent after employment over time, whereas changes in liver function enzymes were not statistically significant. Conclusion With increasing age, workers exhibit a gradual deterioration in liver function and metabolic-related health indicators, reaching abnormality rates comparable to those reported in the general population. These findings suggest that both workplace-related and non-occupational factors jointly contribute to the observed health changes. Consequently, occupational health management should shift towards an "individual-centered" approach. Enhancing workers' health literacy, strengthening workplace health promotion, and extending occupational health services beyond the workplace are essential to comprehensively protect workers' health.

Objective To analyze the coverage of chronic disease-related indicators and the distribution of positive detection rates in occupational medical examinations (OME). Methods A total of 872 862 currently employed workers who underwent OME from 2015 to 2023 were selected as the study participants using a convenience sampling method. According to the year of OME, the study period was divided into three periods: 2015-2017, 2018-2020, and 2021-2023. The examination rates and positive detection rates of five chronic disease-related indicators, including obesity, high blood pressure, hyperglycemia, dyslipidemia, and hyperuricemia, were analyzed. Results Regarding the routine medical examination indicators in OME, the examination rates for systolic and diastolic blood pressure were both 97.4%, while those for body height and weight were both 6.8%. Among the mandatory or optional examination items for specific occupational groups in OMEs, the examination rates of fasting blood glucose, triglycerides, total cholesterol, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol were 35.2%, 18.5%, 18.5%, 7.8%, and 7.9%, respectively. Regarding items not included in OMEs, the examination rates of glycated hemoglobin and uric acid were 0.2% and 19.2%, respectively. The examination rates for systolic and diastolic blood pressure consistently exceeded 95.0%, the examination rates for fasting blood glucose, triglycerides, and uric acid ranged from 32.9% to 37.0%, 16.8% to 21.4%, and 16.9% to 24.1%, respectively, while the rates for body height and weight remained below 10.0% across these three periods. Among workers who completed corresponding OME items, dyslipidemia and hyperuricemia showed relatively higher positive detection rates, with rates of 23.2% and 23.0%, respectively. The positive detection rates of obesity, high blood pressure, and hyperglycemia were relatively lower, at 9.6%, 3.5%, and 1.4%, respectively. Across the three periods, the positive detection rates of dyslipidemia, hyperuricemia, obesity, high blood pressure, and hyperglycemia ranged from 18.0% to 33.7%, 7.0% to 28.5%, 7.8% to 10.9%, 3.3% to 3.9%, and 1.4% to 1.5%, respectively, all showing increasing trends with advancing OME periods (all P<0.01). Conclusion Current OME practices show a mismatch between the coverage of chronic disease-related indicators and the actual health risks among occupational worker populations. Although blood pressure monitoring is well implemented, basic physical indicators (body height and body weight) and key metabolic indicators (blood glucose, blood lipids, and uric acid) are insufficiently covered, limiting comprehensive assessment of occupational health risks. Optimization of monitoring strategies is urgently needed to improve data completeness.

As the prevalence of chronic diseases increases among occupational populations in China, the focus of occupational health management has shifted from ″occupational disease prevention and control″ to ″occupational disease and chronic disease prevention and control″. At present, occupational medical examinations (OME) are mainly focused on occupational disease prevention and control, and generally lack of chronic disease indicators such as blood lipids and blood glucose, limiting the role of OME in life-cycle health management of workers. To address this gap, it is necessary to incorporate chronic disease screening indicators into OME. Integrating OME and chronic disease screening faces bottlenecks, including concerns about increased operating costs in small- and medium-sized enterprises, and the risk of employment discrimination caused by the leakage of employee health privacy. A tiered strategy is recommended. Incorporating low-cost indicators such as blood lipids, waist circumference into routine examinations, and customizing advanced screening for elder or high-risk position workers. The prioritization of screening indicators can be determined by expert consultation and employer-employee discussions. Meanwhile, the results of chronic disease screening indicators should not be included in occupational health surveillance records to protect workers′ privacy. In summary, expanding OME to cover more chronic disease indicators provides valuable economic and social benefits. In the long run, common chronic disease screening indicators should be included in the regular medical examination indicators of OME, thereby reducing the burden of chronic diseases on occupational populations and promoting the health management of occupational populations.

The core idea of occupational health risk assessment models is to systematically evaluate occupational health risks according to target hazard characteristics and relevant exposure levels of workers. Occupational exposure assessment is based on concentration, frequency, exposure time, and other indicators that indicate actual exposure of workers to occupational hazards, which is a critical component of health risk assessment. However, the accuracy and comparability of assessment results are affected by differences in parameter assignment for exposure assessment across different studies, as well as insufficient emphasis on multiple occupational hazard exposure. This review aimed to explore the assignment and standardization of exposure assessment parameters for occupational health risk assessment modeling, and systematically sorted out the meaning, assignment methods, and sources of exposure assessment related parameters in commonly used occupational health risk assessment models, with the goal of providing researchers with standardized assessment tools to enhance the scientific rigor and practicality of occupational health risk assessments. Considering the individual differences and temporal fluctuations in occupational exposure, it is recommended that researchers should adopt appropriate sampling strategies, reasonably select sample subjects and time based on the division of similar exposure group (SEG), and conduct statistical inference on the obtained data to derive representative exposure parameters. For combined exposure to chemicals with similar toxic effects, the health risk assessment methods are relatively mature. However, the assessment of combined exposure to hazards with different properties and health effects still lacks scientific authority and needs further research and discussion.

