Firefighters are exposed to many dangerous working conditions. Many studies have identified the risk of disease for firefighters, but only a few studies have addressed the medical expenses of firefighters, which represents a concrete scale of disease. Our purpose in this study was to determine the medical expenditures of firefighters to assess the overall scale of disease in Korea. We focused on cancer, mental disorders, cardio-cerebrovascular disease, and musculoskeletal disease, the prevalence of which was expected to be high in firefighters.
This study utilized National Health Insurance Service data. We targeted firefighters, police officers, and government officials. We classified disease based on the 10th revision of the International Statistical Classification of Diseases and Related Health Problems codes. We compared prevalence by the age-standardized prevalence rate, considering standard distribution of the population. Medical expenditure of disease was defined as outpatient fees, hospitalization fees, and drug costs. Total medical expenditures were calculated by the sum of those 3 categories.
The age-standardized prevalence of cancer, mental disorders, and cardiovascular disease in firefighters was slightly higher than or similar to that of government officials and police officers (no significant difference). However, medical expenditures for stomach cancer, mental disorders, and most cardio-cerebrovascular diseases were higher in firefighters than in others. In particular, firefighters spent 12 times more money for ischemic heart disease than did government officials. Of musculoskeletal diseases, lumbar disc disorder had the highest expenditures among firefighters.
The age-standardized prevalence of most of diseases of firefighters was not as high as in the other groups, but the medical expenses of firefighters were much higher than those of government officials and police officers.
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The present study aimed to investigate the basic characteristics of carpal tunnel syndrome (CTS) and its differences between occupations using Korea's National Health Insurance (NHI) and National Employment Insurance (NEI).
The study participants were obtained from the NEI and NHI data from 2008 to 2015, with a diagnosis code of G560 (CTS) as the main or sub-diagnosis. Data about gender, age, diabetes mellitus, smoking, drinking, and length of employment, information about type of occupation, and number of employees according to age and occupation were obtained from NHI and NEI data. In total, 240 occupations were classified into blue-collar (BC) and white-collar (WC) work. In addition, each occupation was classified as high-risk and low-risk groups depending on the degree of wrist usage.
The number of patients with CTS per 100,000 individuals increased with advancing age, and it was higher in women (4,572.2) than in men (1,798.5). Furthermore, the number was higher in BC workers (3,247.5) than in WC workers (1,824.1) as well as in the high-risk group than in the low-risk group in both BC workers (3,527.8 vs. 1,908.2) and WC workers (1,829.9 vs. 1,754.4). The number of patients with CTS was higher in the high-risk group than in the low-risk group among male and female BC workers and female WC workers. However, the number was higher in the low-risk group among male WC workers. In the BC category, the number of patients with CTS was highest among food processing-related workers (19,984.5). In the WC category, the number of patients with CTS was highest among social workers and counselors (7,444.1).
The results of this study are expected to help identify occupational differences in patterns of CTS. High number of patients with CTS was seen in new jobs, as well as in previous studies.
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Bus drivers are known to be highly at risk of cardiovascular diseases. In this study, we assessed the cardiovascular disease prevalence of bus company employees in Seoul, South Korea, and compared the results to those of general workers.
We analyzed the 2014 Korean National Health Insurance (NHI) data and defined hypertension, diabetes, dyslipidemia, ischemic heart disease, and cerebrovascular disease based on the KCD-6 medical diagnoses. We used bus company employees as surrogate participants of bus drivers due to the characteristics of Korean NHI data. We identified bus company employees in Seoul based on one’s workplace which the insurance is registered. The prevalence of five diseases was compared between the bus company employees and general workers. We also calculated the odds ratios (OR) of five diseases between the bus company employees and general workers. To compensate the vast demographical differences between the two groups, we performed propensity score matching.
Bus company employees have higher OR for having hypertension (OR 1.33, 95% CI: 1.28–1.39), diabetes mellitus (1.14, 95% CI: 1.08–1.22), and dyslipidemia (1.23, 95% CI: 1.17–1.29) than the general workers or propensity score matched controls. However, the OR of having ischemic heart disease were not significant. The OR of cerebrovascular disease were lower in bus company employees than in the general workers after adjusting the covariates, but similar in the propensity score matched model.
This study showed that the ORs of cardiovascular disease risk factors are high in bus company employees when compared to the general working population. Further studies with the longitudinal design should be conducted to confirm the causal association.
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This study aims to investigate associated factors including the physician and the employer of successful return to work (RTW) in occupationally injured workers.
This study is based on the first panel study of workers’ compensation insurance (PSWCI), published in June 2014. The PSWCI is a sample survey of occupationally injured workers who completed medical care in 2012 (89,921 people). A total of 2000 subjects were sampled based on sex, age, nine metropolitan-based regions, disability ratings, duration of rehabilitation, and whether vocational rehabilitation service was used. We divided the study population into two groups: return to work (RTW) group (job retention, reemployment, unpaid family worker, and self-employment), and non-RTW group (joblessness and economical inactivity). The odds ratios (ORs) and 95 % confidence intervals (CI) related to differences in basic characteristics, part of physician and employer-related factors between those who succeeded to RTW and those who did not were measured using multivariable logistic regression model.
The success of RTW is 70.6 % (
The physician and the employer have a significant impact on the RTW.
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We aimed to ascertain the relationship between several factors and successful return to work using a structural equation model.
We used original data from the Panel Study of Worker’s Compensation Insurance, and defined four latent variables as occupational, individual, supportive, and successful return to work. Each latent variable was defined by its observed variables, including age, workplace size, and quality of the medical services. A theoretical model in which all latent variables had a relationship was suggested. After examining the model, we modified some pathways that were not significant or did not fit, and selected a final structural equation model that had the highest goodness of fit.
All three latent variables (occupational, individual, and supportive) showed statistically significant relationships with successful return to work. The occupational and supportive factors had relationships with each other, but there was no relationship between individual and the other factors. Nearly all observed variables had significance with their latent variables. The correlation coefficients from the latent variables to successful return to work were statistically significant and the indices for goodness of fit were satisfactory. In particular, four observed variables—handicap level, duration of convalescence, working duration, and support from the company—showed construct validities with high correlation coefficients.
All factors that we examined are related to successful return to work. We should focus on the supportive factor the most because its variables are modifiable to promote a return to work by those injured in their workplace.
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