Examining the correlates of cigarette smoking,
e-cigarette use and dual use among Canadian
post-secondary students
Tobacco Use Insights
Volume 17: 1–9
© The Author(s) 2024
Article reuse guidelines:
sagepub.com/journals-permissions
DOI: 10.1177/1179173X241247414
Matthew James Fagan, Jian Kun Zhan, Kelly B. Wunderlich and
Guy Faulkner 
School of Kinesiology, University of British Columbia, Vancouver, Canada.
ABSTRACT
Many Canadians use nicotine products such as cigarettes and e-cigarettes. A particular subpopulation of concern is post-secondary students given
they have a higher prevalence of use. Many correlates of cigarette smoking and e-cigarette use have been identified. However, less focus has been
on examining the correlates of cigarette smoking, e-cigarette use and dual use. This study explores the correlates of different nicotine modality use
in post-secondary students. Using data from the Canadian Campus Wellbeing Survey (CCWS; n = 27,164), a multi-level nominal regression
assessed the correlates of nicotine modality use. In comparison to individuals who were <20, individuals 20-24 (OR = .448, 95% CI .321, .625), 25-29
(OR = .140, 95% CI .093, .212), 30-34 (OR = .076, 95% CI .046, .125) and over 35 (OR = .041, 95% CI .024, .071) had lower odds of e-cigarette use
compared to cigarette smoking. Identifying as a woman (OR = 1.553, 95% CI 1.202, 2.006), non-heterosexual (OR = .642, 95% CI = .485,0.851),
current cannabis user (OR = 1.651, 95% CI 1.296, 2.104), and being an international student (OR = .350, 95% CI .251, .487) also impacted the odds
of e-cigarette use vs only cigarette smoking. When considering dual use vs cigarette smoking, individuals aged 20-24 (OR = .491, 95% CI .337,
.717), 25-29 (OR = .221, 95% CI .137, .357), 30-34 (OR = .163, 95% CI .091, .292) and over 35 (OR = .122, 95% CI .065, .230) had lower odds than
individuals <20. Current cannabis use (OR = 1.680, 95% CI = 1.209, 2.138), binge drinking (OR = 1.885, 95% CI 1.384, 2.568), and international
student status (OR = .689, 95% CI .476, .996) also impacted cigarette smoking vs dual-use. Overall, a minority of young adults (11.5%) at postsecondary institutions in our sample use nicotine products, and the higher prevalence of e-cigarette use warrants continued monitoring. Health
promotion campaigns addressing e-cigarette use are required. Additionally, tailored intervention efforts could prioritize the treatment needs of
international students studying in Canada.
KEYWORDS: E-Cigarette, cigarette, nicotine, post-secondary students, dual use
TYPE: Original Research Article
FUNDING: The author(s) disclosed receipt of the following financial support for the research,
authorship, and/or publication of this article: This work was supported by the The Rossy
Foundation and the University of British Columbia.
DECLARATION OF CONFLICTING INTERESTS: The author(s) declared no potential
conflicts of interest with respect to the research, authorship, and/or publication of this
article.
CORRESPONDING AUTHOR: Matthew James Fagan, School of Kinesiology, University of
British Columbia, Lower Mall Research Station 2259, Lower Mall Rm 337, Vancouver, BC V6T
1Z4, Email: matthew.fagan@ubc.ca
Introduction
E-cigarettes are often promoted as a safer alternative to
smoking and a potential tool for smoking cessation. 7
Additionally, research has indicated that young adults
(aged 20-24) primarily use e-cigarettes for enjoyment
(27%), stress reduction (25%), and smoking cessation
(24%). 6 However, there are concerns that e-cigarette use
may lead to smoking initiation. 8 Studies have shown that ecigarette users have 2 to 4 times higher odds of expressing
an intention to smoke cigarettes compared to non-ecigarette users. 9,10 It is important to note that the relationship between e-cigarette use and smoking initiation is
likely influenced by age. 2 For example, Soneji et al 11 found
that adolescents who reported using e-cigarettes had more
than six times higher odds of starting to smoke cigarettes
than those who had never used e-cigarettes. Understanding
factors related to dual use of e-cigarettes and cigarettes
compared to using only one modality would provide potential avenues for prevention and intervention as there is
RECEIVED: October 31, 2023. ACCEPTED: March 24, 2024.
