Mohammad Ebrahimi Kalan1, Raed Bahelah2, Zoran Bursac3, Kenneth D Ward4, Ziyad Ben Taleb5, Malak Tleis6, Rime Jebai1, Taghrid Asfar7,8, Thomas Eissenberg9, Wasim Maziak1. 1. Department of Epidemiology, Robert Stempel College of Public Health, Florida International University, Miami, FL, USA. 2. School of Health Sciences, Baldwin Wallace University, Berea, OH, USA. 3. Department of Biostatistics, Robert Stempel College of Public Health, Florida International University, Miami, FL, USA. 4. School of Public Health, University of Memphis, Memphis, TN, USA. 5. Department of Kinesiology, College of Nursing and Health Innovation, University of Texas at Arlington, Arlington, TX, USA. 6. Health Promotion and Community Health Department, Faculty of Health Sciences, American University of Beirut, Miami, FL, USA. 7. Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL, USA. 8. Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA. 9. Center for the Study of Tobacco Products, Department of Psychology, Virginia Commonwealth University, Richmond, VA, USA.
Abstract
INTRODUCTION: Adolescence represents a critical period in which nicotine dependence (ND) symptoms are developing. Little is known about waterpipe (WP) smoking and developmental trajectories of ND criteria across adolescence. AIMS AND METHODS: Here, we aimed to identify ND trajectories from early- to late-adolescence in current (past 30 days) WP smokers and examine baseline correlates of each identified trajectory, using the International Classification of Diseases, 10th Revision (ICD-10). The analytical sample consisted of 278 current WP smokers from eight waves of an ongoing longitudinal cohort of eighth to ninth graders in Lebanon. Group-based trajectory modeling was estimated to identify trajectory classes for ICD-10-ND criteria over ages 11-18. RESULTS: A group-based modeling approach yielded a four-class solution that best fit the data and reflected differences in the timing of ND onset during adolescence: no-onset of ND (43.9%), early-onset (16.2%), mid-onset (26.6%), and late-onset (13.3%) of ND criteria. Having a less-educated mother (adjusted odds ratio [aOR] = 4.08, 95% confidence interval [95% CI] = 1.01% to 16.53%) and siblings who smoke WP (aOR = 3.95, 95% CI = 1.08% to 14.42%), exposure to favorite WP-specific advertisements (aOR = 3.33, 95% CI = 1.03% to 10.85%), and being a novelty seeker (aOR = 1.12, 95% CI = 1.02% to 1.23%) were associated with early-onset of ND. Daily (aOR = 3.48, 95% CI = 1.08% to 11.23%) or weekly (aOR = 2.20, 95% CI = 1.05% to 4.62%) WP smokers (vs. monthly) and having higher stress level (aOR = 1.07, 95% CI = 1.00% to 1.14%) were associated with mid-onset trajectory. Believing that WP smoking is not harmful to health (aOR = 0.11, 95% CI = 0.02% to 0.82%) and spending less than 60 minutes on a WP smoking session (aOR = 5.62, 95% CI = 1.20% to 26.44%) were associated with late-onset ND trajectory class. CONCLUSIONS: Monitoring the development of ND trajectories among WP smokers may identify an individual as belonging to one of these four groups with distinct individual and socioenvironmental factors and allow the individual and health care providers opportunities to inform initiate on-time WP-specific tailored prevention and cessation interventions. IMPLICATIONS: The results from this study showed a four-class trajectory of ICD-10-ND criteria and suggested that every ND trajectory class during adolescence could have distinctive characteristics and therefore provides new insights into the process of ND in terms of when and what specific interventions are needed to curb the development of ND and long-term WP smoking among youth.
