Electronic Cigarette Use and Cigarette Smoking among Australian Women
Alemu Sufa Melka
Bsc (Nursing), MPH (Population and Family Health)
Thesis Submitted for Fulfilment of the Award of Doctor of Philosophy (Public Health and
Behavioural Science)
The University of Newcastle
September 2020
This research was supported by an Australian Government Research Training Program
Scholarship i
Declarations
Originality
I hereby certify that the work embodied in the thesis is my own work, conducted under normal supervision. The thesis contains no material which has been accepted, or is being examined, for the award of any other degree or diploma in any university or other tertiary institution and, to the best of my knowledge and belief, contains no material previously published or written by another person, except where due reference has been made. I give consent to the final version of my thesis being made available worldwide when deposited in the University’s Digital Repository, subject to the provisions of the Copyright
Act 1968 and any approved embargo.
Alemu Sufa Melka
Collaboration
I hereby certify that the work embodied in this thesis has been done in collaboration with other researchers. I have included as part of the thesis a statement clearly outlining the extent of collaboration, with whom and under what auspices.
Authorship
I hereby certify that the work embodied in this thesis contains published papers of which I am a joint author. I have included as part of this thesis a written statement, endorsed by my supervisors, attesting to my contribution to the joint publications. ii
Table A
Publication Status of Papers Included in the Thesis
Title of Paper Publication Status Publication Details and DOI Predictors of E-cigarette ☒Published Melka AS, Chojenta CL, Holliday EG, Loxton
Use Among Young ☐Accepted for DJ, ‘Predictors of E-cigarette Use Among Young Australian Women Australian Women’, American Journal of publication Preventive Medicine, 56 293–299 (2019) ☐Submitted for
https://doi.10.1016/j.amepre.2018.09.019 publication ☐ Un-submitted work
Adverse childhood ☒Published Melka A, Chojenta C, Holliday E, Loxton D, experiences and ☐Accepted for ‘Adverse childhood experiences and electronic electronic cigarette use cigarette use among young Australian women’, publication among Young Preventive Medicine, 126 105759-105759 (2019) ☐Submitted for Australian Women https://doi.10.1016/j.ypmed.2019.105759 publication ☐ Un-submitted work
Effectiveness of ☒Published Melka AS, Chojenta CL, Holliday EG, Loxton pharmacotherapy for ☐Accepted for DJ, ‘Effectiveness of pharmacotherapy for smoking cessation: smoking cessation: protocol for umbrella review publication protocol for umbrella and quality assessment of systematic reviews’, ☐Submitted for review and quality SYSTEMATIC REVIEWS, 7 (2018) publication assessment of https://doi.10.1186/s13643-018-0878-3 ☐ Un-submitted work systematic reviews E-cigarette use and ☒Published Melka AS, Chojenta CL, Holliday EG, Loxton cigarette smoking ☐Accepted for DJ. E-cigarette use and cigarette smoking initiation among Publication initiation among Australian women who have Australian women who never smoked. ☐Submitted for have never smoked https://doi.org/10.1111/dar.13131 publication ☐ Un-submitted work
Determinants of ☐Published Melka AS, Chojenta CL, Holliday EG, Loxton smoking cessation ☐Accepted for DJ. Determinants of smoking cessation among among Australian Australian women: The role of e-cigarette use publication women: The role of e- ☒Submitted for cigarette use publication iii
☐ Un-submitted work
Effectiveness of ☐Published Melka AS, Chojenta CL, Holliday EG, Bali AG, pharmacotherapy for ☐Accepted for Loxton DJ. Effectiveness of pharmacotherapy for smoking cessation: smoking cessation: Umbrella review and quality publication Umbrella review and assessment of systematic reviews ☒Submitted for quality assessment of publication systematic reviews ☐ Un-submitted work Thesis by Publication
I hereby certify that this thesis is in the form of a series of papers. I have included as part of the thesis a written declaration from each co-author, endorsed in writing by the
Faculty Assistant Dean (Research Training), attesting to my contribution to any jointly authored papers.
Co-Author Statement
By signing below, I confirm that Alemu Sufa Melka contributed to the listed publications included in this thesis by publication by contributing to each study’s conception and design, developing analyses plans, performing data analyses, interpreting the data and, and leading the writing of the manuscripts.
1. Effectiveness of Pharmacotherapy for Smoking Cessation: Protocol for Umbrella
Review and Quality Assessment of Systematic Reviews
2. Predictors of Electronic Cigarette Use among Young Australian Women
3. Adverse Childhood Experiences and Electronic Cigarette Use among Young
Australian Women
4. E-cigarette use and cigarette smoking initiation among Australian women who have
never smoked. i
Name of Co-Author Dr Catherine Chojenta Contribution to the Supervised development of work, helped in data interpretation and Paper manuscript evaluation. Signature Date 1 September 2020 Name of Co-Author Professor Deborah Loxton Contribution to the Supervised development of work, helped in data interpretation and Paper manuscript evaluation. Signature Date 31 August 2020 Name of Co-Author A/Professor Elizabeth Holliday Contribution to the Supervised development of work, helped in data interpretation and Paper manuscript evaluation. Signature Date 13 March 2020 Endorsed by Faculty of Health and Medicine Assistant Dean (Research Training)
Signature
Associate Professor Lesley MacDonald-Wicks
Date 2/9/2020 ii
Permission
The copyright and license agreement for Chapters 4 can be found in Appendix A. The copyright and license agreement for Chapters 5 can be found in Appendix B. The copyright and license agreement for Chapters 6 can be found in Appendix C. The copyright and license agreement for systematic review protocol is found in Appendix D.
Other Thesis-Related Output Manuscripts
Loxton D, Forder P, Townsend N, Cavenagh D, Holliday E, Chojenta C, Melka AS.
The impact of adverse childhood experiences on the health and health behaviours of young
Australian women (Under review) (Appendix T).
Conferences
Melka, A., Chojenta, C., Holliday, E. & Loxton, D. (2020). Adverse childhood experiences and electronic cigarette use among young Australian women. Poster presented at the Australian Society of Behavioural Health and Medicine conference, Sydney, NSW,
Australia.
Media Coverage Related to Chapter 4
Bedo, S. (2019, 22 January). New study warns of growing public health concern
around vaping for young women. News.com.au. Retrieved from
https://www.news.com.au/lifestyle/health/health-problems/new-study-warns-of-
growing-public-health-concern-around-vaping-for-young-women/news-
story/046d867fcac561f64fef95bc42a28791.
HMRI Press release. A quarter of young women who vape haven’t smoked
cigarettes—yet. (2019, 21 January). Retrieved from https://hmri.org.au/news-
article/quarter-young-women-who-vape-haven%E2%80%99t-smoked-cigarettes-
yet iii
Media Coverage Related to Chapter 6
Trying e-cigarettes may be pushing some Aussie women on to the smokes. (2020,
5 August). Retrieved from https://www.scimex.org/newsfeed/trying-e-cigarettes-
may-be-pushing-some-aussie-women-on-to-the-smokes iv
List of Abbreviations
AIHW: Australian Institute of Health and Welfare ALSWH: Australian Longitudinal Study on Women’s Health AMSTAR: A Measurement Tool to Assess Systematic Reviews AOR: Adjusted Odds Ratio CDC: Centre for Disease Prevention and Control CI: Confidence Interval COR: Crude Odds Ratio ENDS: electronic nicotine delivery system FCTC: Framework Convention on Tobacco Control GNI: Gross National Income HMRI: Hunter Medical Research Institute IPV: intimate partner violence NDSHS: National Drug Strategy Household Survey NHMRC: National Health and Medical Research Council NHIS: National Health Interview Survey NRT: nicotine replacement therapy OR: Odds Ratio PHE: Public Health England SDG: Sustainable Development Goal WHO: World Health Organization v
Acknowledgements
Writing a PhD thesis is not an individual experience; rather, it is accomplished in a social context and includes several persons and institutions who I would like to thank sincerely.
This research was supported by an Australian Government Research Training
Program Scholarship. I would like to thank the University of Newcastle for providing me a full scholarship to undertake my study. This research used data from the Australian
Longitudinal Study on Women’s Health (ALSWH), which was conducted jointly by the
University of Queensland and the University of Newcastle. We are indebted to the Australian
Government Department of Health for financing the study and to the respondents who voluntarily participated in the study. I would also like to acknowledge the University of
Queensland and The University of Newcastle for their approval to use the ALSWH data for my thesis.
I would like to express my deep gratitude to Dr Catherine Chojenta, Professor
Deborah Loxton and Associate Professor Elizabeth Holliday—my research supervisors—for their ongoing direction, wholehearted encouragement and valuable reviews of my work.
Thank you for encouraging my research and for allowing me to grow as a researcher. I wish to acknowledge the support I received from the staff and my PhD colleagues in the Research
Centre for Generational Health and Ageing, as well as the senior librarian at the University of
Newcastle, Debbie Booth, for the her assistance in developing the systematic review search strategy. I am also grateful to Natalia Soeters for her language support.
Capstone Editing provided copyediting and proofreading services, according to the guidelines laid out in the university-endorsed national ‘Guidelines for Editing Research
Theses’. vi
A special thanks to my family—words cannot express how grateful I am to my wife
Tsige Ayalew and my daughters Meti Sufa and Amerti Sufa for all the sacrifices that they have made on my behalf. Your prayers for me were what sustained me thus far. You have given me strength, encouragement and motivation. vii
Abstract
Background. E-cigarette use is a globally contentious topic. Most of the previous studies on e-cigarette use have not examined differences in usage in relation to gender. The findings regarding the association between e-cigarette use and subsequent cigarette smoking initiation and smoking cessation are also inconsistent.
In Australia, no studies have investigated the association between e-cigarette use and smoking initiation among young adults who have never smoked. Additionally, few population-based longitudinal studies have investigated the association of e-cigarette use with smoking cessation, either internationally or in Australia. This thesis aims to identity and investigate the risk and protective factors of e-cigarette use and the role that e-cigarettes play in smoking initiation and cessation in Australian women.
Methods. This thesis uses online survey data collected from the new young cohort of
Australian women who were born between 1989 and 1995 and who participated in the Australian Longitudinal Study on Women’s Health. The research was conducted to identify the risk and protective factors of e-cigarette use and the role that e- cigarettes play in smoking initiation and smoking cessation.
Results. Young age, smoking status, alcohol use, intimate partner violence and adverse childhood experiences (i.e., traumatic childhood experiences) were identified as factors that positively associated with e-cigarette use in the study population. This thesis discovered that although ever e-cigarette use is associated with subsequent cigarette smoking among never smokers, it also hinders subsequent cigarette smoking cessation among current smokers. Conversely, an umbrella review found that most nicotine and non-nicotine drugs (e.g., NRT, bupropion and varenicline) are effective treatments for smoking cessation. viii
Conclusions. Certain efforts are required to prevent young people and non-smokers from nicotine addiction that is acquired through e-cigarette use. Subsequent interventions to curb the use of e-cigarettes among young Australian women should target risk factors such as young people, alcoholics, people with a history of intimate partner violence and people exposed to childhood adversities. ix
Contents
Declarations ...... i Originality ...... i Collaboration ...... i Authorship...... i Thesis by Publication ...... iii Co-Author Statement ...... iii Permission ...... ii Other Thesis-Related Output Manuscripts ...... ii Conferences ...... ii Media Coverage Related to Chapter 4 ...... ii Media Coverage Related to Chapter 6 ...... iii List of Abbreviations ...... iv Acknowledgements ...... v Abstract ...... vii Contents ...... ix List of Tables ...... xiii List of Figures ...... xiv Chapter 1: Introduction ...... 1 1.1 Smoking and the Sustainable Development Goals ...... 3 1.2 Definition and Types of E-Cigarettes ...... 5 1.3 Prevalence, Trends and Reasons for E-Cigarette Use ...... 7 1.4 Short- and Long-Term Health Effects of E-Cigarettes ...... 8 1.5 E-Cigarette Use, Smoking Initiation and Cessation...... 10 1.6 Policies and Regulation of Smoking and E-Cigarettes ...... 11 1.7 Thesis Overview ...... 15 Chapter 2: Literature Review ...... 17 2.1 Literature Search Strategies...... 17 2.2 Prevalence and Trends of Smoking ...... 18 2.3 Prevalence and Trends of E-Cigarette Use ...... 20 2.4 Gender Differences in Tobacco Smoking and E-Cigarette Use ...... 23 2.5 Factors Associated with E-Cigarette Use ...... 25 2.5.1 Personal and sociodemographic factors...... 26 2.5.1.1 Age...... 26 2.5.1.2 Education...... 28 2.5.1.3 Employment and income...... 28 2.5.2 Peer and parental factors...... 29 2.5.3 Adverse childhood experiences...... 30 2.5.4 Mental health...... 31 2.5.5 Intimate partner violence...... 31 2.5.6 Smoking status and other substance use...... 32 2.6 E-Cigarette Use and Smoking Initiation ...... 32 2.7 Gateway and Common Liability Hypothesises ...... 34 2.8 E-Cigarette Use and Smoking Cessation ...... 36 2.9 Pharmacotherapy and Behavioural Therapy for Smoking Cessation...... 38 2.10 Conclusion ...... 39 2.11 Gaps in Current Knowledge ...... 40 2.12 Purpose of the Current Study...... 42 x
Chapter 3: Methods and Materials ...... 43 3.1 Introduction ...... 43 3.2 The Australian Longitudinal Study on Women’s Health ...... 43 3.3 The Australian Longitudinal Study on Women’s Health—1989–1995 Cohort ...... 44 3.4 Ethics and Data Access ...... 47 3.5 Overview of Data Analysis Methods for Specific Aims ...... 48 Chapter 4: Predictors of Electronic Cigarette Use Among Young Australian Women ...... 52 4.1 Introduction ...... 53 4.2 Methods ...... 54 4.2.1 Measures...... 54 4.2.2 Data analysis...... 59 4.3 Results ...... 59 4.4 Discussion ...... 67 4.5 Limitations ...... 68 4.6 Conclusions ...... 68 Chapter 5: Adverse Childhood Experiences and Electronic Cigarette Use among Young Australian Women ...... 69 5.1 Introduction ...... 70 5.2 Methods ...... 72 5.2.1 Study design...... 72 5.2.2 Recruitment of study participants...... 73 5.2.3 Measures...... 73 5.2.4 Data analysis...... 77 5.3 Results ...... 78 5.4 Discussion ...... 88 5.5 Limitations ...... 91 5.6 Conclusion...... 92 Chapter 6: E-Cigarette Use and Cigarette Smoking Initiation Among Australian Women Who Have Never Smoked ...... 93 6.1 Introduction ...... 94 6.2 Methods ...... 97 6.2.1 Study design and data source...... 97 6.2.2 Recruitment of study participants...... 98 6.2.3 Measures...... 99 6.2.3.1 Outcome measure...... 99 6.2.3.2 Exposure variable...... 100 6.2.3.3 Covariates...... 100 6.2.4 Data analysis...... 102 6.3 Results ...... 103 6.3.1 Characteristics of study participants...... 103 6.3.2 Factors associated with lost to follow-up...... 108 6.3.3 Baseline e-cigarette use and cigarette smoking initiation at follow-up...... 110 6.4 Discussion ...... 112 6.5 Limitations and Strength of the Study ...... 115 6.6 Conclusions ...... 116 Chapter 7: Determinants of Smoking Cessation Among Australian Women—The Role of E- Cigarette Use ...... 117 7.1 Introduction ...... 118 7.2 Methods ...... 120 7.2.1 Study design and participant recruitment...... 120 7.2.2 Measures...... 121 7.2.2.1 Outcome measures...... 121 7.2.2.2 Exposure/independent variable...... 122 xi
7.2.2.3 Covariates...... 122 7.2.3 Statistical analysis...... 123 7.3 Results ...... 124 7.4 Discussion ...... 129 7.5 Limitations and Strengths of the Study ...... 131 7.6 Conclusions ...... 132 Chapter 8: Effectiveness of Pharmacotherapy for Smoking Cessation: Umbrella Review and Quality Assessment of Systematic Reviews ...... 133 8.1 Background ...... 134 8.2 Objectives ...... 136 8.3 Methods ...... 137 8.3.1 Protocol registration and reporting of findings...... 137 8.3.2 Inclusion and exclusion criteria...... 137 8.3.3 Information source and search strategy...... 138 8.3.4 Data collection processes...... 139 8.3.5 Assessment of methodological quality...... 139 8.3.6 Data synthesis...... 142 8.4 Results ...... 143 8.4.1 Characteristics of included reviews...... 145 8.4.2 Methodological quality of included reviews...... 159 8.4.3 The effectiveness of pharmacological interventions...... 162 8.4.3.1 Nicotine replacement therapy...... 162 8.4.3.2 Varenicline and bupropion...... 163 8.4.3.3 Combination therapy...... 163 8.4.3.4 Other therapies...... 164 8.5 Discussion ...... 164 8.6 Conclusions ...... 168 Chapter 9: General Discussion...... 169 9.1 Introduction ...... 169 9.2 Factors Associated with E-Cigarette Use ...... 170 9.3 Adverse Childhood Experiences and E-Cigarette Use ...... 173 9.4 E-Cigarette Use and Smoking Initiation and Cessation ...... 175 9.5 Limitations and Strengths of the Study ...... 178 9.6 Conclusions and Future Directions ...... 179 Chapter 10: Policy Brief ...... 181 Executive Summary ...... 181 Aims ...... 181 Addressee ...... 182 Approaches and Findings ...... 182 The Existing E-Cigarette Regulatory Policy Environment in Australia ...... 182 Key challenges ...... 182 Conclusion ...... 183 Policy Option ...... 183 Recommendations ...... 183 Acknowledgements ...... 185 Disclaimer ...... 186 References ...... 187 Appendices ...... 229 Appendix A: Copyright agreement for chapter 4 ...... 229 Appendix B: Copyright agreement for chapter 5 ...... 232 Appendix C: Copyright agreement for chapter 6 ...... 235 Appendix D: License agreement for umbrella review protocol ...... 240 Appendix E: Sample of Literature review matrix ...... 243 xii
Appendix F: Participation and retention of 17,010 women in the 1989–95 cohort of women who were aged 18–23 years at Survey 1 in 2013* ...... 248 Appendix G: Data access approval letter ...... 249 Appendix H: Statement Governing the Analysis, Use and Publication of Data ...... 251 Appendix I: Media release related to chapter 4 ...... 254 Appendix J: Logistic regression analysis steps employed in data analysis ...... 256 Appendix K: Supplementary table for the association between e-cigarette use during survey 3 and subsequent initiation of smoking at survey 4 among 3rd survey never smoker ...... 257 Appendix L: Supplementary table for the Association between e-cigarette use during survey 3 and subsequent cessation of smoking at survey 4 ...... 260 Appendix M: Supplementary table for the Hosmer-Lemeshow test used to determine the goodness of fit for logistic regression model in chapter 6...... 262 Appendix N: Supplementary table for the Hosmer-Lemeshow test used to determine the goodness of fit for logistic regression model in chapter 7 ...... 263 Appendix O: Supplementary table for the multivariable logistic regression for the association between smoking cessation among those who smoked at least 100 cigarette in life time ...... 264 Appendix P: Version of Published paper from chapter 4 ...... 265 Appendix Q: Version of Published paper from chapter 5 ...... 272 Appendix R: Version of Published paper from chapter 6 ...... 280 Appendix S: Version of published umbrella review protocol ...... 290 Appendix T: The impact of adverse childhood experiences on the health and health behaviours of young Australian women...... 296 Appendix U: Survey 3 for the Australian Longitudinal Study on Women’s Health 1989-95 cohort...... 318 Appendix V: Survey 4 for the Australian Longitudinal Study on Women’s Health 1989-95 cohort...... 406 xiii
List of Tables
Table A Publication Status of Papers Included in the Thesis ...... ii Table 4.1 Description and Coding of the Variables Used in Analysis ...... 55 Table 4.2 Past Year and Ever E-cigarette Use a Among Young Australian Women by Selected Background Characteristics ...... 60 Table 4.3 Associations with Past Year and Ever E-Cigarette Use Among Young Australian Women ...... 64 Table 5.1 ACEs Category (Collected in the Third Survey) ...... 74 Table 5.2 Frequency of E-Cigarette Use by Selected Participant Characteristics ...... 80 Table 5.3 Associations of Individual ACEs with Past Year and Ever E-Cigarette Use Among Young Australian Women ...... 85 Table 5.4 Association Between Number of ACEs and Past and Ever E-Cigarette Use ...... 87 Table 6.1 Sample Characteristics: E-Cigarette Use Status and the Subsequent Initiation of Tobacco Smoking Among Baseline Survey Never Smokers ...... 105 Table 6.2 Factors Associated with Lost to Follow-Up Among Baseline Never Smokers ...... 108 Table 6.3 Unadjusted and Adjusted Associations Between Baseline Characteristics and Subsequent Initiation of Cigarette Smoking at Follow-Up Among Never Smokers...... 111 Table 7.1 Characteristics of Study Participants Reached to Follow-Up and Lost to Follow-Up in Terms of Baseline Variables ...... 125 Table 7.2 Association Between E-Cigarette Use During Baseline Survey and Subsequent Cessation of Smoking at Survey 4 ...... 127 Table 8.1 PICOS Elements ...... 138 Table 8.2 AMSTAR 2 Quality Rating Criteria ...... 142 Table 8.3 Characteristics of Included Reviews ...... 147 Table 8.4 Methods of Smoking Cessation Validation, Quality Assessment Tools and Reported Heterogeneity of the Reviews ...... 157 Table 8.5 Systematic Review Quality (N = 10) ...... 160 Table E1 Sample of Literature Review Matrix Used to Manage Information Collected from Literature ...... 243 Table K1 Supplementary Table for the Association between Ever E-cigarette Use and Subsequent Initiation of Tobacco Smoking...... 257 Table L1 Supplementary Table for the Association between Ever E-cigarette Use and Subsequent Smoking Cessation ...... 260 xiv
List of Figures
Figure 1.1. The e-cigarette device...... 6 Source: US Department of Health Human Services (2016c) ...... 6 Figure 1.2. Diversity of e-cigarette devices...... 7 Source: US Department of Health Human Services (2016c) ...... 7 Figure 3.1. Participants and non-responses in the follow-up surveys...... 47 Figure 5.1 Prevalence of individual ACEs and the number of ACEs experienced by young Australian women...... 78 Figure 5.2 Prevalence of past year and ever e-cigarette use by ACE score among Australian young women...... 79 Figure 6.1 Flow of study participants to assess ever e-cigarette use at baseline and cigarette smoking initiation in the follow-up survey...... 100 Figure 7.1 Flow of study participants to assess ever e-cigarette use at baseline and cigarette smoking cessation in the follow-up survey...... 121 Figure 8.1 PRISMA flowchart of the included reviews...... 145 Figure J1. Logistic regression steps used for data analysis...... 256 Figure M1. Hosmer Lemeshow test used to check goodness of fit for logistic regression in chapter 6...... 262 Figure N1. Hosmer Lemeshow test used to check goodness of fit for logistic regression in chapter 7...... 263 Figure O1. Multivariable logistic regression for the association between smoking cessation among those who smoked at least 100 cigarette in life time...... 264 1
Chapter 1: Introduction
The scientific community can be described as divided in regard to the risks and benefits of e-cigarette use. Currently, the evidence concerning the efficacy of e-cigarettes in aiding cessation is mixed. Some studies report that e-cigarette use is an effective smoking- cessation aid (McNeil et al., 2015) and they were as effective or more effective than nicotine replacement therapy for smoking cessation (Brown, Beard, Kotz, Michie & West, 2014).
Conversely, other studies found that e-cigarette use was ineffective for smoking cessation, with some longitudinal studies finding no statistical association between e-cigarette use and smoking cessation at follow-up (Grana, Popova & Ling, 2014; Shi et al., 2016). Most of the findings regarding e-cigarette use and smoking cessation were derived from cross-sectional data (Barnett, Soule, Forrest, Porter & Tomar, 2015; Brown et al., 2014; Wang, Ho, Leung &
Lam, 2015), so researchers have recommended investigating the causal relationship between e-cigarettes, combustible cigarette use and smoking cessation using more robust designs
(Morgan, Breitbarth & Jones, 2019).
Similarly, findings regarding the role of e-cigarettes in the initiation of traditional cigarette smoking among never smokers are inconclusive (McNeil et al., 2015). Despite the
World Health Organization’s (WHO) recommendation to control and ban the use of e- cigarettes, national positions on e-cigarette regulation vary among countries (WHO, 2019b).
Regulatory environments range from including minimal restrictions to the strict regulation of the supply, sale and use of e-cigarette products.
In Australia, the regulatory policy governing e-cigarette use is relatively stricter than other developed countries. In all Australian states and territories, liquid nicotine is classified as a ‘dangerous poison’ under Schedule 7—which includes dangerous substances that entail special precautions in their production, packaging, storing and use (Australian Government 2
Department of Health Therapeutic Goods Administration, 2016). Based on this classification, the sale, supply and use of nicotine-containing e-cigarettes are illegal in all Australian states and territories (Douglas, Hall & Gartner, 2015). However, the law governing the use of non- nicotine e-cigarettes differs from state to state. Despite these regulations, researchers have found that six out of 10 ‘nicotine-free e-liquids’ sold in Australia contain nicotine (Chivers,
Janka, Franklin, Mullins & Larcombe, 2019). Researchers have also found that most
Australian e-cigarette users (85–89%) purchase e-liquids (with or without nicotine) online from overseas vendors (Fraser, Weier, Keane & Gartner, 2015; WHO, 2016). Additionally, available studies have demonstrated that the prevalence of e-cigarette use is increasing in
Australia. For example, in a study conducted in the United Kingdom (UK) and Australia with adult respondents who were aged 18 years and older, the prevalence of current e-cigarette use increased from 0.6 per cent to 6.6 per cent in Australia from 2010 to 2013 (Yong et al.,
2014).
The e-cigarette regulatory policy in Australia depends mainly on the research findings reported from other countries, but longitudinal associations between e-cigarette use and subsequent smoking initiation may differ by country according to whether nicotine e- cigarettes are legally available. Therefore, formulating an appropriate regulatory policy requires research that investigates the risk and protective factors of e-cigarette use and the role that e-cigarettes play in subsequent smoking initiation and cessation. The findings from the current studies can be used to inform policy developments, increase people’s understanding of e-cigarette behaviour in Australia and form a baseline for future studies that investigate issues related to the evolution of e-cigarette use in Australia.
Most of the studies and prevention strategies pertaining to cigarette smoking and e- cigarette use have targeted adolescents with the assumption that smoking behaviour is mostly established by the age of 18 (Evans-Polce, Veliz, Boyd & McCabe, 2020; Harvey, Chadi, 3
Canadian Paediatric Society & Adolescent Health Committee, 2016). However, current smoking trends have indicated that young adults could be an important and mostly ignored sub-population in the development of smoking behaviour. For example, according to a longitudinal study conducted in the United States (US) that used national data, the incidences of ever and current initiation for both combustible cigarette smoking and e-cigarettes among young adults (18–24 years) was higher compared to those for youth (11–17 years) (Perry et al., 2018). Therefore, research that targets young adults is vital for designing prevention policies and strategies. In another research area, biological, psychological and social factors could affect women’s nicotine uptake (Williams & Ziedonis, 2004). Women are disproportionally susceptible to smoking-related morbidity and mortality compared to men
(Allen, Oncken & Hatsukami, 2014). Further, tobacco firms are using gendered tactics to attract girls and women to use tobacco products (Amos, Greaves, Nichter & Bloch, 2012).
This thesis was designed to identify the risk and protective factors that are associated with e-cigarette use among Australian women aged 19–26 years. Moreover, it examines the association between e-cigarette use and subsequent smoking initiation and smoking cessation.
This introductory chapter includes an outline of smoking and the sustainable development goals (SDGs), the definition of e-cigarettes and a description of their chemical contents, usage, trends, reasons for use and short- and long-term health effects. The chapter also overviews e-cigarette use, smoking initiation and smoking cessation, as well as the policies and regulation of tobacco smoking and e-cigarette use both globally and within
Australia. It concludes by presenting an overview of the thesis.
1.1 Smoking and the Sustainable Development Goals
Smoking is a leading cause of morbidity and premature death worldwide, both in developed and developing countries (Gometz, 2011). Mortality from chronic diseases such as cancer has been associated with the number of years that a person has smoked and the 4 number of cigarettes smoked per day (Halpern, Gillespie & Warner, 1993). Smoking cessation, or reduction, can improve survival by decreasing the risk of cancer, heart disease, stroke, chronic obstructive pulmonary disease and other chronic diseases (Gometz, 2011;
Kawachi et al., 1993). Among young adults who smoke, smoking cessation before the age of
35 years has been proven to increase life expectancy by nearly seven years (Taylor Jr,
Hasselblad, Henley, Thun & Sloan, 2002).
Strengthening tobacco control is one of the targets of SDG 3, which aims to realise the World Health Organization Framework Convention on Tobacco Control (WHO FCTC)
(WHO, 2019b). The WHO FCTC seeks to protect current and future generations from the devastating health, social, environmental and economic consequences of tobacco smoking
(Buse & Hawkes, 2015; WHO, 2019b) and it includes both price and non-price measures
(WHO, 2013). Tobacco control efforts positively affect, either directly or indirectly, all 17
SDGs (Buse & Hawkes, 2015).
Australia is successfully reducing the prevalence of tobacco smoking, with data indicating that the prevalence of cigarette smoking has declined over time. According to the
2019 WHO report on the global tobacco epidemic, Australia is among the group of countries displaying the highest level of achievement in regard to implementing the WHO FCTC
(WHO, 2019b). The prevalence of daily smoking among people aged 14 and older had decreased from 24 per cent in 1991 to 12.2 per cent in 2016. Further, the mean age at which people aged 14–24 years smoked their first full tobacco cigarette had increased from 15.9 years in 2013 to 16.3 years in 2016 (Australian Institute of Health and Welfare, 2014a, 2016).
Nicotine replacement therapy (NRT) in different formulations (e.g., gum, patches, lozenges and inhalers) was the first approved pharmacological therapy for smoking cessation by the
Food and Drug Administration (FDA) in the US and other local national health agencies
(American Academy of Family Physicians, 2016). In recent years, the advent of the 5 electronic nicotine delivery system (ENDS)—or electronic cigarettes—has become a global trend (WHO, 2019b).
1.2 Definition and Types of E-Cigarettes
The electronic cigarette—or e-cigarette—is a battery-powered device that is designed to deliver nicotine and/or other substances in the form of an aerosol (without burning tobacco) (Schripp, Markewitz, Uhde & Salthammer, 2013; US Department of Health Human
Services, 2016a). It is known by various names, such as vapes, e-cigs, cigalikes, mods, e- hookahs and vape pens. Other than nicotine, e-cigarette liquid may also contain substances such as propylene glycol, glycerine and flavouring agents (Walley & Jenssen, 2015). The aerosols produced by e-cigarettes can contain dangerous substances, including heavy metals
(e.g., lead), volatile organic compounds and ultrafine particles (Visser et al., 2019).
Researchers have also found that e-cigarettes may contain formaldehyde—a known carcinogen (Chakma, Dhaliwal, Mehrotra & Writing Committee, 2019). Evidence has also indicated that young people are occasionally using e-cigarettes for the delivery of illicit drugs
(Breitbarth, Morgan & Jones, 2018). Although e-cigarettes are diverse in design and shape, most e-cigarette devices comprise a mouthpiece, a sensor or user-actuated button to activate the heating coil, a battery, a heating coil or atomiser and a reservoir or tank/cartridge (Walley,
Wilson, Winickoff & Groner, 2019; Zborovskaya, 2017) (see Figure 1.1). A device closely resembling the current e-cigarette was invented by Herbert Gilbert in 1963, though it was not commercialised (Consumer Advocates for Smoke-Free Alternatives Association, 2018). The prototype of the e-cigarette that is currently used worldwide was introduced by Hon Lik as an alternative nicotine delivery method in China in 2004 (Grana, Benowitz & Glantz, 2014). E- cigarettes were introduced to the European and North American market between 2006 and
2007 (Wellman & O’Loughlin, 2016). 6
Figure 1.1. The e-cigarette device. Source: US Department of Health Human Services
(2016c)
Currently, more than 460 different brands of e-cigarettes are available on the global market (Zhu et al., 2014). Since the invention of modern e-cigarettes, different generations of the products have been developed (see Figure 1.2). The first generation is cigar-like; it resembles a traditional cigarette and is a closed-system (non-refillable) device. The second generation is an open, refillable and rechargeable device that closely resembles a pen, while the third generation is similar to the second and contains a much larger tank, battery and heating device (Chakma et al., 2019). Launched in 2015, JUUL is a fourth-generation e- cigarette with a higher concentration of nicotine compared to previous generations (Walley et al., 2019). Due to its flavour and nicotine concentration, JUUL is currently popular among young people (Fadus, Smith & Squeglia, 2019). Compared to the previous generations of e- cigarettes and traditional tobacco, JUUL can deliver a significantly higher concentration of nicotine to the blood (Rao, Liu & Springer, 2020). It is a closed-system device charged by a
USB and comprises a one-use pod that contains 0.7 mL of nicotine, which corresponds to five per cent of nicotine in weight (Kavuluru, Han & Hahn, 2019). JUUL currently accounts for approximately 50% per cent of the e-cigarette market in the US (Willett et al., 2019). 7
Figure 1.2. Diversity of e-cigarette devices. Source: US Department of Health Human
Services (2016c)
1.3 Prevalence, Trends and Reasons for E-Cigarette Use
The Centers for Disease Control and Prevention (CDC) has declared e-cigarette use by young people to be an epidemic in the US. The prevalence of e-cigarette use among high school students in the US had increased from 1.5 per cent in 2011 to 20.8 per cent in 2018
(Cullen et al., 2018). Similarly, the prevalence of e-cigarette use has also drastically increased in European Union member states (Filippidis, Laverty, Gerovasili & Vardavas, 2017). In some European countries, the prevalence of e-cigarette use surpasses that of traditional tobacco smoking. For example, a cross-sectional study conducted in Wales discovered that among students aged 11–16 years, the prevalence of e-cigarette use was nearly twice as high 8
(18.5%) as the prevalence of conventional tobacco smoking (10.5%) (de Lacy, Fletcher,
Hewitt, Murphy & Moore, 2017). Although the prevalence and trends of e-cigarette use have not been well documented in Australia, available data demonstrate that the prevalence of e- cigarette use, especially by young people, is increasing over time (Yong et al., 2014).
People have provided different reasons for using e-cigarettes—some of which include reducing or quitting the use of combustible cigarettes, reducing the health risks of tobacco use, the lower price (compared to conventional cigarettes), the perception of greater social acceptability than traditional cigarettes, the pleasant flavouring and curiosity (Jancey, Binns,
Smith, Maycock & Howat, 2015). Additionally, reasons for e-cigarette use vary by age. For example, flavouring is the main reason cited by young people for e-cigarette use compared to older people (Patel et al., 2016). In Australia, the main reason cited for e-cigarette use is to quit cigarette smoking and reduce tobacco-related health risks (Dunlop, Lyons, Dessaix &
Currow, 2016).
1.4 Short- and Long-Term Health Effects of E-Cigarettes
Although the health-related effects of e-cigarettes are claimed as minimal compared to traditional tobacco smoking, available research has found that the chemicals and additives in e-liquids can lead to short- and long-term health problems (Chakma et al., 2019; Glantz &
Bareham, 2018; Visser et al., 2019). The health effects vary due to the variety of e-cigarette devices and e-liquid contents (National Academies of Sciences & Medicine, 2018). Since e- cigarettes have not been in widespread use for long enough to permit the evaluation of long- term health effects (Glantz & Bareham, 2018; Grana et al., 2014), researchers believe that the long-term health effects of e-cigarettes have not been well studied yet (Dyer, 2018). The
WHO has reviewed and summarised existing sources of evidence for e-cigarette use outcomes and has concluded that the evidence pertaining to the health effects associated with e-cigarettes is inconclusive (WHO, 2019b). Lung and respiratory-related symptoms are 9 common short-term health effects associated with e-cigarette use (Centers for Disease
Control and Prevention, 2019). According to a recent CDC report that focused on the period of 28 June – 20 August 2019, 200 cases of severe respiratory disease were reported among e- cigarette users in the US (Schier et al., 2019). Additionally, researchers have discovered that direct exposure to propylene glycol, a common e-cigarette liquid ingredient, can cause eye and respiratory irritation (Visser et al., 2019).
Scientific evidence is available regarding the effect nicotine on brain development during the prenatal, postnatal and adolescent period. Many e-cigarettes contain addictive substances (primarily nicotine) that can affect brain development, particularly in young people (Glantz & Bareham, 2018). The chemicals found in e-cigarettes can also lead to long- term health effects, including cardiovascular disorders, cancer and chronic obstructive pulmonary disease (Glantz & Bareham, 2018). For example, the International Agency for
Research on Cancer has classified formaldehyde as a carcinogen (Zborovskaya, 2017).
Further, researchers have found that bystanders’ (passive smokers’) exposure to e-cigarette vapour can lead to health problems such as eye irritation, sore throat, headaches and nausea, though these effects are notably less harmful compared to those caused by traditional cigarette smoking (Visser et al., 2019). The ingestion of e-liquid by children can also cause health consequences that range from mild to severe (Ang, Tuthill & Thompson, 2018;
Govindarajan, Spiller, Casavant, Chounthirath & Smith, 2018).
Use of nicotine not only invite serious physical, mental and social health consequences but these consequences also adversely affect the wellbeing of foetuses during pregnancy and after birth in pregnant women. Some of these adverse short- and long-term consequences during pregnancy include foetal growth retardation, premature birth, congenital disabilities, withdrawal syndrome, low intelligence quotient, delays in motor skills, impaired cognitive functioning and behavioural problems (Chakma et al., 2019; WHO, 2016). 10
1.5 E-Cigarette Use, Smoking Initiation and Cessation
The rising prevalence of e-cigarette use among children and young people has increased the concern that it may lead to smoking initiation among non-smokers (Chakma et al., 2019). Most overseas cross-sectional and longitudinal studies found a positive association between e-cigarette use and subsequent smoking initiation or intention to smoke (Aleyan,
Cole, Qian & Leatherdale, 2018; East et al., 2018; Jongenelis, Jardine, Kameron, Rudaizky &
Pettigrew, 2019; Primack et al., 2017). However, some researchers concluded that the link between e-cigarette use and the initiation of smoking was not evident (McNeil et al., 2015).
Further, the findings regarding the association between e-cigarette use and subsequent cigarette smoking are controversial. Public Health England (PHE) declared that e-cigarettes are 95 per cent safer compared to traditional cigarette smoking and strongly recommends that the products can be used for risk reduction and smoking cessation (McNeil et al., 2015).
However, McKee and Capewell (2015a) condemned PHE’s report, arguing that it included studies with methodological flaws and studies that received funding from tobacco companies.
Polosa (2015) also argued that it was not sound to conclude that e-cigarettes are 95 per cent safer based on the opinion of a small group of people with no predefined expertise in tobacco control. Polosa also argued that the papers on which the PHE’s conclusions were based received financial support from e-cigarette advocates, and that some of the authors were consultants and advisors of e-cigarette producers and distributors. Moreover, Cancer
Research UK and the British Lung Foundation have defended the PHE’s report (Green, Bayer
& Fairchild, 2016). In contrast to the PHE’s recommendations, the US National Academies of Sciences, Engineering and Medicine reported that no sufficient evidence existed concerning the efficacy of e-cigarettes as a smoking-cessation aid (National Academies of
Sciences & Medicine, 2018). 11
1.6 Policies and Regulation of Smoking and E-Cigarettes
A national substance use policy is an official government statement that reflects the government’s ambitions, goals, decisions and commitments to curb the health and socio- economic effects of substance use (WHO, 2001). The WHO recommends formulating policies and strategies that consider the culture and socio-economic context of any specific nation (Monteiro, 2011).
Most countries have adopted the WHO’s tobacco control framework for reducing the social, economic, environmental and emotional effects of tobacco smoking. In brief, the
WHO FCTC is a global recommendation to reduce the demand and supply of tobacco (Stone
& Marshall, 2019; WHO, 2019b). The measures to reduce tobacco demand include:
1. price and tax measures
2. the regulation and disclosure of tobacco product contents
3. protection from being exposed to environmental tobacco smoke
4. packaging and labelling
5. public awareness
6. tobacco dependence and cessation measures, as well as a comprehensive ban and
restriction on tobacco advertising
7. promotion and sponsorship.
The supply-side measures include eliminating the illicit trade of tobacco products, restricting the sales to and by minors and obtaining support for economically viable alternatives for growers (Shibuya et al., 2003). After the WHO FCTC treaty was established in 2005, global tobacco smoking prevalence rates dropped over time (Stone & Marshall,
2019).
Australia has been estimated to lose approximately A$55 million annually from effects related to substance abuse—of which alcohol, tobacco and illicit drugs shared 27.3, 12
56.2 and 14.6 per cent of the total tangible and intangible social costs from 2004 to 2005, respectively (Collins & Lapsley, 2008). To curb health and non–health related substance use problems, the Australian Government has been developing a policy framework in Australia called the National Drug Strategy (NDS) since 1985 (Green, 2002). Following the 2004–2009 and 2010–2015 NDSs, the use of all forms of substances dropped dramatically (Australian
Institute of Health and Welfare, 2017). For example, the prevalence of daily smoking among people aged 14 years and older decreased from 24 per cent in 1991 to 12.2 per cent in 2016, and the mean age at which young people aged 14–24 years smoked their first full tobacco cigarette increased from 15.9 years in 2013 to 16.3 in 2016. The 2017–2026 NDS is a recent document that has been developed to minimise the social, economic and psychological consequences of substance use in the Australian population (Australian Department of
Health, 2017). It includes three pillars that are proposed to minimise the adverse effects of substance use: demand reduction (i.e., reduce the use of substances), supply reduction (i.e., decrease the availability of substances) and harm reduction (reduce the adverse outcomes related to substance use).
As part of the NDS, the government has also formulated the National Tobacco
Strategy, which aims to decrease the social-, economic- and health-related problems that are specifically tied to tobacco smoking (Department of Health and Ageing, 2012; Ministerial
Council on Drug Strategy, 2005). After the 2004–2009 Australian National Tobacco Strategy was introduced, daily tobacco smoking rates declined from 16.6 per cent in 2007 to 15.1 per cent in 2010 and 12.8 per cent in 2013 (Australian Institute of Health and Welfare, 2011,
2014a). However, the prevalence of daily tobacco smoking did not significantly decrease in
2016, compared with the prevalence in 2013 (12.8% in 2013 and 12.2% in 2016). The new
National Tobacco Strategy of 2012–2018 was also developed with the central goal of
13 decreasing the social and economic consequences of tobacco smoking on the Australian population (Department of Health and Ageing, 2012).
The WHO recommends the following regulatory options for preventing the initiation of e-cigarette use by never smokers and young people in countries that have not banned the distribution, sale or use of the ENDS or electronic non-nicotine delivery system (ENNDS), or both (WHO, 2016):
1. Ban the sale and distribution of ENDS/ENNDS to minors.
2. Ban the possession of ENDS/ENNDS by minors.
3. Ban or restrict the advertising, promotion and sponsorship of ENDS.
4. Tax the ENDS/ENNDS at a level that renders the devices and e-liquids unaffordable
to minors.
5. Ban or restrict the use of flavours that appeal to minors.
6. Regulate the places, density and channels for sales.
7. Take measures to combat illicit trade in the ENDS/ENNDS.
No universal agreement currently exists regarding which is the most appropriate policy response or regulatory framework for managing e-cigarettes. Unlike tobacco control strategies, the approaches that countries have adopted to regulate e-cigarettes are diverse and sometimes contradictory (Stone & Marshall, 2019). E-cigarette policy domains are also varied across countries. Some of these domains include a minimum age for purchase, sale, advertising, promotion and sponsorship, and others include the packaging, product regulation, reporting/notification, vape-free alternatives and tax (Institute for Global Tobacco Control,
2015). For example, e-cigarette products in the US are regulated like tobacco products, while they have been recommended as an aid for smoking cessation and risk reduction in England by governmental agencies such as the PHE (McNeil et al., 2015). In contrast, the distribution, 14 sale and use of nicotine in e-cigarettes is illegal in all Australian states and territories.
However, the sale and use of non-nicotine e-cigarettes are legal.
The e-cigarette regulatory policy in Australia is mainly based on study findings that have been reported from abroad. Studies that have investigated the risk and protective factors of e-cigarette use are limited, with no studies having examined the role that e-cigarettes play in smoking initiation and cessation in Australia. In the context of policy variation, research investigating these two under-researched points is crucial for policymakers. Within this political context, this thesis has identified the prevalence, risk and protective factors that are associated with e-cigarette use, as well as the role that e-cigarettes play in smoking initiation and smoking cessation among Australian women. The findings of this thesis will contribute to future policies and strategies that pertain to e-cigarette use. 15
1.7 Thesis Overview
The thesis comprises 10 chapters, of which this introduction forms Chapter 1. Chapter
2 includes a critical review of relevant literature. It begins by describing the prevalence and trends of tobacco smoking and e-cigarette use (both globally and in the Australian context) and then elaborates on the factors that are associated with e-cigarette use. The third section of
Chapter 2 discusses the factors that are associated with smoking initiation and cessation. The methods and materials used in this thesis’s study are then described in Chapter 3. This chapter also details the approaches that were employed to answer the research questions. It additionally provides an overview of the Australian Longitudinal Study on Women’s Health
(ALSWH), including information about the study’s participants, recruitment strategy, inclusion and exclusion criteria and attrition rate. For each specific research question, the specific research methods and procedures are briefly explained.
