Relationship between women's smoking and laryngeal disorders based on the urine cotinine test: results of a national population-based survey

Byeon, Haewon; Lee, Dongwoo; Cho, Sung-Hyoun

doi:10.1136/bmjopen-2016-012169

Cited by 4 publications

(3 citation statements)

References 27 publications

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“…In the Republic of Korea, females tend to hide their health risk behaviors because of the social image that smoking women tend to be sexually promiscuous, which does not apply to smoking men. [31,32] When the difference in smoking rate between genders was evaluated using self-reporting and urine cotinine test, women hid smoking behaviors 6 times more than men. [33] These results support the possibility the health risk behaviors were underestimated for females in the health risk behavior survey and explained why the health risk behavior cluster was found only in males.…”

Section: Discussionmentioning

confidence: 99%

A laryngeal disorders prediction model based on cluster analysis and regression analysis

Byeon

2019

Medicine

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This study provided the baseline for establishing policies for community health promotion programs to propose the clusters of multiple health risk factors and identify the risks of laryngeal disorders according to the clusters by using the national level survey representing the South Korean population. This study targeted 5941 people who completed the 5th Korean National Health and Nutrition Examination Survey. The independent variables were age, sex, smoking, high-risk drinking, education level, occupation, household income, and self-reported voice problems. The identify cluster relationship with laryngeal disorders by conducting 2-way cluster analysis and multinomial logit analysis. The prevalence of laryngeal disorder was 6.7%. The results of analysis, 3 clusters were automatically extracted. Multinomial logistic regression analysis showed that sociodemographic factors, health risk behaviors, and health status clusters were significantly related to the risk of laryngeal disorders. The males who were smoking, high-risk drinking, college graduate and above, high income, and non-manual workers had a higher risk of laryngeal disorders than females who were non-smokers, non-drinkers, 60 years old and older, economically inactive, and high school graduate. The results of this study suggested that it may be effective to classify population according to sociodemographic and health behaviors and develop health education materials and health promotion program accordingly in order to prevent laryngeal disorders.

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Section: Discussionmentioning

confidence: 99%

A laryngeal disorders prediction model based on cluster analysis and regression analysis

Byeon

2019

Medicine

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“…Consequently, it is important to fully understand the etiology of a benign laryngeal mucosal disorder and identify multiple risk factors of the disease in order to perform accurate diagnosis and treatment. Nevertheless, most studies that have evaluated the risk factors of laryngeal disorders have just tried to find individual risk factors using regression analysis [25,26,27,28,29], and only a few studies have explored the multiple risk factors of benign laryngeal mucosal disorders using machine learning [30].…”

Section: Introductionmentioning

confidence: 99%

Is Deep Learning Better than Machine Learning to Predict Benign Laryngeal Disorders?

Byeon¹

2021

IJACSA

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It is important in otolaryngology to accurately understand the etiology of a laryngeal disorder, diagnose it early, and provide appropriate treatment accordingly. The objectives of this study were to develop models for predicting benign laryngeal mucosal disorders based on deep learning, naive Bayes model, generalized linear model, a Classification and Regression Tree (CART), and random forest using laryngeal mucosal disorder data obtained from a national survey and confirm the best classifier for predicting benign laryngeal mucosal disorders by comparing the prediction performance and runtime of the developed models. This study analyzed 626 subjects (313 people with a laryngeal disorder and 313 people without a laryngeal disorder). In this study, deep learning was the best model with the highest accuracy (0.84). However, the runtime of deep learning was 39min 41sec, which was a 10 times longer development time than CART (3min 7sec). This model confirmed that subjective voice problem recognition, pain and discomfort in the last two weeks, education level, occupation, mean monthly household income, high-risk drinker, and current smoker were major variables with high weight for the benign laryngeal mucosal disorders of Korean adults. Among them, subjective voice problem recognition was the most important factor with the highest weight. The results of this study implied that the prediction performance of deep learning could be better than that of machine learning for structured data, such as health behavior and demographic factors as well as video and image data.

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“…[11,12] The difference result of the relationship between smoking and laryngeal disorders possible depend on the survey method used, based on the questionnaire survey or biochemical tests, and difference population and gender sample. [13] The early laryngeal disease symptom before dysphonia was vocal fatigue that can be measured by VHI. VHI is a useful tool in measuring functional outcome for voice disorders on non-symptomatic smokers, it assess not only the bio-psychosocial impact of voice disorders but also screening tool to distinguish between subjects with voice dysfunction or normal.…”

Section: Introductionmentioning

confidence: 99%

The Influence of Smoking Habit to Voice Handicap Index Score

Widuri

Wiratama

2020

Egyptian Journal of Ear, Nose, Throat and Allied Sciences

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Introduction:The health voice production depends on vocal folds vibration and resonance. Anatomical changes of the vocal folds soft tissue (epithelium, ligament, muscle) can cause functional problems results in voice disorders. The vocal folds react to smoking, from irritate the mucous to the tissue changes or dysplasia that degradation of voice performance in daily living. Aim: This study determined the influence of smoking habits to the voice handicap score by Voice Handicap Index score (VHI). Patients and Methods: The design was a retrospective cohort study from 96 adults that identified level of voice fatigue through VHI. They were divided into 3 groups: a group of active smokers, passive smokers and nonsmokers. Chi-square test and regression nominal test was used in this study. Results: Voice fatigue (VHI score > 20) in both active smokers (13.54%) and passive smokers (15.62%) compared with nonsmokers (6.25%), by Chi-square test results was p = 0.047 (p <0.05). Nominal regression test between 3 groups of samples were (RR = 3.0, CI 95% = 0.95 to 9.21) between the group of active smokers and passive smokers (RR = 3.8, CI 95% = 1.23-11.80). Conclusion:Smoking habits influence the voice handicap score in both active smokers and passive smokers compared with nonsmokers. Active smokers have a risk of voice fatigue 3 times than nonsmokers and passive smokers have a risk 3.8 times than non-smokers.

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Relationship between women's smoking and laryngeal disorders based on the urine cotinine test: results of a national population-based survey

Cited by 4 publications

References 27 publications

A laryngeal disorders prediction model based on cluster analysis and regression analysis

A laryngeal disorders prediction model based on cluster analysis and regression analysis

Is Deep Learning Better than Machine Learning to Predict Benign Laryngeal Disorders?

The Influence of Smoking Habit to Voice Handicap Index Score

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