Secondhand Smoke in the Workplace Is Associated With Depression in Korean Workers

Kim, Seunghan; Oh, Joa Sub; Yun, Byungyoon; Cho, Ara; Sim, Juho; Yoon, Jin‐Ha

doi:10.3389/fpubh.2022.802083

Cited by 4 publications

(2 citation statements)

References 32 publications

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“…Our study results suggest that currently, married elderly people can lower their probability of developing depression by improving their GNRI. Regarding the impact of smoking on depression, most studies focus on secondhand smoke [ 26 , 27 ]. Smoking has various states, including quitting, previously smoking, and repeated quitting, so more accurate data are required to describe the smoking situation.…”

Section: Discussionmentioning

confidence: 99%

Association between geriatric nutritional risk index and depression prevalence in the elderly population in NHANES

Li,

Zhang,

Yang

et al. 2024

BMC Public Health

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Background The prevalence of depression is increasing in the elderly population, and growing evidence suggests that malnutrition impacts mental health. Despites, research on the factors that predict depression is limited. Methods We included 2946 elderly individuals from National Health and Nutrition Examination Survey (NHANES) spanning the years 2011 through 2014. Depressive symptoms were assessed using the PHQ-9 scale. Multinomial logistic regression was performed to evaluate the independent association between Geriatric Nutritional Risk Index (GNRI) and depression prevalence and scores. Subgroup analysis was conducted to explore potential factors influencing the negative correlation between GNRI and depression. Restricted cubic spline graph was employed to examine the presence of a non-linear relationship between GNRI and depression. Results The depression group had a significantly lower GNRI than the non-depression group, and multivariate logistic regression showed that GNRI was a significant predictor of depression (P < 0.001). Subgroup analysis revealed that certain demographic characteristics were associated with a lower incidence of depression in individuals affected by GNRIs. These characteristics included being female (P < 0.0001), non-Hispanic black (P = 0.0003), having a moderate BMI (P = 0.0005), having a college or associates (AA) degree (P = 0.0003), being married (P = 0.0001), having a PIR between 1.50 and 3.49 (P = 0.0002), being a former smoker (P = 0.0002), and having no history of cardiovascular disease (P < 0.0001), hypertension (P < 0.0001), and diabetes (P = 0.0027). Additionally, a non-linear negative correlation (non-linear P < 0.01) was found between GNRI and depression prevalence, with a threshold identified at GNRI = 104.17814. Conclusion The GNRI demonstrates efficacy as a reliable indicator for forecasting depression in the elderly population. It exhibits a negative nonlinear correlation with the prevalence of depression among geriatric individuals.

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Section: Discussionmentioning

confidence: 99%

Association between geriatric nutritional risk index and depression prevalence in the elderly population in NHANES

Li,

Zhang,

Yang

et al. 2024

BMC Public Health

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“… 17 , 18 Moreover, most Korean female non-smokers exposed to SHS at home and male non-smokers exposed to SHS at workplaces face an elevated risk of both depression and suicidal ideation. 19 – 21 …”

Section: Introductionmentioning

confidence: 99%

Machine-learning model for predicting depression in second-hand smokers in cross-sectional data using the Korea National Health and Nutrition Examination Survey

Kim,

Han

et al. 2024

DIGITAL HEALTH

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Objective Depression among non-smokers at risk of second-hand smoke (SHS) exposure has been a neglected public health concern despite their vulnerability. The objective of this study was to develop high-performance machine-learning (ML) models for the prediction of depression in non-smokers and to identify important predictors of depression for second-hand smokers. Methods ML algorithms were created using demographic and clinical data from the Korea National Health and Nutrition Examination Survey (KNHANES) participants from 2014, 2016, and 2018 ( N = 11,463). The Patient Health Questionnaire was used to diagnose depression with a total score of 10 or higher. The final model was selected according to the area under the curve (AUC) or sensitivity. Shapley additive explanations (SHAP) were used to identify influential features. Results The light gradient boosting machine (LGBM) with the highest positive predictive value (PPV; 0.646) was selected as the best model among the ML algorithms, whereas the support vector machine (SVM) had the highest AUC (0.900). The most influential factors identified using the LGBM were stress perception, followed by subjective health status and quality of life. Among the smoking-related features, urine cotinine levels were the most important, and no linear relationship existed between the smoking-related features and the values of SHAP. Conclusions Compared with the previously developed ML models, our LGBM models achieved excellent and even superior performance in predicting depression among non-smokers at risk of SHS exposure, suggesting potential goals for depression-preventive interventions for non-smokers during public health crises.

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Association between secondhand smoke exposure across the life course and depressive symptoms among Chinese older adults

Yang,

Sun

et al. 2024

Journal of Affective Disorders

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Secondhand Smoke in the Workplace Is Associated With Depression in Korean Workers

Cited by 4 publications

References 32 publications

Association between geriatric nutritional risk index and depression prevalence in the elderly population in NHANES

Association between geriatric nutritional risk index and depression prevalence in the elderly population in NHANES

Machine-learning model for predicting depression in second-hand smokers in cross-sectional data using the Korea National Health and Nutrition Examination Survey

Association between secondhand smoke exposure across the life course and depressive symptoms among Chinese older adults

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