Michael D Sawdey1, Hannah R Day2, Blair Coleman2, Lisa D Gardner2, Sarah E Johnson2, Jean Limpert2, Hoda T Hammad2, Maciej L Goniewicz3, David B Abrams4, Cassandra A Stanton5, Jennifer L Pearson6, Annette R Kaufman7, Heather L Kimmel8, Cristine D Delnevo9, Wilson M Compton8, Maansi Bansal-Travers3, Raymond S Niaura4, Andrew Hyland3, Bridget K Ambrose2. 1. Office of Science, Center for Tobacco Products, Food and Drug Administration, Silver Spring, MD, USA. Electronic address: Michael.Sawdey@fda.hhs.gov. 2. Office of Science, Center for Tobacco Products, Food and Drug Administration, Silver Spring, MD, USA. 3. Department of Health Behavior, Division of Cancer Prevention & Population Sciences, Roswell Park Cancer Institute, Buffalo, NY, USA. 4. Department of Social and Behavioral Sciences, NYU College of Global Public Health, New York University, New York, NY, USA. 5. Westat, Rockville, MD, USA; Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, USA. 6. Division of Social and Behavioral Sciences/Health Administration and Policy, University of Nevada, Reno, Reno, NV, USA. 7. Tobacco Control Research Branch, Behavioral Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Rockville, MD, USA. 8. National Institute on Drug Abuse, National Institutes of Health, U.S. Department of Health and Human Services, Bethesda, MD, USA. 9. Department of Health Education and Behavioral Science, Center for Tobacco Studies, Rutgers School of Public Health, Piscataway, NJ, USA.
Abstract
INTRODUCTION: Improved understanding of the distribution of traditional risk factors of cigarette smoking among youth who have ever used or are susceptible to e-cigarettes and cigarettes will inform future longitudinal studies examining transitions in use. METHODS: Multiple logistic regression analysis was conducted using data from youth (ages 12-17 years) who had ever heard of e-cigarettes at baseline of the PATH Study (n = 12,460) to compare the distribution of risk factors for cigarette smoking among seven mutually exclusive groups based on ever cigarette/e-cigarette use and susceptibility status. RESULTS: Compared to committed never users, youth susceptible to e-cigarettes, cigarettes, or both had increasing odds of risk factors for cigarette smoking, with those susceptible to both products at highest risk, followed by cigarettes and e-cigarettes. Compared to e-cigarette only users, dual users had higher odds of nearly all risk factors (aOR range = 1.6-6.8) and cigarette only smokers had higher odds of other (non-e-cigarette) tobacco use (aOR range = 1.5-2.3), marijuana use (aOR = 1.9, 95%CI = 1.4-2.5), a high GAIN substance use score (aOR = 1.9, 95%CI = 1.1-3.4), low academic achievement (aOR range = 1.6-3.4), and exposure to smoking (aOR range = 1.8-2.1). No differences were observed for externalizing factors (depression, anxiety, etc.), sensation seeking, or household use of non-cigarette tobacco. CONCLUSIONS: Among ever cigarette and e-cigarette users, dual users had higher odds of reporting traditional risk factors for smoking, followed by single product cigarette smokers and e-cigarette users. Understanding how e-cigarette and cigarette users differ may inform youth tobacco use prevention efforts and advise future studies assessing probability of progression of cigarette and e-cigarette use. Published by Elsevier Ltd.
INTRODUCTION: Improved understanding of the distribution of traditional risk factors of cigarette smoking among youth who have ever used or are susceptible to e-cigarettes and cigarettes will inform future longitudinal studies examining transitions in use. METHODS: Multiple logistic regression analysis was conducted using data from youth (ages 12-17 years) who had ever heard of e-cigarettes at baseline of the PATH Study (n = 12,460) to compare the distribution of risk factors for cigarette smoking among seven mutually exclusive groups based on ever cigarette/e-cigarette use and susceptibility status. RESULTS: Compared to committed never users, youth susceptible to e-cigarettes, cigarettes, or both had increasing odds of risk factors for cigarette smoking, with those susceptible to both products at highest risk, followed by cigarettes and e-cigarettes. Compared to e-cigarette only users, dual users had higher odds of nearly all risk factors (aOR range = 1.6-6.8) and cigarette only smokers had higher odds of other (non-e-cigarette) tobacco use (aOR range = 1.5-2.3), marijuana use (aOR = 1.9, 95%CI = 1.4-2.5), a high GAIN substance use score (aOR = 1.9, 95%CI = 1.1-3.4), low academic achievement (aOR range = 1.6-3.4), and exposure to smoking (aOR range = 1.8-2.1). No differences were observed for externalizing factors (depression, anxiety, etc.), sensation seeking, or household use of non-cigarette tobacco. CONCLUSIONS: Among ever cigarette and e-cigarette users, dual users had higher odds of reporting traditional risk factors for smoking, followed by single product cigarette smokers and e-cigarette users. Understanding how e-cigarette and cigarette users differ may inform youth tobacco use prevention efforts and advise future studies assessing probability of progression of cigarette and e-cigarette use. Published by Elsevier Ltd.
Entities:
Keywords:
Cigarettes; E-cigarettes; Risk factors of tobacco use; Susceptibility
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