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The UK's work-related diseases and occupational injury surveillance system consists of Reporting of Injuries, Diseases and Dangerous Occurrences Regulations 2013 (RIDDOR), Labour Force Survey (LFS), The Health and Occupation Research network in General Practice (THOR-GP), and Reporting to The Health and Occupation Research network by specialist physicians. This article briefly described the scope, content, and methods of each surveillance programme in the UK work-related diseases and occupational injury surveillance system, and summarized their advantages and disadvantages. Among them, employers are required to report to relevant law enforcement authorities by RIDDOR, data are highly accessible but with a concern of serious underreport, and it is the only data source of fatal occupational injuries; LFS, a representative national household sample survey, covering occupational injuries and work-related diseases, is the primary data source of non-fatal occupational injuries and work-related diseases such as stress, anxiety, and depression, but collects non-clinically proven data based on self-perception; general practitioners report clinically confirmed work-related diseases, which is more scientific in attribution and is a good secondary source of work-related diseases; specialist physicians report clinically confirmed cases of higher severity, which is the primary source of data on conditions such as asthma and dermatitis, but may underestimate morbidity. Each surveillance programme of the system has its own characteristics, intersects, and complements each other, which can provide reference for the construction of occupational injury surveillance system in China.
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In order to promote the development of China's occupational injury surveillance system, this paper presented the legal basis, project overview, reporting procedures, definitions and stati statistical scope, data sources and collection standards, statistical data management and analysis points of the European Statistics on Accidents at Work (ESAW), and combined with existing research and related surveillance management system in China, five key points were proposed for constructing China's occupational injury surveillance system: 1) Establish and improve laws and regulations related to occupational injury surveillance; 2) Promote utilization of nation-level data systems; 3) Establish and optimize a sound national occupational injury surveillance system; 4) Provide standardized protocols for data collection and data application of occupational injury statistics; 5) Strengthen supervision and law enforcement targeting industries and enterprises.
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Background The United Kingdom (UK) adopts active surveillance and passive surveillance to jointly collect occupational injury data, and builds a relatively complete occupational injury surveillance system, which can provide reference for the construction of China's occupational injury surveillance system. Objective To compare the results of active surveillance and passive surveillance of occupational injuries in the UK, and to explore the joint application value of active and passive surveillance methods in the field of occupational injury prevention and control. Methods The non-fatal occupational injury active surveillance data from Labor Force Survey were used to calculate indicators such as number of reported cases, reporting rate, lost workdays per year, lost workdays per capita, and average lost workdays per case. The fatal passive surveillance data reported by the employers were used to calculate number of reported deaths, reported mortality, and other indicators. Join-point regression was used to estimate the reported trends of fatal and non-fatal occupational injuries from 2004 to 2020, and the annual percentage change (APC) and average annual percentage change (AAPC) were calculated. Results The active surveillance data showed that from 2004 to 2020, the number of reported cases of absenteeism ≥0 d due to occupational injury decreased from 89.7 (95%CI: 85.2, 94.2) per ten thousand to 44.1 (95%CI: 39.1, 49.2) per ten thousand, and the reporting rate of occupational injury decreased from 32100/100000 (95%CI: 3050/100000, 3370/100000) to 1410/100000 (95%CI: 1250/100000, 1570/100000), showing a linear downward trend (both APC and AAPC were −3.88%, P<0.05); the average lost workdays per case in 2019 was 9.1 (95%CI: 6.8, 11.5) d. The passive surveillance data showed that from 2004 to 2020, the number of reported deaths due to occupational injury decreased from 223 to 142, and the reporting rate of occupational injury decreased from 0.78/100000 to 0.44/100000, showing a linear downward trend (both APC and AAPC were −4.59%, P<0.05). Conclusion The reporting rates of fatal and non-fatal occupational injuries in the UK are showing a linear downward trend. The active surveillance method based on Labor Force Survey provides more surveillance indicators for non-fatal occupational injuries, and the passive surveillance method based on employer report has more advantages in assessment of fatal occupational injuries. Jointly applying the two surveillance modalities and the combination of trend analysis indicators, such as AAPC, provide a more comprehensive picture of the epidemiological characteristics of occupational injuries.