The study’s results are presented and discussed in Chapters 4–8. Chapter 4 reports on the prevalence and factors that are associated with past year and ever e-cigarette use among
Australian women. Chapter 5 reports on the results that were obtained from the same dataset in regard to the association between adverse childhood experiences and past year and lifetime e-cigarette use, while controlling for potential covariates. The findings from this study will identify the risk and protective factors of e-cigarette use among young Australian women, which will enable the targeting of high-risk groups.
Chapter 6 contains a longitudinal analysis that assesses the association of e-cigarette use and subsequent initiation of cigarette smoking among never smokers. The analysis controlled for any potential confounders so that it could identify how e-cigarette use affected the subsequent initiation of cigarette smoking. Chapter 7 discusses the role that e-cigarette use plays in subsequent smoking cessation, while Chapter 8 presents an umbrella review that assesses the effectiveness of pharmacotherapy for smoking cessation. Chapter 9 presents a 16 general discussion that integrates the findings from Chapters 4–8 and provides the limitations of the study, the contribution of the findings and the thesis’s conclusions. Finally, Chapter 10 presents a policy brief that is based on the current study’s findings. There is some repetition among the results chapters, including in the description of study design, data source, and recruitment of study participants. 17
Chapter 2: Literature Review
This literature review examines existing research that has assessed the risk and protective factors of e-cigarette use and the role that e-cigarettes play in smoking initiation and cessation. After describing the methods that were used to search the literature, this chapter will review literature that pertains to the prevalence and trends of tobacco smoking and e-cigarette use (both globally and in the Australian context); the gender differences in smoking tobacco and e-cigarette use; the factors that are associated with e-cigarette use; and the factors that are associated with tobacco smoking initiation and cessation.
2.1 Literature Search Strategies
Comprehensive search strategies were used to identify literature for a critical review.
Search terms that were considered relevant to the literature review subheadings were used to search the literature from different databases. Several databases were used to identify relevant articles, including PubMed, MEDLINE, EMBASE, CINAHIL and PsychINFO. Google
Scholar was also used to find governmental and organisational reports and policies. The search for articles was conducted using the University of Newcastle’s online library, with the ensuing literature review only including articles that were published in peer-reviewed journals or respectable magazines and governmental documents. The search included articles that were published after 2007 since e-cigarettes were not considered popular before this time. A literature review matrix table was used to manage the sources and arguments presented in the literature (see Appendix E). The information collected from each article included the characteristics of study participants (gender, age and sample), location and study setting, year that the study was conducted, data collection method, study design and main findings. 18
2.2 Prevalence and Trends of Smoking
The prevalence of current tobacco smoking has globally declined from 43 per cent in
2000 to 34 per cent in 2016 among males aged 15 years and older and from 11 per cent in
2000 to six per cent in 2016 among females (WHO, 2019c). Achieving the SDG target of a
30 per cent reduction in the prevalence of global tobacco smoking by 2030 will entail the overall prevalence decreasing to 15.5 per cent (25.6% for males and 5.3% for females). If the decline in tobacco prevalence continues at the current pace, then the projected prevalence rate of tobacco smoking among people aged 15 years and older will be 17.3 per cent by 2025
(30% for males and 4.7% for females) (WHO, 2018). This projection reveals that females can reach the global target set by the SDGs well ahead of the target time.
In many countries, the prevalence rates of tobacco smoking among both men and women have revealed a declining trend over time. However, in some countries and communities, the prevalence rates of smoking have increased over time. For example, the prevalence rate of current tobacco smoking in Canada among people older than 15 years increased from 13 per cent in 2015 to 15.1 per cent in 2017 (Reid et al., 2019). In a longitudinal study of North American indigenous adolescents (n = 743) aged 10–13 years at baseline, researchers found that occasional smoking was more prevalent among females and that this prevalence rate increased steadily in survey one from 8.6 per cent (7.3% for males and 10% for females) to 12.7 per cent (16.2% for females and 9.2% for males) in survey two a year later. Similarly, frequent smoking rates increased across the wave among females compared to males, from 3.8 per cent (3.6% for females and 4.1% for males) to 9.1 per cent
(11.2% for females and 7.2% for males) and 19.7 per cent (20.1% for females and 13.8% for males) at survey one, survey two and survey three, respectively (Yu & Whitbeck, 2016).
According to a recent WHO report, the tobacco smoking prevalence rate for people older than 15 years in the European region may be as high as 29 per cent, which represents 19 the highest prevalence rate among all WHO regions (WHO, 2019a). The prevalence rate of current smoking for people older than 18 years in England was 14.4 per cent (16.4% for males and 12.6 % for females) in 2018—which was reduced from 14.9 per cent in 2017 and
19.8 per cent in 2011 (NHS Digital, 2019). In this same study, the prevalence rate of current smoking was highest among people aged 25–34 years (19%). The prevalence rate of current smoking in the US in 2018 was 13.7 per cent (15.6% for males and 12% for females). In this report, current smokers were defined as people who reported smoking at least 100 cigarettes in their lifetime and who, at the time they participated in a survey about this topic, reported smoking every day or for some days (Centers for Disease Control & Prevention, 2016). In a cross-sectional study conducted in China on 13,354 study participants aged 15 years and older, researchers identified that the prevalence of current smokers in China in 2010 was 28.1 per cent and that this prevalence represented 33.6 per cent among young people aged 15–24 years (Li, Hsia & Yang, 2011).
Australia is among the group of countries considered on track for reaching the SDG target of reducing the prevalence of smoking among adults by 2030 (Watson, Thwaites,
Griggs, Kestin & McGrath, 2014). National policies that are designed to decrease cigarette smoking through public awareness actions, advertising bans and increased taxation have contributed to the drop in smoking prevalence rates in Australia. Based on the 2016 National
Drug Strategy Household Survey, the prevalence rate of current smoking (daily, weekly or less often than weekly) among people aged 14 years or older was 14.9 per cent (Australian
Institute of Health and Welfare, 2017). The prevalence rate of daily smoking in Australia was much higher in the age category of 25–34 years (17.3%) and 45–54 years (17.7%) and lowest for people aged 65 years or older (6.8%). From 2014 to 2015, the prevalence rate of daily smoking was high for Western New South Wales (23.3%) and lowest for Northern Sydney
(5.4%) (Australian Institute of Health and Welfare, 2016a) (see Table 2.1). 20
2.3 Prevalence and Trends of E-Cigarette Use
The prevalence of tobacco smoking has globally declined (WHO, 2019c), while the prevalence of e-cigarette use has steadily increased, especially for adolescents and young people (Cullen et al., 2018; de Lacy et al., 2017). Some researchers have attributed this current decline in global tobacco smoking for young people to the advent and popularity of e- cigarettes (Notley, Ward, Dawkins & Holland, 2018; Zhu, Zhuang, Wong, Cummins &
Tedeschi, 2017). Conversely, other scholars believe that this current decline is mainly due to the execution of the WHO FCTC by respective countries (WHO, 2019b). In a cross-sectional study that compared the prevalence of e-cigarette use and cigarette smoking among adolescents in the US and South Korea, the combined prevalence of past 30 days e-cigarette use and cigarette smoking declined in South Korea from 13.2 per cent to 8.5 per cent. It increased in the US from 11.3 per cent to 14, between 2011 to 2015 (Cho, Dutra & Glantz,
2017). Notably, during the study period, e-cigarette regulation was stricter in South Korea compared to the US (as will be discussed in the following paragraph). In a longitudinal study conducted in the UK on adolescents (n = 2,836) aged 13–14 years at baseline, researchers identified that ever e-cigarette use at baseline was associated with the escalation of the cigarette smoking prevalence rate at 12 months follow-up (Conner et al., 2018).
The prevalence of e-cigarette use is mainly determined by policy regulations—that is, the prevalence of e-cigarette use is lower in countries that have established more restrictive policy regulations compared to countries with less restrictive regulatory policies. For example, in a study that compared the prevalence rates of e-cigarette use for young people aged 12–18 years in South Korea and the US, the prevalence rate of e-cigarette use decreased from 4.7 to 4.4 per cent in South Korea, but increased from 0.9 to 11.2 per cent in the US, from 2011 to 2015 (Cho et al., 2017). Before 2016, the regulation of e-cigarettes was stricter in South Korea than in the US. Starting in 2008, South Korea regulated e-cigarettes as 21 tobacco products. The country used the following strategies to regulate e-cigarettes: prohibiting use in all indoor places, prohibiting sale to minors, banning the products’ advertisement, increasing taxes, banning online marketing and rendering health warning labels mandatory. Conversely, the US endorsed no regulation of e-cigarettes before 2016
(Tremblay et al., 2015). E-cigarette products were introduced to the South Korean and US markets at approximately the same time.
Researchers compared the pre-policy and post-policy prevalence rates of e-cigarette use for Pennsylvania adolescents in grades nine to 12 using data from the 2015–2017 Youth
Risk Behaviour Surveillance System. Before 2015, no retail-licensing requirement existed for e-cigarettes in Pennsylvania. However, after 2015, Pennsylvania launched a retail-licensing requirement for e-cigarettes. Based on this policy change, the post-policy prevalence rate of e-cigarette use for adolescents decreased by 21.6 per cent in 2017 from its pre-policy rate in
2015 (Azagba, Shan & Latham, 2020). The global and Australian policy regulations of e- cigarettes have been previously discussed in Section 1.6.
Access to an online e-cigarette market is another factor responsible for the increased prevalence of e-cigarette use, especially regarding young people (US Department of Health
Human Services, 2016). The online market accounts for more than 33 per cent of all global e- cigarette sales (WHO, 2016). The online e-cigarette market contributes more extensively to supply in countries that have established strict regulations for e-cigarettes.
Additionally, the general prevalence of global e-cigarette use has increased over time—again, especially in regard to young people (Australian Institute of Health and
Welfare, 2017; Cullen et al., 2018). For example, the prevalence rate of current e-cigarette use (past 30 days use) for high school students in the US has increased by 78 per cent—from
11.7 per cent in 2017 to 20.8 per cent in 2018 (Cullen et al., 2018). According to more recent data, the prevalence rates of ever e-cigarette use and past 30 days use of e-cigarettes in 22
Canada for people aged 15 years and older were 15.4 per cent and 2.9 per cent, respectively
(Reid et al., 2019). This study by Reid et al. (2019) revealed that the prevalence rate of ever e-cigarette use was higher for males (18.8%) compared to females (12%). According to the study, the ever use rate of e-cigarettes in Canada was more prevalent for people aged 20–24 and 25–34 years compared to the rate for people aged 35 years and older. Based on another study derived from the Health and Lifestyles Survey of 2016, the prevalence rate of ever e- cigarette use in New Zealand for people aged 15 years and older (n = 3,854) was 17 per cent, with the highest prevalence exhibited by young people aged 15–24 years (Oakly, Edwards &
Martin, 2019).
In a study conducted with US high school students (n = 19,018) and middle school students (n = 8,837), the prevalence rate of current e-cigarette use (past 30 days use) was 27.5 and 10.5 per cent for high school students and middle school students, respectively (Cullen et al., 2019). However, the low response rate (66.3%) in this study may have biased the estimated prevalence rate of e-cigarette use for the study population.
According to the 2016 National Drug Strategy Household Survey, the prevalence of ever e-cigarette use for smokers aged 14 years and older was 31 per cent. This report indicated that the prevalence of current e-cigarette use was higher for smokers (4.4%) compared to never smokers (0.6%). Based on this same survey, the prevalence of lifetime e- cigarette use in Australia was revealed to be higher for young smokers aged 18–24 years when compared to older smokers aged 60–69 years (49% v. 18.7%) (Australian Institute of
Health and Welfare, 2017).
The variation in prevalence rates of e-cigarette use between countries may partly be due to the differences in policy regulations among countries, the availability of e-cigarette products, and cultural norms. Moreover, sampling procedures, data collection methods, 23 sample sizes, definitions of e-cigarette use, ages of study participants and gender compositions can also contribute to these differences.
2.4 Gender Differences in Tobacco Smoking and E-Cigarette Use
Several previous studies have found that the prevalence of smoking is higher in males than in females (Flora, Mascie-Taylor & Rahman, 2009; Jiang, Wang, Ho, Leung & Lam,
2016). Gender differences in the prevalence rates of cigarette smoking and nicotine dependence are partly linked to social norms, economic factors and biological factors (US
Department of Health Human Services, 2012; Zhang, Cowling & Tang, 2010). Additionally, low social acceptance of women smoking could contribute to the low prevalence of smoking among women (Obeidat et al., 2014). Some researchers have discovered that males were more likely than females to believe that smoking behaviour was accepted by society
(Chinwong, Mookmanee, Chongpornchai & Chinwong, 2018). Females were also more likely to perceive smoking as being a leading risk of morbidity and mortality than males
(Lundborg & Andersson, 2008).
According to the 2019 WHO world health statistics report, the prevalence of smoking has declined in developed nations for both sexes, but the decline has been slower for females
(WHO, 2019c). In some developed countries, the difference in the prevalence rates of smoking between males and females has narrowed over time (WHO, 2019c). Recent increases in the female-to-male smoking ratio in developed countries have been mainly attributed to decreases in the gender inequality index (Bilal et al., 2016; Hagen, Garfield &
Sullivan, 2016). Researchers have also found that in countries with higher female empowerment and autonomy, the prevalence of smoking is nearly equal between men and women (Hitchman & Fong, 2011). Hitchman and Fong (2011) also found that countries with a higher gross national income per capita exhibited an equal prevalence of smoking for men and women. 24
According to the 2016 National Drug Strategy Household Survey, the prevalence of daily smoking in Australia for males and females aged 18–24 years was 12.3 and 10.8 per cent, respectively; however, the prevalence of daily smoking for males and females aged 50–
59 years was approximately the same for both sexes (14%) (Australian Institute of Health and
Welfare, 2017).
In addition to gender differences in conventional smoking rates, the literature also supports the presence of gender differences in e-cigarette use. A systematic review that included 16 primary studies found that males were more likely to use e-cigarettes compared to females (Kong, Kuguru & Krishnan-Sarin, 2017). In a school-based survey conducted in
Hong Kong with 45,857 study participants, past 30 days e-cigarette use was positively associated with the male gender (Jiang et al., 2016). However, contrary to the overall results and findings for smokers, female never smokers were more likely to use e-cigarettes compared to male never smokers (Lee & Oh, 2019).
In a study conducted in Australia with 1,116 young adults aged 18–25 years (59% female), male smokers were more likely than female smokers to be current e‐cigarette users
(Jongenelis, Brennan, et al., 2019). The researchers of the study also found that male non- smokers were more likely to be current e-cigarette users than female non-smokers. According to another study conducted in New South Wales with people aged 18 years and older
(n = 2,966), males were proven to be more likely to report ever e-cigarette use compared to females (Harrold, Maag, Thackway, Mitchell & Taylor, 2015). Similarly, in yet another study with people aged 18 years and older, which used data from the 2016 National Drug Strategy
Household Survey, researchers found that males were more likely to report current e-cigarette use compared to females (Chan et al., 2019). The high prevalence of e-cigarette use among males could be due to a higher awareness of e-cigarette products, lower perceptions of health risks, more favourable social norms and greater access to the products (Cheah, Teh & Lim, 25
2019; Lundborg & Andersson, 2008; Perikleous, Steiropoulos, Paraskakis, Constantinidis &
Nena, 2018). For example, in an online survey of US adolescents aged 13–18 years
(n = 3000), researchers found that females were more likely to perceive the health risks of using e-cigarette products than males (Lundborg & Andersson, 2008).
Researchers have identified that the risk and protective factors of substance use differ by gender. A country or community’s gender roles and societal norms expose men and women to different risks at different levels (WHO, 2019c). Biological differences are another factor responsible for the gender differences found in relation to the risk and protective factors of substance use (Becker, McClellan & Reed, 2017). Researchers have discovered that the risk and protective factors of tobacco smoking also differ by gender (García-
Rodríguez, Suárez-Vázquez, Secades-Villa & Fernández-Hermida, 2010; Teodoro,
Cerqueira-Santos, Araujo de Morais & Koller, 2008). Therefore, the risk and protective factors of e-cigarette use could be different between males and females. Most of the previous studies have not assessed any gender-specific factors that determine e-cigarette use.
Moreover, despite the gender differences in e-cigarette use caused by tobacco smoking status
(Jongenelis, Brennan, et al., 2019; Lee & Oh, 2019), previous studies were not gender specific in terms of investigating the role that e-cigarette use plays in smoking initiation and cessation. This thesis will identify the risk and preventive factors of e-cigarette use, as well as its role in smoking initiation and cessation for Australian females. The thesis’s findings will help develop gender-based strategies for controlling e-cigarette use.
2.5 Factors Associated with E-Cigarette Use
To understand and design prevention strategies, it is important to first identify the risk and protective factors of e-cigarette use. Existing evidence demonstrates that most factors that are associated with traditional cigarette smoking are also associated with e-cigarette use
(Rennie, Bazillier-Bruneau & Rouëssé, 2016). This section discusses the risk and protective 26 factors associated with e-cigarette use that have been previously reported in the literature.
Previous Australian research that has examined the factors associated with e-cigarette use was limited at the start of this thesis. Based on the available literature, the identified risk and protective factors can be generally categorised under personal and socio-economic factors, peer and parental factors, adverse childhood experiences, physical and mental health, intimate partner violence, and smoking and other substance use.
2.5.1 Personal and sociodemographic factors.
Previous studies from both overseas and Australian contexts have identified several risk and protective factors that relate to personal and sociodemographic factors. These factors are summarised in the ensuing subsections.
2.5.1.1 Age.
E-cigarette use is most common in adolescents and young people (Australian Institute of Health and Welfare, 2017; Cullen et al., 2018; Reid et al., 2019). Most international studies have reported that younger age is positively associated with current and ever e- cigarette use (Lee, Chiang, Kwon, Baik & Chang, 2019; Stallings-Smith & Ballantyne, 2019;
US Department of Health and Human Services, 2016b). For example, in a survey conducted in the US with study participants aged 18 years and older (n = 5989), researchers found that the odds of ever e-cigarette use were higher for people aged 18–34 years than for adults aged
55 years and older (Stallings-Smith & Ballantyne, 2019). In contrast, other studies have linked e-cigarette use with older age (Agaku & Ayo-Yusuf, 2014; Perikleous et al., 2018).
The differences in the findings could be due to differences in age classification, variables adjusted in the model, sample sizes and age restrictions for e-cigarette use.
In a study of 2,966 people aged 18 years and older in New South Wales, the prevalence of current e-cigarette use (daily, weekly, monthly or less than monthly) was found to be higher for people aged 18–29 years than those aged 30–55 years and older (Dunlop et 27 al., 2016). In another study conducted in New South Wales with men and women aged 18 years and older (n = 12,502), the odds of using e-cigarettes were greater for participants aged
18–44 years compared to people aged 45 years and older (Harrold et al., 2015).
Some researchers have linked the high prevalence of e-cigarette use for adolescents and young people with the flavours found in e-liquids (Fadus et al., 2019; Jongenelis et al.,
2018; Pepper, Ribisl & Brewer, 2016). In a cross-sectional study conducted in the US that compared the use of e-cigarettes between adolescents aged 12–17 years (n = 414), young people aged 18–24 years (n = 961) and older adults aged 25 years or older (n = 1711), researchers found that adolescents and young people were more likely to use flavoured e- cigarettes compared to older adults (Soneji, Knutzen & Villanti, 2019). According to an online survey conducted in the US for people aged 18 years or older (n = 1492), it was found that young adults aged 18–24 years were more likely to be motivated by flavoured e- cigarettes to initiate smoking compared to adults aged 35–44 years (Landry et al., 2019).
After e-cigarette use was declared an epidemic for young people in the US, the FDA planned to ban flavoured e-cigarettes (Furlow, 2019b). Based on this initiative, Michigan became the first US state to prohibit flavoured e-cigarette products (Furlow, 2019a). In a study conducted with young adult e-cigarette users aged 18–25 years in Australia, Jongenelis et al. (2018) identified that 92 per cent of smokers, 82 per cent of non-smokers and 95 per cent of never smokers preferred flavoured e-cigarette products. The e-cigarette advertisement strategies that targeted adolescents and young people also contributed to the high prevalence of e- cigarette use among these groups of people (Pepper, Emery, Ribisl, Southwell & Brewer,
2014; Pokhrel et al., 2018; Williams & Knight, 2015).
Although age is a continuous variable, most of these studies have used age as a categorical variable in their analyses. Moreover, the age interval that was used in both of the studies described above was substantially wide and could not capture people in different age 28 categories. In this thesis’s study, the participants were 19–26 years old, and age was used as a continuous variable to estimate the incremental effects that were associated with each additional year of age.
2.5.1.2 Education.
Some studies have alternatively associated the use of e-cigarettes with either low educational attainment or higher educational achievement. In a US study with 2,136 study participants aged 18 years and older, e-cigarette use was not associated with educational attainment in a model that was adjusted for sociodemographic factors, smoking status and state cigarette tax
(Giovenco, Lewis & Delnevo, 2014). In a South Korean study with adult smokers aged 19 years and older (n = 3227), researchers found that the likelihood of using e-cigarettes was higher for people who completed at least a high school qualification, compared to those with only an elementary education (Lee et al., 2019). Another study was conducted to determine the predictors of e-cigarette use for Finnish adults aged 15–69 years (n = 3485), in which current e-cigarette use was found to be positively associated with low education
(Ruokolainen, Ollila & Karjalainen, 2017). The differences in the findings may be explained by the differences in the classification of educational status and the types and numbers of variables that were adjusted in the model. Further, in a cross-sectional New South Wales study with study participants aged 18 years and older (n = 2966), no association was found between educational status and current e-cigarette use (Dunlop et al., 2016). All in all, previous studies have different conclusions regarding the effect of educational status on e- cigarettes use.
2.5.1.3 Employment and income.
Findings in regard to the association between income and e-cigarette use have also been mixed—likely due to the differences in how income variables are measured and defined.
Further, the accessibility of e-cigarette products also affects how income influences e-
29 cigarette use. In countries where e-cigarettes have become strictly regulated, the imbalance between product demand and supply can lead to an increase in the price of e-cigarettes, which in turn decreases the ability of individuals to buy the products. Adolescents and young people tend to use e-cigarettes in countries where e-cigarettes are less expensive than traditional cigarettes (Cantrell et al., 2019). Ruokolainen et al. (2017) aimed to determine the predictors of e-cigarette use among Finnish adults aged 15–69 years (n = 3485) and found that current e-cigarette use was positively associated with unemployment. Previous studies have also found that unemployment was a strong predictor of other kinds of substance use, such as smoking, alcohol and illicit drugs (Compton, Gfroerer, Conway & Finger, 2014). In places in which e-cigarette products are legally available, people who experience financial difficulties might be trying to find inexpensive alternatives to traditional cigarettes (Pesko, Huang,
Johnston & Chaloupka, 2018).
In Dunlop et al.’s (2016) cross-sectional New South Wales study with participants aged 18 years and older (n = 2966), no association was found between socio-economic status and current e-cigarette use. In another cross-sectional New South Wales study with participants in the same age range (n = 3,188), researchers similarly found that income level was not associated with ever e-cigarette use (Twyman, Watts, Chapman & Walsberger,
2018). The same finding was present in Jongenelis et al.’s study, in which no association between socio-economic status and e-cigarette use was found (Jongenelis, Brennan, et al.,
2019).
2.5.2 Peer and parental factors.
Most previous studies have identified a positive association between parental and peer use of cigarette smoking and e-cigarette use (Barrington-Trimis et al., 2015; Fotiou,
Kanavou, Stavrou, Richardson & Kokkevi, 2015; Joung, Han, Park & Ryu, 2016; Rennie et al., 2016). For example, a cross-sectional study conducted in Southern California found that 30 peer and parental e-cigarette use was a strong positive predictor of e-cigarette use for adolescents in the 11th and 12th grades (n = 2084) (Barrington-Trimis et al., 2015).
Conversely, in a school-based survey conducted in Hong Kong with 45,857 students, researchers found no association between current e-cigarette use (in the past 30 days) and peer and parental smoking (Jiang et al., 2016). Peers who abuse substances and exhibit deviant behaviour are considered a risk factor that predisposes adolescents to experience negative behaviour, including substance use (US Department of Health Human Services,
2004). When drawn together, these results suggest that parental and peer factors could be either risk or protective factors for the use of e-cigarettes.
2.5.3 Adverse childhood experiences.
Several studies—including the historical adverse childhood experience study conducted by Felitti et al. (1998)—discovered a strong positive association between childhood adversities and substance use, including smoking, alcohol use and illicit drug use
(Ford et al., 2011; Mersky, Topitzes & Reynolds, 2013). Some studies also found a positive association between ACEs and chronic disease and mental illness, such as diabetes mellitus, cardiovascular disease and depression and anxiety disorders (Felitti et al., 1998; Mersky et al., 2013). Certain parental factors (e.g., parental substance use, mental illness history and conflict) were identified as risk factors for substance use in young people (Blum et al., 2003;
Haase & Pratschke, 2010; Mersky et al., 2013). Traumatic events during childhood can affect brain development by altering the normal structure and chemical activity of neurotransmitters
(Perry, 2009). Since the underdeveloped brain cannot cope naturally with traumatic events, this can lead to negative coping strategies such as substance use and other health-harming behaviours (Shonkoff et al., 2011). After an extensive literature search, no studies that investigated the association between ACEs and e-cigarette use (either globally or in
Australia) were identified. 31
2.5.4 Mental health.
Researchers have identified that people with mental illnesses were more susceptible to substance use, including cigarette smoking and e-cigarette use (Chan et al., 2019; Cummins,
Zhu, Tedeschi, Gamst & Myers, 2014; Yu & Whitbeck, 2016). The unreasonably high rates of nicotine use among people affected by mental illness are likely due to a combination of biological, emotional, psychological and social factors that create an exceptional susceptibility to tobacco addiction (Williams & Ziedonis, 2004). For example, participants aged 18 years and older with a mental illness (depression, anxiety) from an online US survey
(n = 10,041) were more likely to report ever use of e-cigarettes compared to individuals without mental illness (Cummins et al., 2014).
In a study that used data from the 2016 Australian National Drug Strategy Household
Survey for people aged 18 years and older, researchers found that psychological distress was positively associated with current e-cigarette use (Chan et al., 2019). In the same study, no association was found between general health and current e-cigarette use. All the studies mentioned above demonstrate the importance of controlling for mental health variables when identifying factors that are associated with e-cigarette use.
2.5.5 Intimate partner violence.
Although studies that investigate the association between intimate partner violence
(IPV) and e-cigarette use are scarce, researchers have found a positive association between
IPV and other substance use. In previous studies, researchers discovered that women who were exposed to IPV were more likely to abuse substances compared to those who had not experienced violence (Anderson, 2002; Carbone-López, Kruttschnitt & Macmillan, 2006).
Similarly, a systematic review that included 31 primary studies found that those who had experienced IPV were at a higher smoking risk than those who had not (Crane, Hawes &
Weinberger, 2013). The reported experience of physical or emotional abuse has been 32 identified as an important predictor of nicotine use disorder in the analysis of a prospective birth cohort study in Brisbane for Australian women aged 21 years at baseline and 30 years at follow-up (n = 822) (Ahmadabadi et al., 2019). Using nicotine may function as a coping mechanism for stress and anxiety associated with IPV (Crane et al., 2013).
2.5.6 Smoking status and other substance use.
According to a school-based survey conducted in Hong Kong with 45,857 young people, researchers identified that current e-cigarette use was positively associated with cigarette smoking and alcohol consumption (Jiang et al., 2016). A cross-sectional study conducted in Greece with students aged 15 years (n = 1,320) identified that using cannabis and smoking were positive predictors of ever e-cigarette use (Fotiou et al., 2015). In another cross-sectional study conducted in Korea with participants aged 19 years and older
(n = 5338), ever e-cigarette use was positively associated with current smoking and heavy drinking (Lee, Kim & Cho, 2016).
Further, in an Australian online survey with 1,116 participants (59% female) aged 18–
25 years, smokers were found to be more likely to report ever e-cigarette use compared to non-smokers (67% vs. 28%) (Jongenelis, Brennan, et al., 2019). This study was not representative and used only a chi-squared test to compare smokers and non-smokers.
Therefore, the analysis did not control for the potentially confounding effect of other variables when it estimated the independent effect of smoking status. Therefore, this thesis’s study has assessed the effect of smoking status on e-cigarette use after adjusting for potential confounders.
2.6 E-Cigarette Use and Smoking Initiation
This section discusses the role that e-cigarette use plays in smoking initiation as reported in the literature (both overseas and Australian). It has already been mentioned that there is limited research that examines the factors associated with smoking initiation in 33
Australia and that the available studies have not investigated the role that e-cigarette use plays in smoking initiation. The WHO and other professional societies recommend a tight regulation of e-cigarette use by non-smokers and young people, in light of the concern that these smoking behaviours will become renormalised (WHO, 2016). Some researchers are against the claim that e-cigarette use can renormalise smoking behaviour and conclude that e- cigarettes are mainly used by current and ex-smokers (Green et al., 2016; Hallingberg et al.,
2019; McNeil et al., 2015). However, other studies have demonstrated that a significant proportion of non-smokers also reported e-cigarette use. For example, in a UK study with 499 students aged 11–16 years, researchers found that approximately 53 per cent of the e-cigarette users were never smokers (Fulton, Gokal, Griffiths & Wild, 2018). An Australian study also reported a significant prevalence of e-cigarette use among never smokers (Jongenelis et al.,
2018).
Prior studies conducted in the US, UK and Canada have provided evidence that e- cigarette use is associated with an increased risk of subsequent cigarette smoking initiation among never smoking young people (Aleyan et al., 2018; East et al., 2018; Primack et al.,
2017). A systematic review by Soneji et al. (2017) also found that e-cigarette use was associated with subsequent cigarette smoking in young adult individuals. Further, researchers have identified a positive association between e-cigarette use and other substance use (e.g., marijuana and alcohol) (Curran, Burk, Pitt & Middleman, 2018). Most of these studies defined smoking initiation as having had at least a puff of traditional cigarettes at a follow-up survey. However, this definition cannot measure the established use of combustible cigarettes.
According to Kim and Selya (2019), ever and current e-cigarette use was not associated with current combustible cigarette smoking. However, the researchers used cross- sectional data to reach their conclusions, so they could not identify the temporality.
34
In an Australian cross-sectional study with 519 never smokers aged 18–25 years, researchers identified a positive association between ever e-cigarette use and curiosity about tobacco smoking, willingness to smoke and intentions to smoke (Jongenelis, Jardine, et al.,
2019). However, since this study used cross-sectional data, inferring the causal relationship between e-cigarette use and the outcome variables (smoking initiation) is not possible.
Additionally, because this study used a small sample size, generalisability to the broader population of Australian young people is questionable. The gaps in the existing evidence suggest that longitudinal data analysis that uses national longitudinal data—with a large, reasonably representative sample—is required to examine the role of e-cigarette use in subsequent cigarette smoking.
Most of the studies that investigated the association of e-cigarette use to subsequent smoking initiation were conducted in the US and UK. A review of available literature has revealed that no existing longitudinal studies in Australia have investigated the role of e- cigarettes in subsequent smoking initiation. Therefore, the investigation of the longitudinal association between e-cigarette use and subsequent smoking initiation is likely different when compared to similar investigations in countries such as the US and the UK (where nicotine e- cigarettes are legally available).
2.7 Gateway and Common Liability Hypothesises
The gateway hypothesis was preceded by the ‘stepping-stone’ theory, which emerged in 1930 to explain the associations between the use of soft drugs and subsequent initiation of hard drugs (Vanyukov et al., 2012). According to the stepping-stone theory, the practice of using one drug significantly increases the likelihood of using another, more dangerous drug.
Researchers have used the concept of the gateway hypothesis to investigate the relationship between adolescents’ use of tobacco, alcohol and cannabis and their subsequent initiation of stronger illicit drugs in later life (Kandel, 1975). Researchers have also recently used the 35 gateway hypothesis to examine the association between e-cigarette use and subsequent initiation of conventional tobacco smoking (Chaffee, Watkins & Glantz, 2018; Leventhal et al., 2015; Primack et al., 2017).
According to Kandel, Yamaguchi and Klein (2006), the epidemiological studies had to fulfil the following three criteria to establish causal association: the sequence of initiation of one substance should consistently precede the other, the initiations must demonstrate a strong association, and they must control for possible confounding factors. According to
Kandel (2002), the development of a drug use sequence can be affected by several social factors. The usefulness of the gateway theory to investigate e-cigarette use and the initiation of cigarette smoking is not consistently accepted. Some researchers have argued that the gateway theory is not appropriate for investigating e-cigarette use and the subsequent initiation of cigarette smoking (Etter, 2018; Vanyukov et al., 2012). In contrast to gateway hypothesis opponents, other researchers have supported the theory for investigating the association between e-cigarette use and the initiation of cigarette smoking (Chapman,
Bareham & Maziak, 2019). In his paper, ‘Gateway Effects and Electronic Cigarettes’, Etter
(2018) referred to the criteria set mentioned above by Kandel et al. (2006) to argue that the gateway theory was not useful for predicting the causal association between e-cigarette use and subsequent initiation of the traditional cigarette.
Etter (2018) also argued that the conclusion that a puff or a few puffs of an e-cigarette can lead to subsequent smoking initiation was implausible. Chapman et al. (2019) disagreed with Etter’s argument that regular smokers—mostly adolescents—always started with a simple puff of tobacco products. Similarly, a meta-analysis that included eight primary studies found that approximately 69 per cent of ever smokers became daily smokers. Etter
(2018) also believed that because it is difficult to exclude an alternative explanation for the 36 link between e-cigarette use and smoking initiation, the gateway hypothesis is not appropriate for examining this causal relationship.
Some researchers have proposed a common liability hypothesis as an alternative option for examining the correlation between e-cigarette use and cigarette smoking (Etter,
2018; Vanyukov et al., 2012). The common liability hypothesis proposes that vaping is more likely to occur within a population that is more likely to use cigarettes due to shared common risk factors (Vanyukov et al., 2012). According to common liability, people with uncovered personal, social or biological characteristics can be predisposed to experimenting with drugs.
Unlike the gateway hypothesis, the common liability hypothesis posits non-specific liability to several drugs, regardless of the sequence of initiation (Vanyukov et al., 2012). According to Kim and Selya (2019), common liability (e.g., environmental factors and biological vulnerability) can predispose adolescents and young people to e-cigarette use and traditional cigarette smoking. The common liability hypothesis provides precise estimates if all risk factors that are common to both e-cigarette use and cigarette smoking are controlled.
In light of the debate regarding the gateway hypothesis’s suitability for examining the causal link between e-cigarettes and traditional cigarettes smoking, this thesis uses the term
‘association’ rather than ‘causation’ to assess the role of e-cigarettes in the subsequent initiation of smoking (Kandel, 2002).
2.8 E-Cigarette Use and Smoking Cessation
Tobacco companies, and some public health advocates such as PHE, have supported and recommended e-cigarettes as a smoking-cessation device and a safer option to cigarette smoking (McNeil et al., 2015; The Lancet, 2019). However, the WHO has thus far not recommended e-cigarettes as a smoking-cessation aid (WHO, 2016). Further, the research findings regarding the role that e-cigarettes play in smoking cessation are inconsistent.
Kalkhoran and Glantz (2016) conducted a systematic review that included 20 primary studies 37 and found that the likelihood of quitting smoking was 28 per cent lower among e-cigarette users compared to those who had never used e-cigarettes. A Cochrane systematic review with moderate-quality evidence that included 50 primary randomised controlled trials found that e-cigarette use helped with smoking cessation (Hartmann-Boyce et al., 2020).
Another study was conducted in France and used retrospective data from ever smokers (n = 39,115). The study concluded that the odds of transitioning to daily smoking at
17 years were higher for the participants who reported ever use of e-cigarettes compared to never e-cigarette users (Chyderiotis, Benmarhnia, Beck, Spilka & Legleye, 2020). In another study that used data from people older than 15 years (n = 12,608) in 28 European Union countries, researchers found that the likelihood of being a former smoker was lower for those who used nicotine-containing e-cigarettes compared to those who never used e-cigarettes
(Kulik, Lisha & Glantz, 2018). Because this study used a large sample size from 28 European countries, its results may be generalised to the adult population in the region. However, the findings were derived from a cross-sectional survey, from which causation cannot be inferred.
Wang, Zhang, Xu and Gao (2018) conducted a survey with Chinese youth aged 12–18 years (n = 2042) and found that the odds of attempting to quit were 1.6 times higher for those who reported ever use of e-cigarette than for never e-cigarette users. However, this study has several methodological limitations. First, it used cross-sectional data and thus could not identify temporal patterns. Second, it measured quitting attempts rather than the actual quitting rate. Third, since more than half of China’s general population could not access the internet during the period of data collection, the generalisability of the study results was questionable. This thesis’s study uses lager nationwide longitudinal data to measure the longitudinal association between ever e-cigarette use and smoking cessation. 38
Some researchers have found that e-cigarettes are effective for smoking-related harm reduction and smoking cessation. Other researchers have also found that using e-cigarettes was more effective than using NRT for smoking cessation. For example, according to a randomised trial study conducted in the UK to compare e-cigarette use and NRT for smoking cessation among adults who attended a stop-smoking service (n = 886), the one-year abstinence rate was twice in the e-cigarette group compared to the NRT group (Hajek et al.,
2019). However, the randomisation of participants in this study was not blinded and could bias the findings. Moreover, the study’s finding could not be generalised to the population outside the service centre.
Most studies that focus on e-cigarettes examined either the effect of vaping on smoking initiation or smoking cessation, but not both. Simultaneously examining the role that e-cigarettes play in smoking initiation and smoking cessation in the same study population could provide the net public health effects of e-cigarette use on cigarette smoking.
2.9 Pharmacotherapy and Behavioural Therapy for Smoking Cessation
Although some researchers and health authorities have recommended e-cigarettes for smoking cessation and risk reduction, the WHO has not recognised e-cigarette use for these purposes (WHO, 2016). Article 14 of the WHO FCTC emphasises the importance of implementing smoking-cessation intervention for minimising smoking-related morbidity and mortality (WHO, 2013). The WHO recommends approved pharmacotherapies and behavioural counselling for smoking cessation. First-line drugs that are used for treating tobacco (nicotine) dependence in many countries include those in NRT, bupropion and varenicline (Rigotti & Aronson, 2015). These drugs have also been approved by the
Therapeutic Goods Administration (TGA) for smoking-cessation aids in Australia (Zwar et al., 2011). However, the TGA has not approved the nicotine in e-cigarettes to be a smoking- cessation aid.
39
NRT is available in different formulations, including gum, patches, nasal sprays, lozenges, inhalers and sublingual tablets. These NRT formulations can be used to treat withdrawal symptoms that are experienced after smoking cessation. Since the nicotine concentration in NRT is low compared to the concentration in tobacco, these therapies have a low addiction rate (US Department of Health and Human Services, 2016b). Amfebutamone
(bupropion) is the first non-nicotine drug that has been used to treat nicotine dependence. It is a nicotine receptor antagonist and inhibits the reuptake of epinephrine, dopamine and serotonin—thus reducing withdrawal symptoms (Covey et al., 2000; Damaj, Slemmer,
Carroll & Martin, 1999; Roddy, 2004). Varenicline is a nicotine receptor partial agonist that blocks nicotine receptors by binding into α4β2 nicotinic acetylcholine receptors and moderately releasing dopamine, thus reducing the craving and withdrawal symptoms that are associated with the absence of nicotine (Mihalak, Carroll & Luetje, 2006).
Behavioural counselling is also used singularly or in combination for treating nicotine addiction. Several behavioural therapies are available to help smokers quit, such as one-to- one counselling group behavioural therapy (Hiscock et al., 2013) Although most of the previous trials and systematic reviews in the literature supported the effectiveness of behavioural interventions for smoking cessation (Lancaster & Stead, 2017; Mottillo et al.,
2008), the findings were less consistent for pharmacological interventions. Therefore, this thesis’s researcher conducted an umbrella review to summarise how effective pharmacotherapy is for smoking cessation.
2.10 Conclusion
The global prevalence of e-cigarette use has increased over time among adolescents and young people. Despite the tight regulation of e-cigarettes in Australia, the available evidence has indicated an increase over time in the prevalence of e-cigarette use. Globally, the issue of e-cigarettes is still controversial. The findings regarding the role that e-cigarette 40 use plays in smoking initiation and smoking cessation can be concluded as mixed.
Additionally, the available studies that examine the role of e-cigarette use for smoking initiation and smoking cessation were reported from overseas (mainly the US).
Previous studies have identified many risk and protective factors that are associated with e-cigarette use—which can be classified as sociodemographic, personal and socio- economic factors, peer and parental factors, physical and mental health, IPV, and smoking and other substance use. Further, most of the studies conducted in Australia either used a small sample size or focused only on the sample drawn from a single state. Since the regulation of non–nicotine containing e-cigarettes differs from state to state in Australia, the factors may also correspondingly differ from state to state. Additionally, all the existing studies were not gender specific in regard to identifying the risk and protective factors of e- cigarette use; however, these risk and protective factors could differ by gender. Therefore, the current thesis used larger national data to identify the risk and protective factors of e-cigarette use among Australian young adult women. It also examined the association between ACEs and e-cigarette use, which has not been addressed by prior studies. Further, this thesis has assessed the longitudinal associations between e-cigarette use and subsequent smoking initiation and smoking cessation. The ensuing findings of this thesis’s study will be an additional resource for policymakers to refine or strengthen existing e-cigarette policies and regulations.
2.11 Gaps in Current Knowledge
Including the historical study conducted by Felitti et al. (1998), several other studies have identified an association between childhood adversities and tobacco smoking. However, to the researcher’s knowledge, no study has examined the association between ACEs and e- cigarette use. This thesis thus examines the association between childhood adversities and e- cigarette use. Further, to the best of the researcher’s knowledge, there is also no longitudinal 41 study that examines the role that e-cigarettes play in subsequent smoking initiation and smoking cessation in Australia. Any existing evidence for the association between e-cigarette use and subsequent initiation of conventional cigarette use mostly relates to the US population.
The prevalence and trend of e-cigarette use and cigarette smoking can be affected by the regulatory environment of any given country or territory (Cho et al., 2017). In a recent cross-sectional study conducted in the US among people aged 18 years and older
(n = 894,997), researchers found that the prevalence of e-cigarette use was lower in states that exhibited strict regulations of e-cigarette use compared to those with not established regulation. This finding was consistent in terms of the following restrictions: banning the indoor use of e-cigarettes, requiring a licence to sell e-cigarettes, prohibiting the sale of e- cigarettes for minors and applying tax measures to e-cigarette sales (Du et al., 2020). In all
Australian states and territories, the supply, sale and use of nicotine-containing e-cigarettes are prohibited (Douglas et al., 2015). Therefore, the longitudinal associations between e- cigarette use and subsequent smoking initiation are likely to differ when they are compared to countries in which nicotine e-cigarettes are legally available. Moreover, most of the overseas studies used students as study participants.
Although gender was used as an independent variable to predict factors that are associated with e-cigarette use, none of the mentioned studies assessed gender-specific risk and protective factors. Therefore, this thesis used national data to identify the factors associated with e-cigarette use among Australian young adult women. The factors that are associated with smoking initiation and cessation could also differ by gender, but most of the prior studies were not gender specific in terms of evaluating the role that e-cigarettes play in smoking initiation and smoking cessation. Additionally, it has been concluded that previous studies have assessed the role of e-cigarettes either for smoking initiation or smoking 42 cessation, but not for both simultaneously. Therefore, this thesis has identified the role of e- cigarettes in smoking initiation and cessation to understand the net effect of e-cigarette use, using the same study population.
2.12 Purpose of the Current Study
The overall purpose of this study is to identify the prevalence and predictors of e- cigarette use among Australian women and to examine the role that e-cigarettes play in smoking initiation and smoking cessation. Specifically, this thesis is designed to address the following aims:
1. to identify the predictors of e-cigarette use among young Australian women
2. to assess the association between ACEs and e-cigarette use among Australian
women
3. to examine the association of e-cigarette use and cigarette smoking initiation among
Australian women who have never smoked
4. to identify the role that e-cigarettes play in smoking cessation among Australian
women
5. to assess the effectiveness of pharmacotherapy for smoking cessation. 43
Chapter 3: Methods and Materials
3.1 Introduction
The study in this thesis primarily focuses on identifying the risk and protective factors of e-cigarette use and the role that e-cigarette use plays in the subsequent initiation or cessation of traditional cigarette smoking. This study used data from the ALSWH. An umbrella review was additionally conducted to assess the effectiveness of pharmacotherapy for smoking cessation. This chapter details the approach that was employed to answer the study’s research questions. It also overviews the ALSWH and provides information about the study’s participants, recruitment strategy, inclusion and exclusion criteria, and attrition rate.
