Associations of risk factors of e-cigarette and cigarette use and susceptibility to use among baseline PATH study youth participants (2013–2014). (April 2019)

• Record Type: Journal Article
• Authors: Sawdey, Michael D.; Day, Hannah R.; Coleman, Blair; Gardner, Lisa D.; Johnson, Sarah E.; Limpert, Jean; Hammad, Hoda T.; Goniewicz, Maciej L.; Abrams, David B.; Stanton, Cassandra A.; Pearson, Jennifer L.; Kaufman, Annette R.; Kimmel, Heather L.; Delnevo, Cristine D.; Compton, Wilson M.; Bansal-Travers, Maansi; Niaura, Raymond S.; Hyland, Andrew; Ambrose, Bridget K.
• Is Part Of: Addictive behaviors. Volume 91(2019)
• Page Start: 51
• Page End: 60
• Publication Date: 2019-04
• Subjects: E-cigarettes -- Cigarettes -- Risk factors of tobacco use -- Susceptibility; Substance abuse -- Periodicals; Alcoholism -- Periodicals; Drug addiction -- Periodicals; Nicotine addiction -- Periodicals; Smoking -- Periodicals; Gambling -- Psychological aspects -- Periodicals; Electronic journals; 362.29
• DOI: 10.1016/j.addbeh.2018.11.027
• Languages: English
• ISSNs: 0306-4603

Abstract

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. Highlights: Dual users had higher odds of reporting traditional risk factors for smoking, followed by single product users. Youth susceptible to both products had stronger associations with risk factors than those susceptible to one product. Between Wave 1 and 2, compared to continued non-users, a significantly higher proportion of new users reported being older, ever using marijuana and alcohol, having more internalizing, externalizing, and sensation seeking behaviors, having lower grades, and having someone in the household who uses tobacco.