Background: This study aimed to evaluate the association between exposure to occupational hazards and the metabolic syndrome. A secondary objective was to analyze the additive and multiplicative effects of exposure to risk factors. Methods: This retrospective cohort was based on 31,615 health examinees at the Pusan National University Yangsan Hospital in Republic of Korea from 2012–2021. Demographic and behavior-related risk factors were treated as confounding factors, whereas three physical factors, 19 organic solvents and aerosols, and 13 metals and dust were considered occupational risk factors. Time-dependent Cox regression analysis was used to calculate hazard ratios. Results: The risk of metabolic syndrome was significantly higher in night shift workers (hazard ratio = 1.45: 95% confidence interval = 1.36–1.54) and workers who were exposed to noise (1.15:1.07–1.24). Exposure to some other risk factors was also significantly associated with a higher risk of metabolic syndrome. They were dimethylformamide, acetonitrile, trichloroethylene, xylene, styrene, toluene, dichloromethane, copper, antimony, lead, copper, iron, welding fume, and manganese. Among the 28 significant pairs, 19 exhibited both positive additive and multiplicative effects. Conclusions Exposure to single or combined occupational risk factors may increase the risk of developing metabolic syndrome. Working conditions should be monitored and improved to reduce exposure to occupational hazards and prevent the development of the metabolic syndrome.

Background: A job-exposure matrix (JEM) is an important surrogate indicator to evaluate past exposure levels. Although a Korean asbestos JEM has been constructed previously, this JEM includes only a few industrial and occupational groups. This study aimed to reconstruct the JEM by integrating the latest organized data to improve its utility. Methods: We used recent Korean standard industry and occupation codes and extracted 36 articles from a systematic literature review to initiate the reconstruction of the previous Korean asbestos JEM. The resulting data consisted of 141 combinations of industrial and occupational groups. Data from the Netherlands's JEM were also reviewed and categorized into 70 industrial and 117 occupational groups by matching with the Korean data. We also utilized Germany's data, which consisted of 10 industrial and 14 occupational groups. Results: The reconstructed Korean asbestos JEM had 141 combinations of industries and occupations. The time periods are from the 1980s to the 2000s in 10-year intervals. Most of the data were distributed between the 1990s and the 2000s. Occupations with high exposure to asbestos included knitting and weaving machine operators, automobile mechanics or assemblers, ship mechanics or assemblers, mineral ore and stone products processing mechanics, and metal casting machine operators or mold makers. Conclusions The reconstructed Korean asbestos JEM has expanded the type and duration of the occupational groups of the previous JEM and can serve as an important reference tool for evaluating asbestos exposure and designing compensation and prevention policies in Korea.

Background: A job-exposure matrix (JEM) is an important surrogate indicator to evaluate past exposure levels. Although a Korean asbestos JEM has been constructed previously, this JEM includes only a few industrial and occupational groups. This study aimed to reconstruct the JEM by integrating the latest organized data to improve its utility. Methods: We used recent Korean standard industry and occupation codes and extracted 36 articles from a systematic literature review to initiate the reconstruction of the previous Korean asbestos JEM. The resulting data consisted of 141 combinations of industrial and occupational groups. Data from the Netherlands's JEM were also reviewed and categorized into 70 industrial and 117 occupational groups by matching with the Korean data. We also utilized Germany's data, which consisted of 10 industrial and 14 occupational groups. Results: The reconstructed Korean asbestos JEM had 141 combinations of industries and occupations. The time periods are from the 1980s to the 2000s in 10-year intervals. Most of the data were distributed between the 1990s and the 2000s. Occupations with high exposure to asbestos included knitting and weaving machine operators, automobile mechanics or assemblers, ship mechanics or assemblers, mineral ore and stone products processing mechanics, and metal casting machine operators or mold makers. Conclusions The reconstructed Korean asbestos JEM has expanded the type and duration of the occupational groups of the previous JEM and can serve as an important reference tool for evaluating asbestos exposure and designing compensation and prevention policies in Korea.