BACKGROUND:Magnetic resonance imaging is the gold standard for the diagnosis of osteonecrosis of femoral head,and previous methods of predicting osteonecrosis of femoral head collapse based on magnetic resonance images mostly require the combined assessment of coronal and sagittal images.However,osteonecrosis of femoral head tends to occur bilaterally,most hospitals perform bilateral hip magnetic resonance imaging scans during clinical examinations,but the bilateral hip scans can only view coronal and cross-sectional images,and it is difficult to obtain sagittal images,which affects the assessment of the risk of collapse.Therefore,it is of clinical value to establish a method to assess the risk of early osteonecrosis of femoral head collapse by applying the images that can be obtained after bilateral hip magnetic resonance scanning. OBJECTIVE:To establish a method of applying coronal and cross-sectional images of bilateral hip magnetic resonance imaging to assess the risk of osteonecrosis of femoral head collapse. METHODS:The medical records of 111 patients(181 hips)with early-stage osteonecrosis of femoral head diagnosed at the outpatient clinic of Honghui Hospital Affiliated to Xi'an Jiaotong University from October 2017 to October 2019 were retrospectively analyzed.They were categorized into collapsed and non-collapsed groups according to the femoral head collapse at the final follow-up,with 69 hips in the collapsed group and 112 hips in the non-collapsed group.The angle of necrotic range on the images of median coronal plane,transverse plane or one level above and below it was measured on the magnetic resonance imaging system.The sum of the two angles of necrotic angle on the coronal and transverse planes was used as the combined necrotic angle.The average of the three combined necrotic angles of each hip was taken to get the average combined necrotic angle of each hip.Finally,the correlation between the three combined necrotic angles and the average combined necrotic angle with the collapse of osteonecrosis of femoral head was analyzed,and the specificity and sensitivity of the four combined necrotic angles in predicting collapse were evaluated by using receiver operating characteristic curves. RESULTS AND CONCLUSION:(1)Totally 69 hips(38.1%)had femoral head collapse at the last follow-up and were included in the collapsed group;112 hips(61.9%)did not have progression of collapse and were included in the non-collapsed group.(2)The difference between the collapsed group and the non-collapsed group in terms of Association Research Circulation Osseous(ARCO)stage was significant(P<0.001).The difference in age,body mass index,follow-up time,gender distribution,side of onset,and causative factors was not significant(P>0.05).(3)The results of independent samples t-test suggested that all four combined necrotic angles were significantly correlated with collapse(P<0.000 1);and the differences in combined necrotic angles between the collapsed group and the non-collapsed group of ARCO stage I and the two groups of ARCO stage II were all significant(P<0.000 1).(4)In the analysis of the receiver operating characteristic,the area under the curve of the average combined necrotic angle was greater than that of the combined necrotic angle on the lower level of the median,the middle level,and the upper level of the median.(5)The average combined necrotic angle had a higher accuracy in the prediction of collapse than the lower level of the median,the middle level,and the upper level of the combined necrotic angle.(6)It is concluded that the accuracy of the average combined necrotic angle in predicting the risk of osteonecrosis of femoral head collapse is higher,and the clinical practicability is stronger,so we can consider using this method to predict the risk of osteonecrosis of femoral head collapse.

ObjectiveTo investigate the chemical hazards in the production line of lithium batteries, so as to provide a scientific basis for the management of occupational-health risk and to promote the healthy and sustainable development of the lithium battery industry. MethodsAn on-site survey on the process flow of the production of lithium battery was conducted in an enterprise. Volatile organic compounds (VOCs) in the occupational environment were collected by Summa canisters, carbonates and N-methyl pyrrolidone (NMP) were collected using activated carbon tubes, and airborne metals were collected using filter membranes. VOCs, carbonates and NMP were detected by gas chromatography-mass spectrometry (GC-MS), and airborne metal elements in the dust samples were analyzed by inductively coupled plasma mass spectrometry (ICP-MS). ResultsNon-targeted environmental monitoring results indicated that NMP was detected in the negative /positive electrode coating, assembly and drying filling workstations, dimethyl carbonate (DMC) was detected in the assembly, drying and electrolyte injection workstations, and ethyl methyl carbonate (EMC) was detected solely in the electrolyte injection workstation. Semi-quantitative analyses of VOCs identified 136 pollutants, including acrylonitrile and halohydrocarbons. Quantitative targeted environmental monitoring results revealed the highest geometric mean (GM) concentration of EMC (31.450 mg·m^-3) was found in the assembly and drying workstations, diethyl carbonate (DEC) was detected in all workstations. While vinylene carbonate (VC) and ethylene carbonate (EC) were detected only in electrolyte injection, assembly and drying workstations. NMP was detected in all positive electrode coating samples, with a GM concentration of 5.68 mg·m^-3 (concentration range: 4.0‒ 7.4 mg·m^-³). Lithium was exclusively detected in dust samples from the liquid injection workstation (GM: 0.014 μg·m^-³). ConclusionNMP, EMC, DEC, and other chemicals are identified at the key workstations such as the positive electrode coating, electrolyte injection, assembly and drying in the lithium production line. Furthermore, semi-quantitative VOCs analyses identified 136 pollutants, demonstrating a characteristic of multicomponent chemical exposure.