Association between Second-Hand Smoking and Laryngopathy in the General Population of South Korea

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

doi:10.1371/journal.pone.0165337

Cited by 3 publications

(3 citation statements)

References 24 publications

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“…60 Children exposed to secondhand smoke were 70% more likely to present with voice problems. Secondhand smoke has been tied to laryngeal pathology in adults, 61 as cigarette smoke damages vocal fold mucosa on a cellular level. [62][63][64] Specifically, cigarette vapor induces vocal fold injury and disrupts typical cellular immunological response.…”

Section: Discussionmentioning

confidence: 99%

Voice Disorder Prevalence and Vocal Health Characteristics in Children

Fujiki,

Thibeault

2024

JAMA Otolaryngol Head Neck Surg

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ImportanceVoice disorders impede communication and limit quality of life for many children. However, research demonstrating the prevalence of pediatric voice problems and associated voice use patterns is scarce. This investigation examined the prevalence of voice problems and vocal health characteristics of school-aged children.ObjectiveTo examine the prevalence of voice problems in school-aged children throughout the US with reference to lifestyle, demographic characteristics, and voice use patterns.Design, Setting, and ParticipantsThis study used a cross-sectional design to survey a probability sample of caregivers of children aged 4 to 12 years living throughout the US in 2023.Main Outcomes and MeasuresCaregivers were surveyed regarding their children’s voice use, voice symptoms, voice problems, extracurricular activities, and demographic information. Caregivers also competed the Pediatric Voice-Related Quality of Life questionnaire. The Fisher exact test, χ2 tests, and logistic regression were used to compare children with and without voice problems.ResultsOverall, 6293 panelists were invited to complete screening questions for the survey, and 1789 individuals were screened for eligibility between March and April 2023. Of these, 1175 parents (65.7%) completed the survey. Twenty-one (1.8%) were excluded for a combination of either high refusal rates (n = 16), speeding (n = 2), or straight lining (n = 12). The final number of participants included in analysis was 1154 caregivers of children aged 4 to 12 years (559 female children [48.4%]; 595 male children [51.6%]; mean [SD] age, 8.02 [2.49] years). The prevalence of voice problems in children was 6.7% (n = 78), and the lifetime prevalence was 12% (n = 138). Benign vocal fold lesions was the most common diagnosis underlying voice complaints, and other causes included respiratory illness, allergies, autism-related voice issues, and other neurological conditions. Risk factors for pediatric voice problems included being male (odds ratio [OR], 1.47; 95% CI, 1.0-2.1), having more than 4 individuals living in the household (OR, 2.30; 95% CI, 1.2-4.4), poor speech intelligibility (OR, 2.26; 95% CI, 1.2-4.3), maternal history of voice problems (OR, 4.54; 95% CI, 1.2-16.4), participating in online gaming (OR, 1.56; 95% CI, 1.0-2.3), and secondhand smoke exposure (OR, 1.7; 95% CI, 1.1-2.6). Voice use–related risk factors included frequent talking, coughing, throat clearing, tantrums/crying, and vocal strain. Voice problems were associated with substantially detracted quality of life as measured by the Pediatric Voice-Related Quality of Life questionnaire, limited social/extracurricular interactions, increased school absences, and negative attention from adults.ConclusionsThe results of this survey study suggest that pediatric voice problems are relatively common and detract from quality of life. Specific environmental and behaviorial factors are associated with increased risk for voice disorders.

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Section: Discussionmentioning

confidence: 99%

Voice Disorder Prevalence and Vocal Health Characteristics in Children

Fujiki,

Thibeault

2024

JAMA Otolaryngol Head Neck Surg

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“…Those who vaped were 99% more likely to report a voice problem. This supports past work suggesting that secondhand smoke may be associated with increased voice disorder risk, possibly because traditional and electronic cigarette smoke/vapor have been shown to injure vocal fold epithelium, cause mucosal inflammation, and disrupt normal immune responses . Changes reflecting a threat to mucosal health may be associated with anatomical and/or functional voice problems …”

Section: Discussionmentioning

confidence: 99%

Voice Disorder Prevalence and Vocal Health Characteristics in Adolescents

Fujiki,

Thibeault

2024

JAMA Otolaryngol Head Neck Surg

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ImportanceVoice disorders impede communication and detract from quality of life, yet little is known about how voice problems present in adolescents. This study characterized the prevalence of voice problems and vocal health characteristics of adolescents throughout the US.ObjectiveTo determine the prevalence of adolescent voice problems in the US as well as the symptoms, voice use patterns, and risk factors associated with these problems.Design, Setting, and ParticipantsThis prospectively performed, cross-sectional survey study conducted from March to June 2023 included a probability sample of adolescents (aged 13-17 years) who were located across the US.Main Outcome and MeasuresAdolescents were surveyed concerning voice use, voice symptoms, demographic information, and substance use patterns. The Vocal Fatigue Index (VFI) was also completed. Fisher exact tests, χ2 tests, and logistic regression were used to compare those with and without reported voice problems.ResultsFive-hundred and two adolescents participated (51.6% participation rate), including 248 female (49.4%), 7 nonbinary (1.3%), 254 male (50.6%), 4 transfemale (0.7%), and 3 transmale individuals (0.5%) (mean [SD] age, 15.2 [1.3] years). The lifetime prevalence of voice problems was 24.3%, and current prevalence was 7.4%. Voice-related diagnoses included dysphonia associated with acute illness, vocal hyperfunction, benign lesions, and muscle tension. The mean (SD) VFI score was 4.7 (8.09) for part 1, 1.6 (3.2) for part 2, and 2.1 (3.2) for part 3. Adolescents with voice complaints presented with significantly higher VFI scores than those without voice problems (Cohen d for part 1 = 0.78, part 2 = 0.59, and part 3 = 0.79). Voice problems interfered with daily tasks, prevented participation in extracurricular activities, limited the ability to sing, and were associated with school absences. Voice use patterns associated with voice problems included frequent loud voice use, coughing, and/or throat clearing. Risk factors for increased voice problem prevalence included part-time employment in childcare (odds ratio [OR], 6.4; 95% CI, 1.6-26.3), singing (OR, 2.08; 95% CI, 1.3-3.1), performance-related extracurricular activities (ie, musical theater [OR, 2.67; 95% CI, 1.1-6.6 ], karaoke [OR, 3.08; 95% CI, 1.3-6.9]), vaping (OR, 1.99; 95% CI, 1.1-3.5), and/or secondhand smoke exposure (OR, 1.83; 95% CI, 1.2-2.7). Adolescents identifying as transgender were more than 4 times (OR, 4.44; 95% CI, 0.63-31.2) as likely to report voice problems compared with cisgender adolescents.ConclusionsThe results of this survey study illuminate the nature of voice problems in adolescents and may guide clinicians in preventing and treating voice disorders in this population. Future work may define the mechanisms through which these factors are associated with the risk for voice disorders in adolescents and determine whether observed relationships are causal or associative.

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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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Association between Second-Hand Smoking and Laryngopathy in the General Population of South Korea

Cited by 3 publications

References 24 publications

Voice Disorder Prevalence and Vocal Health Characteristics in Children

Voice Disorder Prevalence and Vocal Health Characteristics in Children

Voice Disorder Prevalence and Vocal Health Characteristics in Adolescents

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

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