Background: This study aimed to determine the effect of specific working postures on the development of varicose veins (VV). By using Korea's National Health Insurance (NHI) and National Employment Insurance (NEI) data, we analyzed the general characteristic and difference in proportions of VV cases according to occupational working posture. Methods: From the NEI and NHI data, participant demographics, such as gender, age, body mass index, and number of workers in specific occupations or industries were obtained.We classified the 240 occupations into blue-collar (BC) and white-collar (WC) occupations and subdivided them into standing, sitting, and walking groups according to the dominant working posture. Results: The number of VV patients per 100,000 individuals increased with age, with a higher number of women than men and a higher number of patients in the BC than WC groups. For the BC group, the proportion of VV cases was the highest in the standing group, followed by the walking and sitting groups, but there was no significant difference between standing and walking groups in man. For the WC group, the standing group had a higher proportion of VV cases than the sitting group, but there was no significant difference between the standing and sitting group in man. In the BC group, the proportion of VV cases was the highest among medical and welfare-related elementary workers, bakers and cookie makers, automobile assemblers, cleaning and guarding-related elemental workers, and nurses and dental hygienists. In the WC group, the proportion of VV cases was the highest among food/ lodging/tourism/entertainment/sports-related managers, environment/cleaning/protective services-related managers, finance and insurance clerks, accounting book-keeping clerks, and social welfare and counseling professionals. Conclusions This study was performed to determine the characteristics of VV with different working posture among Korean workers. It is expected to be the basis of further studies on occupational musculoskeletal diseases.

Background: This study aimed to determine the effect of specific working postures on the development of varicose veins (VV). By using Korea's National Health Insurance (NHI) and National Employment Insurance (NEI) data, we analyzed the general characteristic and difference in proportions of VV cases according to occupational working posture. Methods: From the NEI and NHI data, participant demographics, such as gender, age, body mass index, and number of workers in specific occupations or industries were obtained.We classified the 240 occupations into blue-collar (BC) and white-collar (WC) occupations and subdivided them into standing, sitting, and walking groups according to the dominant working posture. Results: The number of VV patients per 100,000 individuals increased with age, with a higher number of women than men and a higher number of patients in the BC than WC groups. For the BC group, the proportion of VV cases was the highest in the standing group, followed by the walking and sitting groups, but there was no significant difference between standing and walking groups in man. For the WC group, the standing group had a higher proportion of VV cases than the sitting group, but there was no significant difference between the standing and sitting group in man. In the BC group, the proportion of VV cases was the highest among medical and welfare-related elementary workers, bakers and cookie makers, automobile assemblers, cleaning and guarding-related elemental workers, and nurses and dental hygienists. In the WC group, the proportion of VV cases was the highest among food/ lodging/tourism/entertainment/sports-related managers, environment/cleaning/protective services-related managers, finance and insurance clerks, accounting book-keeping clerks, and social welfare and counseling professionals. Conclusions This study was performed to determine the characteristics of VV with different working posture among Korean workers. It is expected to be the basis of further studies on occupational musculoskeletal diseases.

BACKGROUND: The International Agency for Research on Cancer (IARC) defined that asbestos is a group 1 substance that causes lung cancer, mesothelioma (pleura and peritoneum), laryngeal cancer, and ovarian cancer in humans. Many studies on lung cancer, and mesothelioma caused by asbestos exposure have been conducted, but there was no case report of ovarian cancer due to asbestos exposure in Korea. We describe a case of ovarian cancer caused by asbestos exposure in a worker who worked at an asbestos textile factory for 3 years and 7 months in the late 1970s. CASE PRESENTATION: A 57-year-old woman visited the hospital because she had difficulty urinating. Ovarian cancer was suspected in radiologic examination, and exploratory laparotomy was performed. She was diagnosed with epithelial ovarian cancer. The patient did not undergo postoperative chemotherapy and recovered. She joined the asbestos factory in March 1976 and engaged in asbestos textile twisting and spinning for 1 year, 2 years and 7 months respectively. In addition, she lived near the asbestos factory for more than 20 years. There was no other specificity or family history. CONCLUSION: Considering the patient’s occupational and environmental history, it is estimated that she had been exposed to asbestos significantly, so we determined that ovarian cancer in the patient is highly correlated with the occupational exposure of asbestos and environmental exposure is a possible cause as well. Social devices are needed to prevent further exposure to asbestos. It is also necessary to recognize that ovarian cancer can occur in workers who have previously been exposed to asbestos, and the education and social compensation for those workers are needed.
Asbestos ; Compensation and Redress ; Drug Therapy ; Education ; Environmental Exposure ; Female ; Humans ; International Agencies ; Korea ; Laparotomy ; Laryngeal Neoplasms ; Lung Neoplasms ; Mesothelioma ; Middle Aged ; Occupational Diseases ; Occupational Exposure ; Ovarian Neoplasms ; Sensitivity and Specificity ; Textile Industry ; Textiles