The specific research questions, chosen research methods and procedures are then explained.
3.2 The Australian Longitudinal Study on Women’s Health
The ALSWH consists of nationally representative data and involves more than 57,000 women. It is funded by the Australian Department of Health and is conducted jointly by the
University of Newcastle and the University of Queensland. The ALSWH project commenced in 1996 by recruiting more than 40,000 women across three cohorts, which comprised young women aged 18–23 years old (born in the period 1973–1978), middle-aged women aged 45–
50 years (born in the period 1946–1951) and older-aged women (born in the period 1921–
1926). The new cohort (young people born between 1989 and 1995) was recruited in the period 2012–2013. The main surveys are linked to different national databases, including the cancer registry, perinatal and admitted patients’ datasets, the Medicare Benefits Schedule, the
Pharmaceutical Benefits Scheme and aged care datasets. The ALSWH has aimed to provide scientific evidence regarding Australian women’s health for policymakers, planners and implementers. The evidence that has been derived from the ALSWH contributes to the development of state and national policies and guidelines, including the 2010 Australian 44
Government’s National Women’s Health Policy (Australia Government Department of
Health and Ageing 2010). Additional details about the ALSWH can be found on their website
(http://alswh.org.au/).
3.3 The Australian Longitudinal Study on Women’s Health—1989–1995
Cohort
This thesis’s study has used nationally representative data from the ALSWH that was collected from the youngest cohort of Australian women, born between 1989 and 1995. This cohort is surveyed annually, and five surveys have currently been completed, which can be viewed on the ALSWH’s website (http://www.alswh.org.au/about/methods). The sixth survey was underway at the time the analyses were conducted. These surveys have external data linkage with the cancer registry, perinatal and admitted patients’ datasets, Medicare Benefits
Schedule, the Pharmaceutical Benefits Scheme and aged care datasets. Information regarding alcohol use, cigarette smoking and illicit drug use were collected in all five surveys.
However, information on e-cigarette use was included only in the third survey.
The recruitment of the 1989–1995 cohort was conducted through traditional means as well as social and online media (e.g., Facebook, Twitter and YouTube) and the ALSWH study website. Emails were also sent to the previous cohorts, ALSWH professional networks,
ALSWH collaborators and professional bodies to refer potential eligible participants to the study’s website. Traditional media (e.g., posters, business cards, leaflets, newspapers, television and radio) was also used to recruit eligible study participants. Links to the online survey tool and information sheet were provided in advertisements. Most of the study participants (70%) reported that they had discovered the survey through Facebook advertisements. Due to this diverse approach to recruitment, calculating a response rate was not possible. The 2011 Australian census data were used to determine the representativeness of the sample. Evidence has shown that the study participants of this nationally representative 45 dataset were comparable with Australian census data in terms of geographical distribution, marital status and age composition, with some over-representation of women with post- secondary education (Loxton et al., 2017; Mishra, Hockey et al., 2014). Therefore, the accuracy of generalising the study findings to Australia’s young population will be high; generalisability is further increased because the study is not limited to a single institution or state.
Women aged 18–23 years were eligible to participate in the 1989–1995 cohort survey.
They were also required to provide online consent for participation and external data linkage for personal information with administrative data. The study included citizens and permanent residents who had a Medicare card (Australian universal insurance). Details about the recruitment strategies and inclusion criteria are published in other studies (e.g., Loxton et al.,
2015b; Mishra et al., 2014; Mishra, Hockey et al., 2014). The participants annually completed online surveys in regard to numerous physical, mental, emotional and social factors that determine the health of Australian women. The following health and health- related variables were included to collect information:
physical health (e.g., symptoms and diagnosis)
mental health (e.g., depression, anxiety and psychological distress)
reproductive and sexual health (e.g., contraceptives, STIs, pregnancy and
abortion)
health behaviours and risk factors (e.g., exercise, diet, smoking, alcohol and illicit
drugs)
life events (e.g., violence, ACEs and available support)
sociodemographic characteristics.
In the baseline survey that was conducted in the period 2012–2013, a total of 17,011 participants completed an online survey. The attrition rates during the second, third, fourth 46 and fifth surveys were 33.3, 47.3, 47.1 and 50.1 per cent, respectively, when compared to the participants who were recruited at the baseline survey. Figure 3.1 presents the response and attrition rates at subsequent surveys. Further, participants’ retention and reasons for attrition are listed in Appendix F. According to a study by Loxton et al. (2019), participants were more likely to participate in the follow-up surveys if they were older, more educated, found follow-up surveys easier to manage on their available income and were recruited through traditional media and referral methods. 47
Young women at the baseline survey (17,069)
Participants Non-response
Second survey (2014) n = 11,344 (66.7%) Age: 19–24 years n = 5667 (33.3%)
Third survey (2015) n = 8,961 (52.7%) n = 8050 (47.3%) Age: 20–25 years
Fourth survey (2016) n = 9,007 (52.9%) n = 8004 (47.1%) Age: 21–26 years
Fifth survey (2017) n = 8495 (49.9%) n = 8516 (50.1%) Age: 22–27 years
Figure 3.1. Participants and non-responses in the follow-up surveys.
3.4 Ethics and Data Access
Ethics approval for the ALSWH is provided by the University of Newcastle (UoN
HREC approval number H-2012-0256) and the University of Queensland (UQ HREC approval number 2012000950) Human Research Ethics Committees and the Department of
Health. In the baseline online survey, the respondents consented to participate in the ALSWH study by completing the survey, agreeing to data linkage and providing their details, which were confirmed by the Australian Department of Human Services. Participants were (and will continue to be) regularly informed that participation is voluntary and that they were free to 48 terminate their participation at any time. Their data are anonymised, and numerical ID labels were used to distinguish study subjects. Researchers can use this data for research purposes by formally requesting data access, which is accomplished by submitting an online expression of interest form. The Data Access Committee of the ALSWH is responsible for approving data access for researchers and collaborators. Permission to analyse the data for this thesis was granted by the ALSWH Data Access Committee, with the approval to access the data being received on 18 September 2017 (see Appendix G). Statements that governed the analysis, use and publication of the data are listed in Appendix H. The data were accessed only by those listed in the expression of interest and were kept on a password-protected computer, with all printed materials related to the data being kept in a locked cabinet on university-controlled premises.
3.5 Overview of Data Analysis Methods for Specific Aims
Since e-cigarette–related information was collected only in the third survey, this thesis used the third and fourth survey. Chapters 4 and 5 used data from survey three, while chapters 6 and 7 used data from surveys 3 and 4. The data analysis method employed for each study aim is detailed in the respective chapters. This section will briefly present the relevant data analysis methods. This thesis exclusively employed quantitative analysis to answer all the research questions. Additionally, all research aims used ALSWH data to answer the research questions (except for the umbrella review).
Aim I—predictors of e-cigarette use among young Australian women. For this aim, cross-sectional data that were collected in the third survey were used to identify the prevalence, risk and protective factors for ever and past year e-cigarette use. Descriptive statistics, such as Chi-squared and t-tests, were used to describe the participants in terms of background characteristics. Binary logistic regression was used to identify the risk and protective factors of ever and past year e-cigarette use. 49
Aim II—ACEs and e-cigarette use among Australian women. This thesis also used cross-sectional data that were collected during the third survey. Chi-squared and t-tests were used to compare the outcome variables in terms of background characteristics. Further, a Chi-square test was used to compare individuals with missing and complete data in terms of the outcome variable (e-cigarette use). Logistic regression was conducted to estimate the unadjusted and adjusted odds ratios that represented the associations of individual ACEs or the cumulative ACE score in regard to past year or ever e-cigarette use. For each outcome variable of interest (past year and ever e-cigarette use), three sets of models were fitted. In the first set of models, separate bivariate logistic regression models were fitted for each of the eight ACE variables and the ACE score. Sociodemographic variables (e.g., age, education level and employment status), parental education levels and the family’s ability to manage income during childhood were included in the second set of models. The third and final set of models incorporated smoking status in addition to covariates that were included in the second model.
Aim III—e-cigarette use and cigarette smoking initiation among Australian women who have never smoked. In this thesis, the National Health Interview Survey
(NHIS) and the Australian Institute of Health and Welfare’s definitions were used to define never smokers. The NHIS and AIHW defined never smokers as ‘a person who does not smoke now and has smoked fewer than 100 cigarettes or the equivalent tobacco in his or her lifetime’ (Ryan, Trosclair & Gfroerer, 2012). The participants in this thesis’s study were considered to have initiated smoking if they reported having smoked at least 100 cigarettes at follow-up. Reaching at least 100 cigarettes is the preferable measure of smoking initiation for young adults and adults.
The descriptive statistics comprised means with standard deviations and frequencies with percentages, with the groups being compared using Pearson’s chi-square or Fisher’s 50 exact test and student’s t-test. Logistic regression was used to identify the factors that were associated with loss to follow-up using baseline sociodemographic factors, mental health factors, binge drinking and childhood adversity scores. The logistic regression was used to estimate the association between baseline e-cigarette use and the follow-up initiation of cigarette smoking. This thesis conducted a sensitivity analysis with two assumptions in mind to assess the effect of loss to follow-up on the findings. In the first assumption, participants who were lost to follow-up were considered never smokers, and in the second assumption, participants were counted as being smoking initiators at follow-up.
Aim IV—determinants of smoking cessation among Australian women: The role of e-cigarette use. Student t-tests or Chi-square tests were used to compare the baseline independent variables for study participants who did and did not return data at their follow- up. Binary logistic regression was used to examine the association between e-cigarette use in the baseline survey and smoking cessation in the follow-up survey, after controlling for potential confounders. Potential effect modification was assessed by including a pairwise interaction term between the exposure variable (ever e-cigarette use) and each covariate (area of residence, ability to manage income, history of depression, K–10 score and binge drinking).
Aim V—effectiveness of pharmacotherapy for smoking cessation: Umbrella review and quality assessment of systematic reviews. Online databases were used to retrieve reviews, including the Cochrane Library, PubMed, MEDLINE, EMBASE, CINAHL,
PsycINFO, Web of Science, Scopus and Google Scholar. This thesis’s umbrella review focused on systematic reviews that included only randomised controlled trials that were designed to assess pharmacotherapeutic interventions that support abstinence from smoking.
Each review was assessed for quality using the revised Assessment of Multiple Systematic
Reviews 2 (AMSTAR 2) tool. Two authors screened the titles and abstracts of all reviews 51 that were obtained by the search strategy and assessed the full text of the selected articles for inclusion and extracted data independently. Two authors also performed a quality appraisal independently, with Cohen’s Kappa statistic being used to assess inter-rater agreement. The findings of the studies were narrated qualitatively to describe the evidence that pertained to the effectiveness of pharmacotherapies for smoking cessation. The published protocol to conduct the umbrella review was annexed. 52
Chapter 4: Predictors of Electronic Cigarette Use Among Young
Australian Women
This chapter answers aim 1 (to identify the predictors of e-cigarette use among young
Australian women). The content of this chapter has also been published in the American
Journal of Preventive Medicine (see Appendix P). The media release related to this chapter is
found in Appendix H.
Melka, A. S., Chojenta, C. L., Holliday, E. G. & Loxton, D. J. (2019) Predictors of e-cigarette
use among young Australian women. American Journal of Preventive Medicine, 56,
293–299. https://doi.10.1016/j.amepre.2018.09.019 53
4.1 Introduction
As previously discussed, e-cigarette use is controversial worldwide (McKee &
Capewell, 2015a; Middlekauff, 2015; Peruga & Fleck, 2014). Many e-cigarettes contain addictive substances (primarily nicotine) that lead to long-term nicotine addiction, which can affect brain development in young people (Cobb, Byron, Abrams & Shields, 2010). Cross- sectional and longitudinal studies have confirmed that a positive relationship exists between e-cigarette use and the consequent initiation of conventional cigarette smoking among non- smokers (Bunnell et al., 2015; Loukas, Marti, Cooper, Pasch & Perry, 2018). Also discussed previously, the WHO does not recognise e-cigarettes as a smoking-cessation aid and instead strongly recommends a control and ban of such products (WHO, 2010).
Most of the previous literature has identified the existence of a positive relationship between traditional tobacco smoking and e-cigarette use among young people. E-cigarette use and traditional tobacco smoking both have many risks and protective factors in common
(Hanewinkel & Isensee, 2015). Factors such as alcohol use and younger age have been identified as risk factors for e-cigarette use (Geidne, Beckman, Edvardsson & Hulldin, 2016;
Harrold et al., 2015; Stuart et al., 2013; Turner, Russell & Brown, 2003). Although most previous studies were not gender specific, several risk factors have been identified as being more prevalent among women, including IPV and mental illness (Crane et al., 2013; Smith,
Colwell, Ahn & Ory, 2012). Although they are not addressed in this study, psychosocial factors (e.g., attitude towards e-cigarettes) and the use of e-cigarettes by household members and friends were also positively associated with e-cigarette use (Barrington-Trimis et al.,
2015). This study will ultimately identify the predictors of e-cigarette use among young
Australian women. 54
4.2 Methods
This study used data from the third wave of the 1989–1995 cohort of the ALSWH.
The participants were recruited from 2012 to 2013, mainly through social and online media
(e.g., Facebook, Twitter and YouTube). To participate in the study, women had to have a
Medicare card, provide their biographic data, agree to participate in longitudinal follow-up surveys and consent to data linkage. Details about the recruitment strategies and inclusion criteria have already been published in other sources (Loxton et al., 2015b, 2017; Mishra,
Hockey et al., 2014). For this analysis, data were taken from the third online survey of participants in 2015. The response rate for the third wave was 53 per cent (n = 8,961) compared to the baseline survey. Out of 8,961 participants, 8,915 (99.5%) aged 19–26 provided data for the outcome variables presented in this study. Participants were more likely to participate in follow-up surveys if they were older, more educated and found managing the participation on their available income easier at baseline. Ethics approval for the ALSWH is provided by the University of Queensland and the University of Newcastle Human Research
Ethics Committees, and The Australian Government Department of Health Human Research
Ethics Committee.
4.2.1 Measures.
The outcome variables were e-cigarette use in the past year and ever e-cigarette use.
Explanatory variables were derived from the previous literature and included sociodemographic factors, cigarette use variables, IPV, mental illness and parental relationship (Crane et al., 2013; Geidne et al., 2016; Hanewinkel & Isensee, 2015; Harrold et al., 2015; Smith et al., 2012; Stuart et al., 2013; Turner et al., 2003) (see Table 4.1).
55
Table 4.1
Description and Coding of the Variables Used in Analysis
Variable assessed Questionnaire item Original response options Analytic coding
Past year e-cigarette Have you used battery-operated e-cigarettes Yes Yes use in the last 12 months? No No
Ever e-cigarette use Have you ever used battery-operated e- Yes Yes
cigarettes? No No
Age of respondents When is your birthday Complete year Complete year
Highest level of What is the highest level of education that Year 10 or below Year 10 or below education you have completed? Year 11 or equivalent Year 11 or 12
Year 12 or equivalent
Certificate I/II Trade/certificate/diploma
Certificate III/IV
Advanced diploma/diploma
Bachelor’s degree University Degree
Graduate diploma/graduate
certificate 56
Variable assessed Questionnaire item Original response options Analytic coding
Postgraduate degree
Marital status What is your current relationship status? Married/de facto Partnered
Separated/divorced/widowed/never Non-partnered
married
Employment status Are you currently unemployed and actively Yes, unemployed for < 6 months Unemployed
seeking work? Yes, unemployed for ≥ 6 months
No Employed
Area of residence The Accessibility/Remoteness Index of Major cities Urban
Australia was used to grouped using postal Inner regional/outer regional Rural
code: Remote/very remote Remote
Financial management How do you manage on the income you have It is impossible Difficulty managing
available? It is difficult all the time income
It is difficult some of the time
It is not too bad Easy managing income
It is easy 57
Variable assessed Questionnaire item Original response options Analytic coding
IPV Have you ever been in a violent relationship Yes Yes
with a partner/spouse? No No
General health rating In general, how would you describe your Excellent Excellent
health: Very good Very good
Good Good
Fair Fair
Poor Poor
History of depression Have you ever been diagnosed or treated for Yes Yes
depression? No No
Smoking status How often do you currently smoke cigarettes Never smoked Never smokers
or any tobacco products? Ex-smoker Ex-smokers
Current smokers Current smokers
Risk of alcohol-related On a day when you drink alcohol, how many Never drink Low risk of alcohol-related harm over a lifetime a standard drinks do you usually have? 1 or 2 drinks per day harm over a lifetime
3 or 4 drinks per day Risk of alcohol-related
5 to 8 drinks per day harm over a lifetime 58
Variable assessed Questionnaire item Original response options Analytic coding
9 or more drinks per day
History of parental During your childhood, did your parents Yes Yes divorce divorce or permanently separate? No No a According to 2009 Australian alcohol guidelines, drinking more than two standard drinks on any day increases the lifetime risk of harm from alcohol- related disease or injury, which is termed ‘risk of alcohol-related harm over a lifetime’. 59
4.2.2 Data analysis.
The descriptive statistics comprised frequencies with percentages, with the differences being assessed using chi-square tests. For each outcome measure, the odds ratios were estimated for each predictor using logistic regression. Variables with a p-value of less than
0.25 were included in a multivariate logistic regression model. The results were presented as crude and adjusted odds ratios with 95% CI; a p-value of 0.05 was used for declaring statistical significance. The data were analysed in 2018, and the data analysis was performed using Stata (version 15).
4.3 Results
The mean age of the study’s participants was 22.5 years. The prevalence of ever and past year e-cigarette use among young Australian women was 11.1 and 6.4 per cent, respectively. Additionally, more than a quarter of past year and ever e-cigarette users had never reported smoking cigarettes (see Table 4.2). 60
Table 4.2
Past Year and Ever E-cigarette Use a Among Young Australian Women by Selected Background Characteristics
Variables N Past year e-cigarette useb P-value Ever e-cigarette usec P-value Missing valued
Yes n (%) No n (%) Yes n (%) No n (%) (%)
Age
19–22 4,428 329 (57.7) 4,099 (49.1) p < 0.001 544 (54.9) 3,884 (49.0) p < 0.001 0 (0%)
23–26 4,487 241 (42.3) 4,246 (50.9) 446 (45.1) 4,041 (51.0)
Highest level of education
Year 10 or below 159 29 (5.3) 130 (1.6) p < 0.001 47 (5.0) 112 (1.5) p < 0.001 241 (2.7)
Year 11 or 12 2,514 182 (33.4) 2,332 (28.7) 317 (33.4) 2,197 (28.4)
Trade/certificate/diploma 2,452 201 (36.9) 2,251 (27.7) 357 (37.6) 2,095 (27.1)
University Degree 3,549 133 (24.4) 3,416 (42.0) 228 (24.0) 3,321 (43.0)
Currently unemployed
Yes 1,124 111 (20.4) 1,013 (12.5) p < 0.001 188 (19.8) 936 (12.1) p < 0.001 242 (2.7)
No 7,549 434 (79.6) 7,115 (87.5) 761 (80.2) 6,788 (87.9)
Marital status
Partnered 2,660 165 (30.4) 2,495 (30.7) p = 0.878 289 (30.5) 2,371 (30.7) p = 0.908 245 (2.8) 61
Non-partnered 6,010 378 (69.6) 5,632 (69.3) 658 (69.5) 5,352 (69.3)
Residence
Urban 6,648 445 (78.8) 6,203 (75.2) p = 0.175 767 (78.4) 5,881 (75.1) p = 0.073 103 (1.2)
Rural 2,055 114 (20.2) 1,941 (23.5) 200 (20.4) 1,855 (23.7)
Remote 109 6 (1.0) 103 (1.3) 12 (1.2) 97 (1.2)
Ability to manage income
Difficulty managing income 4,609 389 (71.6) 4,220 (51.9) p < 0.001 652 (68.8) 3,957 (51.2) p < 0.001 245 (2.8)
Easy managing income 4,061 154 (28.4) 3,907 (48.1) 295 (31.2) 3,766 (48.8)
Violent relationship with a partner/spouse
Yes 1, 008 130 (28.2) 878 (14.2) p < 0.001 240 (30.2) 768 (13.1) p < 0.001 2,254 (25.3)
No 5,653 331 (71.8) 5,322 (85.8) 555 (69.8) 5,098 (86.9)
General health
Excellent 737 21 (3.7) 716 (8.6) p < 0.001 39 (3.9) 698 (8.8) p < 0.001 0 (0)
Very good 3,266 164 (28.8) 3,102 (37.2) 267 (27.0) 2,999 (37.8)
Good 3,474 227 (39.8) 3,247 (38.9) 416 (42.0) 3,058 (38.6)
Fair 1,187 125 (21.9) 1,062 (12.7) 209 (21.1) 978 (12.4)
Poor 251 33 (5.8) 218 (2.6) 59 (6.0) 192 (2.4) 62
Ever had depression
Yes 3,432 312 (54.7) 3,120 (37.4) p < 0.001 550 (55.6) 2,882 (36.4) p < 0.001 4 (0.04)
No 5,479 258 (45.3) 5,221 (62.6) 439 (44.4) 5,040 (63.6)
Smoking status
Never smokers 6,710 147 (25.8) 6,563 (78.6) p < 0.001 268 (27.1) 6,442 (81.3) p < 0.001 0 (0)
Ex-smokers 712 82 (14.4) 630 (7.6) 157 (15.9) 555 (7.0)
Current smokers 1,493 341 (59.8) 1,152 (13.8) 565 (57.0) 928 (11.7)
Risk of alcohol-related harm over a lifetime
Risk of alcohol-related harm 4,789 396 (69.5) 4,393 (52.6) p < 0.001 694 (70.1) 4,095 (51.7) p < 0.001 2 (0.02) over a lifetime
No risk of alcohol-related 4,124 174 (30.5) 3,950 (47.4) 296 (29.9) 3,828 (48.3) harm over a lifetime
Parents’ divorce /permanently separate during childhood
Yes 2,811 232 (41.9) 2,579 (31.4) p < 0.001 411 (42.7) 2,400 (30.7) p < 0.001 142 (1.6)
No 5,962 322 (58.1) 5,640 (68.6) 552 (57.3) 5,410 (69.3) a Column percentage is calculated to compare the past and ever e-cigarette use in terms of background variables. b Used e-cigarettes at least once in the past 12 months. c Used e-cigarettes at least once in their lifetime. d Missing for the two outcome variables: past year and ever e-cigarette use. 63
For each one-year age increase, the odds for past year e-cigarette use decreased by approximately 13 per cent (adjusted odds ratio [AOR] = 0.87, 95% CI, 0.82–0.93). Women who reported their ability to manage with their available income as ‘easy’ were less likely to have used e-cigarettes in the past year compared to those who found it challenging to manage with their available income (AOR = 0.68, 95% CI, 0.54–0.92). Ex-smokers and current cigarette smokers had fivefold (AOR = 5.05, 95% CI, 3.64–7.01) and 10-fold (AOR = 10.01,
95% CI, 7.77–12.89) higher odds for past year e-cigarette use compared to never cigarette smokers. Women who reported drinking at a level of lifetime risk of harm from alcohol- related disease or injury (i.e., drinking more than two standard drinks on any day) were more likely to have used e-cigarettes in the past year (AOR = 1.23, 95% CI, 1.01–1.53) compared to those who did not report drinking at this level. Finally, the results for ever e-cigarette use were similar, with additional associations for rural residence (AOR = 0.74, 95% CI; 0.60–
0.91) and IPV (AOR = 1.44, 95% CI; 1.17–1.76) (see Table 4.3).
64
Table 4.3
Associations with Past Year and Ever E-Cigarette Use Among Young Australian Women
Variables Past year e-cigarette use Ever e-cigarette use
COR (95% CI) AORa (95% CI) COR (95% CI) AORa (95% CI)
Age 0.88 (0.84–0.93) 0.87 (0.82–0.93)** 0.92 (0.89–0.95) 0.92 (0.87–0.97)**
Highest level of education
Year 10 or below (ref) 1.00 1.00 1.00 1.00
Year 11 or 12 0.36 (0.23–0.51) 0.83 (0.50–1.37) 0.38 (0.28–0.49) 1.05 (0.67–1.64)
Trade/certificate/diploma/ 0.44 (0.32–0.62) 0.85 (0.52–1.39) 0.47 (0.36–0.62) 1.04 (0.67–1.61) university Degree 0.19 (0.13–0.27) 0.76 (0.45–1.29) 0.19 (0.14–0.25) 0.77 (0.48–1.22)
Currently unemployed
Yes 1.80 (1.44–2.24) 1.22 (0.93–1.59) 1.80 (1.51–2.13) 1.17 (0.93–1.47)
No (ref) 1.00 1.00 1.00 1.00
Residence
Urban (ref) 1.00 1.00 1.00
Rural 0.81 (0.66–1.01) 0.83 (0.70–0.97) 0.74 (0.60–0.91)**
Remote 0.81 (0.35–1.86) 0.95 (0.52–1.74) 0.88 (0.42–1.86) 65
Ability to manage income
Difficulty managing income (ref) 1.00 1.00 1.00 1.00
Easy managing income 0.43 (0.35–0.52) 0.68 (0.54–0.87)** 0.47 (0.41–0.54) 0.76 (0.63–0.92)**
Violent relationship with a partner/spouse
Yes 2.38 (1.92–2.95) 1.14 (0.89–1.46) 2.87 (2.42–3.40) 1.44 (1.17–1.76)**
No (ref) 1.00 1.00 1.00 1.00
General health
Excellent (ref) 1.00 1.00 1.00 1.00
Very good 1.80 (1.13–2.86) 1.46 (0.84–2.52) 1.59 (1.13–2.25) 1.27 (0.83–1.96)
Good 2.38 (1.51–3.75) 1.16 (0.67–2.01) 2.43 (1.74–3.42) 1.26 (0.82–1.93)
Fair 4.01 (2.50–6.43) 1.66 (0.93–2.93) 3.82 (2.68–5.46) 1.61 (0.98–2.54)
Poor 5.16 (2.93–9.11) 1.36 (0.67–2.73) 5.49 (3.56–8.49) 1.53 (0.88–2.69)
Ever had depression
Yes 2.02 (1.71–2.40) 1.08 (0.87–1.35) 2.19 (1.92–2.50) 1.15 (0.96–1.38)
No (ref) 1.00 1.00 1.00 1.00
Smoking status
Never smokers (ref) 1.00 1.00 1.00 1.00 66
Ex-smokers 5.81 (4.38–7.71) 5.05 (3.64–7.01)** 6.79 (5.48–8.43) 5.34 (4.14–6.89)**
Current smokers 13.22 (10.78–16.19) 10.01 (7.77–12.89) ** 14.63 (12.46–17.18) 10.57 (8.66–12.91)**
Risk of alcohol-related harm over a lifetimeb
Risk of alcohol-related harm over a 2.05 (1.70–2.46) 1.23 (1.01–1.53)* 2.19 (1.90–2.53) 1.40 (1.17–1.68)** lifetime
Low risk of alcohol-related harm over 1.00 1.00 1.00 1.00 a lifetime (ref)
Parents divorced/permanently separated during childhood
Yes (ref) 1.00 1.00 1.00 1.00
No 0.63 (0.53–0.76) 0.92 (0.74–1.14) 0.59 (0.52–0.68) 0.83 (0.69–1.01)
Note: Boldface indicates statistical significance (p < 0.05) for AOR *p < 0.05, **p < 0.01 aEach variable was adjusted for all the other variables listed in this table b2009 Australian National alcohol guidelines classification for lifetime risk of alcohol-related harm. 67
4.4 Discussion
In the 2013 Australian National Drug Strategy Household Survey Report, the prevalence of past year e-cigarette use was 27 per cent among young people aged 18–24, a figure that was higher than this current study (Australian Institute of Health and Welfare,
2014b). The prevalence was higher than that reported in a study of young Swiss men
(Douptcheva, Gmel, Studer, Deline & Etter, 2013). The difference in prevalence could be explained by the difference in gender composition, sample size and age of participants.
In this study, more than one-quarter of respondents who declared past year and ever e- cigarette use had never reported cigarette use. It has been shown in several studies that there is a positive relationship between e-cigarette use and the consequent initiation of conventional tobacco smoking among non-smokers (Bunnell et al., 2015; Loukas et al., 2018;
Soneji et al., 2017). For example, a systematic review that included nine longitudinal studies concluded that e-cigarette use was positively associated with the subsequent traditional cigarette smoking initiation among non-cigarette smokers (Soneji et al., 2017).
Consistent with previous research, this study has found that youth was positively associated with past year and ever e-cigarette use (Giovenco et al., 2014; Hummel et al.,
2015; Ramo, Young-Wolff & Prochaska, 2015; Yong et al., 2014). This could reflect the claim that young people are more likely to experiment with new behaviours than older people. Similar to previous studies, this study also identified that both past year and ever e- cigarette use were strongly associated with being an ex-smoker or current cigarette smoker
(Douptcheva et al., 2013; Farsalinos, Poulas, Voudris & le Houezec, 2016; Harrold et al.,
2015; Jiang et al., 2016; Moore et al., 2015). In previous studies, the binge drinking of alcohol was identified as a risk factor for e-cigarette use (de Lacy et al., 2017; Geidne et al.,
2016; Milicic & Leatherdale, 2017). In the current study, drinking alcohol at risk of alcohol- related harm over a lifetime was also found to be an important predictor of e-cigarette use. 68
This study also determined that urban residence was a risk factor associated with ever e- cigarette use, which aligns with the findings of other previous research (Park, Lee & Min,
2017). One possible reason for this could be the availability of e-cigarette products (mainly in urban areas). As has been discovered in previous research, this study also identified that IPV was positively associated with substance use (Stuart et al., 2013; Turner et al., 2003).
4.5 Limitations
One limitation of this study was participation bias due to the high attrition rate of study participants by the third wave. Moreover, since the current analysis was cross-sectional, inferring causation was not possible. The high rate of missing value that is associated with
IPV could introduce bias. Most previous studies have assessed a history of e-cigarette use in the 30 days preceding the data collection. However, past year e-cigarette use was used in this study to measure recent use. It is not known whether the e-cigarettes that were used by the women contained nicotine or not. However, it has been reported that 70 per cent of the e- cigarettes that are sold in New South Wales contain nicotine (Hasham, 2015).
4.6 Conclusions
This study has crucially identified that although cigarette smoking was strongly associated with e-cigarette use, it was not the only risk factor that was significantly associated with e-cigarette use. Younger age, financial difficulty and alcohol use were also risk factors for both past year and ever e-cigarette use. Further, the high prevalence of e-cigarette use among never cigarette smokers has significant public health implications. Subsequent interventions to curb the use of e-cigarette among young Australian women should focus on these risk factors. 69
Chapter 5: Adverse Childhood Experiences and Electronic Cigarette
Use among Young Australian Women
Chapter 4 identified several sociodemographic and behavioural factors that are
associated with e-cigarette use. In this chapter, aim 2 is addressed (to assess the association
between ACEs and e-cigarette use among Australian women). The content of this chapter has
been previously published in Preventive Medicine (see Appendix Q).
Melka, A., Chojenta, C., Holliday, E. & Loxton, D. (2019). Adverse childhood experiences
and electronic cigarette use among young Australian women. Preventive Medicine,
126, 105759–105759. https://doi.10.1016/j.ypmed.2019.105759 70
5.1 Introduction
Evidence suggests that traumatic events (ACEs) that occur during childhood (before
18 years) can increase the risk of poor health outcomes during adult life (Felitti et al., 1998).
ACEs broadly comprise childhood abuse and household dysfunction. Childhood abuse includes physical, psychological and sexual abuse, while household dysfunction includes household substance abuse, household mental illness, parental separation/divorce, incarcerated household members and witnessing domestic violence. Child abuse is globally documented as a serious public health, human rights, legal and societal issue (Makaruk et al.,
2018). Other than genetic and nutritional factors, childhood brain development can also be affected by childhood adversity, with high levels of stress during childhood interfering with the normal development of brain structures (Maté, 2012).
Researchers have identified a positive association between ACEs and physical health
(e.g., diabetes, coronary heart disease and stroke) (Campbell, Walker & Egede, 2016; Felitti et al., 1998), mental health (e.g., depression, anxiety disorder, post-traumatic stress disorder and suicidal ideation) (Campbell et al., 2016; Felitti et al., 1998) and risky behaviours (e.g., multiple sexual partners, sexually transmitted infections, abortion and substance use) (Bleil et al., 2011; Campbell et al., 2016; Felitti et al., 1998).
The prevalence rates of at least one reported ACE among women in the US ranges from 54.6 to 60.9 per cent (Ford et al., 2011; Ye & Reyes-Salvail, 2014), while the prevalence rate is as high as 85 per cent in Brazil (Soares et al., 2016). In recent research,
Loxton, Townsend, Dolja-Gore, Forder and Coles (2018) found that the prevalence rate was
41 per cent in regard to reporting at least one type of ACE in Australia among a cohort of women born between 1973 and 1978.
The first study of ACEs and adult health outcomes was conducted by Felitti et al.
(1998) in San Diego, Southern California, to investigate the relationship between ACEs and 71 the leading causes of death in adults. In subsequent studies, researchers have identified strong positive relationships between ACEs and substance use, including smoking (Alcalá, von
Ehrenstein & Tomiyama, 2016; Campbell et al., 2016; Ford et al., 2011; Fuller-Thomson,
Filippelli & Lue-Crisostomo, 2013), problem alcohol use (Campbell et al., 2016; Fang &
McNeil, 2017) and illicit drug use (Alcalá et al., 2016). To the researcher’s knowledge, no prior studies have assessed the relationship between ACEs and e-cigarette use. The nicotine that is found in cigarettes reaches the brain and binds to nicotinic cholinergic receptors, which stimulates the release of neurotransmitters such as adrenaline and dopamine
(Benowitz, 2009). It is thus plausible that people who have experienced childhood traumatic stress may use nicotine in later life to produce pleasurable feelings that offset undesirable psychological experiences (Felitti et al., 1998).
Using longitudinal study designs, researchers have found that e-cigarette use can lead to conventional cigarette smoking among young people (Lozano et al., 2017; Unger, Soto &
Leventhal, 2016). There is no convincing evidence that supports the efficacy of e-cigarettes for long-term smoking cessation or the safety of e-cigarettes; there is also little evidence regarding the health consequences of e-cigarettes compared to those of traditional tobacco products (McKee & Capewell, 2015b; Schraufnagel et al., 2014). Conversely, other studies support the use of e-cigarettes as an aid in smoking cessation. For example, a cross-sectional study conducted in England with 5,863 participants concluded that the rate of smoking cessation was higher among e-cigarette users compared to those who used other forms of
NRT (Brown et al., 2014). Researchers in this field have recommended that the efficacy of e- cigarettes for smoking cessation be scientifically examined before policy is developed to govern the sale and use of these products (Middlekauff, 2015; National Academies of
Sciences & Medicine, 2018). 72
In most Australian states, some form of legal restriction and ban exists regarding the supply, possession and use of nicotine in e-cigarettes (Douglas et al., 2015). However, controlling e-cigarettes is difficult because many products are available online through international markets (Dunlop et al., 2016).
This study investigated the association between ACEs and e-cigarette use among young Australian women, with the two hypotheses that were examined as follows:
Individual categories of ACEs will be positively associated with both past year
and ever e-cigarette use
There will be a dose–response relationship between the number of reported ACEs
and both past year and ever e-cigarette use.
5.2 Methods
5.2.1 Study design.
This study used ALSWH data from the new national cohort of young Australian women who were born between 1989 and 1995. The ALSWH involves more than 57,000 women in four cohorts, with the original cohorts aged 18–23, 45–50 and 70–75 when the surveys began in 1996. In the period 2012 to 2013, over 17,000 women aged 18–23 years were recruited to form the new 1989–1995 birth cohort. This analysis included all 1989–1995 birth cohort participants (n = 8,915) who completed the third survey, in which participants responded to a range of physical, mental, psychological, emotional health and health behaviour–related questions (including e-cigarette use). The attrition rate for the third wave was 47 per cent of the respondents in the baseline survey. Respondents were less likely to participate if they were smokers, less educated, challenged by financial management or reported high levels of psychological distress at baseline (Loxton et al., 2019). 73
5.2.2 Recruitment of study participants.
Study participants were mainly recruited via social media, including Facebook,
YouTube and Twitter. Emails were also sent to the previous cohorts, the ALSWH professional network, ALSWH collaborators and professional bodies to refer eligible study subjects to participate. Traditional media such as posters, business cards, leaflets, newspapers, television, and radio were also used to recruit eligible study participants. The majority of participants (70%) were recruited via Facebook. To be eligible, women had to have a Medicare card, agree to provide personal data, agree to participate in a follow-up survey and had to provide consent for external data linkage. Women who agreed to participate were sent an information sheet and consent form to obtain written online consent.
Detailed recruitment procedures are discussed elsewhere (Loxton et al., 2015a; Gita Devi
Mishra et al., 2014). Participants were broadly similar to the 2011 Australian census data regarding geographical distribution, age, and marital status but over-representative of women with a university degree (Gita Devi Mishra et al., 2014). Ethics approval for the ALSWH was granted by the University of Queensland’s and the University of Newcastle’s Human
Research Ethics Committees, and The Department of Health Human Research Ethics
Committee. Written online informed consent was secured from all study participants.
5.2.3 Measures.
Outcome variables. The outcome variables for this study were past year and ever e- cigarette use. To measure past year e-cigarette use, respondents were asked, ‘Have you used battery-operated electronic cigarettes (e-cigarettes) in the last 12 months?’ (with possible responses being ‘yes’ or ‘no’). Ever e-cigarette use was assessed by asking, ‘Have you ever used battery-operated electronic cigarettes? (with the same possible responses).
Exposure variables. Eight ACEs were measured in this study, which can be broadly categorised as childhood abuse (child maltreatment) or household dysfunction. Childhood
74 abuse included psychological abuse (two items), physical abuse (two items) and sexual abuse
(four items), while household dysfunction included household substance abuse (two items), household mental illness (two items), parental separation/divorce (one item), incarcerated household members (one item) and witnessing domestic violence (eight items). Participants were classified as being exposed to a given ACE if they replied ‘yes’ to one or more of the items in that category. All questions relating to ACEs referred to the respondent’s experiences before reaching the age of 18 years. The total ACE score was created by totalling the number of individual ACE types that were reported for a maximum score of eight. Based on this, the ACE score was categorised as zero, one, two, three and four or more. Researchers found a good test–retest reliability for both individual and cumulative ACE scores, which suggests the measurements’ reliability (Pinto, Correia & Maia, 2014). The items that were used to define ACEs are presented in Table 5.1.
Table 5.1
ACEs Category (Collected in the Third Survey)
Abuse/child maltreatment
Psychological Abuse: At least one ‘yes’ response to the following questions
1. Did a parent/adult often swear at, insult or put you down?
2. Did a parent/adult often act in a way that made you afraid that you would be physically
hurt?
Physical Abuse: At least one ‘yes’ response to the following questions
1. Did a parent/adult often push, grab, shove or slap you?
2. Did a parent/adult often hit you so hard that you had marks or were injured?
Sexual Abuse: At least one ‘yes’ response to the following questions
1. Did an adult/5–year older person ever touch or fondle you in a sexual way?
2. Did an adult/5–year older person ever have you touch their body in a sexual way?
3. Did an adult/5–year older person ever attempt oral, anal or vaginal intercourse with you? 75
4. Did an adult/5–year older person ever actually have oral, anal or vaginal intercourse with
you?
Household dysfunction
Substance Abuse (Living with someone with substance abuse): At least one ‘yes’ response to the following questions
1. Did you live with anyone who was a problem drinker or alcoholic?
2. Did you live with anyone who used street drugs?
Witnessing Domestic Violence: At least one ‘yes’ response to the following questions:
1. Was your mother (or stepmother) sometimes, often or very often pushed, grabbed, slapped
or had something thrown at her?
2. Was your mother (or stepmother) sometimes, often or very often kicked, bitten, hit with a
fist or hit with something hard?
3. Was your mother (or stepmother) ever repeatedly hit over at least a few minutes?
4. Was your mother (or stepmother) ever threatened with, or hurt by, a knife or gun?
5. Was your father (or stepfather) sometimes, often or very often pushed, grabbed, slapped or
had something thrown at him?
6. Was your father (or stepfather) sometimes, often or very often kicked, bitten, hit with a fist
or hit with something hard?
7. Was your father (or stepfather) ever repeatedly hit over at least a few minutes?
8. Was your father (or stepfather) ever threatened with, or hurt by, a knife or gun?
Living with Someone with Mental Illness: At least one ‘yes’ response to the following questions
1. Was a household member depressed or mentally ill?
2. Did a household member attempt suicide?
Living with someone who went to jail/prison
1. Did a household member go to prison?
Parental separation or divorce 76
During your childhood, did your parents divorce or permanently separate?
Note: All the ACEs were an event encountered during the first 18 years of life.
Covariates. Previous research has demonstrated that sociodemographic factors such as age, education, employment and residence are risk factors for e-cigarette use among younger people (Filippidis et al., 2017; Melka, Chojenta, Holliday & Loxton, 2019).
Conventional tobacco smoking has also been identified as a risk factor of subsequent e- cigarette use (Filippidis et al., 2017; Jiang et al., 2016). Parental education and parental household income have been strongly associated with adolescent smoking (Soteriades &
DiFranza, 2003), with parental education and parental financial hardship having also been associated with children experiencing ACEs (Halfon, Larson, Son, Lu & Bethell, 2017;
Leung, Wong, Chen & Tang, 2008). For these reasons, this study included sociodemographic parental education, family financial hardship during childhood and smoking status (ever smoker v. never smoker) as model covariates. Participants were asked to report their age in years, which was then included in the analysis as a continuous variable. Other covariates included highest level of education (lower than year 12, year 12 or equivalent, trade/certificate/diploma, university degree), employment status (employed, unemployed), mother’s education (lower than year 12, year 12 or equivalent, trade/certificate/diploma, university degree, do not know), father’s education (lower than year 12, year 12 or equivalent, trade/certificate/diploma, university degree, do not know) and parental financial hardship during primary school. Participants were asked to rate the family’s ability to manage with their available income during childhood (primary school) with the following options: ‘It was easy’, ‘it was not too bad’, ‘it was difficult some of the time’, ‘it was difficult all the time’, ‘it was impossible’ and ‘do not know’. The options were regrouped for analysis as follows: easily managing income (‘it was easy’, ‘it was not too bad’), difficulty managing 77 income (‘it was difficult some of the time’, ‘it was difficult all the time’, ‘it was impossible’) and ‘do not know’.
5.2.4 Data analysis.
Descriptive statistics with chi-squared or student t-tests were used to characterise participants by e-cigarette use and background variables. The chi-square test was used to compare individuals with missing and complete data by outcome variable (e-cigarette use)
(results are not shown). There was a statistically significant difference between individuals with complete and missing data across the variables compared in terms of outcome variables
(history of e-cigarettes use). Participants with missing values were more likely to use e- cigarettes. However, since the non-response rate for many of the exposure variables (ACEs) and other covariates was lower than five per cent (while it was less than 10 per cent for some of the variables), complete case analyses have performed. Logistic regression was conducted to estimate unadjusted and AORs that represent associations of individual ACEs or the cumulative ACE score with past year or ever e-cigarette use. Three sets of models were fitted for each outcome variable of interest (past year and ever e-cigarette use). In the first set of models, separate bivariate logistic regression models were fitted for each of the eight ACE variables and the ACE score. In the second set of models, sociodemographic variables (age, education level and employment status), parental education level and family’s ability to manage income during childhood were included. The third and final set of models included smoking status in addition to the covariates included in the second model. The associations between dependent and independent variables were presented as crude and AORs with 99%
CIs. By using 99% CIs, researchers have implicitly specified a significance threshold of 0.01 rather than 0.05. This accounts for five independent hypothesis tests, which should be ample to account for multiple testing, given the known correlation between different ACEs. In light of the number of significance tests, an alpha = .01 was set (i.e., 99% CI for odds ratios 78 excluding one) (Langkamp, Lehman & Lemeshow, 2010). The data were analysed in the period 2018–2019, and all data analysis was performed using Stata (version 15) (Stata Corp,
2017, College Station, TX, Stata Corp LLC).
5.3 Results
Table 5.2 presents the frequencies of e-cigarette use in terms of selected respondent characteristics. The mean age of the participants was 22.5. The prevalence of past year e- cigarette use among the participants was 6.4 per cent, while the ever e-cigarette use was 11.1 per cent. The prevalence of at least one reported ACE was 65.2 per cent, while the prevalence of four or more ACEs was 14.7 per cent (see Figure 5.1). Both the prevalence of past year and ever e-cigarette use increased with the number of ACEs (see Figure 5.2).
Parental Separation or Divorce Incarcerated family member Household Mental Illness Witnessing Domestic Violence Household Substance Abuse Sexual Abuse Physical Abuse Psychological Abuse
Experienced 4 or more ACEs Experienced 3 ACEs Experienced 2 ACEs Experienced 1 ACEs Experienced 0 ACEs 0 5 10 15 20 25 30 35 40 45 Percent
Figure 5.1 Prevalence of individual ACEs and the number of ACEs experienced by young
Australian women.