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Background The severity of occupational injury in countries such as the United Kingdom, the United States, and Germany is usually analyzed using lost workdays, but in existing occupational injury surveillance research in China, the application of this index is rare. Objective To evaluate the application value of lost workdays in non-fatal occupational injury surveillance, and provide a reference for the construction of occupational injury surveillance index system. Methods The public data of European Statistics on Accidents at Work (ESAW) from 2010 to 2019 on non-fatal injury accidents in 27 member states of the European Union were used. Non-fatal occupational injury is defined as an injury event during occupational activities or at work resulting a victim's absence from work for ≥4 d. According to the European Statistics on Accidents at Work-Summary methodology, the lost workdays were divided into 8 categories (4-6 d, 7-13 d, 14-20 d, 21-30 d, 31-91 d, 92-182 d, 183 d and above, and unknown). Annual percentage change (APC) and the average annual percentage change (AAPC) were used to evaluate the overall trend changes in the incidence rate of non-fatal occupational injury accidents in different lost workdays from 2010 to 2019, and the non-fatal occupational injury accidents in key industries. The characteristics of the occurrence of non-fatal occupational injuries were analyzed in conjunction with the changes in non-fatal occupational injuries in different lost workdays in the industry. Results From 2010 to 2019, the overall incidence of non-fatal occupational injury accidents in the European Union showed a downward trend, and the AAPC was −1.0% (P<0.05). The accident rates of lost workdays of 4-6 d and 92-182 d showed an upward trend, and the AAPC were 7.9% and 5.8% respectively (P<0.05). The average annual accident rates of non-fatal occupational injuries (≥4 d) in Categories C (manufacturing industry), E (water supply, sewage treatment, waste management and remediation), and F (construction industry) showed a linear downward trend, and the AAPC were −3.0%, −2.5%, and −1.5%, respectively (P<0.05). However, among them, the rate of non-fatal occupational injury accidents with 92-182 d of lost workdays in the manufacturing industry showed a significant upward trend, with an AAPC of 3.7% (P<0.001). Conclusion Using lost workdays combined with APC and AAPC by Join-point linear regression analysis can measure the severity and trend changes of non-fatal occupational injury accidents in different industries and different lost workdays. This indicator has an important practical significance in evaluating the effectiveness of occupational injury prevention and control strategies adopted by countries and enterprises.
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Background Occupational injury is one of the important causes of death among the working population and a worldwide hot topic, but there are few relevant studies on the trend and prediction of occupational injury attributable deaths in China. Objective To analyze the trend of occupational injury attributable deaths in China from 2000 to 2019, predict the deaths of occupational injuries in China from 2020 to 2024 by contructing a gray GM(1,1) model, and provid a reference for surveillance and assessment of occupational injuries. Methods Mortality, crude mortality rates, and standardized mortality rates of occupational injuries in China by year, sex, and age groups were calculated using data of the Global Burden of Disease (GBD) 2019 study. Join-point model was used to analyze possible trend of standardized mortality rate from 2000 to 2019, and calculate annual percentage change (APC) and average annual percentage change (AAPC). After a gray model GM(1,1) was established, the accuracy of the model was evaluated by posterior error ratio (C) and small error probability (P) and rated as Level 1 (good, C≤0.35 and P≥0.95) or Level 2 (qualified, 0.35<C≤0.50 and 0.80≤P<0.95). Then the gray model was further used to predict the number of deaths and standardized mortality rates of occupational injuries in China from 2020 to 2024. Results From 2000 to 2019, the deaths due to occupational injuries in China showed a downward trend, the number of deaths decreased from 111557 to 61780, the crude mortality rate decreased from 8.58/100000 to 4.34/100000, the standardized mortality rate decreased from 7.67/100000 to 3.65/100000, and the AAPC of standardized mortality rate was −4.0% (P<0.05); the number of male deaths decreased from 87760 to 49192, and the male standardized mortality rate decreased from 11.78/100000 to 5.68/100000; the number of female deaths decreased from 23797 to 12588, and the female standardized mortality rate decreased from 3.34/100000 to 1.55/100000; the AAPCs of male and female standardized mortality rate were −3.9% and −4.1% respectively. The accuracy of the established gray model for deaths (C=0.09, P=1) was rated as Level 1, and that for standardized mortality rate (C=0.41, P=0.9) was rated as level 2, which allowed for prediction extrapolation. The model showed that from 2020 to 2024, the number of occupational injury attributable deaths would be 76039, 73849, 71721, 69655, and 67649, and the standardized mortality rate would be 4.23/100000, 4.07/100000, 3.92/100000, 3.77/100000, and 3.62/100000, respectively. Conclusion From 2000 to 2019, the standardized mortality rate of occupational injuries in China showed a downward trend, and it is predicted that the standardized mortality rate from 2020 to 2024 will still show a downward trend, but the number of deaths will remain high, so it is necessary to continue to strengthen prevention and control of occupational injuries.
