Jongin Lee

Heavy smoking rate trends and related factors in Korean occupational groups: analysis of KNHANES 2007-2012 data

Objectives The present study was designed to investigate the smoking and heavy smoking trends and identify possible related factors among Korean male workers from 2007 to 2012 by occupational groups. Methods The data were derived from the fourth (2007–2009) and fifth (2010–2012) waves of the Korean National Health and Nutrition Examination Survey (KNHANES). Occupational groups were categorised into three groups, which were non-manual, manual and service and sales groups. Age-adjusted prevalence rates of smoking and heavy smoking (>20 cigarettes/day) in men aged 25–64 years were calculated. Factors associated with heavy smoking were investigated using logistic regression analyses. Results Smoking rate in manual workers decreased gradually over time (p for trend <0.0001). Smoking rate was higher in manual than non-manual workers, but the difference reduced over time (p for trend <0.0001). Heavy smoking rate decreased from 2007 to 2012 (p for trend <0.0001). Heavy smoking rate was higher in manual than non-manual workers; however, this difference increased over time. Stress, depressive mood and long working hours (≥60 h/week) were associated with heavy smoking. Conclusions Antismoking policy should focus on current and heavy smokers. Workplace antismoking programmes should consider working hours and stress, especially in manual workers.

The association between psychiatric disorders and work-related problems among subway drivers in Korea

ObjectivesThis study aimed to find the prevalence and occupational risk factors for major psychiatric disorders among subway drivers in South Korea.MethodsOf all 998 current subway drivers, 995 participated in this study. The Korean version of the Composite International Diagnostic Interview (K-CIDI 2.1) was administered by trained interviewers to diagnose psychiatric disorders in all participants. The questions on socio-demographic characteristics and working conditions included some questions related to a person under train (PUT) experience and work-related problems. One-year prevalence and lifetime prevalence of major depressive disorder (MDD), post-traumatic stress disorder (PTSD), and panic disorder were diagnosed through the interview. The standardized prevalence ratios (SPRs) of these three disorders were calculated in the sample of subway drivers using the 2011 Korean National Epidemiologic Survey data as a basis. Multiple logistic regressions were performed to determine the association between work-related factors and the prevalence of the psychiatric disorders.ResultsThe standardized prevalence ratios (SPRs) for a 1-year prevalence of MDD and PTSD among subway drivers were 1.1 (95% CI 0.7-1.7) and 5.6 (95% CI 3.1-8.8), respectively. Conflict with passengers was significantly associated with an increased risk for both MDD and PTSD in 1-year and in lifetime prevalence. Experiencing a sudden stop due to an emergency bell increased the risk of the lifetime prevalence of MDD (OR 2.61, 95% CI 1.14-6.97) and PTSD (OR 7.53, 95% CI 1.77-32.02). The risk of PTSD significantly increased among drivers who once experienced a near accident in terms of both the 1-year prevalence (OR 8.81, 95% CI 1.96-39.3) and the lifetime prevalence (OR 6.36, 95% CI 2.40-16.90).ConclusionsPTSD and panic disorder were more prevalent among subway drivers than in the general population. We found that having a conflict with passengers, a near accident, and a breakdown while driving can be risk factors for psychiatric disorders among subway drivers. Therefore, a prompt and sensitive approach should be introduced for these high risk groups within the subway company.

0104 The Impact of Occupational Stress on Suicide Ideation of Subway Drivers

Objectives To investigate and manage subway drivers’ mental health, we conducted a temporary mental health checkup on the subway drivers of one transportation company, and analysed the relationship between the suicide ideation and occupational stress factors. Method The subway drivers (n = 995) were asked to fill out a questionnaire and individual interviews were conducted afterwards. Interviews were performed using Korean Composite International Diagnostic Interview (K-CIDI). Participants were classified into two groups; suicide ideation group (SIG) and non suicide ideation group (NSIG). Socio-demographic factors, health information, occupational characteristics, Korean Occupational Stress Scale (KOSS), Davidson Trauma Scale (DTS) and Centre for Epidemiological Studies-Depression Scale (CES-D) were included in the questionnaire. Relationship between occupational stress and the suicide ideation within a year were analysed using multiple logistic regression. Results Logistic regression model after adjusting the age showed that domains of Job demand, Insufficient job control, Interpersonal conflict, Lack of reward, Occupational climate had significant relationship with suicidal ideation. Another model that adjusted factors that was significant in descriptive statistics revealed that domains of Insufficient job control (OR=2.223), Interpersonal conflict (OR=2.478), Lack of reward (OR=2.701) had significant relationship with suicidal ideation. Conclusions Three occupational stress domains of KOSS had statistically significant relationship with the suicidal ideation within a year after adjusting occupational factors that was related to it. To prevent subway drivers’ suicide ideation, stress management program should be applied to this group.

Prediction of Return-to-original-work after an Industrial Accident Using Machine Learning and Comparison of Techniques

Background Many studies have tried to develop predictors for return-to-work (RTW). However, since complex factors have been demonstrated to predict RTW, it is difficult to use them practically. This study investigated whether factors used in previous studies could predict whether an individual had returned to his/her original work by four years after termination of the workers recovery period. Methods An initial logistic regression analysis of 1,567 participants of the fourth Panel Study of Workers Compensation Insurance yielded odds ratios. The participants were divided into two subsets, a training dataset and a test dataset. Using the training dataset, logistic regression, decision tree, random forest, and support vector machine models were established, and important variables of each model were identified. The predictive abilities of the different models were compared. Results The analysis showed that only earned income and company-related factors significantly affected return-to-original-work (RTOW). The random forest model showed the best accuracy among the tested machine learning models; however, the difference was not prominent. Conclusion It is possible to predict a workers probability of RTOW using machine learning techniques with moderate accuracy.

The effects of individual, occupational, and supportive factors on successful return to work using a structural equation model.

ObjectivesWe aimed to ascertain the relationship between several factors and successful return to work using a structural equation model.MethodsWe 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.ResultsAll 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.ConclusionsAll 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.