79
Past year e-cigarette use Ever e-cigarette use
25 20.54 20 14.67 15 12.65 11.74 10 8.5 8.5 6.71 7.1
Prevalence (%) Prevalence 4.81 5 3.94
0 0 ACEs 1 ACE 2 ACEs 3 ACEs 4+ ACEs ACE Score
Figure 5.2 Prevalence of past year and ever e-cigarette use by ACE score among Australian young women. 80
Table 5.2
Frequency of E-Cigarette Use by Selected Participant Characteristics
Variables Past year e-cigarette use p-value Ever e-cigarette use p-value
Yes n (%) No n (%) Yes n (%) No n (%)
Age respondents
19–22 329 (57.7) 4,099 (49.1) p < 0.001 544 (54.9) 3,884 (49.0) p < 0.001
23–26 241 (42.3) 4,246 (50.9) 446 (45.1) 4,041 (51.0)
Highest level of education
Less than Year 12 52 (9.1) 260 (3.1) p < 0.001 83 (8.4) 229 (2.9) p < 0.001
Year 12 or equivalent 159 (27.9) 2,202 (26.4) 281 (28.4) 2,080 (26.3)
Trade/certificate/diploma 201 (35.3) 2,251 (27.0) 357 (36.1) 2,095 (26.4)
University degree 133 (23.3) 3,416 (40.9) 228 (23.0) 3,321 (41.9)
Missing 25 (4.4) 216 (2.6) 41 (4.1) 200 (2.5)
Currently unemployed
Yes 111 (19.5) 1,013 (12.1) p < 0.001 188 (19.0) 936 (11.8) p < 0.001
No 434 (76.1) 7,115 (85.3) 761 (76.9) 6,788 (85.7)
Missing 25 (4.4) 217 (2.6) 41 (4.1) 201 (2.5) 81
Mother’s highest level of education
Less than Year 12 150 (26.3) 2,019 (24.2) p = 0.008 263 (26.6) 1,906 (24.1) p < 0.001
Year 12 or equivalent 77 (13.5) 1,140 (13.7) 145 (14.7) 1,072 (13.5)
Trade/certificate/diploma 120 (21.1) 1,703 (20.4) 199 (20.1) 1,624 (20.5)
University degree 163 (28.6) 2,889 (34.6) 282 (28.5) 2,770 (35.0)
Do not know 46 (8.1) 475 (5.7) 77 (7.8) 444 (5.6)
Missing 14 (2.5) 119 (1.4) 24 (2.4) 109 (1.4)
Father’s highest level of education
Less than Year 12 166 (29.1) 1,959 (23.5) p < 0.001 265 (26.77) 1,860 (23.5) p < 0.001
Year 12 or equivalent 60 (10.5) 941 (11.3) 111 (11.21) 890 (11.2)
Trade/certificate/diploma 94 (16.5) 1,486 (17.8) 155 (15.66) 1,425 (18.0)
University degree 129 (22.6) 2,700 (32.4) 236 (23.84) 2,593 (32.7)
Do not know 73 (12.8) 698 (8.4) 134 (13.54) 637 (8.04)
Missing 48 (8.4) 561 (6.7) 89 (8.9) 520 (6.5)
Family ability to manage income
Easily managing income 287 (50.4) 4,829 (57.9) p < 0.001 485 (49.0) 4,631 (58.4) p < 0.001
82
Difficulty managing 244 (42.8) 3,198 (38.3) 437 (44.1) 3,005 (37.9) income
Do not know 23 (4.0) 192 (2.3) 41 (4.1) 174 (2.2)
Missing 16 (2.8) 126 (1.5) 27 (2.7) 115 (1.5)
Smoking status
Ever smoked 423 (74.2) 1,782 (21.3) p < 0.001 722 (72.9) 1,483 (18.7) p < 0.001
Never smoked 147 (25.8) 6,563 (78.7) 268 (27.1) 6,442 (81.3) 83
In this study’s logistic regression analyses, a statistically significant association existed between individual ACE categories and past year and ever e-cigarette use after adjusting for sociodemographic and parental education level during childhood. However, after adjusting for smoking status, the odds ratio that was associated with each category of
ACE and past or ever e-cigarette use was attenuated (see Table 5.3). In the fully adjusted model, the three childhood abuse variables (psychological, physical and sexual abuse) were positively associated with both past year and ever e-cigarette use. In a separate analysis, specific categories and cumulative ACE scores were strongly associated with traditional tobacco smoking, after controlling for sociodemographic factors (result not shown). In the fully adjusted analysis, the odds of past year e-cigarette use were 1.5 times higher for participants who were exposed to psychological abuse (AOR = 1.45, 99% CI: 1.11, 1.90), 1.3 times higher for those who were exposed to physical abuse (AOR = 1.30, 99% CI: 1.03, 1.82) and 1.4 times higher for those who were exposed to sexual abuse (AOR = 1.41, 99% CI:
1.02, 1.95), as compared with those who were not exposed to such abuse. Participants who grew up in a family that possessed a history of substance abuse were 1.4 times more likely to report ever e-cigarette use compared to their counterparts (AOR = 1.35, 99% CI: 1.08, 1.68).
Additionally, the odds of ever e-cigarette use were 1.3 times greater for those who had witnessed domestic violence when compared to their counterparts. (AOR = 1.28, 99% CI:
1.01, 1.69). The participants who lived with a mentally ill family member had 1.3 times increased likelihood of reporting ever e-cigarette use, as compared to participants who did not live with family members exhibiting a history of mental illness (AOR = 1.28, 99% CI: 1.05,
1.58) (see Table 5.3).
This study confirmed the presence of a strong dose–response relationship between past year or ever e-cigarette use and the number of ACEs. For example, the odds of experiencing past year e-cigarette use and ever e-cigarette use were 1.6 times (AOR = 1.60, 84
99% CI: 1.12, 2.29) and 1.9 times greater (AOR = 1.86, 95% CI: 1.39, 2.48), respectively, among participants who reported four or more ACEs, as compared to those who reported no
ACEs (see Table 5.4). 85
Table 5.3
Associations of Individual ACEs with Past Year and Ever E-Cigarette Use Among Young Australian Women
N Past year e-cigarette use Ever e-cigarette use ACE categories Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 (Reference = No) OR (99% CI) AOR (99% CI) AOR (99% CI) OR (99% CI) AOR (99% CI) AOR (99% CI)
Psychological abuse 8,698 2.27 1.91 1.45 2.12 1.78 1.36
(1.80, 2.87) (1.48, 2.46) (1.11, 1.90) (1.76, 2.55) (1.46, 2.18) (1.09, 1.69)
Physical abuse 8,698 2.32 1.81 1.30 2.13 1.71 1.22
(1.73, 3.11) (1.31, 2.49) (1.03, 1.82) (1.67, 2.70) (1.31, 2.22) (1.01, 1.62)
Sexual abuse 8,635 2.40 1.95 1.41 2.49 2.05 1.50
(1.81, 3.18) (1.43, 2.65) (1.02, 1.95) (2.00, 3.11) (1.60, 2.62) (1.15, 1.96)
Household substance abuse 8,751 2.02 1.67 1.18 2.24 1.87 1.35
(1.60, 2.56) (1.29, 2.16) (0.90, 1.54) (1.87, 2.69) (1.53, 2.29) (1.08, 1.68)
Witnessing domestic violence 8,751 1.85 1.45 1.15 1.98 1.59 1.28
(1.39, 2.47) (1.06, 1.99) (0.82, 1.61) (1.58, 2.49) (1.24, 2.04) (1.01, 1.69)
Mentally ill household member 8,751 1.54 1.34 1.13 1.64 1.50 1.28
(1.23, 1.94) (1.06, 1.71) (0.88, 1.46) (1.38, 1.96) (1.24, 1.80) (1.05, 1.58) 86
Incarcerated household member 8,751 1.52 1.14 0.77 1.66 1.27 0.83
(0.80, 2.90) (0.56, 2.32) (0.37, 1.59) (1.01, 2.75) (0.73, 2.20) (0.46, 1.49)
Parental separation or divorce 8,753 1.58 1.33 1.05 1.68 1.39 1.11
(1.25, 1.98) (1.03, 1.70) (0.81, 1.37) (1.40, 2.00) (1.14, 1.69) (0.89, 1.38)
Note: n = the number of participants who responded for each of the ACE categories. OR, odds ratio; AOR, adjusted odds ratio; CI, confidence interval. Model 1: unadjusted odds ratio of association between ACEs and e-cigarette use. Model 2: association between ACEs and e-cigarette use adjusted for age, education level, employment status, mother’s education during childhood, father’s education during childhood and family’s ability to manage income during childhood. Model 3: association between ACEs and e-cigarette use adjusted for variables in model 2 plus cigarette smoking status. 87
Table 5.4
Association Between Number of ACEs and Past and Ever E-Cigarette Use
N Past year e-cigarette use Ever e-cigarette use ACE score Model 1 Model 2 Model 3 Model 1 Model 2 Model 3
OR (99% CI) AOR (99% CI) AOR (99% CI) AOR (99% CI) AOR (99% CI) AOR (99% CI)
0 2,994 1.0 [Reference] 1.0 [Reference] 1.0 [Reference] 1.0 [Reference] 1.0 [Reference] 1.0 [Reference]
1 2,141 1.23 (0.86, 1.76) 1.19 (0.83,1.72) 1.05 (0.78, 1.40) 1.29 (1.04, 1.69) 1.26 (1.01, 1.56) 1.06 (0.84, 1.34)
2 1,352 1.86 (1.29, 2.68) 1.81 (1.33, 2.46) 1.30 (0.95, 1.79) 2.01 (1.51, 2.67) 1.89 (1.48, 2.40) 1.39 (1.07, 1.80)
3 859 2.26 (1.52, 3.37) 2.12 (1.49, 3.00) 1.45 (1.01, 2.10) 2.39 (1.75, 3.26) 2.15 (1.63, 2.84) 1.51 (1.12, 2.05)
4 and more 1,261 3.24 (2.33, 4.51) 2.82 (2.02, 3.93) 1.60 (1.12, 2.29) 3.59 (2.77, 4.66) 3.15 (2.43, 4.08) 1.86 (1.39, 2.48)
Note: OR, odds ratio; AOR, adjusted odds ratio; CI, confidence interval. Model 1: unadjusted odds ratio of the association between ACEs and e-cigarette use. Model 2: the association between ACEs and e-cigarette use adjusted for age, education level, employment status, mother’s education during childhood, father’s education during childhood and family’s ability to manage income during childhood. Model 3: association between ACEs and e-cigarette use adjusted for variables in model 2 plus cigarette smoking status. 88
5.4 Discussion
To the researcher’s knowledge, this is the first study to investigate the relationship between ACEs and e-cigarette use using extensive national Australian data. The prevalence of at least one ACE (65%) was higher compared to the findings reported by Loxton et al
(2018) (41%) in regard to Australian women aged 18–23 years old. The difference in the prevalence of at least one ACE between this study and Loxton et al. could be because of the difference in timing of the study and sample size. The prevalence of four or more ACEs was comparable with the values reported in other studies (13.7%) (Campbell et al., 2016; Fang &
McNeil, 2017). All of the ACEs that related to the three types of childhood abuse were associated with past year or ever e-cigarette use among young Australian women. Although this study revealed that no previous studies investigated the relationship between ACEs and e-cigarette use, other researchers have found that physical, emotional and sexual abuse are stronger predictors of smoking among women (Fuller-Thomson et al., 2013). Similarly, in this study, all ACE categories were strongly associated with conventional tobacco smoking after controlling for sociodemographic factors, parental education and parental financial hardship during childhood. Moreover, a dose–dependent relationship existed between the cumulative ACE score and tobacco smoking (results not shown). After adjusting for the participants’ smoking status, the odds ratios that were associated with each of the ACE categories and ACE score were attenuated. The attenuation of odds ratios suggests that the positive relationship between ACEs and e-cigarette use is substantially mediated by conventional cigarette smoking. Other evidence has also indicated that e-cigarette use is more prevalent among ever and current smokers, as compared to never smokers (Filippidis et al.,
2017; Jiang et al., 2016). To cope with anxiety and depression, individuals who were exposed to childhood adversities may be more likely to use nicotine to reduce their negative emotions, through the stimulation of neurotransmitters (e.g., epinephrine and dopamine) (Anda, 89
Butchart, Felitti & Brown, 2010). The findings in this study emphasised the importance of protecting children from psychological, physical and sexual abuse during their childhood so that subsequent substance use and other adverse outcomes can be prevented.
Growing up in a family with a history of substance abuse was found to be a risk factor for ever e-cigarette use. In previous studies, researchers also identified a positive association between a family member’s history of substance abuse and smoking and heavy drinking
(Anda et al., 1999; Campbell et al., 2016). Witnessing domestic violence during childhood was an important risk factor of substance use, such as smoking and binge drinking during later life (Crouch, Radcliff, Strompolis & Wilson, 2018; Ford et al., 2011). Similar in this study, the odds of ever e-cigarette use were 28 per cent greater for those who had witnessed domestic violence, as compared to those who had not witnessed such violence. In this study, a family history of mental illness was also a positively associated factor with ever e-cigarette use. Previous studies have found that a family history of mental illness was a risk factor for substance use, such as smoking and alcohol abuse (Fuller-Thomson et al., 2013; Rehkopf et al., 2016).
Several previous studies have concluded that health risk behaviours such as smoking, problem drinking and illicit drug use increased with an increase in the number of ACEs (Fang
& McNeil, 2017; Ford et al., 2011). Similarly, in this study, a dose–response relationship was found between ACEs and past and ever e-cigarette use. The ACE score may reflect the accumulated exposure of the developing brain to the activated stress response—which has been suggested as being the main pathway by which ACEs exert their effects on adult behaviours (Anda et al., 2010). The presence of a strong dose–response association between the number of ACEs and e-cigarette use magnifies the importance of designing strategies and interventions that can address all forms of ACEs. 90
Previous studies have identified sociodemographic factors, smoking status and peer and parental smoking as risk factors of e-cigarette use (Filippidis et al., 2017; Jiang et al.,
2016). Previous studies have also examined the association between smoking and childhood adversities; however, no studies have investigated the association between ACEs and e- cigarette use. This study’s findings add to the existing evidence that establishes the association of childhood adversities with other substance use (e.g., alcohol, tobacco and illicit drugs). This finding highlights the importance of considering childhood adversities along with other risk factors when designing strategies and interventions that target the prevention of nicotine addiction through e-cigarette use.
Researchers have previously concluded that ACEs were an important pathway for many health-harming behaviours and morbidities. For example, Felitti et al.’s (1998) historical ACEs study identified that ACEs were important risk factors for several health risk behaviours and disorders, such as smoking, obesity, physical inactivity, depression and suicide attempts. As mentioned previously, traumatic events during childhood can affect brain development by altering the normal structure and chemical activity of the neurotransmitters (Perry, 2009). Since the underdeveloped cannot cope naturally, this can lead to negative coping strategies such as substance use and other health-harming behaviours
(Shonkoff et al., 2011). The effects of maltreatment on brain development depend on factors such as the child’s age during the abuse, the severity of the abuse and the chronicity of the abuse (Shonkoff et al., 2011).
Childhood adversities are a source of social, emotional and cognitive impairment that can subsequently lead to the adoption of health risk behaviours. In the long term, health risk behaviours could result in disease, disability, social problems and premature death (Rose, Xie
& Stineman, 2014). Some of the strategies that have been proposed to reduce the effects of
ACEs include offering financial support to families, providing quality care and education 91 early in life, enhancing parental nurturing skills to promote healthy child development, training clinicians to regularly investigate ACEs using trauma-informed practices and strengthening legal protections for children (Fortson, Klevens, Merrick, Gilbert & Alexander,
2016; Jorm & Mulder, 2018).
5.5 Limitations
This study should be interpreted by accounting for the following limitations. The first limitation is the cross-sectional nature of the study. Most of the ACE-related questions were related to sensitive issues that could have influenced the respondents and led them to offer more socially acceptable answers. Additionally, the high attrition rate for the third survey could affect the generalisability of the findings to the general population. Due to the retrospective nature of reporting ACEs, some recall bias is also expected. However, researchers have found a good test–retest reliability for both individual and cumulative ACE scores, which suggests the measurements’ reliability (Pinto et al., 2014). The data concerning the type of e-cigarette device used and its contents were not collected, so whether the device contained nicotine of any concentration could not be ascertained. However, in a recent study, researchers have identified that six out of 10 ‘nicotine-free e-liquids’ in Australia contain nicotine (Chivers et al., 2019).
The recruitment of study participants using the internet and social media may have resulted in self-selection bias. Compared to the 2011 Australian census data, the participants of this cohort were over-represented by women with tertiary education, which could reduce the generalisability of results to Australian women of the same age. Since the recruitment of the participants was not conducted randomly, the data were not weighted to balance this over- representation of women. Despite these limitations, this study is the first to investigate the relationship between ACEs and e-cigarette use using national data. Overall, it suggests that an important relationship exists between ACEs and e-cigarette use during early adult life.
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5.6 Conclusion
The findings of the current study are consistent with those of previous studies that have linked the experience of childhood adversities with substance use. Evidence has shown that people who experience stress and anxiety use nicotine for mood modulation (Cosci,
Pistelli, Lazzarini & Carrozzi, 2011). Therefore, individuals who are exposed to childhood adversities may use nicotine to control their level of arousal and undesirable mood states in later life. This study’s findings highlight the importance of protecting children from childhood adversities—as well as the early identification and intervention for children who are exposed to adversities—so that substance abuse like e-cigarettes can be minimised during later life. Family practitioners and clinicians should adopt a practical guidance role when educating parents, childcare providers, policymakers and the public about the devastating consequences of childhood adversities. Moreover, strategies should be established to help people with a history of childhood adversities to adopt positive coping mechanisms rather than health-harming behaviours—including nicotine addiction. 93
Chapter 6: E-Cigarette Use and Cigarette Smoking Initiation Among
Australian Women Who Have Never Smoked
Chapter 5 examined the association between ACEs and e-cigarette use. This chapter
will address aim 3 (to examine the association between e-cigarette use and cigarette smoking
initiation among Australian women who have never smoked). The content of this chapter has
also been published in the Drug and Alcohol Review (see Appendix R).
Melka, A. S., Chojenta, C. L., Holliday, E. G. & Loxton, D. J. (2020). E-cigarette use and
cigarette smoking initiation among Australian women who have never smoked. Drug
and Alcohol Review. https://doi.org/10.1111/dar.13131 94
6.1 Introduction
Tobacco smoking is a leading risk factor for many chronic diseases, including cancer, cardiovascular disease, stroke and chronic obstructive pulmonary disease (Sherman, 1991).
Although smoking rates have declined globally over time, as of 2015, the annual global tobacco-related death toll was as high as six million (Reitsma et al., 2017). In addition to health effects, smoking has environmental, economic and social influences (Venkatesh,
2013). For example, the tangible and intangible social cost related to tobacco smoking in
Australia in the period 2004–2005 was more than 31 billion AUD (Collins & Lapsley, 2008).
E-cigarettes release smokeless inhalants that containing chemicals, such as propylene glycol and flavouring agents (with or without nicotine) (Douglas et al., 2015; Schripp et al.,
2013). Additionally, some e-cigarette products may be attractive to young people due to their pleasant flavours (Harrell, Loukas, Jackson, Marti & Perry, 2017). E-cigarette product classifications are known to differ from country to country. For example, they are classified as a tobacco product in the US, a medicinal/consumer good in the UK and a poison/consumer good in Australia (Kennedy, Awopegba, de León & Cohen, 2017).
Researchers found that the global prevalence of conventional tobacco smoking has declined (Reitsma et al., 2017), while the prevalence of e-cigarette use has increased over time among young people in some countries (Cullen et al., 2018; de Lacy et al., 2017). For example, Cullen et al. (2018) conducted a study among high school students in the US and found that the prevalence of current e-cigarette use increased from 1.5 to 21 per cent between
2011 and 2018. A different cross-sectional study conducted in Wales found that the prevalence of e-cigarette use among students aged 11–16 was nearly twice as high (18.5%) as the prevalence of conventional tobacco smoking (10.5%) (de Lacy et al., 2017). In a study conducted in the UK and Australia among adult respondents aged 18 years and older, the 95 prevalence of current e-cigarette use had increased from 4.5 to 18.8 per cent in the UK and from 0.6 to 6.6 per cent in Australia between 2010 and 2013 (Yong et al., 2014).
Previous studies that were conducted in the US, UK and Canada have provided evidence that e-cigarette use is linked to an increased risk of subsequent cigarette smoking among never smoking youth (Aleyan et al., 2018; East et al., 2018; Primack et al., 2017). A systematic review and meta-analysis by Soneji et al. (2017) also revealed that e-cigarette use was associated with subsequent cigarette smoking among young adults. In contrast, some researchers have linked the current decline in global tobacco smoking among young people with the advent and popularity of e-cigarettes (Notley et al., 2018; Zhu et al., 2017).
In a cross-sectional study conducted in Australia among 519 never smokers who were aged 18–25 years, Jongenelis, Jardine et al. (2019) identified a positive association between ever e-cigarette use and curiosity about tobacco smoking, willingness to smoke and intentions to smoke. The available evidence regarding the association between e-cigarette use and the subsequent initiation of cigarette smoking has mostly been reported from the US (Primack et al., 2017; Wills et al., 2017). There are no longitudinal studies in Australia that investigate the association between e-cigarette use and subsequent conventional cigarette use among people who have never smoked. The prevalence and trends of e-cigarette use and cigarette smoking can be affected by the regulatory environment of any given country (Cho et al., 2017). In all
Australian states and territories, the supply, sale and use of nicotine-containing e-cigarettes are prohibited (Douglas et al., 2015). Therefore, examinations of the longitudinal association between e-cigarette use and subsequent smoking initiation are likely to differ when compared to countries in which nicotine e-cigarettes are legally available. Most previous longitudinal studies that investigated the association between e-cigarette use and the initiation of combustible cigarette smoking among never smokers recruited participants from schools
96
(Aleyan et al., 2018; Barrington-Trimis et al., 2016; Wills et al., 2017). However, in this thesis, large, community-based national data were used to answer the research question.
Most of studies and prevention strategies cigarette smoking and e-cigarette use related studies and prevention strategies have targeted adolescents with the assumption that smoking behaviour is mostly established by the age of 18 (Evans-Polce et al., 2020; Harvey et al.,
2016). Nevertheless, current trends indicated that young adults could be important and mostly ignored sub-population in the development of smoking behaviour. For instance, according to a longitudinal study conducted in the US using national data, the incidence of ever and current initiation of both combustible cigarette smoking and e-cigarettes among young adults
(18–24 years old) was higher compared to youth (11–17 years old) (Perry et al., 2018).
Therefore, research that targets young adults is essential to design prevention policies and strategies.
In recent years, researchers have used the gateway hypothesis to examine the association between e-cigarette use and the subsequent initiation of conventional tobacco smoking (Chaffee et al., 2018; Leventhal et al., 2015; Primack et al., 2017). However, the usefulness of the gateway theory for investigating e-cigarette use and the initiation of cigarette smoking is not consistently accepted. Some researchers have argued that the gateway theory is not appropriate for investigating e-cigarette use and the subsequent initiation of cigarette smoking (Etter, 2018; Vanyukov et al., 2012). In contrast, some researchers support the use of the gateway theory to investigate this association (Chapman et al., 2019). Etter (2018) had argued in an article that the gateway theory is not useful for predicting the causal association between e-cigarette use and subsequent initiation of traditional cigarette smoking. According to Etter, epidemiological studies must fulfil the following criteria to establish a causal association: the sequence of initiation of one substance should consistently precede the other, the substances must demonstrate a strong association, 97 and they must control for possible confounding factors. Further, Schneider and Diehl (2015), based on their evidence, found that the gateway hypothesis did not explain the reasons for the transition from e-cigarettes to traditional cigarettes.
Some researchers have proposed a common liability hypothesis as an alternative option for examining the correlation between e-cigarette use and cigarette smoking (Etter,
2018; Vanyukov et al., 2012). The common liability hypothesis proposes that vaping is more likely to occur within populations more likely to use cigarettes due to shared common risk factors (Vanyukov et al., 2012). Unlike the gateway hypothesis, the common liability hypothesis suggests non-specific liability to a range of drugs regardless of the sequence of initiation (Vanyukov et al., 2012). According to Kim and colleagues, common liability such as environmental factors and biological vulnerability can predispose adolescents and young people for e-cigarette and traditional cigarette smoking (S. Kim & Selya, 2019).
Given the debate on the suitability of gateway hypothesis to examine the causal link between e-cigarettes and traditional cigarettes smoking, we used the term ‘association’ rather than ‘causation’ to assess the role of e-cigarette in subsequent initiation of smoking (Kandel
D, 2002). In this study, we hypothesised that e-cigarette use among tobacco-naive young adult women would be associated with subsequent cigarette smoking.
6.2 Methods
6.2.1 Study design and data source.
This study has used the third and fourth survey data from the new young cohort of
ALSWH Australian women who were born in the period 1989–1995. The ALSWH, as previously discussed, is the largest and longest-running national Australian study that collects information on the physical health, mental health, psychological wellbeing, lifestyle, substance use and health service utilisation of Australian women.
98
The new young cohort is surveyed annually, and five surveys have currently been completed. The participants completed the third and fourth online surveys in 2015 and 2016, respectively. In the third survey, 8,961 participants responded to several health and health- related issues—of which 8,917 and 8,915 participants provided information regarding cigarette smoking and ever e-cigarette use, respectively. Of the 8,665 participants who responded to having ever smoked 100 cigarettes in their lifetime, 6,711 (75.3%) were never smokers (i.e., they did not smoke at the time and had not smoked at least 100 cigarettes in a lifetime). From the third survey, 6,710 never smokers responded to the e-cigarette use items.
During the follow-up survey (fourth survey), the age of the participants ranged from 20–27 years. For the purposes of reporting, survey 3 was considered the ‘baseline’ and survey 4 the
‘follow-up’.
6.2.2 Recruitment of study participants.
Participants in the new young cohort were recruited using both traditional and social media. Social media such as Facebook, YouTube, and Twitter were used to recruit study participants meeting inclusion criteria. Traditional media (e.g., newspaper, leaflet, TV and radio) were also used to recruit study participants. Professional organisations and collaborators were also requested to refer to potential study participants. The majority of study participants were recruited using Facebook (70%). Women aged 18–23 were eligible to participate in the new young cohort survey. Women were also required to provide online consent for participation and external data linkage of personal information with administrative data. Moreover, the study included only permanent residents who had a
Medicare (Australian universal insurance) card. Details about participant recruitment strategies and procedures have been published elsewhere (Loxton et al., 2015b; Gita Devi
Mishra et al., 2014). The study was approved by the human research ethics committees at the
University of Newcastle (UoN HREC approval number H-2012-0256), the University of 99
Queensland (UQ HREC approval number 2012000950) and the Australian Government
Department of Health.
6.2.3 Measures.
6.2.3.1 Outcome measure.
The outcome variable that this study investigated was the initiation of cigarette smoking at the follow-up survey among participants who were never smokers during the baseline survey. Never smokers were defined as those people who do not smoke currently and who had not smoked 100 cigarettes or more in their lifetime (Centers for Disease Control
& Prevention, 1994; Ryan et al., 2012). Participants in this study who were never smokers at baseline and who reported ‘ever smoking’ at the follow-up survey have been defined as initiated smoking (see Figure 6.1).
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Figure 6.1 Flow of study participants to assess ever e-cigarette use at baseline and cigarette smoking initiation in the follow-up survey.
6.2.3.2 Exposure variable.
The main exposure variable in this study was ever e-cigarette use (lifetime use) as reported in the baseline survey. Participants were asked the question, ‘Have you ever used battery-operated electronic cigarettes’, with possible responses of yes or no. The respondents were not asked about the nicotine content of the e-liquid that was used.
6.2.3.3 Covariates.
By referring to previous literature, the variables reported as causally affecting both e- cigarette use and conventional cigarette smoking were identified as potential confounders and were included in the adjusted model (Ayo-Yusuf & Szymanski, 2010; Edwards, Anda, Gu, 101
Dube & Felitti, 2007; van der Deen, Carter, Wilson & Collings, 2011). All these covariates were measured during the baseline survey.
Sociodemographic. The age of the participants when they completed the third survey was used as a continuous variable. Using postal codes, the place of residence was classified based on the Remoteness Index of Australia Plus (ARIA+) as major city, inner regional, outer regional and remote/very remote (Chojenta et al., 2014). The participants’ level of education was categorised as lower than year 12, year 12 and equivalent, trade/certificate/diploma and university degree or higher. Marital status was dichotomised as having a partner (married/de facto relationship) and having no partner (separated, divorced, widowed and never married).
Employment status was also dichotomised as employed and unemployed (unemployed for fewer than six months, unemployed for six months or more). The ability to manage with available income was assessed by asking, ‘How do you manage on the income you have available?’, with the responses being dichotomised as difficulty managing income (it is impossible, it is difficult all the time and it is difficult some of the time) and easily managing income (it is not too bad, it is easy). The participants were also asked whether they were living with one or both of their parents.
Mental health. Participants’ history of depression was assessed by the question,
‘Have you ever been diagnosed or treated for depression’, with possible answers of yes or no.
The 10-item Kessler Psychological Distress Scale (K10) was used to measure psychological distress (Yiengprugsawan, Kelly & Tawatsupa, 2014). Each item asked about specific feelings and was scored from one, ‘none of the time’, to five, ‘all of the time’. Based on this, the minimum score was 10 and the maximum 50, in which higher scores reflected greater distress. Categorising psychological distress using a meaningful cut point facilitates clinical decision-making and interpretation of psychological distress levels. The severity of 102 psychological distress was categorised as low (10–15), moderate (16–21), high (22–29) and very high (30–50) (Carter, van der Deen, Wilson & Blakely, 2014).
Binge drinking at least monthly. Binge drinking was assessed by the question,
‘How often do you have five or more standard drinks of alcohol on one occasion?’, with responses ranging from ‘never’, ‘less than once a month’, ‘about once a month’, ‘about once a week’ and ‘more than once a week’. The responses were dichotomised as no binge drinking at least once a month (never, less than once a month) and binge drinking at least once a month (about once a month, about once a week and more than once a week).
ACE score. Eight ACEs were assessed in the third survey. They include childhood abuse (psychological, physical and sexual abuse) and household dysfunction (household substance abuse, witnessing domestic violence, a mentally ill household member, an incarcerated household member and parental separation or divorce). A cumulative score was created by adding the eight specific childhood adversities to form ordinal variables, in which higher scores reflected exposure to more forms of adversity.
6.2.4 Data analysis.
The descriptive statistics comprised the mean with standard deviation and frequencies with percentages, with groups compared using Pearson’s Chi-square or Fisher’s exact test and student’s t-test. Logistic regression was used to identify the factors that were associated with loss to follow-up using baseline sociodemographic factors, mental health factors, binge drinking and childhood adversity scores. The logistic regression was used to estimate the association between baseline e-cigarette use and follow-up initiation of cigarette smoking.
The traditional p-value cut-off point of 0.05 fails to retain important variables for the final model, and researchers recommend the use of a p-value range from 0.15–0.2 for variable selection (Bursac, Gauss, Williams & Hosmer, 2008). Therefore, variables that achieved p < 0.2 in the univariate analysis were included in the multivariable analysis. Variables that 103 were excluded from the model were then individually re-entered into the model, and the nested models were compared using a likelihood ratio test. Based on this test, none of these variables was retained in the final model. The estimates were expressed as odds ratio with
95% CI, and a p-value threshold of 0.05 was used to declare statistical significance. The steps that were employed for logistic regression are found in Appendix J. An additional analysis that includes all independent variables, irrespective of the univariate p-value, is contained in
Appendix K.
In a separate sensitivity analysis, penalised maximum likelihood estimation was used to estimate regression parameters and minimise bias due to data sparsity for some covariates
(Firth, 1993). The findings from the penalised maximum likelihood estimation were consistent with the maximum likelihood estimates. Therefore, maximum likelihood estimates were reported in this study. The researcher conducted another sensitivity analysis with two alternate assumptions in mind to assess the sensitivity of the study findings regarding loss to follow-up. In the first scenario, participants lost to follow-up were considered never smokers, while in the second scenario, they were counted smoking initiators at follow-up (results not shown). In both scenarios, ever e-cigarette use was a strong predictor of the subsequent initiation of smoking. Therefore, loss to follow-up did not majorly affect the findings. The goodness of fit was assessed using the Hosmer-Lemeshow goodness-of-fit test (see Appendix
M).
6.3 Results
6.3.1 Characteristics of study participants.
Table 6.1 presents the characteristics of the participants in regard to e-cigarette use at the baseline and smoking initiation at the follow-up, with selected baseline sociodemographic factors, mental health factors, binge drinking and childhood adversity scores. The mean
(± SD) age of the participants at baseline (third survey) was 22.5 (± 1.7). Ever e-cigarette 104 users and never users among baseline never smokers at baseline were different regarding most of the background characteristics (e.g., age, education, marital status, employment and ability to manage income). Similarly, participants who were smokers and never smokers at follow-up differed on baseline background characteristics such as age, education and ability to manage income. 105
Table 6.1
Sample Characteristics: E-Cigarette Use Status and the Subsequent Initiation of Tobacco Smoking Among Baseline Survey Never Smokers
Ever e-cigarette use at 3rd survey (n = 6710) p-value Initiation of cigarette smoking 4th survey (n = p-value
5,398) Variables Yes No Yes No
n (%) n (%) n (%) n (%)
Age (±SD) 22.0 (±1.7) 22.5 (±1.7) <0.001a 22.1 (±1.7) 22.5 (±1.8) 0.003
Area of residence
Major cities 212 (79.1) 4,780 (74.2) 0.420b 146 (75.6) 3,845 (73.9) 0.639 b
Inner regional 40 (14.9) 1,065 (16.5) 27 (14.0) 881 (16.9)
Outer regional 13 (4.9) 447 (6.9) 16 (8.3) 365 (7.0)
Remote/Very remote 2 (0.7) 75 (1.2) 1 (0.5) 60 (1.2)
Missing 1 (0.4) 75 (1.2) 3 (1.6) 54 (1.0)
Higher level of education
Less than Year 12 6 (2.2) 131 (2.0) 0.017b 6 (3.1) 90 (1.7) <0.001b
Year 12 and equivalent 91 (34.0) 1,708 (26.5) 61 (31.6) 1,374 (26.4)
Trade/certificate/diploma 70 (26.1) 1,547 (24.0) 62 (32.1) 1,205 (23.2)
University degree 98 (36.6) 2,907 (45.2) 61 (31.6) 2,474 (47.5)
Missing 1 (1.1) 149 (2.3) 3 (1.6) 62 (1.2)
Marital status 106
Has a partner 65 (24.2) 1,852 (28.8) <0.001c 50 (25.9) 1,526 (29.3) 0.33b
Has no partner 198 (73.9) 4,439 (68.9) 140 (72.5) 3,614 (69.4)
Missing 5 (1.9) 151 (2.3) 3 (1.6) 65 (1.3)
Employment status
Unemployed 44 (16.4) 696 (10.8) 0.007b 24 (12.4) 559 (10.7) 0.41b
Employed 221 (82.5) 5,596 (86.9) 166 (86.0) 4,583 (88.1)
Missing 1 (1.1) 150 (2.3) 3 (1.6) 63 (1.2)
Ability to manage income
Difficulty managing income 158 (58.9) 3,050 (47.4) <0.001c 112 (58.0) 2,451 (47.1) 0.002 b
Easy managing income 105 (39.2) 3,241 (50.3) 78 (40.4) 2,689 (51.7)
Missing 5 (1.9) 151 (2.3) 3 (1.6) 65 (1.2)
Live with one or both parents
Yes 125 (46.6) 2,467 (38.3) <0.001c 65 (33.7) 1,994 (38.3) 0.23b
No 138 (51.5) 3,824 (59.4) 125 (64.8) 3,146 (60.4)
Missing 5 (1.9) 151 (2.3) 3 (1.5) 65 (1.3)
Ever had depression
Yes 104 (38.8) 2,101 (32.6) 0.033b 91 (47.2) 1,672 (32.1) <0.001b
No 163 (60.8) 4,338 (67.3) 101 (52.3) 3,532 (67.9)
Missing 1 (0.4) 3 (0.1) 1 (0.5) 1 (0.0)
Kessler Psychological Distress Scale
Low 98 (36.6) 3,355 (52.1) <0.001b 85 (44.0) 2,772 (53.2) 0.004b 107
Moderate 67 (25.0) 1,335 (20.7) 43 (22.3) 1,071 (20.6)
High 46 (17.2) 835 (13.0) 23 (11.9) 691 (13.3)
Very high 54 (20.1) 776 (12.0) 39 (20.2) 608 (11.7)
Missing 3 (1.1) 141 (2.2) 3 (1.6) 63 (1.2)
Binge drinking at least once a month
Yes 97 (36.2) 1,627 (25.3) <0.001b 93 (48.2) 1,282 (24.6) <0.001b
No 171 (63.8) 4,813 (74.7) 100 (51.8) 3,922 (75.4)
Missing 0 (0.0) 2 (0.03) 0 (0.00) 1 (0.02)
ACE score
0 80 (29.8) 2,468 (38.3) <0.001c 39 (20.2) 2,055 (39.5) <0.001b
1 61 (22.8) 1,607 (24.9) 54 (28) 1,317 (25.3)
2 52 (19.4) 927 (14.4) 37 (19.2) 730 (14.0)
3 30 (11.2) 564 (8.8) 26 (13.5) 443 (8.5)
4 or more 37 (13.8) 682 (10.6) 34 (17.6) 543 (10.4)
Missing 8 (3.0) 194 (3.0) 3 (1.5) 117 (2.3)
Note: a p-value derived from student t-test. b p-value derived from Fisher exact test. c p-value derived from chi-square test.
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6.3.2 Factors associated with lost to follow-up.
Multivariable logistic regression was used to identify factors that were associated with loss to follow-up in the fourth survey. Table 6.2 outlines the factors that were associated with participants lost to follow-up in the follow-up survey. Overall, 1,313 out of 6,711 (19.6%) of the baseline never smokers were lost to follow-up in the fourth survey. E-cigarette use at the baseline was positively associated with loss to follow-up. Participants who resided in an inner regional area and who had a high level of education were more likely to participate in the follow-up survey. However, no difference was observed between participants who were lost to follow-up and those who reached follow-up regarding age, marital status, employment status, ability to manage with the available income, status of living with one or both parents, history of ever depression, Kessler Psychological Distress Scale, status of binge drinking and
ACE score.
Table 6.2
Factors Associated with Lost to Follow-Up Among Baseline Never Smokers
Predictor variables Unadjusted OR Adjusted ORa
OR (95% CI) OR (95% CI)
Ever e-cigarette use
Yes 1.51 (1.14, 1.99) 1.52 (1.14, 2.03)
No 1 [Reference] 1 [Reference]
Age 0.97 (0.94, 1.01) 1.00 (0.96, 1.05)
Area of residence
Major cities 1 [Reference]
Inner regional 0.87 (0.73, 1.02) 0.81 (0.68, 0.97)
Outer regional 0.83 (0.64, 1.06) 0.79 (0.60, 1.03)
Remote/Very remote 1.04 (0.60, 1.82) 1.02(0.56, 1.84)
Higher level of education
Less than year 12 1 [Reference] 1 [Reference] 109
Year 12 and equivalent 0.59 (0.40,0.87) 0.57 (0.38, 0.84)
Trade/certificate/diploma 0.65 (0.44, 0.95) 0.63 (0.42, 0.93)
University degree 0.43 (0.30, 0.63) 0.42 (0.28, 0.63)
Marital status
Has a partner 1 [Reference]
Has no partner 1.08 (0.95, 1.25)
Employment status
Unemployed 1.19 (0.99, 1.45) 1.09 (0.89, 1.33)
Employed 1 [Reference]
Ability to manage income
Difficulty managing income 1 [Reference] 1 [Reference]
Easy managing income 0.83 (0.73, 0.94) 0.95 (0.83, 1.08)
Live with one or both parents
Yes 1.23 (1.08, 1.39)
No 1 [Reference]
Ever had depression
Yes 1.05 (0.93, 1.19)
No 1 [Reference]
Kessler Psychological Distress Scale
Low 1 [Reference] 1 [Reference]
Moderate 1.24 (1.06, 1.45) 1.15 (0.98, 1.35)
High 1.12 (0.93, 1.36) 1.01 (0.82, 1.23)
Very high 1.36 (1.13, 1.63) 1.15 (0.94, 1.41)
Binge drinking at least once a month
Yes 1.06 (0.93, 1.22)
No 1 [Reference]
ACE score
0 1 [Reference] 1 [Reference]
1 1.04 (0.89, 1.22) 0.99 (0.85, 1.16)
2 1.19 (0.99, 1.44) 1.11 (0.91, 1.34) 110
3 1.27 (1.00, 1.61) 1.11 (0.86, 1.42)
4 or more 1.09 (0.86, 1.40) 0.88 (0.67, 1.15)
Note: a Adjusted model includes variables achieving p < 0.2 in the univariate analysis. OR, odds ratio; CI, confidence interval; ACE, adverse childhood experience.
6.3.3 Baseline e-cigarette use and cigarette smoking initiation at follow-up.
From the third survey, 268/6710 never smokers (4%) had ever used e-cigarettes at baseline. From the baseline survey, out of never smokers who participated in the follow-up survey (n = 5,398), 193 (3.6%) of the participants initiated smoking. Among study participants who initiated cigarette smoking in the follow-up survey, 13 per cent reported ever using e-cigarettes. The proportion of cigarette smoking initiation at follow-up among baseline e-cigarette users was higher compared to the proportion of cigarette smoking initiation among baseline never e-cigarette users, at 168/5200 users (25/197 [12.7%] v.
[3.2%]).
In the analysis that was adjusted for level of education, ability to manage income, history of depression, Kessler Psychological Distress Scale, binge drinking and ACE score, the odds of subsequent smoking initiation were 3.7 times higher among baseline survey ever e-cigarette users compared to never e-cigarette users (AOR = 3.71, 95% CI: 2.33, 5.93).
Compared to the participants with no history of depression in the baseline survey, those with a history of depression had 60 per cent greater odds of smoking initiation in the follow-up survey (AOR = 1.60, 95% CI: 1.14, 2.24). The odds of tobacco smoking initiation were 3.3 times greater for participants who reported binge drinking at least once a month, as compared to non-binge drinkers (AOR = 3.29, 95% CI: 2.43, 4.46). A dose–response relationship was observed between the ACE score and the initiation of tobacco smoking in the follow-up survey. For example, the odds of smoking initiation was 2.4 times higher among study participants who had an ACE score of four or more, as compared to participants with an ACE score of zero (AOR = 2.38, 95% CI: 1.43, 3.98) (see Table 6.3). This study tested for two- 111 way interactions between the main exposure variable (ever e-cigarette use) and age, education, ability to manage income, history of ever depression, Kessler Psychological
Distress Scale, binge drinking and ACE score. None of these interaction terms was statistically significant at p < 0.05.
Table 6.3
Unadjusted and Adjusted Associations Between Baseline Characteristics and Subsequent
Initiation of Cigarette Smoking at Follow-Up Among Never Smokers
Unadjusted OR Adjusted OR a p-value Predictor variables n OR (95% CI) OR (95% CI)
Ever e-cigarette use (5,397) b
Yes 197 4.35 (2.78, 6.81) 3.71 (2.33, 5.93) <0.001
No 5,200 1 [Reference] 1 [Reference]
Age (5,398) c 5,398 0.88 (0.81, 0.96) 0.94 (0.86, 1.03) 0.21
Area of residence (5,341) c
Major cities 3,991 1 [Reference] - -
Inner regional 908 0.81 (0.53, 1.22) - -
Outer regional 381 1.15 (0.68, 1.96) - -
Remote/Very remote 61 0.43 (0.06, 3.19) - -
Higher level of education (5,333) c
Less than year 12 96 1 [Reference] 1 [Reference] -
Year 12 and equivalent 1,435 0.67 (0.28, 1.58) 0.73 (0.30, 1.78) 0.49
Trade/certificate/diploma 1,267 0.77 (0.32, 1.83) 0.91 (0.37, 2.21) 0.83
University degree 2,535 0.37 (0.16, 0.88) 0.53 (0.21, 1.31) 0.17
Marital status (5,330) c
Has a partner 1,576 1 [Reference] - -
Has no partner 3,754 1.18 (0.85, 1.64) - -
Employment status (5,332) c
Unemployed 583 1.18 (0.77, 1.83) - -
Employed 4,749 1 [Reference] -
Ability to manage income (5,330) c
Difficulty managing income 2,563 1 [Reference] 1 [Reference] 0.18
112
Easy managing income 2,767 0.63 (0.47, 0.85) 0.81 (0.59, 1.11)
Live with one or both parents (5,330) c
Yes 2,059 0.82 (0.60, 1.11) - -
No 3,271 1 [Reference] -
Ever had depression (5,396) c
Yes 1,763 1.90 (1.43, 2.5) 1.60 (1.14, 2.24) 0.007
No 3,633 1 [Reference] 1 [Reference]
Kessler Psychological Distress Scale (5,332) c
Low 2,857 1 [Reference] 1 [Reference] -
Moderate 1,114 1.31 (0.90, 1.90) 0.96 (0.65, 1.42) 0.84
High 714 1.08 (0.68, 1.73) 0.69 (0.42, 1.14) 0.14
Very high 647 2.09 (1.42, 3.09) 1.11 (0.71, 1.75) 0.65
Binge drinking at least ones a month (5,397) c
Yes 1,375 2.84 (2.1, 3.80) 3.29 (2.43, 4.46) <0.001
No 4,022 1 [Reference] 1 [Reference]
ACEs score (5,278) c
0 2,094 1 [Reference] 1 [Reference] -
1 1,371 2.16 (1.42, 3.28) 2.03 (1.32, 3.11) 0.001
2 767 2.67 (1.69, 4.22) 2.24 (1.39, 3.61) 0.001
3 469 3.09 (1.86, 5.13) 2.69 (1.59, 4.58) 0.001
4 or more 577 3.29 (2.06, 5.27) 2.38 (1.43, 3.98) 0.001
Note: a Adjusted model included variables achieving p < 0.2 in the univariate analysis. The final model adjusted for age, education, ability to manage income, ever had depression, Kessler Psychological Distress Scale, Binge drinking and ACE score. b Main exposure variable. c Covariates. OR, odds ratio; CI, confidence interval.
