Comprehensive review of the national surveys that assess E-cigarette use domains among youth and adults in the United States

Boakye, Ellen; Erhabor, John; Obisesan, Olufunmilayo H.; Tasdighi, Erfan; Mirbolouk, Mohammadhassan; Osuji, Ngozi; Osei, Albert D.; Lee, Jieun; DeFilippis, Andrew P.; Stokes, Andrew; Hirsch, Glenn A.; Benjamin, Emelia J.; Robertson, Rose Marie; Bhatnagar, Aruni; El-Shahawy, Omar; Blaha, Michael

doi:10.1016/j.lana.2023.100528

Cited by 4 publications

(4 citation statements)

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“… 26 Furthermore, our study highlights the importance of collecting and analyzing data continuously, as they play a vital role in monitoring epidemiologic shifts and informing policies. 39 These data should be consistently repeated and compared across surveys to ensure accurate and up-to-date information. 39 …”

Section: Discussionmentioning

confidence: 99%

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E-Cigarette Use Among US Adults in the 2021 Behavioral Risk Factor Surveillance System Survey

Erhabor,

Boakye,

Obisesan

et al. 2023

JAMA Netw Open

Self Cite

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ImportanceAfter the initial disruption from the COVID-19 pandemic, it is unclear how patterns of e-cigarette use in the US have changed.ObjectiveTo examine recent patterns in current and daily e-cigarette use among US adults in 2021.Design, Setting, and ParticipantsThis cross-sectional study used data from the 2021 Behavioral Risk Factor Surveillance System (BRFSS) database. The BRFSS is the largest national telephone-based survey of randomly sampled adults in the US. Adults aged 18 years or older, residing in 49 US states (all except Florida), the District of Columbia, and 3 US territories (Guam, Puerto Rico, and the US Virgin Islands), were included in the data set. Data analysis was performed in January 2023.Main Outcomes and MeasuresThe main outcome was age-adjusted prevalence of current and daily e-cigarette use overall and by participant characteristics, state, and territory. Descriptive statistical analysis was conducted, applying weights to account for population representation.ResultsThis study included 414 755 BRFSS participants with information on e-cigarette use. More than half of participants were women (51.3%). In terms of race and ethnicity, 0.9% of participants were American Indian or Alaska Native, 5.8% were Asian, 11.5% were Black, 17.3% were Hispanic, 0.2% were Native Hawaiian or Other Pacific Islander, 62.2% were White, 1.4% were of multiple races or ethnicities, and 0.6% were of other race or ethnicity. Individuals aged 18 to 24 years comprised 12.4% of the study population. The age-standardized prevalence of current e-cigarette use was 6.9% (95% CI, 6.7%-7.1%), with almost half of participants using e-cigarettes daily (3.2% [95% CI, 3.1%-3.4%]). Among individuals aged 18 to 24 years, there was a consistently higher prevalence of e-cigarette use, with more than 18.6% reporting current use and more than 9.0% reporting daily use. Overall, among individuals reporting current e-cigarette use, 42.2% (95% CI, 40.7%-43.7%) indicated former combustible cigarette use, 37.1% (95% CI, 35.6%-38.6%) indicated current combustible cigarette use, and 20.7% (95% CI, 19.7%-21.8%) indicated never using combustible cigarettes. Although relatively older adults (aged ≥25 years) who reported current e-cigarette use were more likely to report former or current combustible cigarette use, younger adults (aged 18-24 years) were more likely to report never using combustible cigarettes. Notably, the proportion of individuals who reported current e-cigarette use and never using combustible cigarettes was higher in the group aged 18 to 20 years (71.5% [95% CI, 66.8%-75.7%]) compared with those aged 21 to 24 years (53.0% [95% CI, 49.8%-56.1%]).Conclusion and RelevanceThese findings suggest that e-cigarette use remained common during the COVID-19 pandemic, particularly among young adults aged 18 to 24 years (18.3% prevalence). Notably, 71.5% of individuals aged 18 to 20 years who reported current e-cigarette use had never used combustible cigarettes. These results underscore the rationale for the implementation and enforcement of public health policies tailored to young adults.