INTRODUCTION: Adolescence represents a critical period in which nicotine dependence (ND) symptoms are developing. Little is known about waterpipe (WP) smoking and developmental trajectories of ND criteria across adolescence. AIMS AND METHODS: Here, we aimed to identify ND trajectories from early- to late-adolescence in current (past 30 days) WP smokers and examine baseline correlates of each identified trajectory, using the International Classification of Diseases, 10th Revision (ICD-10). The analytical sample consisted of 278 current WP smokers from eight waves of an ongoing longitudinal cohort of eighth to ninth graders in Lebanon. Group-based trajectory modeling was estimated to identify trajectory classes for ICD-10-ND criteria over ages 11-18. RESULTS: A group-based modeling approach yielded a four-class solution that best fit the data and reflected differences in the timing of ND onset during adolescence: no-onset of ND (43.9%), early-onset (16.2%), mid-onset (26.6%), and late-onset (13.3%) of ND criteria. Having a less-educated mother (adjusted odds ratio [aOR] = 4.08, 95% confidence interval [95% CI] = 1.01% to 16.53%) and siblings who smoke WP (aOR = 3.95, 95% CI = 1.08% to 14.42%), exposure to favorite WP-specific advertisements (aOR = 3.33, 95% CI = 1.03% to 10.85%), and being a novelty seeker (aOR = 1.12, 95% CI = 1.02% to 1.23%) were associated with early-onset of ND. Daily (aOR = 3.48, 95% CI = 1.08% to 11.23%) or weekly (aOR = 2.20, 95% CI = 1.05% to 4.62%) WP smokers (vs. monthly) and having higher stress level (aOR = 1.07, 95% CI = 1.00% to 1.14%) were associated with mid-onset trajectory. Believing that WP smoking is not harmful to health (aOR = 0.11, 95% CI = 0.02% to 0.82%) and spending less than 60 minutes on a WP smoking session (aOR = 5.62, 95% CI = 1.20% to 26.44%) were associated with late-onset ND trajectory class. CONCLUSIONS: Monitoring the development of ND trajectories among WP smokers may identify an individual as belonging to one of these four groups with distinct individual and socioenvironmental factors and allow the individual and health care providers opportunities to inform initiate on-time WP-specific tailored prevention and cessation interventions. IMPLICATIONS: The results from this study showed a four-class trajectory of ICD-10-ND criteria and suggested that every ND trajectory class during adolescence could have distinctive characteristics and therefore provides new insights into the process of ND in terms of when and what specific interventions are needed to curb the development of ND and long-term WP smoking among youth.
Authors: Brian A Primack; Omar F Khabour; Karem H Alzoubi; Galen E Switzer; Ariel Shensa; Mary V Carroll; Mohammed Azab; Thomas Eissenberg Journal: Nicotine Tob Res Date: 2014-02-26 Impact factor: 4.244
Authors: Mohammad Ebrahimi Kalan; Raed Bahelah; Zoran Bursac; Ziyad Ben Taleb; Joseph R DiFranza; Malak Tleis; Rima Nakkash; Rime Jebai; Mohammad Masudul Alam; Miguel Ángel Cano; Matthew T Sutherland; Kristopher Fenni; Taghrid Asfar; Thomas Eissenberg; Kenneth D Ward; Wasim Maziak Journal: Drug Alcohol Depend Date: 2020-10-12 Impact factor: 4.492
Authors: Mohammad Ebrahimi Kalan; Raed Behaleh; Joseph R DiFranza; Zoran Bursac; Ziyad Ben Taleb; Malak Tleis; Taghrid Asfar; Rima Nakkash; Kenneth D Ward; Thomas Eissenberg; Wasim Maziak Journal: J Adolesc Health Date: 2020-07-02 Impact factor: 5.012
Authors: J R DiFranza; J A Savageau; N A Rigotti; K Fletcher; J K Ockene; A D McNeill; M Coleman; C Wood Journal: Tob Control Date: 2002-09 Impact factor: 7.552
Authors: Taghrid Asfar; Sara Chehab; Michael Schmidt; Kenneth D Ward; Wasim Maziak; Rima Nakkash Journal: Nicotine Tob Res Date: 2022-08-06 Impact factor: 5.825