6.4 Discussion
This longitudinal study identified a strong association between e-cigarette use and subsequent tobacco smoking one year later, even after adjusting for other covariates. The subsequent initiation of cigarette smoking during the follow-up survey was also associated with a history of depression, binge drinking and higher ACE scores. Although the sale, 113 supply and use of nicotine-containing e-cigarettes are prohibited in all Australian states, researchers from a recent study found that six out of 10 ‘nicotine-free e-liquids’ that are sold in Australia contain nicotine (Chivers et al., 2019). Fraser et al. (2015) also found that most
(89%) of Australian e-cigarette users purchase e-liquids that may or may not contain nicotine online from overseas vendors.
This finding is consistent with other longitudinal studies reported from the US, UK and Canada, which investigated the association between e-cigarette use and the subsequent initiation of combustible cigarette smoking among young people (Aleyan et al., 2018; East et al., 2018; Primack et al., 2017). However, none of these studies measured the nicotine content of the e-cigarette at baseline. In a cross-sectional study that was conducted in Australia among 519 never smokers aged 18–25 years, researchers have identified a positive association between ever e-cigarette use and curiosity about tobacco smoking, willingness to smoke and intentions to smoke (Jongenelis, Jardine et al., 2019). Most of these studies have defined e-cigarette use as lifetime use and smoking initiation as any puff of a cigarette in the follow-up survey. However, in this study, smoking initiation has been defined as smoking at least 100 cigarettes in a lifetime at follow-up. In the absence of RCTs, the best evidence will be provided by well-designed and carefully analysed observational studies, such as longitudinal studies. However, statistical associations between e-cigarette use and smoking initiation do not necessarily suggest a causal association.
Researchers have argued that a simple trial of e-cigarettes could not be sufficient to study the gateway effect of e-cigarette use transitioning to subsequent cigarette use. Since nicotine is responsible for the development of addiction, researchers have highlighted the importance of assessing the nicotine contents of e-cigarettes (Glasser, Abudayyeh, Cantrell &
Niaura, 2019). Most studies (including the one in this study) have investigated the causal association between e-cigarette use and the subsequent initiation of e-cigarettes and have 114 failed to consider the nicotine content of the e-liquid that non-smokers use. Therefore, it could be difficult to conclude a causal association between e-cigarette use and smoking initiation.
Some researchers have linked the current decline in global tobacco smoking among young people with the prominence of e-cigarettes. For example, Levy et al. (2019) concluded that the increase in the prevalence of e-cigarette use by young adults in the US has contributed to the decline in the prevalence of combustible cigarette smoking in the US. In contrast, others believe that the current decline in global tobacco smoking is mainly due to the execution of the WHO FCTC.
In agreement with previous studies, the initiation of tobacco smoking was positively associated with a history of depression. Individuals with mental illness have reportedly used addictive substances, such as nicotine, to cope with the unpleasant feelings that are associated with depressive symptoms through the stimulation of neurotransmitters (Benowitz, 2009).
Smoking and nicotine addiction are highly prevalent among individuals who have a mental illness (Lawrence, Mitrou & Zubrick, 2009). Aligned with previous studies, this study also found that binge drinking was a strong predictor of subsequent tobacco smoking initiation
(Jiang et al., 2016; Morgenstern, Nies, Goecke & Hanewinkel, 2018).
Childhood adversities are known risk factors of substance abuse—such as tobacco smoking, alcohol and illicit drug use (Campbell et al., 2016; Felitti et al., 1998). Prior studies have identified a strong dose–response association between the number of childhood adversities and smoking behaviours in later life. Similarly, in the current study, a strong dose–response relationship exists between the ACE score and the subsequent initiation of smoking in the fourth survey. Individuals who were exposed to ACEs used a psychoactive substance such as nicotine to cope with the anxiety and depression that was encountered due to the traumatic effect of childhood adversities (Felitti et al., 1998). 115
6.5 Limitations and Strength of the Study
This study’s analysis has adjusted for a range of sociodemographic, mental health, childhood adversities and alcohol-related variables. However, it has not adjusted for other variables that are associated with both e-cigarette use and tobacco smoking in the literature, such as parental and peer smoking behaviours and personality traits (Gilman et al., 2009;
Hwang & Park, 2016). Studies have revealed that shared environmental factors such as growing up in the same family contributed much to the inter-individual variation in smoking behaviour among monozygotic twins (Boomsma, Koopmans, van Doornen & Orlebeke,
1994). Researchers also found that social influence, such as peer and parental factors, have contributed to an increase in the prevalence of e-cigarette use (Chao, Hashimoto & Kondo,
2019). Since e-cigarettes were not described to the study participants through pictures or other names (e.g., vaping), it is possible that the participants did not clearly understand what the term signified.
This study did not assess the frequency of e-cigarette use. It only assessed whether participants had ever used e-cigarettes to investigate the association between e-cigarette use and subsequent smoking initiation. Therefore, to better understand the link between e- cigarette use and subsequent smoking initiation, studies that measure both the frequency of e- cigarette use and cigarette smoking are required. Self-reported e-cigarette use and smoking could affect the study’s findings. However, previous studies have confirmed a good agreement between self-reported smoking and the biochemical method for adults (McDonald,
Maguire & Hoy, 2003). In the current study, respondents were not asked about the nicotine content of the e-liquid that they used. Since nicotine is responsible for the development of addiction, researchers have emphasised the importance of assessing the nicotine contents of e-cigarettes (Glasser et al., 2019). Participants lost to follow-up differed from those who participated in the follow-up survey in terms of baseline e-cigarette use. Therefore, the 116 selection bias that could have been introduced due to attrition may bias the estimates.
However, the findings were consistent in the sensitivity analysis that considered participants lost to follow-up as smoking initiators and never smokers. This study focuses on specific age groups and the female gender, which may limit generalisability to age groups and the male gender. The use of open recruitment methods may have resulted in selection bias, which in turn affects the generalisability of the study. However, the sample was found to be comparable to census data for this age group in terms of demographics with some over- representation of women with tertiary education and some under-representation of women from non–English speaking backgrounds (Loxton et al. 2019; Mishra et al., 2014).
6.6 Conclusions
This study is one of the few longitudinal studies that has used a large, community- based sample to examine the association between e-cigarette use and tobacco smoking. The study’s finding together with similar previous longitudinal studies that were conducted overseas suggests that e-cigarette use is associated with the subsequent initiation of cigarette smoking among never smokers. Enforcing the existing restriction of the sale and supply of e- liquid containing nicotine is essential for preventing never smokers from nicotine addiction via e-cigarettes. Future quantitative and qualitative studies that investigate the mechanism through which e-cigarette use leads to subsequent smoking initiation among never smokers is required.
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Chapter 7: Determinants of Smoking Cessation Among Australian
Women—The Role of E-Cigarette Use
Chapter 6 has assessed the association of e-cigarette use and cigarette smoking
initiation among Australian women who have never smoked. In this chapter, aim 4 is
addressed (to identify the role that e-cigarettes play for smoking cessation among Australian
women). The content of this chapter is under review for publication.
Melka, A. S., Chojenta C. L., Holliday. E. G Loxton DJ. E-cigarette use and cigarette
smoking initiation among Australian women who have never smoked. 118
7.1 Introduction
Smoking is one of the leading causes of morbidity and premature death, both in developed and developing countries (Gometz, 2011). Mortality from chronic diseases such as cancer has been associated with the number of years that a person has smoked, as well as the number of cigarettes smoked per day (Halpern et al., 1993). Smoking cessation can improve survival by decreasing the risk of cancer, heart disease, stroke, chronic obstructive pulmonary disease and other chronic diseases (Gometz, 2011; Kawachi et al., 1993). Among young adults who smoke, smoking cessation before the age of 35 years has been known to increase life expectancy by nearly seven years (Taylor Jr et al., 2002).
As mentioned previously, young people cite that their main reason for using e- cigarettes is to reduce or quit combustible cigarettes (Dunlop et al., 2016; Keogan, Taylor,
Babineau & Clancy, 2016). Some people who adopt smoking also report a preference for e- cigarettes over traditional cigarettes for reasons including reduced health risks, lower price and a perception of greater social acceptability (Jancey et al., 2015).
National positions on e-cigarette regulation vary among countries. For example, in
England, governmental agencies such as PHE have recommended e-cigarette use as an aid for smoking cessation and risk reduction (McNeil et al., 2015). Contrary to this, Canada, Mexico,
Brazil and Australia have not approved the use of e-cigarettes as a smoking-cessation aid and have developed regulatory mechanisms to control the supply, possession and use of e- cigarette products (Douglas et al., 2015; Gardenier, Higgins & Prochnow, 2018; Jancey et al.,
2015). In Australia, the sale or supply and use of nicotine in an e-cigarette are illegal
(Douglas et al., 2015). Although some studies have recommended e-cigarette use as a smoking-cessation aid, the WHO does not recognise e-cigarettes for smoking cessation therapy and strongly recommends a control and ban of these products (WHO, 2010). To date, the evidence concerning the efficacy of e-cigarettes in aiding cessation is mixed (Wolfenden, 119
Stockings & Yoong, 2017). Some studies report evidence that e-cigarette use is an effective smoking-cessation aid (Mantey, Cooper, Loukas & Perry, 2017), and other researchers have also reported that e-cigarettes were as effective or more effective than NRT for smoking cessation (Brown et al., 2014). For example, a cross-sectional study conducted in England found that individuals who used e-cigarettes for smoking cessation were more likely to report sustained abstinence than those who used NRT for smoking cessation (Brown et al., 2014).
However, some longitudinal studies found no statistical association between e-cigarette use and smoking cessation at follow-up (Grana et al., 2014; Shi et al., 2016).
A limited number of population-based longitudinal studies have investigated the association between e-cigarette use and smoking cessation (both internationally and in
Australia) (Shi et al., 2016). Research investigating the role that e-cigarettes play in smoking cessation is required for formulating strategies and policies that govern the regulation of e- cigarette use (Etter, 2015; Wolfenden et al., 2017). In the absence of pertinent data regarding the effect of e-cigarette use on the subsequent cessation of conventional cigarette smoking, it is challenging for policymakers to design evidence-based policies and strategies that control tobacco smoking (Sæbø & Scheffels, 2017). Most of the findings on smoking cessation are derived from cross-sectional data (Barnett et al., 2015; Brown et al., 2014; Wang et al.,
2015), with researchers having recommended investigating the causal relationship between e- cigarette use and combustible cigarette use (Morgan et al., 2019). The National Academies of
Sciences, Engineering and Medicine also recommend the use of longitudinal data to examine the association between e-cigarette use and cigarette smoking initiation (National Academies of Sciences & Medicine, 2018). In this study, the researcher hypothesised that baseline e- cigarette use could be independently associated with smoking cessation at follow-up after adjusting for confounders.
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7.2 Methods
7.2.1 Study design and participant recruitment.
For the current analysis, this study used data from the 1989–1995 birth cohort of the
ALSWH, who was recruited through traditional media, referrals, social media and the
ALSWH website. Professional organisations and existing cohort members also referred participants to participate. Nearly 70 per cent of the participants were recruited via Facebook.
The 1989–1995 cohort has been surveyed annually since 2013, and five surveys have been completed to date. The current analysis used the third (2015) and fourth (2016) surveys to examine the association between e-cigarette use at baseline and subsequent cessation of cigarette smoking at follow-up. For ease of reporting, we refer to the third survey as the baseline survey and the fourth survey as the follow-up survey. During the baseline survey,
8,919 study participants responded to smoking-related items, of which 16.8 per cent (1,496) were current smokers. Sixty-eight per cent of the baseline current smokers completed the follow-up survey. Figure 7.1 presents the flow of participants in the baseline and follow-up surveys. 121
Total number of respondents in the baseline (third survey) (N=8,919)
Current smoker Non-current smoker baseline survey baseline survey (n=1,496) (n=7,423)
Current smoker Current smoker lost reached for follow-up to follow-up in the in the fourth survey fourth survey (n=475) (n= 1,021)
Respondents Respondents stopped smoking continued current in the follow-up smokers in the survey (ex- follow-up survey smokers) (n=272) (n=749)
Figure 7.1 Flow of study participants to assess ever e-cigarette use at baseline and cigarette smoking cessation in the follow-up survey.
7.2.2 Measures.
7.2.2.1 Outcome measures.
Smoking cessation at one-year follow-up was the outcome variable that was measured in this study. The current status was measured in the baseline and follow-up surveys by asking the question, ‘How often do you currently smoke cigarettes or tobacco products?’, with response options ranging from ‘daily’, ‘at least weekly (but not daily)’, ‘less often than 122 weekly’ to ‘not at all’. The responses were dichotomised into current smokers (‘daily’, ‘at least weekly but not daily’, ‘less often than weekly’) and non-current smokers (‘not at all’).
The WHO and Australian National Drug Strategy Household Survey defined current smokers as those who use any tobacco products either daily or occasionally during the data collection period (Australian Institute of Health and Welfare, 2014b; WHO, 2017). Baseline current smokers who were classified as ex-smokers at the follow-up survey were considered to have ceased smoking.
7.2.2.2 Exposure/independent variable.
The exposure variable used in this study was ever e-cigarette use, as measured in the third survey (baseline). Ever e-cigarette use was measured by asking the question, ‘Have you ever used battery-operated electronic cigarettes (e-cigarettes)?’, with yes or no responses.
7.2.2.3 Covariates.
Regression models controlled for the variables that were associated with e-cigarette use or cigarette smoking in previous cross-sectional studies (Ayo-Yusuf & Szymanski, 2010;
Edwards et al., 2007; van der Deen et al., 2011). Age in years was included as a continuous variable and postcodes were used to classify the area of residence based on the Remoteness
Index of Australia Plus (ARIA+; major cities, inner regional, outer regional or remote/very remote). Participant’s education levels were re-categorised as ‘less than Year 12’, ‘Year 12 and equivalent’, ‘trade/certificate/diploma’ or ‘university degree’. Marital status was dichotomised as partnered (married, de facto) or non-partnered (separated, divorced, widowed, and never married). Study participants’ employment status was categorised as employed or unemployed. Participants were asked to rate their ability to manage on their available income using the question: ‘How do you manage on the income you have available?’ with response options ‘it is impossible’, ‘it is difficult all the time’, ‘it is difficult some of the time’, ‘it is not too bad’, or ‘it is easy’. Responses were then dichotomised as 123 difficult managing income (‘it is impossible’, ‘it is difficult all the time’, or ‘it is difficult some of the time’) and easy managing income (‘it is not too bad’ or ‘it is easy’). Participants were also asked whether they were living with their parents or not.
History of depression was assessed by asking the question: ‘Have you ever been diagnosed with or treated for depression?’ with response options ‘yes’ or ‘no’. The ten-item
Kessler Psychological Distress Scale (K10) was used to assess anxiety and depressive symptoms experienced in the past four weeks (Kessler et al., 2003). For each of the items, the response ranged from 1, ‘none of the time, to 5, ‘all of the time’, with a minimum total score of 10 and a maximum of 50. The severity of psychological distress was classified as low (10–
15), moderate (16–21), high (22–29), or very high (30–50). Binge drinking at least once a month was categorised as ‘yes’ or ‘no’. In this study, eight forms of adverse childhood experiences (ACEs) were assessed based on the study by Felliti et al. (1998). The ACEs include three types of abuse (physical abuse, psychological abuse and sexual abuse) and five household dysfunctions (household substance use, witnessing domestic violence, household mental illness, imprisoned family members and parental divorce or separation). Ordinal response categories were created based on the number of childhood adversities an individual experienced before the age of 18 years. The score was ordered as 1, 2, 3, and 4 or more.
7.2.3 Statistical analysis.
A student t-test or chi-square test was used to compare baseline independent variables for the participants who did and did not return for data follow-up. Binary logistic regression was used to examine the association between e-cigarette use in the baseline survey and smoking cessation in the follow-up survey after controlling for potential confounders.
Univariate analysis was used to select candidate variables that were associated with the outcome variable (smoking cessation). Variables that attained a univariable p-value of less than or equal to 0.2 were included in the initial multivariable logistic regression model. 124
Variables that were not included using this p-value threshold were individually entered into the model and assessed for inclusion using a likelihood ratio test for the two nested models.
Based on the likelihood ratio test, none of these variables was important in the models at the
0.05 significant level. Potential effect modification was assessed by including a pairwise interaction term between the exposure variable (ever e-cigarette use) and each covariate (area of residence, ability to manage income, history of depression, K–10 score and binge drinking). No interaction term was statistically significant at p-value < 0.05. An additional analysis that includes all independent variables irrespective of the univariate p-value is contained in Appendix L. The Hosmer-Lemeshow goodness-of-fit test was employed to assess model fit (see Appendix N). Effect estimates were reported as odds ratios with 95%
CI, and statistical significance was declared at 95% CI and a p-value threshold of 0.05. Stata
(version 15) was used for analysing the data (which was completed in 2019).
7.3 Results
The mean age of the study participants during the baseline survey was 22.5 years and most had completed post-secondary education. Baseline current smokers who were lost to follow-up differed from those who were reached for the follow-up survey in terms of education level, employment status, Kessler Psychological Distress Scale, ever e-cigarette use and ACE score. Participants who were retained and lost to follow-up were similar concerning age, area of residence, marital status, ability to manage income, living arrangement, history of depression and binge drinking (see Table 7.1).
125
Table 7.1
Characteristics of Study Participants Reached to Follow-Up and Lost to Follow-Up in Terms of Baseline Variables
Variables Current smokers reached Current smokers lost p-value
for follow-up to follow-up
N % N %
Age (±SD) 22.3 (±1.7) 22.3 (±1.8) 0.992
Area of residence
Major cities 787 77.9 362 77.4 0.369
Inner regional 158 15.6 70 15.0
Outer regional 50 5.0 32 6.8
Remote/very remote 15 1.5 4 0.9
Highest level of education
Lower than year 12 81 8.1 42 9.9 0.002
Year 12 and equivalent 289 28.7 128 30.1
Trade/certificate/diploma 366 36.4 181 42.5
University degree 270 26.8 75 17.6
Marital status
Partnered 307 30.5 135 31.7 0.660
Non-partnered 699 69.5 291 68.3
Employment Status
Unemployed 175 17.4 96 22.5 0.023
Employed 831 82.6 330 77.5
Ability to manage income
Difficulty managing income 675 67.1 301 70.7 0.186
Easily managing income 331 32.9 125 29.3
Live with one or both parents
Yes 345 34.3 150 35.2 0.739
No 661 65.7 276 64.8 126
Ever had Depression
Yes 576 56.4 282 59.4 0.282
No 445 43.6 193 40.6
Kessler Psychological Distress Scale
Low 316 31.5 108 25.0 0.005
Moderate 213 21.2 77 17.9
High 163 16.2 94 21.8
Very high 313 31.1 152 35.3
Binge drinking at least monthly
Yes 511 50.1 243 51.5 0.606
No 510 49.9 229 48.5
Ever e-cigarette use
Yes 367 36.0 198 42.0 0.026
No 654 64.0 274 58.0
ACEs score
0 256 25.9 116 27.0 0.013
1 244 24.7 87 20.2
2 206 20.9 74 17.2
3 111 11.3 48 11.2
4 or more 170 17.2 105 24.4
Notes: Boldface indicates p < 0.05.
Among the 1,021 baseline survey of current smokers who reached follow-up, 272
(26.6%) reported quitting smoking. A total of 367 (40%) participants who reached follow-up were ever e-cigarette users at the baseline survey. Among the participants who were current smokers at baseline, the number who quit smoking at follow-up was lower in baseline e- cigarette users than in participants who had never used e-cigarettes (21.8% v. 29.4%).
In the multivariable analysis that was adjusted for area of residence, financial management ability, history of depression, Kessler Psychological Distress Scale and binge
127 drinking, the odds of smoking cessation at the follow-up survey among baseline current smokers decreased by 68 per cent among the baseline ever e-cigarette users, as compared to never e-cigarette users (AOR = 0.68, 95% CI: 0.50–0.93). None of the covariates controlled in the model (area of residence, ability to manage income, history of ever depression, Kessler
Psychological Distress Scale and binge drinking) was associated with smoking cessation behaviour in the follow-up survey (see Table 7.2). When this study’s definition of current smoking was restricted to individuals who had smoked at least 100 cigarettes in their lifetime and who had smoked daily or occasionally at baseline, no significant association was observed between ever e-cigarette use and smoking cessation at the follow-up survey (see
Appendix O). This finding suggested that e-cigarette use does not affect the smoking cessation rate among established smokers.
Table 7.2
Association Between E-Cigarette Use During Baseline Survey and Subsequent Cessation of
Smoking at Survey 4
Smoking cessation Unadjusted OR Adjusted ORa p-value
Predictor variables Yes No OR (95% CI) OR (95% CI)
n (%) n (%)
Ever e-cigarette use
Yes 80 (29.4) 287 (38.3) 0.67 (0.50–0.90)* 0.68 (0.50, 0.93) 0.017
No 192 (70.6) 462 (61.7) 1 [Reference] 1 [Reference]
Age (mean +SD) 22.3 (1.7) 22.3 (1.7) 0.99 (0.91, 1.07) -
Area of residence
Major cities 209 (77.7) 578 (78.0) 1 [Reference] 1 [Reference] -
Inner regional 45 (16.7) 113 (15.2) 1.10 (0.75, 1.61) 1.17 (0.79, 1.72) 0.421
Outer regional 14 (5.2) 36 (4.9) 1.08 (0.57, 2.03) 0.99 (0.51, 1.94) 0.996
Remote/very remote 1 (0.4) 14 (1.9) 0.20 (0.03, 1.51)* 0.19 (0.03, 1.48) 0.114
Higher level of education 128
Lower than year 12 19 (7.1) 62 (8.4) 1 [Reference] - -
Year 12 and equivalent 76 (28.5) 213 (28.8) 1.16 (0.65, 2.07) - -
Trade/certificate/diploma 91 (34.1) 275 (37.2) 1.08 (0.61, 1.90) - -
University degree 81 (30.3) 189 (25.6) 1.40 (0.79, 2.49) - -
Marital status
Partnered 84 (31.5) 223 (30.2) 1 [Reference] -
Non-partnered 183 (68.5) 516 (69.8) 0.94 (0.69, 1.27) -
Employment status
Unemployed 43 (16.1) 132 (17.9) 0.88 (0.61, 1.29) -
Employed 224 (83.9) 607 (82.1) 1 [Reference] -
Ability to manage income
Difficulty managing 168 (62.9) 507 (68.6) 1 [Reference] 1 [Reference] 0.369 income
Easily managing income 99 (37.1) 232 (31.4) 1.29 (0.96, 1.73)* 1.15 (0.85, 1.56)
Live with one or both parents
Yes 91 (34.1) 254 (34.4) 0.99 (0.73, 1.33) -
No 176 (65.9) 485 (65.6) 1 [Reference] -
Ever had depression
Yes 141 (51.8) 435 (58.1) 0.77 (0.59, 1.03)* 0.89 (0.65, 1.21) 0.464
No 131 (48.2) 314 (41.9) 1 [Reference] 1 [Reference]
Kessler Psychological Distress Scale
Low 92 (34.5) 224 (30.4) 1 [Reference] 1 [Reference] -
Moderate 69 (25.8) 144 (19.5) 1.16 (0.80, 1.70 1.20 (0.81, 1.77) 0.359
High 41 (15.4) 122 (16.5) 0.82 (0.53, 1.26 0.91 (0.58, 1.43) 0.687
Very high 65 (24.3) 248 (33.6) 0.64 (0.44, 0.92)* 0.76 (0.50, 1.14) 0.180
Binge drinking at least ones a month
Yes 148 (54.4) 363 (48.5) 1.26 (0.96, 1.68)* 1.21 (0.91, 1.62) 0.186
No 124 (45.6) 386 (51.5) 1 [Reference] 1 [Reference]
ACEs score
0 60 (23.0) 145 (20.0) 1 [Reference] - - 129
1 75 (28.7) 144 (19.8) 1.25 (0.85, 1.83) - -
2 40 (15.3) 151 (20.8) 0.73 (0.47, 1.12) - -
3 32 (12.3) 113 (15.6) 0.79 (0.48, 1.34 - -
4 or more 54 (20.7) 173 (23.8) 0.80 (0.51, 1.25) - - a The analysis was adjusted for variables attaining a p-value of < 0.02 in univariate analysis. OR, odds ratio; CI, confidence interval.
7.4 Discussion
Australian national data were used to examine the association between ever e- cigarette use and smoking cessation among current smokers. This study is unique because it included only the female gender to investigate the association between e-cigarette use and smoking cessation (through national longitudinal data). In this thesis’s study, smoking cessation was more common in participants who had never used e-cigarettes compared to participants who had used e-cigarettes (29.4% v. 21.8%).
Baseline e-cig users had 68% the odds of cessation compared with never e-cigarette users. Consistent with this study, previous longitudinal studies have also concluded that e- cigarette use can inhibit smoking cessation. In a cohort study conducted among US smokers aged 18 years and older, ever e-cigarette users were less likely to quit smoking for at least 30 days during one year of follow-up compared to never e-cigarette users (Shi et al., 2016).
Another US study also found that e-cigarette use at baseline was not associated with smoking cessation after a follow-up one year later (Grana et al., 2014). Another US study found that women were less likely to quit cigarette smoking compared to men, regardless of their e- cigarette use history. This thesis’s study also found that both daily and non-daily e-cigarette use did not increase the likelihood of smoking cessation in women (Verplaetse et al., 2018).
Conversely, other research has supported the claim that e-cigarette use can work as an aid for smoking cessation (Berry et al., 2019).
130
Prior studies have found that sociodemographic factors (e.g., increased age, higher education and living with a partner) were positively associated with successful smoking cessation (Ayo-Yusuf & Szymanski, 2010; Holm et al., 2017; Lee & Kahende, 2007).
Researchers have identified that alcohol users were less likely to quit smoking compared to non-alcohol users (Ayo-Yusuf & Szymanski, 2010). Additionally, mental illnesses (e.g., depressive disorder, anxiety disorder and psychological distress) were also positively associated with smoking-cessation rates in previous studies (van der Deen et al., 2011; Xu,
Wang & Gong, 2018). ACEs were another factor that reduced the likelihood of smoking cessation in previous studies (Edwards et al., 2007). However, in this study, none of these factors was associated with smoking cessation.
PHE has declared that e-cigarettes are 95 per cent safer compared to traditional cigarette smoking. The group strongly recommends them for a risk reduction and smoking- cessation aid (McNeil et al., 2015). However, McKee and Capewell (2015a) condemned
PHE’s report, arguing that it included studies with methodological flaws and studies that received funding from tobacco companies (McKee & Capewell, 2015a). Polosa, in his editorial published in The Lancet, also argued that it was not sound to conclude that e- cigarettes are 95 per cent safer compared to cigarette smoking based on the opinion of a small group of people with no predefined expertise in tobacco control (Polosa, 2015). Moreover, the PHE’s report has also been defended by Cancer Research UK and the British Lung
Foundation (Green et al., 2016). In contrast to the PHE’s recommendations, the US National
Academies of Sciences, Engineering and Medicine reported no sufficient evidence regarding the efficacy of e-cigarettes as a smoking-cessation aid (National Academies of Sciences &
Medicine, 2018). The findings of the current study, along with previous similar studies, emphasise the importance of randomised controlled trials for strengthening the evidence base of this topic. 131
The American College of Preventive Medicine (ACPM) recently formulated a practice statement regarding e-cigarette use after conducting a rapid literature review of the evidence supporting the effectiveness and harm of an ENDS (or e-cigarettes) (Livingston et al., 2019). The review ultimately found that the evidence for both the harms and benefits of e- cigarettes was inconclusive. The ACPM recommended that clinicians, public health experts and policymakers support efforts to prevent the use of e-cigarettes mainly in youth because the long-term health effects have not been established and e-cigarettes can potentially serve as a gateway to conventional cigarette smoking. The practice statement also emphasises the need for formulating policies that regulate the use of e-cigarettes. Researchers have also recommended that future Australian e-cigarette regulations should protect young people from nicotine addiction through e-cigarettes as a possible gateway to cigarette smoking (Morgan et al., 2019). Policymakers, public health experts and clinicians have insufficient information regarding on what to base their stand on e-cigarettes; there is thus no strong consensus
(Jancey et al., 2015). Therefore, the findings of this study can help policymakers design regulatory mechanisms that govern e-cigarette use.
7.5 Limitations and Strengths of the Study
This study could not distinguish whether the e-cigarettes that participants used contained nicotine or not. Although the final model has controlled for known confounders, some of the variables that are associated with both e-cigarette use and cigarette smoking by peers and parents, as well as sensation-seeking propensity, were not controlled for in the final model. Therefore, the overlooked variables might determine the association between e- cigarette use and the subsequent cessation of cigarette smoking. The respondents available for follow-up and who were lost to follow-up were also different concerning some key variables, which may limit the generalisability of the findings. 132
Despite these limitations, this study has strengths. Since the current study used longitudinal data to determine the association between e-cigarette use and subsequent cessation of cigarette smoking, it can better show the temporal relationship between exposure and effect. Moreover, hypotheses were tested using a large, national dataset.
7.6 Conclusions
The use of e-cigarettes inhibited subsequent smoking cessation among current smokers. Therefore, regulating e-cigarette use is vital for enhancing smoking control efforts and for preventing the renormalisation of smoking behaviours among the population. The
Australian NDS should incorporate the issue of e-cigarettes to prevent nicotine addiction and renormalisation of cigarette smoking, especially among youth and non-smokers. Research investigating why e-cigarette users are unsuccessful in quitting traditional cigarettes is important. Further, research investigating the effect of regular e-cigarette use on smoking cessation is also required to further strengthen the evidence. Future research should compare the role of nicotine-containing and non–nicotine containing e-cigarettes for smoking cessation. 133
Chapter 8: Effectiveness of Pharmacotherapy for Smoking
Cessation: Umbrella Review and Quality Assessment of
Systematic Reviews
In the previous chapters, this thesis has established that young age, smoking status, alcohol use, IPV and ACEs (traumatic childhood experiences) were factors positively associated with e-cigarette use in the study population. This research found evidence that although ever e- cigarette use is associated with subsequent cigarette smoking among never smokers, it also hinders subsequent cigarette smoking cessation among current smokers. Given that the utility of e-cigarettes as smoking-cessation aids is the subject of debate, in this chapter, aim 5 is addressed (to assess the effectiveness of pharmacotherapy for smoking-cessation). The WHO has not approved the use of e-cigarettes as a smoking-cessation aid and recommends that countries use effective pharmacotherapies and behavioural therapies to treat smoking dependence instead. Most of the national guidelines also included behavioural and pharmacological quit-smoking support for smoking cessation. Therefore, this umbrella review can provide evidence on the effectiveness of different pharmacotherapy for smoking cessation. The content of this chapter is under review for publication.
Melka, A. S., Chojenta C. .L, Holliday, E. G., Bali A. G. & Loxton D. J. Effectiveness of
pharmacotherapy for smoking cessation: Umbrella review and quality assessment of
systematic reviews.
Melka, A. S., Chojenta, C. L., Holliday, E. G. & Loxton D. J. (2018). Effectiveness of
pharmacotherapy for smoking cessation: Protocol for umbrella review and quality
assessment of systematic reviews. Systematic Reviews, 7, (2018).
http://dx.doi.org/10.1186/s13643-018-0878-3
134
8.1 Background
In 2012, the global estimated prevalence of daily tobacco smoking among men and women aged 15 years and older was 31.1 and 6.2 per cent, respectively (Ng et al., 2014).
Smoking seriously affects almost all organs in the body. Tobacco smoking can lead to many short- and long-term health effects, including lung and other organ cancers, chronic bronchitis, emphysema, stroke and heart attack (National Institute on Drug Abuse, 2017).
Tobacco smoking is responsible for 90 per cent of all cases of lung cancer and 90 per cent of all deaths due to chronic obstructive pulmonary disease (COPD) (US Department of Health and Human Services, 2016). According to the WHO, tobacco smoking kills about six million people globally per annum (WHO, 2015). Second-hand smoke contains hundreds of chemicals that are responsible for diseases such as respiratory disorders, cancer and cardiovascular disease. The combustible chemicals found in tobacco smoke are responsible for disorders such as cancer, cardiovascular and pulmonary diseases, through mechanisms that involve DNA damage, inflammation and oxidative stress (Centers for Disease Control &
Prevention, 2010). Globally, second-hand smoking affects women and children more than men (Health & Services, 2006; Öberg, Jaakkola, Woodward, Peruga & Prüss-Ustün, 2011).
Tobacco-related disability-adjusted life years account for four per cent of the global burden of disease, with the burden significantly higher for developed nations (Rehm, Taylor &
Room, 2006).
Tobacco contains about 4,000 chemicals, of which nicotine is the one responsible for addictive behaviour (Jiloha, 2014). While smoking, the nicotine components of tobacco are absorbed through the respiratory mucous membranes, entering the bloodstream and then the brain. After entering the brain, nicotine stimulates the release of epinephrine and dopamine, which subsequently increases blood pressure, heart rate and respiration rate and produces pleasurable feelings (Jiloha, 2014; US Department of Health and Human Services, 2016c). 135
In the long term, smoking cessation decreases the risk of cancer, stroke and cardiovascular disease, as well as improves life expectancy (Taylor Jr et al., 2002; US
Department of Health and Human Services, 2016a). By improving natural lung function, smoking cessation can also decrease the risk of respiratory infections (e.g., pneumonia, influenza and COPD) (General, 1990). Kahler, Spillane, Metrik, Leventhal and Monti (2009) and Eddy et al. (2009) have shown that smoking cessation is associated with significant reductions in risk of COPD, myocardial infarction, stroke and coronary heart disease. In the long term, smoking cessation is an effective intervention for reversing the course of atherosclerosis.
The range of available smoking-cessation interventions can broadly be categorised as motivational, behavioural/psychological or pharmacological. The WHO recommends that countries prioritise different smoking-cessation strategies, depending on their available resources, national health system and political will to implement the cessation strategies (da
Costa e Silva, 2003). It recommends treating tobacco dependence as one strategy within the organisation’s comprehensive tobacco control policy, along with using measures such as taxation and price policies, advertising restrictions, dissemination of information and establishment of smoke-free public places (da Costa e Silva, 2003). Treating tobacco smoking, like any other forms of substance dependence, necessitates pharmacological interventions to minimise cravings and treat withdrawal symptoms that are associated with dependence (Jiloha, 2014). NRTs in different formulations (e.g., patches, gums and nasal sprays) can be used to treat withdrawal symptoms after smoking cessation. Since the nicotine concentration in NRTs is low compared to tobacco, these therapies have a low addiction rate
(US Department of Health and Human Services, 2016a).
As mentioned previously, amfebutamone is the first non-nicotine drug that has been used to treat nicotine dependence. It is a nicotine receptor antagonist and inhibits the reuptake 136 of epinephrine, dopamine and serotonin, thus reducing withdrawal symptoms (Covey et al.,
2000; Damaj et al., 1999; Roddy, 2004). Varenicline is a nicotine receptor partial agonist that blocks nicotine receptors by binding into α4β2 nicotinic acetylcholine receptors and moderately releasing dopamine, thus reducing the craving and withdrawal symptoms that are associated with an absence of nicotine (Mihalak et al., 2006).
Although most of the previous trials and systematic reviews support the effectiveness of behavioural interventions for smoking cessation (Lancaster & Stead, 2017; Mottillo et al.,
2008), the findings are less consistent for pharmacological interventions. Presently, many trials and systematic reviews have been conducted to assess the effectiveness of smoking- cessation interventions. Therefore, a sound next step in terms of providing evidence to health care decision-makers is to review existing systematic reviews (Aromataris et al., 2015). The umbrella review in this thesis has thus assessed the effectiveness of different pharmacotherapies, as well as the methodological quality of the included reviews.
8.2 Objectives
The umbrella review synthesised the findings of previous reviews that were performed to evaluate the effects of pharmacotherapies for smoking cessation and assessed the consistency of conclusions among previous systematic reviews. It summarised the effects of pharmacological interventions as reported by each review of smoking cessation, specifically addressing the following objectives:
to summarise existing systematic reviews that have assessed the effects of
pharmacological interventions for smoking cessation
to assess the methodological quality of previously conducted systematic reviews.
137
8.3 Methods
8.3.1 Protocol registration and reporting of findings.
The protocol for this review involved following the guidelines of preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) (Shamseer et al.,
2015). The protocol was registered in PROSPERO (registration number CRD42017080906).
The systematic review’s findings were reported in accordance with the recommendation of
Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) (Moher,
Liberati, Tetzlaff, Altman & Group, 2009). The Joanna Briggs Institute Reviewers’ Manual was also used to guide and organise the review processes (The Joanna Briggs Institute, 2014).
8.3.2 Inclusion and exclusion criteria.
Since the primary aim of the umbrella review was to identify the effect of pharmacological interventions on smoking cessation, only reviews that included randomised control trials were reviewed. Since smoking-cessation interventions are mostly targeted at adults aged 15 years and older, this umbrella review included studies of young people and adults aged 15 and older who were smokers (Zwar, Mendelsohn & Richmond, 2014). All systematic reviews that used randomised control trial studies that were designed to assess the effect of pharmacotherapy in any setting were also included in this review. The umbrella review included only reviews for which the full text was available. The outcome variable measured in this study was smoking cessation, and the control or comparison groups were either standard care or placebo, no intervention or alternative pharmacotherapy groups. This review included only reviews that reported on the pooled effects of the included studies. It also only included studies that were published in English.
If the review was an update of a previous review, then the most recent review data were included. Reviews that assessed combined pharmacotherapy and behavioural interventions were excluded unless the review reported the effect of pharmacotherapy 138 separately (in which case, it was included). The summary of inclusion criteria based on population, intervention, comparator and outcome and study design (PICOS) is presented in
Table 8.1.
Table 8.1
PICOS Elements
PICOS Criteria elements
Population The population included young people and adults who were aged 15 years and
older and who were smokers.
Intervention Reviews that assessed only the effect of pharmacotherapy on smoking cessation
were included. Reviews that assessed combined pharmacotherapy and
behavioural interventions were excluded.
Comparator The control may be either standard care or placebo, no intervention or
alternative pharmacotherapy.
Outcome The outcome variable measured in this study was smoking cessation.
Study design The study design included reviews that only used randomised control trials.
8.3.3 Information source and search strategy.
To trace related reviews, databases such as the Cochrane Library, PubMed,
MEDLINE, EMBASE, CINAHL, PsycINFO, Web of Science, Scopus and Google Scholar were used without limits on the publication period. Each database was searched up to 2
September 2019. Additional reviews were sought using the reference lists of the retrieved articles, and additional articles were traced from daily email alerts from the MEDLINE database throughout the review process. The search strategy was developed in consultation with a senior librarian, in which different keywords/search terms were used to access reviews from the database, including ‘smoking cessation’, ‘smoking abstinence’, ‘pharmacotherapy’,
139
‘nicotine replacement therapy (NRT)’, ‘bupropion’, ‘varenicline’, ‘combination therapy’,
‘non-nicotine drug’, ‘nicotine receptor partial agonist’, ‘meta-analysis’ and ‘systematic review’.
8.3.4 Data collection processes.
Studies that did not fulfil the inclusion criteria were first excluded by reading the title and then the abstract of the articles. Full articles were then accessed, and those that did not fit the objectives of the review were excluded. The excluded studies were recorded, along with the reason for exclusion at each stage. The Cochrane data abstraction format was used to extract information from the studies. Two authors (AM, AB) screened the titles and abstracts of all publications that were obtained by the search strategy and assessed the full text of selected articles for inclusion and extracted and checked data independently. Discrepancies were resolved by discussions between the authors. The data extraction form was designed to extract data relating to the objectives of the study, study design, study inclusion and exclusion criteria, number of articles and participants included, participant characteristics, intervention, control, outcome and pooled effect.
8.3.5 Assessment of methodological quality.
The methodological quality of the included reviews was assessed using the
Assessment of Multiple Systematic Reviews 2 tool (an update of the AMSTAR tool), which contains 16 domains (Shea et al., 2017). The tool includes 10 items from the original
AMSTAR tool, while two items were created by splitting a single item from the original
AMSTAR tool (Shea et al., 2007). In total, four domains were added in AMSTAR 2 that were not originally found in it. The response option for most domains consisted of ‘yes’ and
‘no’, while some domains contained the third option, ‘partial yes’. AMSTAR has been demonstrated to have good inter-rater reliability for assessing the quality of systematic reviews. From the 16 AMSTAR tool items, seven were critical domains upon which the 140 quality rating of individual systematic reviews depends. Based on the overall score, the quality of each systematic review was rated as high, moderate, low and critically low. Table
8.2 presents the criteria that were used to rate the quality of systematic reviews. Scores for each item were reported separately for each systematic review. As mentioned previously, the quality assessment was conducted by AM and AB, with any disagreement between the two reviewers being resolved with discussion or the aid of another reviewer (CC). Studies were not excluded based on their quality, but the assessment serves to judge the strength of evidence that was generated by the included studies. The following are items in AMSTAR 2:
1. Did the research questions and inclusion criteria for the review include the
components of PICOS?
2. Did the report of the review contain an explicit statement that the review methods
were established before the review was conducted and did the report justify any
significant deviations from the protocol?
3. Did the review authors explain their selection of the study designs for inclusion in
the review?
4. Did the review authors use a comprehensive literature search strategy?
5. Did the review authors perform study selection in duplicate?
6. Did the review authors perform data extraction in duplicate?
7. Did the review authors provide a list of excluded studies and justify the exclusions?
8. Did the review authors describe the included studies in adequate detail?
9. Did the review authors use a satisfactory technique to assess the risk of bias (RoB)
in individual studies?
10. Did the review authors report on the sources of funding for the studies that were
included in the review?
141
11. If meta-analysis was performed, then did the review authors use appropriate
methods for the statistical combination of results?
12. If meta-analysis was performed, then did the review authors assess the potential
impact of RoB in individual studies in terms of the results of the meta-analysis or
other evidence synthesis?
13. Did the review authors account for RoB in individual studies when
interpreting/discussing the review results?
14. Did the review authors provide a satisfactory explanation for, and discussion of,
any heterogeneity that was observed in the review’s results?
15. If the review authors performed quantitative synthesis, did they undertake an
adequate investigation of publication bias (small study bias) and discuss its likely
effect on the review’s results?
16. Did the review authors report any potential sources of conflict of interest, including
any funding that they received for conducting the review? 142
Table 8.2
AMSTAR 2 Quality Rating Criteria
Quality rating Criteria AMSTAR 2 critical domains
High No or one non-critical Protocol registered before commencement of the
weakness review (item 2)
Moderate More than one non-critical Adequacy of the literature search (item 4)
weakness
Low One critical flaw with or Justification for excluding individual studies
without non-critical weakness (item 7)
Critically low More than one critical flaw
with or without non-critical RoB from individual studies being included in
weakness the review (item 9)
Appropriateness of meta-analytical methods
(item 11)
Consideration of RoB when interpreting the
results of the review (item 13)
Assessment of presence and likely impact of
publication bias (item 15)
8.3.6 Data synthesis.
In this review, a meta-analysis was not conducted because the data from individual studies are likely to be represented more than once across the systematic reviews, which could likely lead to over or underestimation of the true effect size (Aromataris et al., 2015).
The required information was collected using a pretested checklist based on the objectives of the review (Higgins & Green, 2011). A narrative synthesis method was employed to reveal the effects of different pharmacotherapies on smoking cessation. The narrative presentation included the overall effect size as reported by systematic review authors, along with statistical heterogeneity and methodological quality. The evidence was summarised in a table to outline 143 the types of intervention, comparators, outcome measures, number of participants, number of included primary studies and pooled results from each review, heterogeneity and review author’s conclusions.