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Section: Discussionmentioning

confidence: 99%

“… 39 These data should be consistently repeated and compared across surveys to ensure accurate and up-to-date information. 39 …”

Section: Discussionmentioning

confidence: 99%

E-Cigarette Use Among US Adults in the 2021 Behavioral Risk Factor Surveillance System Survey

Erhabor,

Boakye,

Obisesan

et al. 2023

JAMA Netw Open

Self Cite

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“…This study was primarily designed to develop a ML model for substance use prediction among adolescents utilizing three distinct nationwide datasets: KYRBS from South Korea [10], YRBS from the United States [11], and Ungdata from Norway [12]. KYRBS was initially used to train the ML model, followed by the external validation process utilizing the YRBS and Ungdata.…”

Section: Study Design and Participantsmentioning

confidence: 99%

Machine learning-based prediction of substance use in adolescents: Derivation and validation in multinational datasets in South Korea, USA, and Norway (Preprint)

Lee,

Kim,

Kim

et al. 2024

Preprint

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BACKGROUND We aimed to address gaps in global understanding of cultural and social variations by employing a high-performance machine learning model to predict adolescent substance use across three national datasets. OBJECTIVE This study aims to develop a predictive model for adolescent substance use using multinational datasets and machine learning(ML). METHODS The study utilized the Korea Youth Risk Behavior Web-Based Survey(KYRBS) from South Korea(n=1,145,178) to train ML models. For external validation, we employed the Youth Risk Behavior Survey(YRBS) from the USA(n=1,690,108) and Norwegian nationwide Ungdata surveys(Ungdata) from Norway(n=793,879). After developing diverse tree-based models, we further evaluated feature importance. RESULTS The study utilized nationwide adolescent datasets for ML model development and validation, analyzing data from 1,145,178 KYRBS adolescents, 54,709 YRBS Asian subset participants, and 720,812 from Ungdata. The random forest model was the top performer on the KYRBS, achieving an AUROC of 80.8%(95% CI, 80.7-80.8) with sensitivity of 72.9%(72.8-73.0), specificity of 72.9%(72.9-73.0), accuracy of 72.9%(72.8-73.0), and balanced accuracy of 72.9%(72.8-73.0). The model's AUROC scores were 73.2% for YRBS and 75.7% for Ungdata in external validation. The top features for predicting substance use were smoking status, body mass index (BMI), and alcoholic consumption. CONCLUSIONS With multinational datasets from South Korea, USA, and Norway, the findings of this study underscore the potential efficacy of ML models in predicting adolescent substance use with smoking status, body mass index, and alcoholic consumption identified as key predictors. The random forest model exhibited notable performance in this prediction. These findings could be a basis for future studies exploring more comprehensive factors influencing adolescent substance use or developing intervention strategies based on these predictors.

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“…Since the introduction of e-cigarettes in the last decade, vaping has become an epidemic, where global e-cigarette usage has drastically increased from 7 million users in 2011 to 82 million in 2021 [ 1 , 2 ]. E-cigarette products originally gained a large amount of interest as an aid in smoking cessation; however, e-cigarettes are now commonly used for recreational purposes and popular amongst young populations who have never smoked tobacco [ 3 ], and the number of e-cigarette users continues to increase [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Third-Hand Exposure to E-Cigarette Vapour Induces Pulmonary Effects in Mice

Thorpe,

Donovan,

Kim

et al. 2023

Toxics

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In the last decade, e-cigarette usage has increased, with an estimated 82 million e-cigarette users globally. This is, in part, due to the common opinion that they are “healthier” than tobacco cigarettes or simply “water vapour”. Third-hand e-vapour exposure is the chemical residue left behind from e-cigarette aerosols, which is of concern due to its invisible nature, especially among young children. However, there is limited information surrounding third-hand e-vapour exposure. This study aimed to investigate the pulmonary effects of sub-chronic third-hand e-vapour exposure in a murine model. BALB/c mice (4 weeks of age) were exposed to a towel containing nicotine free (0 mg) e-vapour, nicotine (18 mg) e-vapour, or no e-vapour (sham) and replaced daily for 4 weeks. At the endpoint, lung function was assessed, and bronchoalveolar lavage fluid and lungs were collected to measure inflammation and fibrosis. Mice exposed to third-hand e-vapour without nicotine had alveolar enlargement compared to sham exposed controls. Mice exposed to third-hand e-vapour with nicotine had reduced bronchial responsiveness to provocation, increased epithelial thickening in large airways, increased epithelial layers in small airways, alveolar enlargement, and increased small airway collagen deposition, compared to sham exposed controls. In conclusion, our study shows that third-hand e-vapour exposure, particularly in the presence of nicotine, negatively affects the lung health of mice and highlights the need for greater public awareness surrounding the dangers of third-hand exposure to e-cigarette vapour.

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Comprehensive review of the national surveys that assess E-cigarette use domains among youth and adults in the United States

Cited by 4 publications

References 55 publications

E-Cigarette Use Among US Adults in the 2021 Behavioral Risk Factor Surveillance System Survey

E-Cigarette Use Among US Adults in the 2021 Behavioral Risk Factor Surveillance System Survey

Machine learning-based prediction of substance use in adolescents: Derivation and validation in multinational datasets in South Korea, USA, and Norway (Preprint)

Third-Hand Exposure to E-Cigarette Vapour Induces Pulmonary Effects in Mice

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