Factors Associated with E-Cigarette Use in U.S. Young Adult Never Smokers of Conventional Cigarettes
A Machine Learning Approach
Datos Bibliográficos
| ID | 17801092 |
|---|---|
| Autores | Nkiruka C Atuegwu (0000-0001-9516-008X, University of Connecticut, autor de correspondencia), Cheryl Oncken (University of Connecticut), Reinhard Laubenbacher (0000-0002-9143-9451, University of Florida), Reinhard C Laubenbacher (Department of Medicine, University of Florida College of Medicine, Gainesville, FL 32610, USA), Mario Perez (0000-0002-7441-0331, University of Connecticut), Eric M Mortensen (0000-0002-3880-5563, University of Connecticut) |
| Año | 2020 |
| Volumen | 17 |
| Número | 19 |
| Páginas | 7271-7271 |
| Fecha de publicación | 2020-10-05 |
| Peer Reviewed | Sí |
| Open Access | Sí |
| Tipo | ARTICLE |
| Revista | International Journal of Environmental Research and Public Health (JOURNAL) |
| Identificadores de la revista | ISSN: 1661-7827 • E-ISSN: 1660-4601 |
| Editorial | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijerph17197271 |
| PMID | 33027932 |
| OpenAlex | W3091102599 |
| Idioma | EN |
| Citas recibidas | 6 |
| Referencias citadas | 67 |
E-cigarette use is increasing among young adult never smokers of conventional cigarettes, but the awareness of the factors associated with e-cigarette use in this population is limited. The goal of this work was to use machine learning (ML) algorithms to determine the factors associated with current e-cigarette use among US young adult never cigarette smokers. Young adult (18-34 years) never cigarette smokers from the 2016 and 2017 Behavioral Risk Factor Surveillance System (BRFSS) who reported current or never e-cigarette use were used for the analysis ( n = 79,539). Variables associated with current e-cigarette use were selected by two ML algorithms (Boruta and Least absolute shrinkage and selection operator (LASSO)). Odds ratios were calculated to determine the association between e-cigarette use and the variables selected by the ML algorithms, after adjusting for age, gender and race/ethnicity and incorporating the BRFSS complex design. The prevalence of e-cigarette use varied across states. Factors previously reported in the literature, such as age, race/ethnicity, alcohol use, depression, as well as novel factors associated with e-cigarette use, such as disabilities, obesity, history of diabetes and history of arthritis were identified. These results can be used to generate further hypotheses for research, increase public awareness and help provide targeted e-cigarette education
Cigarette smoking · Developmental psychology · Environmental health · Sociology · Young adult · Demography · Medicine · Nutritional Studies and Diet · Obesity, Physical Activity, Diet · Psychology · Smoking Behavior and Cessation · Internal Medicine
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| Obras citantes distintas | 6 |
|---|---|
| Citas por año | 1,2 |
| Intervalo de citas | 2021 - 2026 (6) |
| Velocidad de citación | current |
| Altamente citado | No |
| Tipos de cita | Neutras: 6 |