To calculate the degree of overlap, the corrected covered area (CCA) was calculated by dividing the frequency of repeated occurrence of index publication in other reviews by the product of index publications and reviews, less the number of index publications. The CCA was rated as follows: a CCA of less than five was rated as a slight overlap, 6–10 was considered a moderate overlap, 11–15 a high degree of overlap and greater than 15 a very high degree of overlap (Pieper, Antoine, Mathes, Neugebauer & Eikermann, 2014):
푁−푟 퐶퐴 = , where 푟푐−푟
N is the number of included publications (including double counting), r is the number of rows
(the number of index publications), and c is the number of columns (number of reviews).
8.4 Results
The search identified 218 studies from a range of databases and other sources that used comprehensive and sensitive search terms. After removing duplicates, 156 studies were assessed by reading their titles and abstracts, of which 136 studies were removed because they were not relevant to the review question. Finally, 20 full-text articles were assessed, of which 10 were excluded. Reasons for exclusion included a combined intervention with behavioural therapy (Stanton & Grimshaw, 2013; Stead, Koilpillai, Fanshawe & Lancaster,
2016; Windle et al., 2016), the measured cost-effectiveness of pharmacological smoking- cessation therapies (Aumann, Rozanski, Damm & Graf, 2016), the pooled effect not provided
(Cox, Okuyemi, Choi & Ahluwalia, 2011; King, Pomeranz & Merten, 2016; Schepis & Rao,
2008), reviews not being published in English (Wu et al., 2014), inclusions of non- randomised controlled trials in the review (Cawkwell, Blaum & Sherman, 2015) and the 144 inclusion of participants aged younger than 15 years (Kim et al., 2011). The PRISMA flow diagram is depicted in Figure 8.1.
145
Records identified through Additional records identified database searching through other sources (n = 216) (n =2)
Records after duplicates removed (n = 156) Identification
Records screened Records excluded by title (n = 156) and abstract (n = 136)
Full-text articles Screening assessed for eligibility Full-text articles excluded, with (n = 20) reasons (n = 10): Combined interventions (3) Evaluated cost effectiveness (1) Pooled effect not provided (3) Non-English review (1) Studies included in qualitative
Non-RCT design included synthesis (1) (n = 10) Included age range <15 years (1) Eligibility
Studies included in quantitative synthesis (meta-analysis) (n = 0) Included
Figure 8.1 PRISMA flowchart of the included reviews.
8.4.1 Characteristics of included reviews.
Table 8.3 displays the detailed characteristics of the included systematic reviews. Of the ten included reviews, three assessed the effectiveness of NRT (Etter & Stapleton, 2006;
Lindson & Aveyard, 2011; Moore et al., 2009), two assessed the effects of multiple 146 pharmacotherapies (Apollonio, Philipps & Bero, 2016; Wu, Wilson, Dimoulas & Mills,
2006), one compared combination therapy (NRT and varenicline v. only varenicline) (Chang et al., 2015), one compared opioid antagonists to placebos or an alternative therapy (David et al., 2014), one evaluated the effectiveness of silver acetate products (e.g., gum, lozenge and spray) (Lancaster & Stead, 2012), one compared the combined effect of NRT with different formulations (Shah, Wilken, Winkler & Lin, 2008) and one assessed the effectiveness of cytisine (Hajek, McRobbie, & Myers, 2013). In total, 142 trials were included in nine systematic reviews (mean per review was 14.2, with a range of 2–86). The calculated CCA was 8.3 per cent, which indicated moderate overlaps of primary publication in the included reviews. The included reviews consisted of a total of 63,568 participants (mean per review was 7063.1). However, data from individual studies are likely to be represented more than once across the systematic reviews. Of the included reviews, two were Cochrane reviews.
Seven studies included in their reviews only studies that verified smoking cessation/abstinence using biochemical methods, while three included studies that used both self-reported and biochemical techniques (see Table 8.4). 147
Table 8.3
Characteristics of Included Reviews
Review authors and No. of trials Review Outcome Inclusion criteria Summary findings Author comments
year included/no. of questions/objectives measures/intervention
participants
Apollonio et al. (2016) 35 RCT, (11 assessed To assess whether Smoking No exclusions based Pharmacotherapy Overall, the results
pharmacotherapy)/5796 interventions for abstinence/pharmacotherapy on the language of increased smoking suggest that smoking-
(1808 smoking cessation are of NRT and non-NRT publication or abstinence (RR = 1.88, cessation interventions
pharmacotherapy) related to smoking publication status 95% CI: 1.35, 2.57; 11 that incorporate The study included studies, 1,808 abstinence for people pharmacotherapy adults aged 15 years participants, low- in concurrent should be and older who were quality evidence) treatment for or in incorporated into treated for alcohol When the analysis was recovery from alcohol clinical practice dependence restricted to those dependence studies that evaluated
only NRT, the
treatment effect
remained significant
(RR = 7.74, 95% CI: 148
Review authors and No. of trials Review Outcome Inclusion criteria Summary findings Author comments year included/no. of questions/objectives measures/intervention
participants
3.00, 19.94; 3 studies,
635 participants)
Chang et al. (2015) 3 trials/904 participants To examine the Smoking abstinence Only published RCTs The results Larger RCTs are
effectiveness of rates/Combination therapy with an adult demonstrated a needed to make more
varenicline combined (NRT and varenicline) v. population aged 18 and significant increase in robust conclusions older were included with NRT for smoking varenicline and placebo the smoking
cessation patch abstinence rate during
early measurement in
the combined wing
compared with
varenicline-only
treatment (OR = 1.50,
95% CI: 1.14, 1.97;
three trials) and with
late outcome
measures (OR = 1.62, 149
Review authors and No. of trials Review Outcome Inclusion criteria Summary findings Author comments year included/no. of questions/objectives measures/intervention
participants
95% CI: 1.18, 2.23; two
trials)
David et al. (2014) 8 trials/1,213 To assess the Smoking Adult smokers that Eight trials gave no The findings indicated
participants effectiveness of opioid abstinence/comparing opioid reported data on evidence of a treatment no beneficial effect of
antagonists in helping antagonists to placebo or an abstinence for a effect (RR = 0.97; 95% naltrexone alone nor CI: 0.76, 1.24) long-term smoking alternative therapy for minimum of six as an adjunct to NRT For the four studies cessation smoking cessation months on smoking abstinence that examined
naltrexone versus
placebo as an adjunct
to NRT (n = 768), the
pooled estimate was
RR = 0.95; 95% CI:
0.70, 1.30
Etter et al. (2006) 12/4,792 participants To evaluate whether Smoking cessation at the Studies with a final The pooled effect NRT permanently
(2,408 NRT, 2,384 the outcome of a time of follow-up/Nicotine follow-up of more than provided evidence for affects smoking
control) single treatment replacement therapy one year after the start the efficacy of NRT in cessation
150
Review authors and No. of trials Review Outcome Inclusion criteria Summary findings Author comments year included/no. of questions/objectives measures/intervention
participants
episode with NRT of treatment and only sustaining smoking
enhances smoking study arms of standard cessation beyond 12
cessation over many recommended doses months (OR = 1.99,
years of NRT were included 95% CI: 1.50, 2.64)— fixed effect gives the
same result
Lancaster et al. (2012) 2 trials/976 participants To determine the Sustained abstinence from There were RCTs of The pooled estimate Silver acetate has no
efficacy of silver smoking at 6–12 silver acetate for for the risk ratio for role in promoting
acetate products months/silver acetate smoking cessation quitting was RR = 1.04, smoking cessation
(gum, lozenge, spray) with reports of 95% CI: 0.69; two trials
in helping smoking smoking status at least
cessation six months after the
beginning of
treatment
Lindson et al. (2011) 8 trials/2,813 (1,403 To update the nicotine Short-term abstinence and Only RCT participants There was a very weak The review found a
intervention, 1410 preloading efficacy long-term abstinence at least were cigarette smokers positive effect of weak, non-significant
control) six months after quit attempting to quit, and preloading on short- effect of nicotine only if abstinence was term abstinence (RR = 151
Review authors and No. of trials Review Outcome Inclusion criteria Summary findings Author comments year included/no. of questions/objectives measures/intervention
participants
day/NRT while smoking reported at a six-month 1.05, 95% CI: 0.92, preloading on smoking
before quitting (preloading) follow-up or later 1.19) abstinence
The mean age of study The pooled effect on
participants was 42 long-term abstinence
years was not significant (RR
= 1.16, 95% CI: 0.97,
1.38)
Moore et al. (2009) 7 trials/2,767 To identify the efficacy Six months’ sustained Only RCTs were The proportion of NRT is an effective
participants and safety of NRT for abstinence starting at any eligible smokers achieving intervention for
smoking cessation time before the end of The population sustained abstinence at achieving sustained six months with NRT treatment/gum or inhaler comprised smokers smoking abstinence was double that of the NRT who were unable or placebo group (RR = unwilling to stop 2.06, 95% CI: 1.34, abruptly 3.15; five studies)
The proportion of
smokers achieving
sustained abstinence
152
Review authors and No. of trials Review Outcome Inclusion criteria Summary findings Author comments year included/no. of questions/objectives measures/intervention
participants
at the end of follow-up
was RR = 1.72, 95% CI:
1.31, 2.26; seven
studies
Wu et al. (2006) NRT = 70 trials/28,343 To evaluate the Smoking cessation at one Chemical confirmation Smoking cessation NRT, bupropion and
participants; effectiveness of year of smoking cessation; favoured NRT over varenicline all provide
bupropion = 12 pharmacotherapy for short-term smoking cessation randomised controlled controls at one year therapeutic effects in trials/5,228 participants trials (OR = 1.71, 95% CI: smoking cessation (three months)/any RCT of assisting with smoking varenicline = 4 1.55, 1.88) NRT of any delivery method cessation Smoking cessation trials/2,528 participants (bupropion or varenicline) favoured NRT over
controls at three
months (59 trials, n =
25,294 participants,
OR = 1.98, 95% CI:
1.77–2.21)
Bupropion was
effective for smoking 153
Review authors and No. of trials Review Outcome Inclusion criteria Summary findings Author comments year included/no. of questions/objectives measures/intervention
participants
cessation compared to
control at one year (12
trials/5,228
participants, OR =
1.56, 95% CI: 1.10,
2.2, P = 0.01)
NRT was superior to
bupropion for smoking
cessation at one year
(two trials, n = 548,
OR = 1.14, 95% CI:
0.20, 6.42)
Varenicline was
effective for smoking
cessation compared to
placebo, both in the
long term (one year)
(OR = 2.96, 95% CI: 154
Review authors and No. of trials Review Outcome Inclusion criteria Summary findings Author comments year included/no. of questions/objectives measures/intervention
participants
2.12, 4.12; four trials,
n = 2,528) and short-
term evaluation (OR =
3.75, 95% CI: 2.65,
5.30; four trials, n =
2,528)
Varenicline was more
effective than
bupropion at one year
(OR = 1.58, 95% CI:
1.22, 2.05) and three
months (three trials,
OR = 1.61, 95% CI:
1.16, 2.21)
Shah et al. (2008) 5 trials/2,204 To determine whether Abstinence at three, six and Double-blind Comparing the The author
participants combination therapy 12 months of follow- randomised placebo- combination and recommended that
for smoking cessation up/clinical trials evaluating controlled trial single-agent therapy at future research three months, the rate 155
Review authors and No. of trials Review Outcome Inclusion criteria Summary findings Author comments year included/no. of questions/objectives measures/intervention
participants
with first-line agents is combination therapy using Study duration of one of abstinence was RR considers optimal
more effective than first-line agents (all trials year or more = 1.42, 95% CI: 1.21, therapy combination,
monotherapy include nicotine replacement Sample size ≥ 200 1.67 (four trials) duration of therapy
patches along with one other Using first-line Comparing the and preferred agents smoking-cessation combination and agent) for special populations therapies single-agent therapy at
six months, the rate of
abstinence was RR =
1.54, 95% CI: 1.19,
2.00 (four trials)
Comparing the
combination and
single-agent therapy at
12 months, the rate of
abstinence was RR =
1.58, 95% CI 1.25, 1.99
(four trials) 156
Review authors and No. of trials Review Outcome Inclusion criteria Summary findings Author comments year included/no. of questions/objectives measures/intervention
participants
Hajek et al. (2013) 7 trials/4020 To assess the efficacy Smoking quit rate/cytisine Only RCTs are eligible Cytisine is an effective Cytisine is an effective
participants (2141 of cytisine in smoking therapy treatment for smoking treatment for smoking
intervention, 1,879 cessation cessation compared to cessation
control) placebo ((RR = 1.57, 95% CI: 1.42 to 1.74)
at two to three years
follow-up
Data from two high-
quality studies
revealed that Cytisine
is effective at six
months follow-up (RR
=3.29. 95% CI: 1.84 to
5.90) 157
Table 8.4
Methods of Smoking Cessation Validation, Quality Assessment Tools and Reported Heterogeneity of the Reviews
Authors and Validation of smoking Quality assessment tool and Meta-analysis Reported Reported heterogeneity of the
year cessation of included source model effect size reviews
review
Apollonio et Self-reported tobacco use or Cochrane risk of assessment Fixed effect RR I2 = 64% for overall al. (2016) biochemical validation tool pharmacotherapy
Chang et al. Biochemical verification Jadad score Fixed effect OR I2 = 0% for early outcome measure
(2015) and 54% for late outcome measures
David et al. Self-reported or biochemical Cochrane risk of assessment Fixed effect RR I2 = 0%
(2014) verification tool
Etter et al. Biochemically verified Not stated Random effect OR Q statistics was 18.7 (p = 0.08)—No
(2006) abstinence evidence of heterogeneity
Lancaster et Biochemically verified Cochrane risk of assessment Fixed effect RR I2 = 0.0% al. (2012) abstinence tool
Lindson et al. Biochemically verified Cochrane risk of assessment Fixed effect RR For short-term abstinence I2 of 69%
(2011) abstinence and/or self-report tool and for 158
Authors and Validation of smoking Quality assessment tool and Meta-analysis Reported Reported heterogeneity of the
year cessation of included source model effect size reviews
review
long-term abstinence I2 of 39%
Moore al. Biochemical (exhaled Standard guidelines of NHS Fixed effect RR I2 = 52.4%
(2009) carbon monoxide) Centre for Reviews and
Dissemination
Wu et al. Biochemically verified Cochrane risk of assessment Random effect RR I2 = 20.5 to 71.5%
(2006) tool
Shah et al. Biochemically verified Not stated Random effect OR I2 = 0% to 37%
(2008)
Hajek et al. Self-reported or National Institute for Health and Fixed effect RR I2 = 14 to 76 %
(2013) Biochemically verified Clinical Excellence (NICE)
Check list 159
8.4.2 Methodological quality of included reviews.
The reviews were assessed for methodological quality using the AMSTAR 2 quality appraisal tool for systematic reviews. Table 8.5 displays the score of each item for specific systematic reviews. Based on the AMSTAR 2 rating, one review scored a high quality, two scored a moderate quality, four scored a low quality, and three scored a critically low quality.
Among the 16 AMSTAR 2 domains that were assessed, all the other reviews failed to develop or report the presence of written protocol (item 2), except for two reviews. Of the 10 reviews included, only one review reported on the sources of funding for the studies included in the review (item 10). Conversely, all reviews satisfied items that related to the selection of the study designs for inclusion in the review (item 3) and reported potential sources of conflict of interest and funding (item 16). 160
Table 8.5
Systematic Review Quality (N = 10)
AMSTAR 2 Apollonio et al. Chang et al. David et al. Etter. et al. Lancaster et al. Lindson et al. Moore et al. Wu et al. Shah et al. Hajek et al.
itemsa (2016) (2015) (2014) (2006) (2012) (2011) (2009) (2006) (2008) (2013)
Q1 Y Y Y Y Y Y Y N Y Y
Q2* PY N N N PY N N N N N
Q3 Y Y Y Y Y Y Y Y Y Y
Q4* Y Y N N Y N PY N N PY
Q5 Y Y Y N Y N Y Y N Y
Q6 Y Y Y Y Y N Y Y N Y
Q7* Y N Y Y Y N Y N Y N
Q8 Y Y PY PY PY PY PY N N PY
Q9* Y Y PY N PY PY Y PY N PY
Q10 Y N N N N N N N N N
Q11* Y Y Y Y Y N Y Y Y Y
Q12 Y Y N N N N Y Y N Y
Q13* Y N Y N N Y Y N N Y
Q14 Y Y Y Y Y Y Y Y N N
Q15* Y Y N Y N Y Y Y N N
Q16 Y Y Y Y Y Y Y Y Y Y
Quality of review High Low Low Low Low Critically Low Moderate Critically Low Critically Low Moderate
Items: Q1) Did the research questions and inclusion criteria for the review include the components of PICO? Q2) Did the report of the review contain an explicit statement that the review methods were established before the review was conducted and did the report justify any significant deviations from the protocol? Q3) Did the review authors explain their selection of the study designs for inclusion in the review? Q4) Did the review authors use a 161 comprehensive literature search strategy? Q5) Did the review authors perform a study selection in duplicate? Q6) Did the review authors perform data extraction in duplicate? Q7) Did the review authors provide a list of excluded studies and justify the exclusions? Q8) Did the review authors describe the included studies in adequate detail? Q9) Did the review authors use a satisfactory technique for assessing the RoB in individual studies? Q10) Did the review authors report on the sources of funding for the studies that were included in the review? Q11) If meta-analysis was performed, did the review authors use appropriate methods for statistical combination of results? Q12) If meta-analysis was performed, did the review authors assess the potential influence of RoB in individual studies in terms of the results of the meta-analysis or other evidence synthesis? Q13) Did the review authors account for RoB in individual studies when interpreting/discussing the results of the review? Q14) Did the review authors provide a satisfactory explanation for, and discussion of, any heterogeneity that was observed in the results of the review? Q15) If they performed quantitative synthesis, did the review authors undertake an adequate investigation of publication bias (small study bias) and discuss its likely effect on the results of the review? Q16) Did the review authors report any potential sources of conflict of interest, including any funding that they received for conducting the review? * Critical domains. AMSTAR, assessing the methodological quality of systematic reviews; Y, yes; N, no; PY, partial yes. 162
8.4.3 The effectiveness of pharmacological interventions.
8.4.3.1 Nicotine replacement therapy.
In one review—which included 11 randomised controlled trials involving 1,808 study participants—researchers found that pharmacotherapy significantly increased the smoking cessation rate, as compared to the placebo group (RR = 1.88, 95% CI: 1.35, 2.57) at six weeks to 18 months follow-up. Similarly, the pooled effect from a sub-analysis of three trials using only NRT indicated a significant positive effect on smoking-cessation rate (RR = 7.74,
95% CI: 3.00, 19.94; three studies, 635 participants) (Apollonio et al., 2016). In the quality appraisal, the review was scored as having ‘high’ methodological quality.
In a review of seven studies (with a ‘moderate’ methodological quality review),
Moore et al. (2009) found that NRT increased smoking cessation for at least six months when compared with the placebo (RR = 2.06, 95% CI: 1.34, 3.15; five studies). Another study that assessed the pooled effect from 12 randomised controlled trials (with a ‘fair’ methodological quality review) supported the favourable effect that NRT has on sustaining smoking cessation beyond 12 months, as compared to a placebo (OR = 1.99, 95% CI: 1.50, 2.64) (Etter &
Stapleton, 2006). Further, the pooled effect from a review that included 70 trials (n = 28,343) found that the odds of smoking cessation at one year were higher among participants using
NRT compared to the control group (OR = 1.71, 95%, CI: 1.55, 1.88) (Wu et al., 2006). In this review, the finding was consistent across all NRT formulations (e.g., gum and patch).
Additionally, the pooled effect of 59 trials (n = 25,294) demonstrated that NRT supported the efficacy of smoking cessation in the short-term follow-up (three months) compared to the control group (OR = 1.98, 95% CI: 1.77, 2.21) (Wu et al., 2006). In the quality appraisal, this review scored a ‘critically low’ methodological quality. Conversely, a review by Lindson and
Aveyard (2011) (receiving a ‘critically low’ methodological quality review) included eight studies and 2,813 participants and found NRT to have no significant effects over the placebo 163 group for the treatment of smoking cessation in the short-term follow-up (four to 12 weeks)
(RR = 1.05, 95% CI: 0.92, 1.19) and long-term follow-up (six to 12 months) (RR = 1.16,
95% CI: 0.97, 1.38).
8.4.3.2 Varenicline and bupropion.
The pooled effect of 12 trials that included 5,228 participants revealed that bupropion was more effective for smoking cessation compared to the control group at the one-year follow-up (OR = 1.56, 95% CI: 1.10, 2.21). Moreover, bupropion was also more effective than the placebo at the three-month follow-up (OR = 2.13, 95% CI: 1.72, 2.64; 11 trials) (Wu et al., 2006). The pooled effect of four studies (n = 2,528) revealed that varenicline was effective for smoking cessation compared to the placebo both at the long-term follow-up (one year) (OR = 2.96, 95% CI: 2.12, 4.12) and short-term follow-up evaluations (three months)
(OR = 3.75, 95% CI: 2.65, 5.30). Similarly, varenicline was more effective than bupropion at the one-year follow-up (OR = 1.58, 95% CI: 1.22, 2.05; three trials) and three-month follow- up (OR = 1.61, 95% CI: 1.16, 2.21; three trials) (Wu et al., 2006).
8.4.3.3 Combination therapy.
Chang et al. (2015) (receiving a ‘low’ methodological quality review) found that participants on a combined regimen (NRT and non-NRT) were more likely to abstain from smoking compared with those in a non-NRT (varenicline) only treatment group, both in the short-term (measured at four to 12 months before treatment completion; OR = 1.50, 95% CI:
1.14, 1.97; three trials) and long-term (measured at the end of two to 24 months after treatment completion; OR = 1.62, 95% CI: 1.18, 2.23; two trials). Combining naltrexone and
NRT did not favour smoking cessation compared to the placebo group, based on the six- month reported abstinence rate (RR = 0.95, 95% CI: 0.70, 1.30; four studies) (David et al.,
2014).
164
In a ‘critically low’ methodological quality review that included 2,204 study participants from five trials, a combination therapy of nicotine replacement patches with other nicotine formulation drugs (nicotine gum, inhaler or nasal spray) was found to be more effective than monotherapy at the three-month (RR = 1.42, 95% CI: 1.21, 1.67; four trials), six-month (RR = 1.54, 95% CI: 1.19, 2.00; four trials) and 12-month follow-up (RR = 1.58,
95% CI: 1.25, 1.99; four trials) (Shah et al., 2008).
8.4.3.4 Other therapies.
The pooled effect of a study that included eight randomised control trials with 1,213 participants identified that opioid antagonist therapy had no effect on smoking-cessation rates, based on the six-month reported abstinence rate (RR = 0.97; 95% CI: 0.76, 1.24). Five studies that assessed the effect of naltrexone (a long-acting form of opioid antagonist) also demonstrated that there was no significant effect on the smoking abstinence rate (RR = 1.00;
95% CI: 0.66, 1.51) (David et al., 2014). In the quality appraisal, this review scored a ‘low’ methodological quality. Silver nitrate was not effective for smoking cessation compared to the placebo at a minimum of six months follow-up (RR = 1.04, 95% CI: 0.69, 1.57; two trials) (Lancaster & Stead, 2012). This review was ranked as having a ‘low’ methodological quality. Another review that included seven trials (4,020 participants) found that cytisine was effective for smoking cessation at three weeks to two years of follow-up (RR = 1.57, 95% CI:
1.42 to 1.74), as compared to the placebo group. The pooled effect of two high-quality reviews also confirmed the effectiveness of cytisine for smoking cessation at six months of follow-up compared to the placebo (RR = 3.29. 95% CI: 1.84 to 5.90) (Hajek et al., 2013).
This review was ranked as having a ‘moderate’ methodological quality.
8.5 Discussion
This umbrella review aimed to assess the effect of different pharmacotherapies on smoking cessation. Most of the included reviews found supportive evidence for NRT being 165 an efficacious treatment for withdrawal symptoms that are associated with nicotine dependence. NRT in different formulations is used as a first-line drug for the treatment of nicotine addiction in many settings (McDonough, 2015). Non-nicotine pharmacotherapy such as bupropion and varenicline was also found to be effective for smoking cessation. Reviews also revealed that the combination of NRT and varenicline was more effective for smoking cessation compared with varenicline alone. Moreover, a combination of different formulations of NRT (e.g., gum and nasal spray) with nicotine patches was found to be more effective than nicotine patch monotherapy. The evidence suggests that using other formulations of NRT in combination with nicotine patches helps supplement blood nicotine concentrations at times of craving or risk of smoking relapse (McDonough, 2015). NRT, bupropion and varenicline are drugs approved by the US FDA and other countries for the treatment of smoking cessation (Jaén et al., 2008). Some randomised trial studies reported the superiority of cytisine for smoking cessation, as compared to NRT (Walker et al., 2014).
Cytisine is the cheapest drug treatment compared to other smoking cessation drugs such as
NRT, bupropion and varenicline (Leaviss et al., 2014).
The severity of nicotine dependence is one factor that can affect the effectiveness of pharmacotherapies for smoking cessation. Some researchers found that the rate of smoking cessation was lower among participants who smoked a greater number of cigarettes per day compared to those who smoked fewer cigarettes per day (Argüder et al., 2013; Jiloha, 2014).
The level of treatment compliance is also an important factor for attaining and sustaining smoking abstinence (da Costa e Silva, 2003; Kahler et al., 2009).
Findings from previous systematic reviews and reviews of reviews have demonstrated the effectiveness of behavioural interventions for smoking cessation (Lancaster & Stead,
2017; Mottillo et al., 2008). Moreover, other studies have identified the effectiveness of combined pharmacological and behavioural interventions for smoking cessation (Stead,
166
Koilpillai, Fanshawe & Lancaster, 2016). In a systematic review of the reviews, researchers found that compared to NRT alone, a combination of non-pharmacological interventions such as brief counselling and pharmacotherapy has been more effective for smoking cessation
(Schmelzle, Rosser & Birtwhistle, 2008). Moreover, behavioural interventions have been recommended for preventing relapse and for sustaining the smoking cessation that was achieved by pharmacotherapy (Rigotti et al., 2014). Therefore, combining counselling and pharmacotherapy could be more efficacious for smoking cessation. A Cochrane systematic review with moderate-quality evidence that included 50 primary randomised controlled trials found that e-cigarette use helped with smoking cessation (Hartmann-Boyce et al., 2020).
However, the findings regarding the effectiveness of e-cigarette for smoking cessation were inconsistent.
Overall, the quality of the systematic reviews that were included in the umbrella review was rated as ‘critically low’ to ‘high’. Most of the included reviews scored a ‘low’ and ‘critically low’ methodological quality. All the reviews that were included in the umbrella review were published before the development of the updated AMSTAR 2.
Therefore, the authors of the reviews could not follow the AMSTAR 2 checklist in conducting the reviews. Future reviews should follow the AMSTAR 2 checklist and guidelines to produce quality evidence that informs policy. Most of the reviews addressed item 11 (appropriateness of meta-analytical methods). In the previous systematic review of reviews, these items were also well addressed by review authors (Kowalczuk, Adamich,
Simunovic, Farrokhyar & Ayeni, 2015; Pussegoda et al., 2017; Sorgente et al., 2017). The duplicate selection of articles to be included in a systematic review can decrease the chances of missing articles. In the current review, item 5 (‘duplicate study selection’) and item 6
(‘duplicate data extraction’) were well addressed by most of the included reviews. 167
Formulating a review protocol is an important step before conducting a systematic review. It can determine whether the review was conducted according to plan and, if not, justify any reasons for amending the plan (Schlosser, 2007). In almost all of the included reviews, no statement was made under the first criteria regarding the protocol registration and/or publication (‘Was an “a priori” design provided?’). This finding was consistent with a previous review that undertook a quality assessment of systematic reviews in paediatric surgery (Cullis, Gudlaugsdottir & Andrews, 2017). Clinicians and decision-makers must assure themselves that the basic approaches and methods that are used to collect and combine the findings of individual studies are relevant and sound before using the evidence for patient care and policy development. The observed quality improvement in the reviews that were published after the development of AMSTAR demonstrated the importance of encouraging review authors to adhere to guidelines that advance excellence in conducting systematic reviews. Improving the methodological quality of systematic reviews is fundamental for precisely informing clinical decision-making (Yao, Vella & Brouwers, 2018).
There are several limitations to this review that should be acknowledged. First, the timing of the outcome measure was not consistent across the included reviews. Some of the studies measured short-term effects or long-term effects, while others measured both. Since the data were not retrieved from the primary studies, the umbrella review was restricted by the evidence reported by the review authors with respect to aspects relating to the explanation of the intervention, method, outcomes and conclusions. Despite a duplicate study selection, subjectivity in data extraction and quality appraisal is not avoidable. Another limitation of this review was the review’s restriction to English-language articles. Despite these limitations, this umbrella review considered only systematic reviews that included primary studies with a randomised controlled trial design. Article selection, data abstraction and quality appraisal were also conducted in duplicate, which minimising selection bias.
168
8.6 Conclusions
In this review, NRT, bupropion and varenicline were found to be effective for smoking cessation. Similarly, the combination of a nicotine patch with other nicotine formulations and a combination of nicotine with non-nicotine pharmacotherapy were found to be effective for smoking cessation when compared to nicotine monotherapy. However, silver nitrate was not found to be effective for smoking cessation. The quality of the reviews that were included in this umbrella review ranged from high to critically low. This study recommends that review authors adopt and follow the AMSTAR tool to improve the methodological and reporting quality of systematic reviews. The findings of the current review will improve clinical decision-making and can be used as a baseline for future studies. 169
Chapter 9: General Discussion
9.1 Introduction
This thesis examines the factors that are associated with e-cigarette use and the role that e-cigarettes play in smoking initiation and cessation. An umbrella review was also conducted to identify and summarise the evidence regarding the effectiveness of pharmacotherapy for smoking cessation. Results for the five main aims were presented in consecutive chapters (Chapters 4–8). Chapters 4–8 used data from the new young ALSWH cohort who were born between 1989 and 1995. Chapter 4 identified the risk and protective factors for e-cigarette use among Australian women. Chapter 5 examined the association between childhood adversities and ever and past year e-cigarette use among Australian women. Chapters 6 and 7 respectively evaluated the role of e-cigarettes in subsequent smoking initiation and smoking cessation. Chapter 8 used an umbrella review to assess the effectiveness of pharmacotherapy.
This thesis’s main findings are summarised as follows:
1. The prevalence of ever and past year e-cigarette use among young Australian
women was 11.1 and 6.4 per cent, respectively. The use of e-cigarettes in the past
year was associated with younger age financial difficulties and with being an ex-
smoker or current cigarette smoker who drinks at a level of lifetime risk of harm
from alcohol-related disease or injury. Ever e-cigarette use revealed similar
associations and was also linked to rural residence and IPV.
2. Participants who reported past year e-cigarette use were more likely to report
childhood psychological abuse, physical abuse or sexual abuse. All abuse types that
were associated with past year e-cigarette use were also associated with ever e-
cigarette use. Ever e-cigarette use was in turn associated with household substance 170
abuse, witnessing domestic violence or having a mentally ill household member
(when compared with people who did not experience these factors). A positive
dose–response relationship was observed between the number of ACEs and the
odds of e-cigarette use.
3. Ever e-cigarette use at baseline was positively associated with smoking initiation at
follow-up. A history of depression, binge drinking and a higher childhood adversity
score were also risk factors for subsequent smoking initiation in the follow-up.
4. Smoking cessation was more common in participants who had never used e-
cigarettes, as compared to participants who had used e-cigarettes (29.4% v. 21.8%).
Baseline current smokers who ever used e-cigarettes were 68 per cent less likely to
have quit smoking by the follow-up survey compared to baseline current smokers
who never used e-cigarettes.
5. Most of the reviews that were included in the umbrella review reported that NRT,
bupropion and varenicline were effective for smoking cessation. The combination
of a nicotine patch with other nicotine formulations was also more effective than
monotherapy. Similarly, the combination of nicotine with varenicline was also
found to be more effective than varenicline alone.
This current chapter discusses the overall findings of the study, along with its contribution to policy and future research. Finally, the conclusion section summarises the findings and implications of the study and then offers recommendations for future research.
9.2 Factors Associated with E-Cigarette Use
The current study revealed that more than one in 10 Australian women aged 19–26 years had used e-cigarettes, of whom about a quarter were never smokers. Compared to overseas studies, the prevalence of ever e-cigarette use in this study was lower (Oakly et al.,
2019; Reid et al., 2019). The difference in the prevalence rates could be due to the relatively 171 strict regulation of nicotine-containing e-cigarettes in Australia. Moreover, although previous studies were not gender specific in terms of estimating the prevalence of e-cigarette in the current study, the calculated prevalence here was specifically for women. Researchers have found that e-cigarette use was more prevalent among males than females (Jongenelis,
Brennan et al., 2019; Kong et al., 2017). Social norms, economic factors and biological factors have contributed to the differences in the prevalence of smoking and e-cigarette use among males and females (US Department of Health Human Services, 2012; Zhang et al.,
2010).
If public health agendas are to control the uptake of e-cigarette by young people, then understanding the relevant risk and protective factors is crucial. In agreement with prior studies, the current study has identified that young age is positively associated with e- cigarette use (Australian Institute of Health and Welfare, 2017; Cullen et al., 2018; Reid et al., 2019; Twyman et al., 2018). The high prevalence of e-cigarette use among young people may be partly explained by the low price of the products in comparison to the price of traditional cigarettes (Cantrell et al., 2019), as well as by the youth-appealing flavours that are found in e-liquids (Jancey et al., 2015; Patel et al., 2016) and the low health risk perceptions (Dunlop et al., 2016). Youth-focused marketing tactics that tobacco industries use
(e.g., advertisements on social media and online marketing) are among the main factors that contribute to the high prevalence of vaping among children and young people (Pepper et al.,
2014; Pokhrel et al., 2018).
In the current study, smokers and ex-smokers were more likely to use e-cigarettes compared to non-smokers, which aligns with the findings of previous studies (Jongenelis,
Brennan et al., 2019; Lee et al., 2016). According to the 2016 National Drug Strategy
Household Survey, the prevalence of current e-cigarette use was higher among smokers
(4.4%) than among never smokers (0.6%) (Australian Institute of Health and Welfare, 2017).
172
The high prevalence of e-cigarette use among smokers could be due to this group using e- cigarettes as a smoking-cessation aid. Moreover, since smokers are already experienced with nicotine through cigarette smoking, they are more prone to experimenting with alternative nicotine delivery systems, such as e-cigarettes. Although the prevalence of e-cigarette use is higher among smokers in the current study, a significant proportion of never smokers (25%) also reported the ever use of e-cigarettes.
Previous studies have found that alcohol consumption was positively associated with both cigarette smoking and e-cigarette use (Geidne et al., 2016; Jiang, Lee & Ling, 2014).
Similarly, in this thesis, drinking more than two standard drinks on any day was positively associated with e-cigarette use. Alcohol and nicotine have a synergetic effect that stimulates neurotransmitters that in turn, alleviate mental conditions such as depression and anxiety by producing pleasurable feelings (Little, 2000). Both alcohol and nicotine bind to receptors in the brain’s mesolimbic dopamine system, which is responsible for the transportation of dopamine in the brain (Funk, Marinelli & Lê, 2006). Dopamine is responsible for stimulating the pleasure and reward centre in the brain. This finding reveals that integrating the prevention of alcohol use by young people into nicotine prevention strategies is crucial.
Supply reduction can be also achieved by restricting tobacco/e-cigs from being sold in alcohol licensed venues. While enforcing the restriction of importation, sale and supply of e- liquid containing nicotine, monitoring of the contents of e-liquid in retailers is also required to prevent the sale of nicotine containing e-liquid.
Prior studies have found a strong positive association between IPV and cigarette smoking among women (Crane et al., 2013; Jun, Rich-Edwards, Boynton-Jarrett & Wright,
2008). As was similarly found in the current study, IPV was a strong predictor of ever e- cigarette use. IPV is an established risk factor for mental illnesses such as depression, anxiety and post‐traumatic stress disorder (Park, Park, Jun & Kim, 2017). Therefore, to cope with 173 mental illnesses that are associated with IPV, women could be more prone to substance use, including nicotine.
9.3 Adverse Childhood Experiences and E-Cigarette Use
Apart from personal, social and demographic factors, childhood adversity also affects the future of an individual’s health and behaviour. For this reason, this thesis also examined the association between ACEs and e-cigarette use. Although no previous studies have investigated the relationship between ACEs and e-cigarette use, researchers have found that physical, emotional and sexual abuse are strong predictors of smoking behaviours (Felitti et al., 1998; Ford et al., 2011; Fuller-Thomson et al., 2013). Similarly, this thesis found that the three childhood abuse dimensions (psychological, physical and sexual abuse) were positively associated with both past year and ever e-cigarette use, after controlling for sociodemographic factors, smoking status, parental education and parental financial hardships during childhood.
Childhood adversity is a known risk factor for substance abuse, including tobacco smoking, alcohol and illicit drugs (Campbell et al., 2016; Felitti et al., 1998). Prior studies have identified a strong dose–response association between the number of childhood adversities and smoking behaviour in later life (Felitti et al., 1998; Ford et al., 2011), For example, in the historical ACE study conducted by Felitti et al. (1998), a strong statistical association was found between higher numbers of childhood adversities and smoking behaviour. This thesis has also found a positive dose–response relationship between childhood adversity and e-cigarette use. Individuals who were exposed to ACEs used a psychoactive substance such as nicotine to cope with the anxiety and depression caused by the traumatic effects of childhood adversities (Felitti et al., 1998). Researchers have also found an association between mental conditions such as depression and anxiety disorder and nicotine use (Bandiera, Loukas, Li, Wilkinson & Perry, 2017; Obisesan et al., 2019).
174
Individuals with mental illnesses have reportedly used addictive substances, such as nicotine, to cope with the unpleasant feelings that arise with depressive symptoms through the stimulation of neurotransmission (Benowitz, 2009).
In another article related to the same study population, the researcher noted a strong dose–response association between ACE scores and substance use. Further, the same study also found a positive dose–response association between e-cigarette use and physical health conditions such as diabetes mellitus and cardiovascular disease. This article is annexed for reference in Appendix T. Felitti et al. (1998) also identified a positive, graded association between ACE scores and a range of substance use behaviours and physical health conditions.
These findings highlight the importance of designing strategies that prevent ACEs, as well as the importance of counselling services for those who have been exposed to ACEs so that never smokers can be protected from nicotine addiction. Some of the strategies and interventions that experts have proposed to prevent ACEs include developing positive parental nurturing skills, expanding access to quality early childcare services and providing quality counselling services for those exposed to childhood adversities (Oral et al., 2016).
Overall, the current study found that e-cigarette use by this study population shares common risk and protective factors with traditional cigarette smoking. Targeting high-risk groups such as young people, alcoholics, those with a history of IPV and those who are exposed to childhood adversities is required to prevent nicotine addiction via e-cigarettes.
Family practitioners and clinicians should adopt a practical guidance role in educating parents, childcare providers, policymakers and the public about the devastating consequences of childhood adversities. Moreover, strategies should be established to help people with a history of childhood adversities adopt positive coping mechanisms rather than health-harming behaviours such as nicotine addiction. 175
9.4 E-Cigarette Use and Smoking Initiation and Cessation
In addition to investigating the risk and protective factors of e-cigarette use, investigating the role of e-cigarette in smoking initiation and smoking cessation will help people understand the net effect of e-cigarettes on smoking behaviours (US Department of
Health and Human Services, 2016c). Consistent with previous overseas studies, this thesis found a positive association between e-cigarette use and the subsequent initiation of traditional cigarette smoking (Aleyan et al., 2018; East et al., 2018; Primack et al., 2017). In a cross-sectional study conducted in Australia among 519 never smokers aged 18–25 years, researchers identified a positive association between ever e-cigarette use and curiosity about tobacco smoking, willingness to smoke and intentions to smoke (Jongenelis, Jardine et al.,
2019). In a study conducted in New South Wales, researchers found that novelty and flavourings were the main reasons for never smokers to use e-cigarettes (Twyman et al.,
2018).
Many national organisations and professional associations, including the WHO, have recommended strong e-cigarette policy regulations to prevent non-smokers and young people from nicotine addiction via e-cigarettes and from renormalising smoking behaviours in society (WHO, 2016; Zorbas, Buchanan, Gannon, Johns & Aranda, 2018). The National
Academies of Sciences, Engineering and Medicine concluded that e-cigarette use among young people who have never smoked is a gateway for subsequent cigarette smoking
(National Academies of Sciences & Medicine, 2018). Contrary to this, PHE believes that e- cigarette experimentation draws very few young people who have never smoked into regular traditional cigarette smoking (McNeil et al., 2015).
This thesis’s findings emphasise the importance of enforcing the existing regulatory policy in Australia to prevent non-smokers and young people from nicotine addiction via e- cigarette use. The sale of nicotine-containing e-liquids is prohibited in all Australian states 176
(Morgan et al., 2019). Still, a recent study revealed that six out of 10 ‘nicotine-free e-liquids’ in Australia contain nicotine (Chivers et al., 2019). Therefore, while enforcing the existing restrictions for the sale and supply of e-liquids containing nicotine, monitoring the contents of e-liquid in retailers is also required to prevent the sale of nicotine-containing e-liquid. Since the online market is one of the main sources of e-cigarette products for young people in
Australia, the regulation of online e-cigarette sales is equally important. Both overseas and
Australian studies have reported that both smokers and non-smokers were supportive of restrictive e-cigarette policy regulations (Fraser et al., 2015; Sanders-Jackson, Tan, Bigman,
Mello & Niederdeppe, 2016). Drawing these arguments together, it can be claimed that strengthening and enforcing the regulation of e-cigarettes in Australia is vital for protecting young people and non-smokers from nicotine addiction via e-cigarette use.
Investigating the role of e-cigarettes both for smoking initiation and smoking cessation enables an understanding of their net effect on smoking behaviours in this study population. This current study has revealed that e-cigarette use hinders smoking cessation.
Current smokers who reported ever use of e-cigarettes at baseline were less likely to quit smoking at follow-up compared to never e-cigarette users. This finding was consistent with similar studies reported from overseas (Kulik et al., 2018; Shi et al., 2016). Conversely, a study reported from China identified that e-cigarette use increases the rate of smoking cessation (Wang et al., 2018). The differences in the findings could be partly explained by the differences in sample size, ages of participants, gender, cultural backgrounds, availability of e-cigarette products and policy regulations between countries.
E-cigarette products are advocated by the tobacco industry as risk reduction and cigarette smoking-cessation aids (WHO, 2019b). However, the WHO emphasised that the introduction of alternative nicotine delivery methods such as e-cigarettes is a tactic used by e- cigarette companies to sustain nicotine addiction (Grana, Benowitz & Glantz, 2013). In a
177 cross-sectional study conducted in New South Wales among study participants aged 18 years and older (n = 2966), people aged 18–29 years were less likely to report using e-cigarettes to quit smoking compared to people aged 29–54 years (18% v. 42%). In this same study, people aged 18–29 years were more likely to report reduced health risks and flavouring as reasons for using e-cigarettes (Dunlop et al., 2016).
The topic of e-cigarettes has not been addressed in the previous NDS. The three pillars of the NDSs that are used to minimise the effects of tobacco smoking—namely supply reduction, demand reduction and harm reduction—are required to reduce the social, physical, psychological and economic influence of e-cigarettes. Supply reduction entails the enforcement of available e-cigarette restriction to minimise product availability. This could also include measures to reduce the online internet marketing of e-cigarette products.
Demand reduction focuses on raising community awareness about the effects of e-cigarettes on subsequent smoking initiation and smoking cessation. In designing these policy goals, policymakers should consider the risk factors that predispose people to use e-cigarettes.
Findings from previous systematic reviews and overviews of reviews have demonstrated the effectiveness of behavioural interventions for smoking cessation (Lancaster
& Stead, 2017; Mottillo et al., 2008). Similarly, there are also primary studies and systematic reviews that have investigated the effectiveness of pharmacotherapy for smoking cessation.
In this thesis, the researcher conducted an umbrella review to examine the effectiveness of different pharmacotherapies for smoking cessation. The WHO has not approved the use of e- cigarettes as a smoking-cessation aid and recommends that countries use effective pharmacotherapies and behavioural therapies to treat smoking dependence instead (WHO,
2016). Most of the national guidelines also included behavioural and pharmacological quit- smoking support for smoking cessation (Verbiest et al., 2017). Smoking-cessation guidelines in Australia have supported behavioural and pharmacotherapies for smoking-cessation 178 interventions (Zwar et al., 2011). This thesis also found that although e-cigarette use hinders smoking cessation, most of the drugs that are used to treat nicotine dependence are effective for smoking cessation.
Researchers have found that new brands of e-cigarettes (e.g., JUUL) can deliver a substantially higher amount of nicotine to the blood compared to the older generation of e- cigarettes and combustible cigarettes (Rao et al., 2020). Further, unlike traditional cigarettes and NRT, the nicotine content of e-cigarettes can be controlled by users (Grana, Benowitz &
Glantz, 2013). Researchers believe that the nicotine salt found in JUUL masks the bitter taste of nicotine and encourages users to consume a higher amount. However, NRT is less addictive, as well as designed to taper off nicotine dependence by preventing nicotine withdrawal symptoms (Flowers, 2016).