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Background Occupational injuries are one of the leading causes of death or disability in occupational populations. According to the World Health Organization and the International Labour Organization, occupational injuries were the occupational contributor responsible for the largest loss of disability-adjusted life years (DALY) globally in 2016. Objective To analyze the burden of deaths attributed to occupational injuries in Chinese population from 1990 to 2019, and provide a reference for further construction of occupational injury surveillance system. Methods Using the results and data of the Global Burden of Disease 2019 (GBD 2019), this study estimated the burden of deaths attributable to occupational injuries by year, sex, and age groups, and the indicators included deaths, years of life lost (YLL), mortality, and YLL rates. Age-standardized rates of deaths and YLL rates were calculated using a world standard population presented by GBD 2019. Annualized rate of change (ARC) was use to evaluate changes in the indicators over time. All results were presented as point estimates with 95% uncertainty intervals (95%UI). Results In 2019, the deaths attributable to occupational injuries among women in China accounted for 33.16% of that among world's women, their YLL accounted for 31.88%, and the two indicators among Chinese men accounted for 17.98% and 17.09%, respectively. Compared with 1990, the standardized mortality rate and the standardized YLL rate attributable to occupational injuries in China in 2019 decreased, among which the ARCs of the standardized mortality rate in the whole population, men, and women were −0.68 (95%UI: −0.78, −0.51), −0.68 (95%UI: −0.80, −0.47), and −0.68 (95%UI: −0.82, −0.46), respectively. The ARCs of the standardized YLL rate in the whole population, men, and women were −0.68 (95%UI: −0.78, −0.51), −0.67 (95%UI: −0.80, −0.48), and −0.68 (95%UI: −0.81, −0.44), respectively. Absolute values of the ARCs of the standardized mortality rate and the standardized YLL rate attributable to occupational injuries from 1990 to 2010 were higher than those from 2010 to 2019. The ARCs of the standardized YLL rate for road injuries, falls, and drowning from 1990 to 2010 were −0.55 (95%UI: −0.67, −0.36), −0.57 (95%UI: −0.73, −0.38), −0.77 (95%UI: −0.84, −0.63), and the ARCs from 2010 to 2019 were −0.27 (95%UI: −0.46, −0.02), −0.07 (95%UI: −0.34, −0.26), −0.06 (95%UI: −0.32, −0.29), respectively. In 2019, the standardized mortality rate attributable to occupational injuries among Chinese men was 5.68/100000 (95%UI: 3.89/100000, 8.23/100000), and the standardized YLL rate was 286.27/100000 (95%UI: 197.58/100000, 411.38/100000); the standardized mortality rate attributable to occupational injuries among Chinese women was 1.55/100000 (95%UI: 0.99/100000, 2.36/100000), and the standardized YLL rate was 80.85/100000 (95%UI: 51.61/100000, 122.07/100000). Conclusion From 1990 to 2019, the burden of deaths attributable to occupational injuries in China is declined, but the rate of decline is slowed down in the last decade. The burden of deaths attributable to occupational injuries among women in China still accounts for a high proportion of the global burden among women. The burden of deaths attributable to occupational injuries among Chinese men is higher than that among Chinese women.
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Background Identification and analysis of influencing factors of occupational injury is an important research content of feature selection. In recent years, with the rise of machine learning algorithms, feature selection combined with Boosting algorithm provides a new analysis idea to construct occupational injury prediction models. Objective To evaluate applicability of Boosting algorithm-based model in predicting severity of miners' non-fatal occupational injuries, and provide a basis for rationally predicting the severity level of miners' non-fatal occupational injuries. Methods The publicly available data of the US Mine Safety and Health Administration (MSHA) from 2001 to 2021 on metal miners' non-fatal occupational injuries were used, and the outcome variables were lost working days < 105 d (minor injury) and ≥ 105 d (serious injury). Four different feature sets were screened out by four feature selection methods including least absolute shrinkage and selection operator (Lasso) regression, stepwise regression, single factor + Lasso regression, and single factor + stepwise regression. Logistic regression, gradient boosting decision tree (GBDT), and extreme gradient boosting (XGBoost) were selected to construct prediction models by training with the four feature sets. A total of 12 prediction models of severity of miners' non-fatal occupational injuries were built and their area under the curve (AUC), sensitivity, specificity, and Youden index were calculated for model evaluation. Results According to the results of four feature selection methods, age, time of accident occurrence, total length of service, cause of injury, activities that triggered injury occurrence, body part of injury, nature of injury, and outcome of injury were identified as influencing factors of non-fatal occupational injury severity in miners. Feature set 4 was the optimal set screened out by single factor+stepwise regression and the GBDT model presented the best predictive performance in predicting the severity of non-fatal occupational injuries. The associated specificity, sensitivity, and Youden index were 0.7530, 0.9490, and 0.7020, respectively. The AUC values of logistic regression, GBDT, and XGBoost models trained by feature set 4 were 0.8526 (95%CI: 0.8387, 0.8750), 0.8640 (95%CI: 0.8474, 0.8806), and 0.8603 (95%CI: 0.8439, 0.8773), respectively, higher than the AUC values trained by feature set 2 [0.8487 (95%CI: 0.8203, 0.8669), 0.8110 (95%CI: 0.8012, 0.8344), and 0.8439 (95%CI: 0.8245, 0.8561), respectively] . The AUC values of GBDT and XGBoost models trained by feature set 4 were higher than that of logistic regression model. Conclusion The performance of the prediction models constructed by predictors screened out by two feature selection methods is better than those by single feature selection methods. At the same time, under the condition of optimal feature set, the performance of model prediction based on Boosting is better than that of traditional logistic regression model.