BACKGROUND: The goal of this study is to develop a general population job-exposure matrix (GPJEM) on asbestos to estimate occupational asbestos exposure levels in the Republic of Korea. METHODS: Three Korean domestic quantitative exposure datasets collected from 1984 to 2008 were used to build the GPJEM. Exposure groups in collected data were reclassified based on the current Korean Standard Industrial Classification (9th edition) and the Korean Standard Classification of Occupations code (6th edition) that is in accordance to international standards. All of the exposure levels were expressed by weighted arithmetic mean (WAM) and minimum and maximum concentrations. RESULTS: Based on the established GPJEM, the 112 exposure groups could be reclassified into 86 industries and 74 occupations. In the 1980s, the highest exposure levels were estimated in “knitting and weaving machine operators” with a WAM concentration of 7.48 fibers/mL (f/mL); in the 1990s, “plastic products production machine operators” with 5.12 f/mL, and in the 2000s “detergents production machine operators” handling talc containing asbestos with 2.45 f/mL. Of the 112 exposure groups, 44 groups had higher WAM concentrations than the Korean occupational exposure limit of 0.1 f/mL. CONCLUSION: The newly constructed GPJEM which is generated from actual domestic quantitative exposure data could be useful in evaluating historical exposure levels to asbestos and could contribute to improved prediction of asbestos-related diseases among Koreans.
Asbestos* ; Classification ; Dataset ; Mesothelioma ; Occupational Exposure ; Occupations* ; Republic of Korea* ; Talc

There is a more urgent call for educational methods of machine learning in medical education, and therefore, new approaches of teaching and researching machine learning in medicine are needed. This paper presents a case using machine learning through text analysis. Topic modeling of news articles with the keyword ‘asbestos’ were examined. Two hypotheses were tested using this method, and the process of machine learning of texts is illustrated through this example. Using an automated text analysis method, all the news articles published from January 1, 1990 to November 15, 2016 in South Korea which included ‘asbestos’ in the title and the body were collected by web scraping. Differences in topics were analyzed by structured topic modelling (STM) and compared by press companies and periods. More articles were found in liberal media outlets. Differences were found in the number and types of topics in the articles according to the partisanship and period. STM showed that the conservative press views asbestos as a personal problem, while the progressive press views asbestos as a social problem. A divergence in the perspective for emphasizing the issues of asbestos between the conservative press and progressive press was also found. Social perspective influences the main topics of news stories. Thus, the patients' uneasiness and pain are not presented by both sources of media. In addition, topics differ between news media sources based on partisanship, and therefore cause divergence in readers' framing. The method of text analysis and its strengths and weaknesses are explained, and an application for the teaching and researching of machine learning in medical education using the methodology of text analysis is considered. An educational method of machine learning in medical education is urgent for future generations.
Asbestos ; Education, Medical ; Humans ; Korea ; Machine Learning ; Methods ; Social Problems ; Social Responsibility

No abstract available.
Korea* ; Lung Neoplasms* ; Lung* ; Radon*

Acknowledgements section was missing. The publisher apologises for these errors.