The findings of this thesis will help policymakers enforce and refine existing policies that govern e-cigarette use in Australia. Further, the findings will also help counsellors, clinicians and others working on the prevention of smoking and nicotine addiction provide evidence-based information for their clients.
9.5 Limitations and Strengths of the Study
The findings of this thesis’s study should be interpreted considering the following limitations. First, although the analysis to identify the role of e-cigarettes in smoking initiation and smoking cessation was adjusted for numerous confounders, some potential confounders were not measured and could not be adjusted in the statistical models. Examples include variables that are associated with both e-cigarette use and tobacco smoking in the literature (e.g., parental and peer smoking behaviours and personality traits). Second, the use of open recruitment methods may have resulted in selection bias, which in turn could have affected the generalisability of the study. However, the sample was found to be comparable to census data for this age group in terms of demographics, with some over-representation of
179 women with tertiary education and some under-representation of women from non–English speaking backgrounds (Loxton et al., 2019; Mishra et al., 2014). Third, in the investigation of the role that e-cigarette use plays in smoking initiation and cessation, participants who were lost to follow-up differed from those who participated in the follow-up survey in terms of baseline e-cigarette use. Therefore, attrition may have biased the estimates. Nevertheless, the results were consistent in the sensitivity analysis that considered participants lost to follow-up in the analysis.
Despite these limitations, the study has its strengths. Since the current study used longitudinal data to determine the association between e-cigarette use and subsequent cessation of cigarette smoking, it can better demonstrate the temporal relationship that exists between exposure and effect. Moreover, the study’s hypotheses were tested using a large, national dataset. The simultaneous examination of the role of e-cigarettes in smoking initiation and cessation in the current study helps people understand the net effect of e- cigarettes on smoking behaviours.
9.6 Conclusions and Future Directions
This thesis’s study relates to e-cigarettes—specifically to the factors that are associated with e-cigarette use and the role that e-cigarettes play in smoking initiation and cessation. Further, an umbrella review was conducted to assess the effectiveness of pharmacotherapy for smoking cessation. This study is unique for three reasons. First, to the researcher’s knowledge, no previous studies have examined the association between ACEs and e-cigarette use. Second, this study examined both the association of e-cigarettes and subsequent smoking initiation and smoking cessation to understand the net effect of e- cigarettes on cigarette smoking. Third, this study solely included female participants to answer the research questions. Young age, smoking status, alcohol use, IPV and ACEs
(traumatic childhood experiences) were among the factors that were positively associated 180 with e-cigarette use among this study population. This thesis identified that although ever e- cigarette use is linked with subsequent cigarette smoking among never smokers, it also hinders the subsequent cigarette smoking cessation among current smokers. Conversely, the umbrella review found that most of the nicotine and non-nicotine drugs (e.g., those in NRT, bupropion and varenicline) are effective for treating smoking cessation. Great efforts are required to prevent young people and non-smokers from developing nicotine addiction through e-cigarette use. The findings of this study, along with those of other previous studies, will help policymakers design or strengthen existing e-cigarette regulatory policies.
It can be concluded that research that investigates the effects of regular e-cigarette use on subsequent smoking cessation is required. Future research should compare the role of nicotine-containing and non–nicotine containing e-cigarettes for smoking initiation and smoking cessation. Future studies with randomised control trials that investigate the association between e-cigarette use and smoking initiation and cessation are required to strengthen the evidence on this issue. Research investigating why e-cigarette users are unsuccessful at quitting traditional cigarettes is also essential for strengthening available evidence, as well as research that examines the role that the regular use of e-cigarettes plays in smoking cessation.
The next and final chapter presents the policy brief that was created based on the current study’s findings.
181
Chapter 10: Policy Brief
Protecting non-smokers and young people from nicotine addiction via vaping: Translating
evidence from the Australian Longitudinal Study on Women’s Health to inform policy.
Executive Summary
The international community is divided regarding the issue of e-cigarette use. Some
researchers have recommended that e-cigarettes can be used as smoking cessation
and risk reduction aids. Contrary to this, others have recommended that a strong
policy regulation for e-cigarette use is needed to prevent non-smokers and young
people from developing nicotine addiction through e-cigarettes, as well as from
renormalising smoking behaviours in society. This policy brief aims to highlight the
findings of a series of studies that were conducted on e-cigarette and cigarette
smoking using nationally representative ALSWH data. One out of 10 women in their
twenties has reported ever use of e-cigarettes, from which a quarter had never
previously smoked. Young age, intimate partner violence, alcohol use, smoking status
and adverse childhood experiences (traumatic childhood experiences) are considered
risk factors for e-cigarette use. In this study, the researcher identified that although
ever e-cigarette use is linked to subsequent cigarette smoking initiation among never
smokers, it also hinders subsequent cigarette smoking cessation among current
smokers. Therefore, concentrated efforts are required to prevent young people and
non-smokers from developing nicotine addiction through e-cigarette use.
Keywords: e-cigarette, vaping, smoking initiation, smoking cessation, Australia
Aims
This brief aims to provide policy recommendations that can help mitigate the effects of e-cigarette use. 182
Addressee
Some of the beneficiaries of this policy brief include the Australian National Drug
Strategy, the Australian National Tobacco Strategy, Australian cancer foundations, the
Australian Department of Health and the National Health and Medical Research Council.
Approaches and Findings
The current study has used survey data from a cohort of women born between 1989 and 1995 who participated in the ALSWH. The ALSWH is one of the largest national
Australian health studies and is run jointly by the University of Queensland and The
University of Newcastle. It is funded by the Australian Government Department of Health.
The researcher identified an association between e-cigarette use and subsequent initiation of cigarette smoking.
E-cigarette use is a known risk factor for the subsequent initiation of cigarette smoking among Australian women who had never previously smoked. It has inhibited the ability to quit among Australian women who were current smokers. Along with other research, this study also concluded that e-cigarettes had no net positive effect on tobacco control.
The Existing E-Cigarette Regulatory Policy Environment in Australia
In Australia, the sale, supply and possession of nicotine in e-cigarettes are illegal, with nicotine listed as a dangerous poison under Schedule 7.
Key challenges
Despite regulations, most (89%) of Australian e-cigarette users purchase e-liquids online from overseas vendors (Fraser et al., 2015)—and six out of 10 nicotine-free e-liquids have been found to actually contain nicotine (Chivers et al., 2019).
The National Drug Strategy and National Tobacco Strategy are policy frameworks that are designed to minimise the adverse social, economic and psychological consequences
183 of cigarette smoking among the Australian population. The issue of e-cigarettes was not addressed in these strategies. Therefore, the current policy brief is intended to contribute to the public discussion by highlighting the need for including e-cigrattes in the discussion.
Conclusion
Young age, intimate partner violence, alcohol use, smoking status and adverse childhood experiences were among some of the factors that were positively associated with e- cigarette use. E-cigarette use was a risk factor for the subsequent initiation of cigarette smoking among Australian women who had never previously smoked. Further, e-cigarette use has decreased the odds of subsequent smoking cessation among current cigarette smokers.
Policy Option
Australia is one of the countries that have a strict regulatory policy on e-cigarette products. This policy brief is not intended to replace this policy regulation; rather, it aims to provide specific recommendations that can reinforce it.
Recommendations
The National Drug Strategy and National Tobacco Strategy should address
the issue of e-cigarette use. Regulation is required to guarantee that e-cigarettes
do not become prevalent among non-smoking young people so that addiction to
nicotine and subsequent initiation of cigarette smoking can be prevented.
Although tobacco control efforts have been generally effective in terms of
decreasing young smoking rates, in Australia, e-cigarettes may lead to a
renormalisation of smoking and undermine the progress made in young
smokers who are decreasing their use. The three pillars of the National Drug
Strategy approaches that are used to minimise the adverse effects of tobacco 184
smoking—namely supply reduction, demand reduction and harm reduction—
could be investigated as tools for preventing the abuse of e-cigarettes.
E-liquid products available in Australia should be monitored. Monitoring of
the contents of e-liquid in retailers is required to prevent the sale of nicotine-
containing e-liquids. Since the online market is one of the main sources of e-
cigarette purchases for young people, the regulation of online e-cigarette sales is
equally important.
Risk groups should be targeted to prevent nicotine addiction through e-
cigarettes. Targeting high-risk groups such as young people, those with financial
difficulties, those with a history of mental illness and those who are exposed to
childhood adversities is required to prevent nicotine addiction via e-cigarettes.
Family practitioners and clinicians should adopt a practical guidance role when
they educate parents, childcare providers, policymakers and the general public
about the devastating consequences of childhood adversities. Moreover, strategies
should be established to help people with a history of childhood adversities adopt
positive coping mechanisms by using trauma-informed care strategies rather than
health-harming behaviours like nicotine addiction.
Since nicotine free e-cigarette device cannot be certainly distinguished from
nicotine containing e-cigarette device, this could hindered the regulation of e-
cigarette. Therefore, we also recommend that non-nicotine containing e-cigarette
should be regulated under the same framework as those that do contain nicotine.
From first of January, 2021 importation of nicotine fluid will be illegal in
Australia without prescription of medical practitioners. The proposed regulatory
change will be consistent across the states. The purpose of the proposed change in
the current importation of nicotine vaporizer guidelines is to prevent nicotine
185
addiction and renormalization of smoking behaviours among children and young
people. The proposed change in guidelines governing nicotine liquid importation
will have substantially benefit in preventing children, young people and never
smokers from nicotine addiction via nicotine containing e-cigarettes
Further research is required. Research that investigates the effect of regular e-
cigarette use on smoking cessation is also required to strengthen the evidence base
further. Future research should compare the role of nicotine-containing and non–
nicotine containing e-cigarettes in smoking initiation and cessation.
Note: The authors refer to e-cigarettes as a broad term to define all categories of electronic aerosolising devices, such as vape pens, e-hookahs, e-cigars, ‘mods’ and JUUL.
Acknowledgements
The research on which this policy brief is based uses data from the ALSWH. The researchers are indebted to the Australian Government Department of Health for financing the study. The conclusions stated in this policy brief are those of the researchers and not an official position of the Australian Government Department of Health.
Authors: Alemu Melka1 (Mr), Catherine Chojenta1 (Dr), Deborah Loxton1 (Prof.),
Elizabeth Holliday2 (A/Prof.)
1 Research Centre for Generational Health and Ageing, Faculty of Health and
Medicine, the University of Newcastle, Newcastle, Australia.
2 School of Medicine and Public Health, Faculty of Health and Medicine, the
University of Newcastle, Newcastle, Australia.
Source/publication
1. Melka, A. S., Chojenta, C. L., Holliday, E. G. and Loxton, D. J. (2019). Predictors
of e-cigarette use among young Australian women. American Journal of Preventive
Medicine, 56, 293–299. https://doi.10.1016/j.amepre.2018.09.019 186
2. Melka, A., Chojenta, C., Holliday, E. & Loxton, D. (2019). Adverse childhood
experiences and electronic cigarette use among young Australian women.
Preventive Medicine, 126, 105759–105759.
https://doi.10.1016/j.ypmed.2019.105759
3. Melka, A. S., Chojenta, C. L., Holliday, E. G. & Loxton, D. J. (2020). E-cigarette
use and cigarette smoking initiation among Australian women who have never
smoked. Drug and Alcohol Review. https://doi.org/10.1111/dar.13131
4. Melka, A. S., Chojenta, C. L., Holliday, E. G. & Loxton, D. J. (under review).
Determinants of smoking cessation among Australian women: The role of e-
cigarette use.
Disclaimer
The conclusions stated in this policy brief are those of the researchers and not an official position of the Australian Government Department of Health. 187
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Appendices
Appendix A: Copyright agreement for chapter 4
American Journal of Preventive Medicine
Article: Predictors of E-cigarette Use Among Young Australian Women Corresponding author: Mr. Alemu Sufa Melka E-mail address: [email protected];[email protected] Journal: American Journal of Preventive Medicine Our reference AMEPRE5681 PII: S0749-3797(18)32326-2 DOI: 10.1016/j.amepre.2018.09.019 Your Status: I am one author signing on behalf of all co-authors of the manuscript
Assignment of Copyright
I hereby assign to American Journal of Preventive Medicine the copyright in the manuscript identified above (where Crown Copyright is asserted, authors agree to grant an exclusive publishing and distribution license) and any tables, illustrations or other material submitted for publication as part of the manuscript (the "Article"). This assignment of rights means that I have granted to American Journal of Preventive Medicine, the exclusive right to publish and reproduce the Article, or any part of the Article, in print, electronic and all other media (whether now known or later developed), in any form, in all languages, throughout the world, for the full term of copyright, and the right to license others to do the same, effective when the Article is accepted for publication. This includes the right to enforce the rights granted hereunder against third parties. Supplemental Materials
"Supplemental Materials" shall mean materials published as a supplemental part of the Article, including but not limited to graphical, illustrative, video and audio material. With respect to any Supplemental Materials that I submit, American Journal of Preventive Medicine shall have a perpetual worldwide, non-exclusive right and license to publish, extract, reformat, adapt, build upon, index, redistribute, link to and otherwise use all or any part of the Supplemental Materials in all forms and media (whether now known or later developed), and to permit others to do so. Research Data
"Research Data" shall mean the result of observations or experimentation that validate research findings and that are published separate to the Article, which can include but are not limited to raw data, processed data, software, algorithms, protocols, and methods. With respect to any Research Data that I wish to make accessible on a site or through a service of American Journal of Preventive Medicine, American Journal of Preventive Medicine shall have a perpetual worldwide, non-exclusive right and license to publish, extract, reformat, adapt, build upon, index, redistribute, link to and otherwise use all or any part of the Research Data in all forms and media (whether now known or later developed) and to permit others to do so. Where I have selected a specific end user license under which the Research Data is to be made available on a site 230 or through a service, the publisher shall apply that end user license to the Research Data on that site or service. Reversion of rights
Articles may sometimes be accepted for publication but later rejected in the publication process, even in some cases after public posting in "Articles in Press" form, in which case all rights will revert to the author (see https://www.elsevier.com/about/our-business/policies/article-withdrawal). Revisions and Addenda
I understand that no revisions, additional terms or addenda to this Journal Publishing Agreement can be accepted without American Journal of Preventive Medicine's express written consent. I understand that this Journal Publishing Agreement supersedes any previous agreements I have entered into with American Journal of Preventive Medicine in relation to the Article from the date hereof. Author Rights for Scholarly Purposes
I understand that I retain or am hereby granted (without the need to obtain further permission) the Author Rights (see description below), and that no rights in patents, trademarks or other intellectual property rights are transferred to American Journal of Preventive Medicine. The Author Rights include the right to use the Preprint, Accepted Manuscript and the Published Journal Article for Personal Use and Internal Institutional Use. They also include the right to use these different versions of the Article for Scholarly Sharing purposes, which include sharing:
the Preprint on any website or repository at any time; the Accepted Manuscript on certain websites and usually after an embargo period; the Published Journal Article only privately on certain websites, unless otherwise agreed by American Journal of Preventive Medicine.
In the case of the Accepted Manuscript and the Published Journal Article the Author Rights exclude Commercial Use (unless expressly agreed in writing by American Journal of Preventive Medicine), other than use by the author in a subsequent compilation of the author's works or to extend the Article to book length form or re-use by the author of portions or excerpts in other works (with full acknowledgment of the original publication of the Article). Author Representations / Ethics and Disclosure / Sanctions
I affirm the Author Representations noted below, and confirm that I have reviewed and complied with the relevant Instructions to Authors, Ethics in Publishing policy, Declarations of Interest disclosure and information for authors from countries affected by sanctions (Iran, Cuba, Sudan, Burma, Syria, or Crimea). Please note that some journals may require that all co-authors sign and submit Declarations of Interest disclosure forms. I am also aware of the publisher's policies with respect to retractions and withdrawal (https://www.elsevier.com/about/our- business/policies/article-withdrawal). For further information see the publishing ethics page at https://www.elsevier.com/about/our- business/policies/publishing-ethics and the journal home page. For further information on sanctions, see https://www.elsevier.com/about/our-business/policies/trade-sanctions Author representations
The Article I have submitted to the journal for review is original, has been written by the stated authors and has not been previously published. 231
The Article was not submitted for review to another journal while under review by this journal and will not be submitted to any other journal. The Article and the Supplemental Materials do not infringe any copyright, violate any other intellectual property, privacy or other rights of any person or entity, or contain any libellous or other unlawful matter. I have obtained written permission from copyright owners for any excerpts from copyrighted works that are included and have credited the sources in the Article or the Supplemental Materials. Except as expressly set out in this Journal Publishing Agreement, the Article is not subject to any prior rights or licenses and, if my or any of my co-authors' institution has a policy that might restrict my ability to grant the rights required by this Journal Publishing Agreement (taking into account the Author Rights permitted hereunder, including Internal Institutional Use), a written waiver of that policy has been obtained. If I and/or any of my co-authors reside in Iran, Cuba, Sudan, Burma, Syria, or Crimea, the Article has been prepared in a personal, academic or research capacity and not as an official representative or otherwise on behalf of the relevant government or institution. If I am using any personal details or images of patients, research subjects or other individuals, I have obtained all consents required by applicable law and complied with the publisher's policies relating to the use of such images or personal information. See https://www.elsevier.com/about/our-business/policies/patient-consent for further information. Any software contained in the Supplemental Materials is free from viruses, contaminants or worms. If the Article or any of the Supplemental Materials were prepared jointly with other authors, I have informed the co-author(s) of the terms of this Journal Publishing Agreement and that I am signing on their behalf as their agent, and I am authorized to do so.
Governing Law and Jurisdiction
This Agreement will be governed by and construed in accordance with the laws of the country or state of American Journal of Preventive Medicine ("the Governing State"), without regard to conflict of law principles, and the parties irrevocably consent to the exclusive jurisdiction of the courts of the Governing State. For information on the publisher's copyright and access policies, please see http://www.elsevier.com/copyright. For more information about the definitions relating to this agreement click here. I have read and agree to the terms of the Journal Publishing Agreement. 232
Appendix B: Copyright agreement for chapter 5
Elsevier Inc.
Adverse childhood experiences and electronic cigarette use among Article: young Australian women Corresponding author: Mr. Alemu Sufa Melka E-mail address: [email protected] Journal: Preventive Medicine Article number: 105759 Our reference YPMED_105759 PII: S0091-7435(19)30235-X DOI: 10.1016/j.ypmed.2019.105759 Your Status
I am one author signing on behalf of all co-authors of the manuscript Some of the authors are employees of the UK, Canadian or Australian Government but Crown Copyright is not asserted
Data Protection & Privacy
I do wish to receive news, promotions and special offers about products and services from Elsevier Inc. and its affiliates worldwide.
Assignment of Copyright
I hereby assign to Elsevier Inc. the copyright in the manuscript identified above (where Crown Copyright is asserted, authors agree to grant an exclusive publishing and distribution license) and any tables, illustrations or other material submitted for publication as part of the manuscript (the "Article"). This assignment of rights means that I have granted to Elsevier Inc., the exclusive right to publish and reproduce the Article, or any part of the Article, in print, electronic and all other media (whether now known or later developed), in any form, in all languages, throughout the world, for the full term of copyright, and the right to license others to do the same, effective when the Article is accepted for publication. This includes the right to enforce the rights granted hereunder against third parties. Supplemental Materials
"Supplemental Materials" shall mean materials published as a supplemental part of the Article, including but not limited to graphical, illustrative, video and audio material.
With respect to any Supplemental Materials that I submit, Elsevier Inc. shall have a perpetual worldwide, non-exclusive right and license to publish, extract, reformat, adapt, build upon, index, redistribute, link to and otherwise use all or any part of the Supplemental Materials in all forms and media (whether now known or later developed), and to permit others to do so. Research Data
"Research Data" shall mean the result of observations or experimentation that validate research findings and that are published separate to the Article, which can include but are not limited to raw data, processed data, software, algorithms, protocols, and methods.
With respect to any Research Data that I wish to make accessible on a site or through a service of Elsevier Inc., Elsevier Inc. shall have a perpetual worldwide, non-exclusive right and license to 233 publish, extract, reformat, adapt, build upon, index, redistribute, link to and otherwise use all or any part of the Research Data in all forms and media (whether now known or later developed) and to permit others to do so. Where I have selected a specific end user license under which the Research Data is to be made available on a site or through a service, the publisher shall apply that end user license to the Research Data on that site or service. Reversion of rights
Articles may sometimes be accepted for publication but later rejected in the publication process, even in some cases after public posting in "Articles in Press" form, in which case all rights will revert to the author (see https://www.elsevier.com/about/our-business/policies/article-withdrawal). Revisions and Addenda
I understand that no revisions, additional terms or addenda to this Journal Publishing Agreement can be accepted without Elsevier Inc.'s express written consent. I understand that this Journal Publishing Agreement supersedes any previous agreements I have entered into with Elsevier Inc. in relation to the Article from the date hereof. Author Rights for Scholarly Purposes
I understand that I retain or am hereby granted (without the need to obtain further permission) the Author Rights (see description below), and that no rights in patents, trademarks or other intellectual property rights are transferred to Elsevier Inc.. The Author Rights include the right to use the Preprint, Accepted Manuscript and the Published Journal Article for Personal Use and Internal Institutional Use. They also include the right to use these different versions of the Article for Scholarly Sharing purposes, which include sharing:
the Preprint on any website or repository at any time; the Accepted Manuscript on certain websites and usually after an embargo period; the Published Journal Article only privately on certain websites, unless otherwise agreed by Elsevier Inc..
In the case of the Accepted Manuscript and the Published Journal Article the Author Rights exclude Commercial Use (unless expressly agreed in writing by Elsevier Inc.), other than use by the author in a subsequent compilation of the author's works or to extend the Article to book length form or re- use by the author of portions or excerpts in other works (with full acknowledgment of the original publication of the Article). Author Representations / Ethics and Disclosure / Sanctions
I affirm the Author Representations noted below, and confirm that I have reviewed and complied with the relevant Instructions to Authors, Ethics in Publishing policy, Declarations of Interest disclosure and information for authors from countries affected by sanctions (Iran, Cuba, Sudan, Burma, Syria, or Crimea). Please note that some journals may require that all co-authors sign and submit Declarations of Interest disclosure forms. I am also aware of the publisher's policies with respect to retractions and withdrawal (https://www.elsevier.com/about/our- business/policies/article-withdrawal). For further information see the publishing ethics page at https://www.elsevier.com/about/our- business/policies/publishing-ethics and the journal home page. For further information on sanctions, see https://www.elsevier.com/about/our-business/policies/trade-sanctions Author representations
The Article I have submitted to the journal for review is original, has been written by the stated authors and has not been previously published. The Article was not submitted for review to another journal while under review by this journal and will not be submitted to any other journal. 234
The Article and the Supplemental Materials do not infringe any copyright, violate any other intellectual property, privacy or other rights of any person or entity, or contain any libellous or other unlawful matter. I have obtained written permission from copyright owners for any excerpts from copyrighted works that are included and have credited the sources in the Article or the Supplemental Materials. Except as expressly set out in this Journal Publishing Agreement, the Article is not subject to any prior rights or licenses and, if my or any of my co-authors' institution has a policy that might restrict my ability to grant the rights required by this Journal Publishing Agreement (taking into account the Author Rights permitted hereunder, including Internal Institutional Use), a written waiver of that policy has been obtained. If I and/or any of my co-authors reside in Iran, Cuba, Sudan, Burma, Syria, or Crimea, the Article has been prepared in a personal, academic or research capacity and not as an official representative or otherwise on behalf of the relevant government or institution. If I am using any personal details or images of patients, research subjects or other individuals, I have obtained all consents required by applicable law and complied with the publisher's policies relating to the use of such images or personal information. See https://www.elsevier.com/about/our-business/policies/patient-consent for further information. Any software contained in the Supplemental Materials is free from viruses, contaminants or worms. If the Article or any of the Supplemental Materials were prepared jointly with other authors, I have informed the co-author(s) of the terms of this Journal Publishing Agreement and that I am signing on their behalf as their agent, and I am authorized to do so.
Governing Law and Jurisdiction
This Agreement will be governed by and construed in accordance with the laws of the country or state of Elsevier Inc. ("the Governing State"), without regard to conflict of law principles, and the parties irrevocably consent to the exclusive jurisdiction of the courts of the Governing State. For information on the publisher's copyright and access policies, please see http://www.elsevier.com/copyright. For more information about the definitions relating to this agreement click here.
I have read and agree to the terms of the Journal Publishing Agreement. 235
Appendix C: Copyright agreement for chapter 6
Drug and Alcohol Review
Published by Wiley on behalf of Australasian Professional Society on Alcohol and other Drugs (the "Owner")
COPYRIGHT TRANSFER AGREEMENT
Date: June 23, 2020
Contributor name: Alemu Sufa Melka
Contributor address:
Manuscript number: CDAR-2019-0284.R2
Re: Manuscript entitled E-cigarette use and cigarette smoking initiation among Australian women who have never smoked (the "Contribution") for publication in Drug and Alcohol Review (the "Journal") published by John Wiley & Sons Australia, Ltd ("Wiley")
Dear Contributor(s):
Thank you for submitting your Contribution for publication. In order to expedite the editing and publishing process and enable the Owner to disseminate your Contribution to the fullest extent, we need to have this Copyright Transfer Agreement executed. If the Contribution is not accepted for publication, or if the Contribution is subsequently rejected, this Agreement shall be null and void.
Publication cannot proceed without a signed copy of this Agreement.
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1. The Contributor assigns to the Owner, during the full term of copyright and any extensions or renewals, all copyright in and to the Contribution, and all rights therein, including but not limited to the right to publish, republish, transmit, sell, distribute and otherwise use the Contribution in whole or in part in electronic and print editions of the Journal and in derivative works throughout the world, in all languages and in all media of expression now known or later developed, and to license or permit others to do so. For the avoidance of doubt, “Contribution” is defined to only include the article submitted by the Contributor for publication in the Journal (including any embedded rich media) and does not extend to any supporting information submitted with or referred to in the Contribution (“Supporting Information”). To the extent that any Supporting Information is submitted to the Journal, the Owner is granted a perpetual, non-exclusive license to publish, republish, transmit, sell, distribute and otherwise use this Supporting Information in whole or in part in electronic and print editions of the Journal and in derivative works throughout the world, in all languages and in all media of expression now known or later developed, and to license or permit others to do so.
2. Reproduction, posting, transmission or other distribution or use of the final Contribution in whole or in part in any medium by the Contributor as permitted by this Agreement requires a citation to the Journal suitable in form and content as follows: (Title of Article, Contributor, Journal Title and 236
Volume/Issue, Copyright © [year], copyright owner as specified in the Journal, Publisher). Links to the final article on the publisher website are encouraged where appropriate.
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Notwithstanding the above, the Contributor or, if applicable, the Contributor’s employer, retains all proprietary rights other than copyright, such as patent rights, in any process, procedure or article of manufacture described in the Contribution.
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1. Submitted Version. The Owner licenses back the following rights to the Contributor in the version of the Contribution as originally submitted for publication (the "Submitted Version"):
a. The right to self-archive the Submitted Version on: the Contributor’s personal website; a not for profit subject-based preprint server or repository; a Scholarly Collaboration Network (SCN) which has signed up to the STM article sharing principles [http://www.stm-assoc.org/stm- consultations/scn-consultation-2015/] ("Compliant SCNs"); or the Contributor’s company/ institutional repository or archive. This right extends to both intranets and the Internet. The Contributor may replace the Submitted Version with the Accepted Version, after any relevant embargo period as set out in paragraph C.2(a) below has elapsed. The Contributor may wish to add a note about acceptance by the Journal and upon publication it is recommended that Contributors add a Digital Object Identifier (DOI) link back to the Final Published Version. b. The right to transmit, print and share copies of the Submitted Version with colleagues, including via Compliant SCNs, provided that there is no systematic distribution of the Submitted Version, e.g. posting on a listserve, network (including SCNs which have not signed up to the STM sharing principles) or automated delivery.
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a. The right to self-archive the Accepted Version on: the Contributor’s personal website; the Contributor’s company/institutional repository or archive; Compliant SCNs; and not for profit subject-based repositories such as PubMed Central, all subject to an embargo period of 12 months for scientific, technical and medical (STM) journals and 24 months for social science and humanities (SSH) journals following publication of the Final Published Version. There are separate arrangements with certain funding agencies governing reuse of the Accepted Version as set forth at the following website: http://www.wileyauthors.com/funderagreements. The Contributor may not update the Accepted Version or replace it with the Final Published Version. The Accepted Version posted must contain a legend as follows: This is the accepted version of the following article: FULL CITE, which has been published in final form at [Link to final article]. This article may be used for non-commercial purposes in accordance with the Wiley Self-Archiving Policy [ http://www.wileyauthors.com/self-archiving]. b. The right to transmit, print and share copies of the Accepted Version with colleagues, including via Compliant SCNs (in private research groups only before the embargo and publicly after), provided that there is no systematic distribution of the Accepted Version, e.g. posting on a listserve, network (including SCNs which have not signed up to the STM sharing principles) or automated delivery. 237
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Appendix E: Sample of Literature review matrix
Table E1
Sample of Literature Review Matrix Used to Manage Information Collected from Literature
Study author, Age Sampling and Location and study Study Data collection Data analysis Main findings Limitations year range/Gender Sample size setting design/data approach
source
Cullen KA et al., students in grades 3-stage cluster US cross-sectional self-administered Weighted Current e-cigarette use (past 30 Response rate of
2019 6 to 12/ (M+F) sampling N = 19 018 School survey /National questionnaire prevalence days) was 27.5% among HS 66.3%
(n = 10 097 high Youth Tobacco estimates and students (59.1 use JUUL) 10.5% Out of school
school, n = middle Survey (NYTS)/ 95%CIs among MS students (54.1 use youth are not
school students JUUL). represented
8837)
Jongenelis et.al, 18–25/ Study participants Australia Cross-sectional Online survey Chi-square was Smokers were more likely than Cross-sectional
2019 Female (59%) recruited through used to assess the non‐smokers to report ever use of Non-
internet and radio difference in use ENDS (67% vs 28%). representative
advertisement. of ENDS by Male smokers were more likely sample
N =1116 smoking status and than female smokers to be current
SES. e‐cigarette users.
Cummins SE, >=18 years/ Probability US Cross-sectional Online survey logistic regression individuals with MHC were more -
2014 Female (52%) sampling, Population study analysis likely to have tried e-cigarettes
N = 10041(3111 (14.8%) and to be current users of
were current 244
smokers, 3676 were E-cigarettes (3.1%) than those
former smokers and without MHC (6.6% and 1.1%,
3254 were never respectively; p < 0.01).
smokers)
Jongenelisa et al., 18–25/ (55% Study participants Australia Cross-sectional Online survey Descriptive Curiosity about tobacco smoking, Cross-sectional
2019 female) recruited through statistics willingness to smoke, and
internet and radio Hierarchical intentions to smoke were
advertisement. multiple linear significantly higher among users
N = 519 (90 ever e- regression of e-cigarettes than never users.
cig users analyses
Harrold TC et al., >=18 years/ Random sample N = NSW, (Data from Cross-sectional telephone-based Relative risks and Current smokers were 7.5 times
2015 12 502 NSW Population survey 95% confidence more likely to be current e-
Health Survey) intervals cigarette users than non-smokers
Australia While males and people aged 18–
44 years were more likely to have
ever tried an e-cigarette compared
with females and people aged 45
years or older, respectively.
Dunlop S et al., >=18 years Random selection NSW (data from The cross-sectional Telephone survey logistic regression both mid-aged and older adults The study did
2016 Women ,51% N = 2966 Cancer Institute analyses were less likely to be current users not assess
Tobacco Tracking than younger adults whether
Survey) individuals were
using nicotine- 245
containing e-
cigarettes
Soneji SS et al., Adol 12–17yars address-based, area- US, Population cross-sectional Audio Computer- weighted logistic Compared with older adults, -
2019 /(female 43.1%) probability sampling Assessment for Assisted Self- regression models adolescents and young adults
Adult young 18– design/ Tobacco and Health Interviewing were more likely to use flavoured
24 (female 37.7%) adol (n = 414) Study data e-cigarettes
Old adult ≥25 young adult (n =
(female 48.4%) 961)
old adult (n = 1711)
Chan G et al., >=18 years Randomly selected Australia, cross-sectional Mainly Drop and logistic regression Gender, age, psychological NDSHS
2019 54% Female using a multi-stage 2016 National Drug collect, paper form, distress, cannabis use and excludes
stratified design Strategy Household Online survey smoking status were associated participants
N = 22,354 Survey with current e-cig use. without a fixed
home address
Jiang N et al., Mean age, 14.8 proportionate Hong Kong, School- cross-sectional Self-administered logistic regression Current e-cigarette use was cross-sectional,
2016 stratified sampling based Survey questionnaire. associated with male sex, poor low response rate
N = 45,857 knowledge about the harm of
smoking, cigarette smoking, use
of other tobacco products, and
alcohol consumption.
Giovenco DP et >=18 years probability-based US, cross-sectional Online Survey Multivariate the odds of being an established e- al., 2014 sampling logistic regression cigarette user were greater for
N = 2,136 former smokers 246
Barrington-Trimis 11th- and 12th- Adolescent entire Children’s Health cross-sectional self-administered Polytomous Home use of each product,
JL et al., 2015 grade students classrooms in Study (CHS) questionnaire regression models friends’ use strongly positively
Male 50.4% schools throughout associated both with e-cigarette
southern California and cigarette use.
N = 2084
Fotiou A et al., probability sample, Greek , Health cross-sectional self-completed multivariable Cannabis and smoking of Self-report of
2015 n=1320 Behaviour in School- questionnaire logistic regression conventional tobacco were variables
aged Children models independently associated with
Survey (HBSC) ever e-cigarettes.
Lee et al., 2016 ≥19 years old stratified multi-stage Korea, National cross-sectional - multivariate ever e-cigarette use was highest in cross-sectional
Female 51% probability sampling Health and Nutrition study logistic regression current smokers, former smokers, nature of the
Examination Survey and daily heavy drinkers study
Ruokolainen et al. aged 15–69 Random sample Finnish, Population cross-sectional self-administered A multinomial Unemployment and lower 50% response
n = 3485 Information System study anonymous logistic regression education were associated with rate
online/postal model current e-cigarette
questionnaire
Wang et al., 2018 Age, 12 and 18 Non-probability China, population- cross-sectional mobile internet logistic regression The odds ratio for ever users of e- Less than 50% of
years sampling based survey study survey model cigarettes to have tried to quit the total
Female 16.45% N = 2042 smoking conventional cigarettes population have
was 1.60 that of never users. an access to
internet in China
during data
collection. 247
Kulik et al., 2018 aged ≥15 years multi-stage 2014 Eurobarometer Cross-sectional Interviews logistic regression E-cigarette was associated with Cross-sectional
n = 12,608 probability sample survey of 28 inhibiting smoking cessation.
of Europeans European Union
countries
Stallings-Smith et aged ≥18 years multi-stage US, National Health Cross-sectional Self-reported logistic regression Association was found between Cross-sectional al., 2019 female 52% probability sample and Nutrition questionnaire. sociodemographic Factors and e-
Examination Survey cigarette use.
(NHANES)
Lee et al., 2019 aged ≥18 years multi-stage Korean National Cross-sectional Survey multivariable E-cigarette was associated with cross-sectiona
n = 3227 probability sample Health and Nutrition logistic regressi male gender, younger age an
Examination Surve higher education
Conner et al.,2018 12–17 years old cluster randomised England, School. Cluster Face to face Multilevel logistic Baseline ever use of e-cigarettes
Female 50.2% controlled trial randomised interview regression was strongly associated with
N = 2,836 controlled trial subsequent initiation and
escalation 248 Appendix F: Participation and retention of 17,010 women in the 1989–95 cohort of women who were aged 18–23 years at Survey 1 in 2013*
Survey Survey 2 Survey 3 Survey 4 Survey 5
Age in years 19–24 20–25 21–26 22–27
Deceased 1 6 8 13
Frailty (e.g. mental impairment) 1 1 1 1
Withdrawn 681 694 1744 1943
Total ineligible 683 701 1753 1957
contacted but did not return survey 2362 3879 1850 1813
unable to contact participant 2621 3469 4400 4745
Total non-respondents 4983 7348 6250 6558
Respondents completed survey 11,344 8,961 9,007 8495
Eligible at current survey 16,327 16,309 15,257 15053
Response rate as % eligible 69.5% 54.9% 59.0% 56.4% 249 Appendix G: Data access approval letter
Dear Alemu and colleagues,
Thank you for submitting your amendment to EoI #A701: Substance use and sexual behaviours among young Australian women. We note the amendment restricts the focus to cigarette and e- cigarette smoking and removes sexual behaviors and substance use as specific foci. This amendment has been approved by the ALSWH Data Access Committee and you have permission to use data from the ALSWH in the project. This amended EoI will be referred to as EoIA 701A, titled Electronic cigarette and conventional cigarette smoking among Australian women and replace the original EoI A701.
All collaborators who receive permission to use ALSWH survey data AND/OR to link ALSWH survey data with data from external datasets must abide by the following conditions of approval:
Access to, use and management of data
That collaborators ensure that only researchers who have received permission to use the data (that is, are named on the EoI) and who have signed all appropriate agreements have access to the data.
That if the analysis plan or the people involved with the project change at any time, the collaborator/s will submit a revised EoI for consideration by the Data Access Committee. All changes require approval before they can be implemented.
That the survey data provided by the ALSWH are securely stored and protected by the use of firewalls, automatic screen locking and/or secure encrypted pathways;
That collaborators will complete regular progress updates when requested by the ALSWH;
That the lead collaborator ensures the collaborators or students involved have adequate facilities and resources to enable the project to progress in a reasonable manner to its conclusion;
That in the event of unforeseen circumstances such that the project cannot proceed, the Data Access Committee is notified so the project can be terminated;
Publication and acknowledgment of outcomes / outputs (Note: outputs/outcomes include any manuscripts/articles/books/book chapters for publication, abstracts for conferences/symposia, and reports).
That any outcome (that is, manuscripts for publication, abstracts for conferences/symposia, other reports for funders) are reviewed/approved by the ALSWH liaison person before it is circulated beyond the collaborators named in the EoI;
That all outcomes include the standard ALSWH acknowledgement: “The research on which this [paper, book, monograph, abstract or report] is based was conducted as part of the Australian Longitudinal Study on Women's Health by the University of Queensland and the University of Newcastle. We are grateful to the Australian Government Department of Health for funding and to the women who provided the survey data."
When using the full food frequency questionnaire (FFQ) or nutrients derived from FFQ (from any survey) Professor Graham Giles should be contacted [email protected] and offered the opportunity (or his representative from Cancer Council Victoria) to collaborate in any papers.
Where the full FFQ has been used, Cancer Council Victoria must be acknowledged with the statement "The authors thank Professor Graham Giles of the Cancer Epidemiology Centre of Cancer Council Victoria, for permission to use the Dietary Questionnaire for Epidemiological Studies (Version 2), Melbourne: Cancer Council Victoria, 1996." Furthermore, all parties are to notify each other before presenting any DQES data at a conference, seminar or other forum, and, where appropriate, 250 must provide copies of the presentation, papers etc. to the Director of the Cancer Epidemiology Centre.
You are required to sign a 'Statement Governing the Analysis, Use and Publication of Data' and Confidentiality Statement before you can receive any data files or commence analysis. This ensures that your analysis is covered by the Study’s ethical approvals. We will organise this paperwork as required in a separate email.
We wish you the best with this project and look forward to seeing the results.
Kind regards
Leigh
Associate Professor Leigh Tooth
(Chair, ALSWH Data Access Committee) Principal Research Fellow and Deputy Director, Australian Longitudinal Study on Women's Health School of Public Health, Level 3, Public Health Building, 288 Herston Rd The University of Queensland Herston QLD 4006 Australia 251 Appendix H: Statement Governing the Analysis, Use and Publication of Data
Statement Governing the Analysis, Use and Publication of Data
between
Deborah Loxton
The University of Newcastle
and
Alemu Sufa Melka, Catherine Chojenta, Liz Holliday and Peta Forder
of
The University of Newcastle
Stating terms and conditions for collaboration on the Australian Longitudinal Study on Women's Health (ALSWH)
1. The research covered by this statement includes research into:
Electronic cigarette and conventional cigarette smoking among Australian women as detailed in Proposal ID A701A.
Alemu Sufa Melka, Catherine Chojenta, Liz Holliday and Peta Forder will be accorded Collaborator status on the Australian Longitudinal Study on Women’s Health (ALSWH) project, limited to the research described in Clause 1, in this instance.
2. In this instance, Deborah Loxton will liaise regularly on behalf of the ALSWH researchers.
3. All Collaborators will abide by the “Policy and Procedures for Data Access, Analysis and Publication” (Document B), “Policy and Procedures for Substudies” (Document C) and ‘Privacy Protocol’ (Document E), that govern all ALSWH projects.
4. The results of the research specified in clause 1 will not be used to seek funding for any further research using ALSWH without a new proposal to the Data Access Committee. 5. All quantitative and qualitative data collected as part of a substudy will be provided to ALSWH for secure electronic archiving. These data may be used in future projects, and 252 the project leader who collected the data will always be invited to participate in these future analyses. Hard copies of data will be the responsibility of the project leader and must be stored and disposed of in accordance with HREC and NHMRC guidelines http://www.nhmrc.gov.au/publications/synopses/r39syn.htm
6. Access to data from the following listed external datasets [not applicable] will be restricted to [not applicable]. No other collaborator named on this EoI will have access to these data. Analyses on external linked datasets may only be conducted at the University of Queensland, the University of Newcastle or through the SURE facility at the Sax Institute https://www.saxinstitute.org.au/our-work/sure
It is a condition of use that linked data are not transferred via the internet, email or copied to a USB memory stick, laptop or to other removable media.
7. Access to both ALSWH data and data from any external datasets (such as MBS, PBS, Perinatal, Cancer Registry and Admitted Patients Data Collections) will be available for two years from the date of data release. After this time, researchers must follow the guidelines set out in “Policy and Procedures for Data Access, Analysis and Publication” (Document B), section II.5.
I have read the above terms and conditions and I agree to them.
Deborah Loxton Alemu Sufa Melka
The University of Newcastle The University of Newcastle
Date …………………………… Date…………………………………
...... ………………...... ………………......
Catherine Chojenta Liz Holliday
The University of Newcastle The University of Newcastle
Date …………………………… Date…………………………………
...... ………………......
Peta Forder
The University of Newcastle
Date ………………………… 253
As project leader I accept the following responsibilities:
Completion of regular progress updates when requested by the ALSWH; AND
That if I do not provide progress reports when requested that the permission to conduct the project may be withdrawn; AND
Ensuring the investigators/collaborators or students involved have adequate facilities and resources to enable the project to progress in a reasonable manner to its conclusion; AND Ensuring that only the researchers who are named as having access to the linked data use these data (Please note that if researchers who do not have permission to use linked data are found to be using such data, approval of the EOI will immediately be withdrawn; OR
That in the event of unforeseen circumstances that if a project cannot proceed that the Data Access Committee is notified so that the project can be terminated.
...... ………………......
Alemu Sufa Melka
The University of
Newcastle
Date…………………… 254
Appendix I: Media release related to chapter 4
New study warns of growing public health concern around vaping for young women
Alarming new research shows how many young Aussie women are trying vaping and many of them have never touched a cigarette. news.com.au January 22, 2019; 8:27pm
Researchers say the popularity of vaping among young Australian women is a growing public health concern, with new data revealing how many are taking up the habit.
A study has shown that more than one in 10 Aussie women aged 19–26 have tried vaping, but that more than a quarter of users have never smoked cigarettes.
Researcher Alemu Melka, of the University of Newcastle and the Hunter Medical Research Institute said that figure might seem positive, but it was actually concerning because some research indicated that e-cigarette users were more likely to go on to smoke cigarettes.
“If public health programs are to curb the uptake of e-cigarettes by young women then we need to understand the risk factors to target,” he said.
“A lot of the risk factors for e-cigarette use are similar to traditional tobacco use.”
Researchers also found young women who are current smokers are 10-times more likely to use e- cigarettes, and ex-smokers five-times more likely than those who had never smoked.
They surveyed almost 9000 women from the Australian Longitudinal Study on Women’s Health.
Younger women were more likely to have used e-cigarettes in the previous 12 months than older women.
They also found young women who drank more than two standard drinks per day or who were in financial difficulty were also more likely to have use e-cigarettes recently than other young women.
Mr Melka said women who lived in urban areas were more likely to have tried vaping, likely because e-cigarettes were more easily available.
He said women who had experienced domestic abuse were also more likely to have tried e- cigarettes.
Fellow researcher Dr Catherine Chojenta said e-cigarettes were touted as being a healthier alternative to tobacco and a quit smoking aid but the scientific evidence was not there yet.
“There are concerns about the harmful effects of nicotine and other compounds used in e-cigarette flavourings but it could be another 10 or 20 years before we can say whether there are long term health consequences,” Dr Chojenta said. 255
Mr Melka is conducting follow up research to investigate whether e-cigarette users from the study go on to become tobacco smokers.
Worldwide e-cigarette use is controversial because many contain addictive substances, primarily nicotine that lead to long-term nicotine addiction, which can affect brain development in young people.
South Australia has been at the forefront of debate about the vaping industry after its clampdown recently.
New laws passed will ban online sales and regulate vaping devices under the Tobacco Act.
Late last year New Zealand politicians announced strict new vaping rules that would see e-cigarettes treated more like tobacco products.
The country’s government has proposed legal changes that would ban vaping devices and smokeless tobacco products the same as cigarettes in being banned from bars, restaurants and workplaces, as well introducing restrictions on how they can be displayed in stores.
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Appendix J: Logistic regression analysis steps employed in data analysis
Step 1 Chi-squared test/Fisher’s exact test/univariate logistic regression
Step 2 Variables achieving p-value <0.2 in the univariate analysis and variable with known biological important were included in the multivariable analysis
Step 3 Excluded variables were re-entered into the model one by one and the nested models compared using likelihood ratio test
Step 4 Test for two way interaction among the main exposure variable and other covariates
Step 5 Estimates were expressed as odds ratio with 95% CI, with a p-value threshold of 0.05 used to declare statistical significance.
Figure J1. Logistic regression steps used for data analysis. 257
Appendix K: Supplementary table for the association between e-cigarette use during survey 3 and subsequent initiation of smoking at survey 4 among 3rd survey never smoker
Table K1
Supplementary Table for the Association between Ever E-cigarette Use and Subsequent Initiation of Tobacco Smoking
Predictor variables Model 1 Model 2 Model 3 Model 4 Model 5 p-value
OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI)
Ever e-cigarette use
Yes 4.35 (2.78, 6.81) 4.25 (2.70, 6.70) 4.18 (2.64, 6.63) 3.84 (2.41, 6.12) 3.76 (2.35, 6.02) <0.001
No 1 [Reference] 1 [Reference] 1 [Reference] 1 [Reference]
Age 0.93 (0.85, 1.03) 0.93 (0.84, 1.02) 0.94 (0.85, 1.04) 0.95 (0.86, 1.04) 0.265
Area of residence
Major cities 1 [Reference] 1 [Reference] 1 [Reference] 1 [Reference] -
Inner regional 0.71 (0.46, 1.10) 0.71 (0.46, 1.09) 0.75 (0.48, 1.16) 0.72 (0.46, 1.12) 0.144
Outer regional 1.09 (0.64, 1.88) 1.04 (0.60, 1.81) 1.07 (0.61, 1.86) 1.056 (0.60, 1.85) 0.847
Remote/Very remote 0.45 (0.06, 3.29) 0.46 (0.064, 3.46) 0.40 (0.05, 2.99) 0.43 (0.058, 3.20) 0.410
Higher level of education
Less than year 12 1 [Reference] 1 [Reference] 1 [Reference] 1 [Reference] - 258
Year 12 and equivalent 0.57 (0.24, 1.40) 0.70 (0.29, 1.7) 0.62 (0.25, 1.53) 0.68 (0.27, 1.68) 0.398
Trade/certificate/diploma 0.77 (0.32, 1.85) 0.91 (0.38, 2.20) 0.87 (0.36, 2.12) 0.91 (0.37, 2.22) 0.829
University degree 0.40 (0.17, 0.97) 0.52 (0.21, 1.27) 0.45 (0.18, 1.11) 0.49 (0.20, 1.25) 0.137
Marital status
Partnered 1 [Reference] 1 [Reference] 1 [Reference] 1 [Reference] 0.250
Non-partnered 1.35 (0.94 , 1.95) 1.33 (0.93, 1.92) 1.19 (0.83, 1.72) 1.24 (0.86, 1.80)
Employment status
Unemployed 0.91 (0.58, 1.44) 0.84 (0.53, 1.34) 0.88 (0.55, 1.40) 0.88 (0.55, 1.40) 0.580
Employed 1 [Reference] 1 [Reference] 1 [Reference] 1 [Reference]
Ability to manage income
Difficulty managing income 1 [Reference] 1 [Reference] 1 [Reference] 1 [Reference] 0.189
Easy managing income 0.74 (0.54, 1.01) 0.79 (0.57, 1.09) 0.78 (0.57, 1.08) 0.80 (0.58, 1.11)
Live with one or both parents
Yes 0.67 (0.48, 0.95) 0.67 (0.47, 0.94) 0.74 (0.52, 1.04) 0.78 (0.55, 1.10) 0.151
No 1 [Reference] 1 [Reference] 1 [Reference] 1 [Reference]
Ever had depression
Yes 1.63 (1.17, 2.27) 1.78 (1.28, 2.50) 1.56 (1.10, 2.19) 0.012
No 1 [Reference] 1 [Reference] 1 [Reference]
Kessler Psychological Distress Scale 259
Low 1 [Reference] 1 [Reference] 1 [Reference] -
Moderate 1.05 (0.71, 1.56) 1.04 (0.70, 1.54) 1.01 (0.68, 1.51) 0.944
High 0.77 (0.47, 1.26) 0.77 (0.47, 1.26) 0.71 (0.43, 1.18 0.183
Very high 1.23 (0.79, 1.94) 1.27 (0.81, 1.99) 1.16 (0.73, 1.84) 0.524
Binge drinking at least ones a month
Yes 3.01 (2.22, 4.09) 3.16 (2.32, 4.30) <0.001
No 1 [Reference] 1 [Reference]
ACEs score
0 1 [Reference]
1 1.60 (1.07, 2.38) 0.049
2 2.18 (1.39, 3.43) 0.001
3 1.79 (1.00, 3.18) 0.049
4 or more 2.28 (1.32, 3.93) 0.003
Model 1: Univariate analysis Model 2: Association between e-cigarette and sociodemographic variables Model 3: model 2 plus mental conditions Model 4:: model 3 plus binge drinking Model 5: model 4 plus ACE score 260
Appendix L: Supplementary table for the Association between e-cigarette use during survey 3 and subsequent cessation of smoking at survey 4
Table L1
Supplementary Table for the Association between Ever E-cigarette Use and Subsequent Smoking Cessation
Predictor variables Model 1 Model 2 Model 3 Model 4 Model 5 p-value OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) Ever e-cigarette use Yes 0.67 (0.50–0.90) 0.67 (0.49, 0.90) 0.69 (0.51, 0.95) 0.69 (0.51, 0.95) 0.72 (0.52, 0.98) 0.040 No 1[Reference] 1 [Reference] 1 [Reference] 1 [Reference] 1 [Reference] Age 0.96 (0.88, 1.05) 0.95 (0.87, 1.05) 0.96 (0.88, 1.06) 0.96 ( 0.87, 1.06) 0.433 Area of residence Major cities 1 [Reference] 1 [Reference] 1[Reference] 1[Reference] - Inner regional 1.17 (0.79, 1.7) 1.17 (0.79, 1.73) 1.19(0.81, 1.76) 1.22(0.82, 1.82) 0.316 Outer regional 1.01 (0.52, 1.97) 1.01 (0.52, 1.99) 1.02(0.52, 1.99) .95(0.47, 1.90) 0.876 Remote/Very remote 0.20 (0.03, 1.52) 0.20 (0.03, 1.55) 0.20 (0.03, 1.52 0.19 (0.02, 1.50) 0.115 Higher level of education Less than year 12 1 [Reference] 1 [Reference] 1 [Reference] 1 [Reference] - year 12 and equivalent 1.04 (0.57, 1.87 1.02 (0.56, 1.85) 1.01 (0.56, 1.82) 1.01 (0.54, 1.86) 0.984 Trade/certificate/diploma 0.99 (0.56, 1.77 0.97 (0.54, 1.7) 0.97 (0.54, 1.74 1.01 (0.56, 1.84) 0.966 University degree 1.26 (0.69, 2.31) 1.22 (0.66, 2.26) 1.20 (0.65, 2.22) 1.16 (0.61, 2.18) 0.653 Marital status Partnered 1 [Reference] 1 [Reference] 1 [Reference] 1 [Reference] 0.359 Non-partnered 0.92 (0.66, 1.29) 0.93 (0.67, 1.31) 0.91 (0.65, 1.27) 0.85 (0.60, 1.20) Employment status Unemployed 0.98 (0.67, 1.45) 1.03 (0.70, 1.54) 1.06 (0.72, 1.58) 1.13 (0.75, 1.69) 0.558 Employed 1 [Reference] 1 [Reference] 1 [Reference] 1 [Reference] Ability to manage income 261
Difficulty managing income 1 [Reference] 1 [Reference] 1 [Reference] 1 [Reference] 0.286 Easy managing income 1.21 (0.89, 1.65) 1.13 (0.83, 1.56) 1.14 (0.84, 1.56) 1.19 (0.86, 1.64) Live with one or both parents Yes 0.99 (0.71, 1.38) 1.01 (0.73, 1.41) 1.02 (0.73, 1.43) 1.01 (0.72, 1.41) 0.970 No 1 [Reference] 1 [Reference] 1 [Reference] 1 [Reference] Ever had depression Yes 0.89 (0.66, 1.22) 0.90 (0.66, 1.23) 0.91 (0.66, 1.25) 0.562 No 1 [Reference] 1 [Reference] 1 [Reference] Kessler Psychological Distress Scale Low 1 [Reference] 1 [Reference] 1[Reference] Moderate 1.19 (0.81, 1.77) 1.19 (0.80, 1.76) 1.19 (0.80, 1.78) 0.380 High 0.91 (0.58, 1.45) 0.91 (0.58, 1.45) 0.93 (0.58, 1.48) 0.764 Very high 0.74 (0.49, 1.11) 0.75 (0.49, 1.13) 0.75 (0.49, 1.16) 0.196 Binge drinking at least ones a month Yes 1.21 (0.90, 1.62) 1.29 (0.95, 1.73 0.101 No 1 [Reference] 1[Reference] ACEs score 0 1 [Reference] - 1 1.40 (0.94, 2.09) 0.097 2 0.85 (0.54, 1.34) 0.494 3 0.97 (0.57, 1.66) 0.913 4 or more 1.06 (0.65, 1.74) 0.809 Model 1: Univariate analysis Model 2: Association between e-cigarette and sociodemographic variables Model 3: model 2 plus mental conditions Model 4:: model 3 plus binge drinking Model 5: model 4 plus ACE score. 262
Appendix M: Supplementary table for the Hosmer-Lemeshow test used to determine the goodness of fit for logistic regression model in chapter 6.
(Table collapsed on quantiles of estimated probabilities)
Group Prob Obs_1 Exp_1 Obs_0 Exp_0 Total
1 0.0079 3 3.6 528 527.4 531 2 0.0120 4 5.3 522 520.7 526 3 0.0151 7 7.3 527 526.7 534 4 0.0201 5 8.8 499 495.2 504 5 0.0247 10 11.6 513 511.4 523
6 0.0307 17 14.4 510 512.6 527 7 0.0392 22 18.1 500 503.9 522 8 0.0504 23 23.4 502 501.6 525 9 0.0731 36 31.5 487 491.5 523 10 0.5301 61 64.0 460 457.0 521
number of observations = 5236 number of groups = 10 Hosmer-Lemeshow chi2(8) = 4.49 Prob > chi2 = 0.8106
Figure M1. Hosmer Lemeshow test used to check goodness of fit for logistic regression in chapter 6. 263
Appendix N: Supplementary table for the Hosmer-Lemeshow test used to determine the goodness of fit for logistic regression model in chapter 7
(Table collapsed on quantiles of estimated probabilities)
Group Prob Obs_1 Exp_1 Obs_0 Exp_0 Total
1 0.1806 18 14.9 82 85.1 100 2 0.2100 28 28.2 113 112.8 141 3 0.2298 16 14.6 50 51.4 66 4 0.2440 20 24.0 80 76.0 100 5 0.2659 19 25.1 79 72.9 98
6 0.2826 29 28.8 75 75.2 104 7 0.3036 30 26.1 58 61.9 88 8 0.3235 32 34.2 76 73.8 108 9 0.3551 45 35.0 57 67.0 102 10 0.4364 27 33.2 61 54.8 88
number of observations = 995 number of groups = 10 Hosmer-Lemeshow chi2(8) = 11.05 Prob > chi2 = 0.1987
Figure N1. Hosmer Lemeshow test used to check goodness of fit for logistic regression in chapter 7. 264
Appendix O: Supplementary table for the multivariable logistic regression for the association between smoking cessation among those who smoked at least 100 cigarette in life time
smokces Odds Ratio Std. Err. z P>|z| [95% Conf. Interval]
everecig yes 1.006346 .2135072 0.03 0.976 .6639809 1.525243 age .9295495 .0612976 -1.11 0.268 .8168484 1.0578
ariapgpestimated Inner regional .8023402 .2270744 -0.78 0.436 .4607397 1.397209 Outer regional .8131834 .3755439 -0.45 0.654 .3289176 2.010434 Remote .273495 .2929333 -1.21 0.226 .0335158 2.231768
demo154 Year 12 or equivalent .6642723 .2563541 -1.06 0.289 .3117833 1.41527 Trade/certificate/diploma .906661 .3208088 -0.28 0.782 .4531684 1.81397 University Degree 1.008418 .4001936 0.02 0.983 .463275 2.19504
marital Non-partnered .6958811 .1610116 -1.57 0.117 .4421665 1.095177
empl033 Yes 1.063683 .305613 0.21 0.830 .605687 1.867996
demo013 Easy managing income 1.331931 .311891 1.22 0.221 .8417065 2.107672
demo157 Yes .7881576 .1965807 -0.95 0.340 .4834015 1.285044
medh396 Yes .9116573 .2035644 -0.41 0.679 .588527 1.412202
k10gpabs Moderate 1.026695 .2956443 0.09 0.927 .5838909 1.805307 High .9287539 .3067622 -0.22 0.823 .4861313 1.774384 Very high .8154902 .2405032 -0.69 0.489 .4574921 1.45363
singlerisk single occasion risk at least once a month 1.508819 .3175939 1.95 0.051 .9987718 2.279334
aces036 1 .9045725 .2885241 -0.31 0.753 .4841084 1.690224 2 .586245 .2070821 -1.51 0.131 .2933629 1.171529 3 .7045401 .246761 -1.00 0.317 .3546316 1.399697 4 .7392606 .2464389 -0.91 0.365 .3846291 1.420865
_cons 2.864961 4.523801 0.67 0.505 .1297367 63.26659
Note: _cons estimates baseline odds.
.
Figure O1. Multivariable logistic regression for the association between smoking cessation among those who smoked at least 100 cigarette in life time.
265
Appendix P: Version of Published paper from chapter 4 266 267 268 269 270 271 272
Appendix Q: Version of Published paper from chapter 5 273 274 275 276 277 278 279
280
Appendix R: Version of Published paper from chapter 6 281 282 283 284
285 286 287 288 289
290
Appendix S: Version of published umbrella review protocol 291 292
293 294 295
296
Appendix T: The impact of adverse childhood experiences on the health and health behaviours of young Australian women.
Authors Deborah Loxton1, Peta M Forder1, Dom Cavenagh1, Natalie Townsend1, Elizabeth Holliday 2, Catherine Chojenta1, Alemu Sufa Melka1
Institutional affiliations 1 Research Centre for Generational Health and Ageing, Faculty of Health, University of Newcastle, Newcastle NSW, Australia 2 School of Medicine and Public Health, Faculty of Health and Medicine, University of Newcastle, Newcastle NSW, Australia 297
ABSTRACT
Introduction: This study aims to estimate the prevalence of adverse childhood expereinces among young Australian women (aged 20-25) and examine associations between adverse childhood experiences and adult health behaviours and both physical and mental health.
Methods: Data came from the Australian Longitudinal Study on Women’s Health (ALSWH) for women born 1989-95, who completed the Adverse Childhood Experiences Scale (ACES) at Survey 3 in 2015 (N=8609). Outcomes included: self-rated health, sexual health, K10 psychological distress, depression, anxiety, suicide ideation, self-harm, substance abuse (drinking, smoking, illicit drugs), as well as severe obsesity and exercise. Prevalence of childhood adversities were presented, with the association between childhood adversity and outcomes evaluated using log-binomial multivariable regressions (with 99% CI).
Results: While 59% women reported experiencing at least one childhood adversity, around 10% of participants reported adverse childhood experiences across four or more domains, indicating a significant burden of risk for young Australian women. Women reporting four or more ACES domains had higher rates of poor physical health (adjPR=1.79, 99%CI=1.51- 2.12), sexual transmitted infections (adjPR=1.36, 99%CI=1.11-1.67) , and poor mental health (adjPR=2.78, 99%CI=2.34-3.32), as well as increased rates of severe obesity (adjPR=2.14, 99%CI=1.61-2.86) and smoking (adjPR=2.23, 99%CI=1.89-2.64).
Conclusion: Using nationally representative data, the current study clearly shows adverse childhood experiences directly impact physical health, mental health and health behaviours in adulthood among young Australian women. The management of health and wellbeing in adulthood should look beyond the contemporaneous factors, incorporating a focus on how childhood adversity may negatively influence health behaviour, health and wellbeing in later life. 298
INTRODUCTION
Poor health behaviours such as substance use, unhealthy diet and low levels of physical activity are established proximal predisposers for chronic diseases such as diabetes, liver dysfunction, cancers, and cardiovascular disease.1-3 To date, preventive medicine has focussed on health promotion and interventions designed to support quitting poor behaviours and the adoption of healthy behaviours.4 Yet despite punishing taxes attached to tobacco and the availability of cheaper alternatives (eg patches), prohibition of some drugs, and alcohol controls, substance use and abuse remain a significant problem in the community. Similarly, no current health promotion activity has slowed the increasing trend of obesity and overweight, with young women’s BMIs increasing with each generation and over the life span.5 It is therefore imperative that we turn our attention to the distal factors that predispose adults to the adoption of poor health behaviours. This paper examines the impact of adverse childhood experiences on young women’s health behaviours and health using data collected from a representative community sample of Australian women.
Adverse childhood experiences include two major themes of adversity; childhood abuse and household dysfunction during childhood. Childhood abuse includes psychological, sexual and physical abuse. Household dysfunction includes exposure to substance abuse in the household, mental illness in the home, having a household member incarcerated, and witnessing domestic violence against a father or mother during childhood. The prevalence of an individual reporting at least one ACE varies from 46.4% in a nationally representative British study6 and 72% in a representative Canadian sample7 to 82% in a small Saudi Arabian study.8 Co-occurrence of multiple childhood adversities is common, with the prevalence of two or more adverse childhood experiences reported as 37% in a Canadian population study7 and 66% in a subsample of women from the Adverse Childhood Experiences Study.9 The recent Australian Royal Commission into childhood sexual abuse and the appointment of an Australian Human Rights Commissioner for children has drawn attention to the often catastrophic outcomes of childhood abuse and adversity in Australia.10 However, the current study will be the first to report the prevalence of adverse childhood experiences with nationally representative Australian data.
Previous studies report links between experiences of adversity in childhood and poor physical health in adulthood. Relationships between adverse childhood experiences and later heart disease,8,11,12 respiratory disease,8 sexually transmitted disease,8,13 autoimmune disease,14 299 obesity,15 cancer,6 diabetes,6 asthma,16 gastrointestinal problems,17 and disability18 have been consistently reported. Not surprisingly, adverse childhood experiences are also related to a greater propensity to access health care7, high healthcare costs 19, and poor health related quality of life.20
Many health-behaviours predisposing to preventable diseases have also been associated with adverse childhood experiences, including alcohol consumption,8,21,22 illicit drug use,8,23 and smoking.8,24-26 In turn, a large body of research shows associations between substance use and mental health issues27,28, with the concept of ‘self-medicating’ to alleviate the symptoms of mental illness being commonplace29. However, despite evidence for associations between experiences of adversity in childhood and poor mental health,30-33 including suicide ideation,8,34 depression,8,35-38 and obsessive compulsive symptoms,39 few studies have considered the role of adverse childhood experiences as a distal factor for poor health behaviour, poor physical health, and mental health problems using nationally representative data provided by a community based sample.
This study aims to estimate the prevalence of experiences of adversity in childhood among young Australian women and assess associations between adverse childhood experiences and adult health behaviours and both physical and mental health. It is hypothesised that the prevalence of childhood adverse exposures in young Australian women will be similar to those previously reported in the United States, that adverse childhood experiences will be associated with poor health behaviour, and poorer physical and psychological health, and that the association between adverse childhood experiences and physical health will be mediated by poor health behaviour.
METHODS
Study sample
In 2012-13, 17012 women born between 1989 and 1995 were recruited into the most recent cohort of the Australian Longitudinal Study on Women’s Health, hereafter named the ALSWH 1989-95 cohort. Internet, email and social networking sites were used to invite women to complete an online survey.40 Comparisons with the 2011 Census and the 2011-12 Australian Health Survey demonstrated that respondents were broadly representative of women of the same age in the Australian population, with some over-representation of more educated women.41 The women were invited to participate in annual surveys, with 8961 eligible participants completing the third survey online (54.9% response rate), which included 300 the Adverse Childhood Experiences Scale. Ethics approval for the ALSWH 1989-95 cohort was obtained from the Human Research Ethics Committees of the Universities of Newcastle and Queensland.
Measures
Unless otherwise stated, all variables were collected in 2015 from survey three of the ALSWH 1989-95 cohort.
Adverse childhood experiences: The Adverse Childhood Experience Scale (ACES) was first published in 1998 by Felitti and colleagues.42 The original questionnaire has two major themes (abuse to the individual and household dysfunction) with a total of seven categories measured across 17 items. Questions asked about: (i) psychological abuse (2 items); (ii) physical abuse (2 items); and (iii) childhood sexual abuse (4 items). From a childhood household environment perspective, there were questions that asked about: (iv) substance abuse (2 items); (v) mental illness within the home (2 items); (vi) witnessing mother being treated violently home (4 items); and (vii) criminal behaviour (1 item).
Feedback from the ALSWH pilot survey indicated that participants wanted father-equivalent questions that reflected the ACES category for witnessing violence against the father in the home. This modification was included, such that the third ALSWH survey included eight categories measuring adverse childhood experiences (21 individual items).
Socio-demographics: Participants were categorised as partnered (currently living with a partner) or not. Highest educational qualification was categorised into three groups - ‘Year 12 or less’, ‘Certificate or Diploma’ or ‘University degree or more’. The question, ‘How do you manage on the income you have available?’ had five response options. The first two responses were collapsed together as ‘Impossible/Difficult all the time’, the third response option was unchanged as ‘Difficult some of the time’ and the last two responses were collapsed as ‘Easy/Not too bad’.
Physical health: Self-rated health was measured by the question, ‘In general, would you say your health was: ‘excellent’, ‘very good’, ‘good’, ‘fair’ or ‘poor’. Responses were dichotomised to indicate fair/poor self-rated health versus good/very good/excellent health. History of sexually transmitted infections (STI) was also reported (yes/no), including chlamydia, gonorrhoea, genital herpes, genital warts, Hepatitis B or Hepatitis C. A dichotomous variable was created to indicate whether the participants had indicated ever 301 being diagnosed for one of the listed STI. Chronic conditions diagnosed or recently treated and reported at either Survey 1 or Survey 3 included the following conditions: diabetes (type I or II), heart disease, thrombosis, thyroid condition, hypertension, asthma, endometriosis and polycystic ovary syndrome. The number of conditions reported by Survey 3 were summed and categorised as ‘0’, ‘1’ or ‘2 or more’.
Mental health: The Kessler Psychological Distress Scale (K10) measures psychological distress over the last 30 days, with a score ranging from 10 to 50. The K10 scale is often categorised into low distress (10-15), moderate distress (16-21), high distress (22-29) and very high distress (30-50).43,44 For this study the score was dichotomised to create categories of “very high psychological distress” (score of 30 or more) and “lower levels of distress” (score of 29 or less). Women also reported if they had ever been diagnosed with depression or anxiety, with dichotomous response options (yes/no). Suicide ideation was captured with the question “Have you been feeling that life isn’t worth living?” while self-harm was captured with the question “Have you deliberately hurt yourself or done anything that you knew might have harmed or even killed you?” Response options to these two questions included ‘Never’, ‘Yes, in the last 12 months’ and ‘Yes, more than 12 months ago’, with the latter two response options combined to create an ‘ever’ indicator.
Health behaviours: Participants were asked about their behaviours with respect to alcohol consumption, tobacco smoking and use of illicit drugs. Alcohol consumption was determined using several questions about drinking frequency and quantities,45 where ‘frequent binge drinking’ was defined as five or more drinks at any occasion, at least once a month. Smoking behaviour was categorised as ‘current smoker’ or not. Illicit drug use included responses regarding the use of marijuana or other illicit drugs in the last 12 months or more than 12 months ago, which were then collapsed to create a dichotomous ‘ever exposed’ variable (yes/no). Severely obese was defined as having a Body Mass Index (BMI, measured as kg/m2) of 35 or greater.46 Exercise was measured using several questions about time spent in various activities (walking, moderate or vigorous exercise), and later dichotomised as ‘sedentary/no exercise’ versus ‘some exercise or more’.
Statistical analyses
Descriptive statistics were used to summarise demographic characteristics, risk factors and the physical and mental health of groups of women, by adverse childhood experiences (yes/no), with chi-squared tests used to assess general associations. To examine if adversity 302 in childhood (measured by ACES) was associated with health, prevalence ratios with 99% confidence intervals (using log-binomial models) were calculated for each of the physical and mental health outcomes, initially without controlling for other covariates and also after sequentially controlling for socio-demographic factors and health behaviours (where appropriate). Logistic regression (providing odds ratios) was not deemed to be appropriate as some prevalences were high (>20%) and the interpretation of odds ratios with high prevalences is questionable.47,48 All analyses were conducted using SAS software (version 9.4).
RESULTS
In 2015, 8961 women completed the third survey provided to the 1989-95 cohort participants, which included the modified ACES with eight categories. The ACES items were completed by 8607 (96.0%) women, with nearly 59% of these women indicating exposure to at least one of the adverse childhood categories. Around 26% of women reported one category, 23% of women indicated two or three categories, and 10% of women indicated four or more categories of childhood adversity. As shown in Table 1, the most reported childhood adversity was living with someone who had a mental illness (41.4%), followed by psychological abuse (24.1%) and substance abuse within the home (24.3%). Childhood sexual abuse was reported by 11.5% of women, with 10.1% of women reporting childhood physical abuse. Abuse of parents was also reported, with maternal and paternal abuse indicated by 9.3% and 4.5% of women, respectively.
Table 1: Prevalence of childhood exposure to abuse and household dysfunction for 8607 Australian women aged 20-25 years old in 2015
Item Category Category Item prevalence prevalence % % CHILDHOOD ABUSE Psychological abuse (While you were growing up during the first 18 years of your life, did a 24.1 parent or other adult in the household ...) Often or very often swear at, insult or put you down? 21.6 Often or very often act in a way that made you afraid that 14.1 you would be physically hurt? Physical abuse (While you were growing up during the first 18 years of your life, did a 10.1 parent or other adult in the household ...) Often or very often push, grab, shove or slap you? 8.9 303
Item Category Category Item prevalence prevalence % % Often or very often hit you so hard that you had marks or 5.6 were injured? Childhood sexual abuse (While you were growing up during the first 18 years of your life, did an 11.5 adult or person at least 5 years older ever ...) Touch or fondle you in a sexual way? 10.4 Have you touch their body in a sexual way? 5.5 Attempt oral, anal or vaginal intercourse with you? 4.3 Actually have oral, anal or vaginal sex with you? 3.6 HOUSEHOLD DYSFUNCTION Substance abuse (While you were growing up during the first 18 years of your life, did 24.3 you…) Live with anyone who was a problem drinker or alcoholic? 18.8 Live with anyone who used street drugs? 11.3 Mental illness in home 41.4 (While you were growing up during the first 18 years of your life, …) Was a household member depressed or mentally ill? 40.7 Did a household member attempt suicide? 8.8 Mother treated violently (While you were growing up during the first 18 years of your life, was your 9.3 mother (or stepmother)…) Sometimes often or very often pushed, grabbed, shoved, 8.5 slapped, or had something thrown at her? Sometimes often or very often kicked, bitten, hit with a fist, 4.3 or hit with something hard? Ever repeatedly hit over at least a few minutes? 2.9 Ever threatened with or hurt by a knife or gun? 3.1 Father treated violently* (While you were growing up during the first 18 years of your life, was your 4.5 father (or stepfather)…) Sometimes often or very often pushed, grabbed, shoved, 3.2 slapped, or had something thrown at him? Sometimes often or very often kicked, bitten, hit with a fist, 1.6 or hit with something hard? Ever repeatedly hit over at least a few minutes? 1.0 Ever threatened with or hurt by a knife or gun? 1.4 Criminal behaviour 2.2 (While you were growing up during the first 18 years of your life, …) Did a household member go to prison? 2.2 EXPOSURE TO ANY CATEGORY Original scale (excludes ‘Father treated violently’) 58.5 304
Item Category Category Item prevalence prevalence % % Modified scale (includes ‘Father treated violently’)* 58.6 * The questions relating to witnessing the father treated violently were not part of the original ACE Study by Felitti and colleagues.42
Women with a university degree reported fewer adverse childhood exposure categories than women with less education (p<0.001, Table 2), while women living with a partner were more likely to report a higher number of exposure categories than women not living with a partner (p<0.001). Difficulty managing on available income was also associated with reporting more ACES categories (p<0.001). There was no evidence of a difference in the distribution of exposure categories by age (p=0.15) or area of residence (p=0.27).
Table 2: Distribution of adverse childhood experiences according to demographic characteristics for 8607 Australian women aged 20-25 years old in 2015
Number of ACES categories (%)* 0 1 2 3 4 or more Demographic characteristics (n=3560) (n=2228) (n=1248) (n=682) (n=889) Age (years) 20 14.9 15.8 16.7 18.0 17.5 21 16.4 17.1 18.3 15.4 19.0 22 16.2 18.1 17.1 15.0 16.1 23 17.9 16.3 15.6 19.2 15.7 24 18.5 17.4 18.3 17.6 17.3 25 16.1 15.4 14.0 14.8 14.3 Highest qualificationa Year 12 or below 28.8 31.3 30.9 32.6 36.4 Trade certificate/diploma 22.1 26.6 33.2 35.6 44.5 University degree or above 49.1 42.1 35.9 31.8 19.1 Area of residence Major city 76.0 76.1 76.2 75.0 71.3 Inner regional 16.3 15.4 16.0 17.6 20.7 Outer regional 6.7 7.1 6.6 6.5 6.5 Remote/very remote 1.1 1.4 1.2 0.9 1.5 Living arrangementsa Not living with a partner 68.7 67.6 63.9 61.0 54.9 Living with a partner 31.3 32.4 36.1 39.0 45.1 Stress of managing on available incomea 305
Number of ACES categories (%)* 0 1 2 3 4 or more Demographic characteristics (n=3560) (n=2228) (n=1248) (n=682) (n=889) Easy/not too bad 55.9 47.5 40.0 36.8 26.5 Difficult some of the time 31.7 35.3 38.2 35.6 39.1 Impossible/difficult all the time 12.4 17.2 21.8 27.6 34.4 * row percent a statistically significant, p<0.001
Women who reported experiences in four or more ACES categories had more than double the prevalence of smoking (adjusted PR=2.23; Table 3), and an estimated 60% increase in reporting illicit drug use (adjusted PR=1.60) relative to women who reported no childhood adversity. Higher childhood adversity exposure was also associated with severe obesity, with women reporting 4 or more ACES categories having more than double the prevalence of severe obesity compared to women with no ACES categories (adjusted PR=2.14). After adjusting for other variables, the number of ACES categories was not associated with frequent binge drinking. Sedentary lifestyle was associated with adversity in childhood in the univariate analysis but after adjustment, these associations were reduced.
Table 3: Prevalence of adverse childhood experience categories on health behaviours and attributes among Australian women aged 20-25 years old, with corresponding prevalence ratios (with 99% confidence intervals)
Number of PREVALENCE RATIO (PR) ACES Prevalence Unadjusted PR Adjusted PRa Health behaviours categories (%) (99% C.I.) (99% C.I.) Current smoker 0 10.5 1 1 1 14.9 1.42 (1.21, 1.68) 1.31 (1.11, 1.54) 2 22.2 2.12 (1.79, 2.51) 1.83 (1.55, 2.17) 3 22.0 2.10 (1.71, 2.57) 1.72 (1.40, 2.10) 4 or more 32.1 3.06 (2.60, 3.59) 2.23 (1.89, 2.64)
Frequent binge drinkerb 0 30.8 1 1 1 31.7 0.99 (0.95, 1.03) 0.98 (0.94, 1.03) 2 30.3 1.01 (0.96, 1.06) 0.99 (0.94, 1.04) 3 28.6 1.03 (0.97, 1.10) 1.02 (0.96, 1.08) 4 or more 25.2 1.08 (1.03, 1.14) 1.04 (0.99, 1.10)
Used illicit drugs (ever) 0 25.8 1 1 1 31.6 1.22 (1.11, 1.35) 1.19 (1.08, 1.31) 2 37.2 1.44 (1.29, 1.61) 1.35 (1.21, 1.50) 306
3 40.8 1.58 (1.39, 1.79) 1.44 (1.27, 1.63) 4 or more 47.5 1.84 (1.66, 2.04) 1.60 (1.43, 1.78)
Severely obese (BMI≥35) 0 4.0 1 1 1 6.5 1.63 (1.24, 2.13) 1.46 (1.12, 1.92) 2 7.8 1.97 (1.46, 2.66) 1.65 (1.23, 2.23) 3 10.0 2.54 (1.82, 3.53) 1.90 (1.36, 2.65) 4 or more 13.2 3.33 (2.51, 4.40) 2.14 (1.61, 2.86)
Sedentary/no exercise 0 4.7 1 1 1 6.8 1.45 (1.13, 1.87) 1.31 (1.02, 1.69) 2 6.5 1.38 (1.02, 1.88) 1.19 (0.87, 1.62) 3 8.2 1.74 (1.23, 2.46) 1.44 (1.02, 2.04) 4 or more 8.6 1.83 (1.34, 2.49) 1.34 (0.97, 1.85) a adjusted for demographics (age, education, living with a partner, income stress, area of residence) b frequent binge drinking : five or more drinks per occasion, at least monthly
Increasing exposure to childhood abuse and household dysfunction was associated with poor physical health, STI diagnoses and poor mental health (Table 4). Compared to women who did not report childhood adversity, women who responded positively to four or more ACES categories had more than double the prevalence of poor self-reported health, even when demographic factors were controlled for (unadjusted PR=3.12, adjusted PRa=2.31). This diminished slightly when health behaviours were also considered (adjusted PRb=1.79). The prevalence of a STI was 36% higher for women who had indicated 4 or more ACE categories (adjusted PRb=1.36). Mental health outcomes were also associated with higher numbers of ACE categories. Compared to women with no ACE categories, the prevalence of recent very high psychological distress was tripled for women who had 4 or more ACE categories, even after adjusting for demographic and health behaviour factors (adjusted PRb=2.78, Table 4). Similar elevated prevalences were observed for increasing numbers of ACE categories with respect to diagnoses of depression and anxiety, and for reported suicide ideation and self- harm (Table 4). 307
Table 4: Prevalence of adverse childhood experience categories on health outcomes among Australian women aged 20-25 years old, with corresponding prevalence ratios (with 99% confidence intervals)
Number of Outcome PREVALENCE RATIO (PR) ACES Prevalence Unadjusted PR Adjusted PRa Adjusted PRb Health outcomes categories (%) (with 99% C.I.) (with 99% C.I.) (with 99% C.I.) PHYSICAL HEALTH Poor/fair health 9.4 1 1 1 (self-rated) 0 1 16.1 1.72 (1.46, 2.03) 1.58 (1.34, 1.86) 1.51 (1.29, 1.78) 2 19.2 2.04 (1.70, 2.45) 1.73 (1.44, 2.08) 1.59 (1.33, 1.90) 3 25.2 2.69 (2.21, 3.27) 2.23 (1.83, 2.71) 1.94 (1.62, 2.34) 4 or more 29.2 3.12 (2.63, 3.70) 2.31 (1.93, 2.76) 1.79 (1.51, 2.12)
Sexual transmitted 10.6 1 1 1 infection 0 (ever) 1 13.2 1.24 (1.05, 1.47) 1.25 (1.05, 1.48) 1.17 (0.99, 1.39) 2 15.2 1.44 (1.19, 1.74) 1.39 (1.14, 1.70) 1.22 (1.01, 1.48) 3 17.0 1.61 (1.28, 2.01) 1.52 (1.21, 1.92) 1.33 (1.06, 1.67) 4 or more 19.0 1.79 (1.47, 2.19) 1.66 (1.35, 2.04) 1.36 (1.11, 1.67)
MENTAL HEALTH Very high 8.2 1 1 1 psychological distress 0 (K10 score = 30 or 16.1 1.98 (1.66, 2.35) 1.78 (1.50, 2.11) 1.76 (1.48, 2.10) more) 1 2 19.7 2.41 (2.00, 2.90) 2.04 (1.70, 2.46) 1.95 (1.62, 2.36) 3 25.9 3.17 (2.59, 3.87) 2.46 (2.02, 3.00) 2.28 (1.87, 2.78) 4 or more 38.3 4.69 (3.98, 5.53) 3.26 (2.75, 3.86) 2.78 (2.34, 3.32)
Depression (ever) 0 23.0 1 1 1 1 41.4 1.80 (1.64, 1.98) 1.73 (1.58, 1.90) 1.70 (1.55, 1.87) 2 47.4 2.06 (1.86, 2.28) 1.90 (1.72, 2.09) 1.83 (1.65, 2.02) 3 56.3 2.45 (2.20, 2.72) 2.16 (1.94, 2.40) 2.03 (1.84, 2.24) 4 or more 65.1 2.83 (2.58, 3.10) 2.31 (2.10, 2.54) 2.03 (1.84, 2.24)
Anxiety (ever) 0 21.3 1 1 1 1 36.0 1.69 (1.53, 1.87) 1.62 (1.47, 1.79) 1.58 (1.43, 1.75) 2 40.7 1.91 (1.71, 2.13) 1.77 (1.58, 1.97) 1.70 (1.52, 1.90) 3 45.7 2.15 (1.90, 2.43) 1.94 (1.71, 2.20) 1.83 (1.62, 2.07) 4 or more 53.9 2.53 (2.28, 2.81) 2.15 (1.93, 2.40) 1.89 (1.69, 2.12) 308
Suicide ideation 43.3 1 1 1 (ever) 0 1 63.3 1.46 (1.38, 1.55) 1.41 (1.33, 1.50) 1.41 (1.33, 1.49) 2 72.0 1.66 (1.56, 1.77) 1.56 (1.47, 1.65) 1.55 (1.46, 1.64) 3 76.7 1.77 (1.66, 1.89) 1.63 (1.53, 1.74) 1.60 (1.50, 1.70) 4 or more 86.9 2.01 (1.90, 2.12) 1.74 (1.65, 1.85) 1.64 (1.54, 1.74)
Self-harm intention 25.1 1 1 1 (ever) 0 1 41.2 1.65 (1.50, 1.80) 1.57 (1.43, 1.71) 1.54 (1.41, 1.69) 2 49.6 1.98 (1.80, 2.17) 1.79 (1.63, 1.97) 1.73 (1.57, 1.90) 3 58.4 2.33 (2.11, 2.58) 2.05 (1.85, 2.27) 1.88 (1.70, 2.08) 4 or more 71.6 2.86 (2.63, 3.10) 2.36 (2.16, 2.57) 2.03 (1.84, 2.23) a adjusted for demographics (age, education, living with a partner, income stress, area of residence) b adjusted for demographics (age, education, living with a partner, income stress, area of residence) and health behaviours/attributes (current smoker, frequent binge drinking, use of illicit drugs, severely obese with BMI≥35, sedentary/no exercise, and number of chronic conditions)
DISCUSSION
Around 10% of participants reported experiencing adverse childhood experiences across four or more catalogues, indicating a significant burden of risk for young Australian women. This prevalence was similar to other rates previously reported in the literature,49,50 with differences likely due to differing samples. For example, Felitti et al. included males and females where the current sample included females only.51
Available evidence has shown that multiple childhood adversities during childhood can lead to irreversible damage to the evolving brain and also affects the immune, neurological and endocrine systems, predisposing individuals to unhealthy behaviours, mental illness, and chronic diseases.52 In this study, we identified a strong dose-response relationship between adverse childhood experiences and a range of health conditions and health-related behaviours using nationally representative Australian data.
In agreement with previous similar studies, there was a dose response relationship between ACE score and smoking,49,53 and illicit drug use54,55 behaviours. Previous research has shown that experiencing multiple adversities leads to underdeveloped brain architecture, which may 309 in turn lead to substance abuse as a means of coping.56 Substance abuse screening, prevention, and treatment should also consider childhood adversities as a potential root cause of the behaviours. In our study severe obesity was strongly associated with experiencing childhood adversities across four or more domains, which is consistent with findings from a previous similar study.57 The association between experiences of adversity in childhood and chronic disease conditions such as diabetes, heart disease, hypertension, and stroke may thus be mediated by obesity.
Inconsistent with previous studies51,58 experiencing four or more ACES categories was not associated with frequent binge drinking after adjusting for other variables. The difference might be due to the difference in the definition of the outcome variable across the studies. For instance a study by Felitti and colleagues51 did not assess the frequency or quantity of alcohol consumed, rather it assessed whether the individual considered themselves an alcoholic or not. Moreover, the majority of previous studies were not gender-specific in investigating the association between adverse childhood experiences and binge drinking.
Findings from this study are in line with an earlier study that found significant associations between four and more ACES categories and poor self-rated health.59-61 Researchers have identified that poor self-reported health is associated with substance use and mental health.62 The current study suggests that a common distal predisposing factor to all of these factors, that is, poor self-rated health, substance use and mental health, is the experience of adversity in childhood.
Researchers have previously identified a positive causal relationship between the number of ACES categories and risky sexual behaviour outcomes such as sexually transmitted infection.63,64 Likewise, this study found a dose-response relationship between exposure to adverse childhood experiences and sexually transmitted infections.
All of the psychological distress related health conditions such as a history of depression, anxiety, and suicidal ideation have positive and strong associations with experiencing four or more ACES categories. Similar community-based surveys have reached the same conclusion.65-67 A systematic review and meta-analysis which included 37 primary studies found that individuals who reported four or more ACES categories were more likely to develop mental illness such as depression, anxiety, posttraumatic stress disorder compared with individuals experiencing no childhood adversities.68 The finding highlights the 310 importance of routine screening of adverse childhood experiences for clients who present with psychological distress and mental disorders.
Primary prevention strategies such as building parenting skills, financial support for families and language and communication development support have been effective in averting health and health related conditions linked to childhood adversities.52,69 Moreover, training of health professionals, psychiatrists and counsellors about screening and early intervention for children exposed to childhood adversities are effective measures to minimize the impact of adverse childhood experiences in later life.52 It is important for treating health practitioners to consider the possibility of childhood events when treating women with complex health problems. Similalry, it is also important that practitioners providing psychological support for women who have experienced adverse childhood experiences consider the health behaviour and physical health problems that may need to be addressed in order to optimise quality of life. Taken together, the findings imply the need for an intersectoral approach for addressing the long term impact of adverse childhood experiences.
Limitations
The findings of this study should be interpreted with the following limitations in mind. The outcome and exposure variables in this study were assessed using self-reported items thus the response may be subjected to under or over reporting of the prevalence. The possibility of underreported outcome and exposure variables may bias the finding to the null hypothesis. Therefore the relationship between outcome variables and ACES score is possibly realistic. Adverse childhood experiences were assessed retrospectively which can lead to recall bias. However, researchers have identified the reliability of ACES items to measure a range of childhood adversities.70 It is noteworthy that the restricted age range may have led to the insignificant findings for age. The study participants recruited in this study were found to be comparable with Australian census data in terms of geographical distribution, marital status, and age composition.71,72 Therefore, the results should be broadly generalizable to young women across Australia with the exception of overrepresentation of study participants with tertiary education.
Conclusion
Taken together, the results indicate the need for a holistic understanding of factors that drive poor health behaviour and underlie poor health. The current study clearly shows adverse childhood experiences directly impact both mental health and health behaviour. Furthermore, 311 adverse childhood experiences indirectly impacted on physical health to some extent and showed a clear direct relationship with poorer physical health after adjustment for demographics and health behaviour. Health promotion and prevention activities that focus on changing current behaviour outside of the context in which the behaviour arose is at best ineffective and at worst further blaming individuals who have in reality already experienced sufficient abuse. 312
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Appendix U: Survey 3 for the Australian Longitudinal Study on Women’s
Health 1989-95 cohort 319 320 321 322 323 324 325 326 327 328 329
330 331 332
333 334 335 336 337 338 339 340 341 342 343 344
345 346 347 348
349 350 351 352 353 354 355
356 357
358 359 360
361 362 363
364 365
366 367
368 369
370 371 372 373 374 375 376 377
378 379 380 381 382 383
384 385 386 387
388 389
390 391
392 393
394 395
396 397
398 399
400 401 402 403 404 405 406
Appendix V: Survey 4 for the Australian Longitudinal Study on Women’s
Health 1989-95 cohort 407 408 409 410
411 412 413
414 415 416
417 418
419 420
421 422
423 424 425 426 427
428 429
430 431 432 433
434 435 436 437 438 439 440