Nicotine, a highly addictive and hazardous psychoactive
substance, can have detrimental effects on various organs in
the human body.1 Unsurprisingly, tobacco consumption, a
primary source of nicotine, continues to contribute significantly to global mortality and morbidity rates.2 In
Canada, cigarette smoking and e-cigarette use are the
predominant modes of nicotine consumption.3 According
to Statistics Canada,4 the number of individuals smoking
cigarettes aged 12 years and older decreased from 5.3
million in 2015 to 3.8 million in 2021. However, there has
been a significant surge in e-cigarette usage in recent years.
Globally, e-cigarette users rose from 7 million in 2011 to 41
million in 2018.5 E-cigarette use is particularly prevalent
among Canadian youth and young adults.6 In 2021, nearly
half (48%) of young adults aged 20-24 and approximately
29% of youth aged 15-19 reported trying e-cigarettes,
compared to only 13% of adults over the age of 25 6.
Creative Commons CC BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/
licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as
specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
Tobacco Use Insights
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currently no agreement on a recommended general approach for the treatment of people who are both smoking
and vaping and are seeking help to quit. 12
Several studies have explored the factors associated with
cigarette smoking or e-cigarette use.13-16 For instance,
demographics (such as age, mental health, sex, gender, and
sexuality), health behaviours (including physical activity and
nutrition), and institutional factors (such as school closures
due to COVID-19) have been identified as correlates of
smoking.13-16 Many of these factors also play a role in ecigarette use.13-18 However, there have been conflicting
findings regarding the correlates of e-cigarette use and
smoking. For example, sport participation has been positively associated with e-cigarette use but negatively associated with smoking among secondary students.15 Further
research is needed to understand the factors associated with
the dual use of e-cigarettes and cigarette smoking. There is
limited work that has attempted to identify the correlates of
dual use of cigarettes and e-cigarettes.19-21 One study found
that depressive symptoms and adverse childhood experiences (ACEs) were associated with all trajectories of dual
use.19 Piper et al. found that dual users were more likely to
be younger and white and did not differ in their intentions
to quit smoking. 20 Finally, Zavala-Arciniega et al, found
that in a Mexican sample, compared to individuals only
smoking cigarettes, dual users were more likely to be
younger, have a higher smoking dependence, and recently
quit smoking.21
Understanding the factors contributing to different
nicotine use patterns among post-secondary students could
inform targeted interventions for educational institutions.
These interventions could consider demographic factors
such as gender, age, education level, and socioeconomic
status. Additionally, it is important to consider healthrelated factors such as alcohol consumption and physical
activity levels. There is also limited literature on the dual use
of e-cigarettes and cigarettes compared to the singular use of
either product among post-secondary students. This study
explores the factors associated with the dual use of cigarettes
and e-cigarettes vs cigarettes alone.
Methods
Data collection
This study uses data from the 2022 Winter deployment of
the Canadian Campus Wellbeing Survey (CCWS; https://
www.ccws-becc.ca/). The Winter cycle included 21 postsecondary institutions across Canada with a mean response
rate of 18.8%. This cycle from the CCWS has been used
previously.22 Please see Faulkner et al.23 for further description of the CCWS. This project was approved by the
appropriate institutional ethics board (approval H19-01907)
and the CCWS Data Access Committee approved using
aggregated data.
n
Measures
Outcome measure
Modality of nicotine use was the primary outcome of this study.
This includes three categories: smoking cigarettes, e-cigarette
user or dual user. Cigarette smoking was collected in the CCWS
by asking, “Which of the following best applies to you?” with
response options ranging from “I smoke cigarettes every day” to “I
have never been a smoker”. Consistent with previous work,22 a
dichotomous measure of currently smoking cigarettes was
created by including “I smoke cigarettes every day” and “I smoke
cigarettes but not every day” as current use. This was done as
current use is typically defined as use within the last month.24
The CCWS collects e-cigarette use by asking, “During the
past 30 days, how often did you use an e-cigarette or vaping device?”
with the response options ranging from daily or almost daily to
never. Consistent with previous work,22 a dichotomous variable
was created to reflect current use.24 Finally, if an individual was
found to use cigarettes and e-cigarettes through these measures,
they were coded as dual users. It should be noted that the
response options for both cigarette use and e-cigarette use
include “I don’t know” and “I prefer not to answer”. If an individual selected one of these options for either cigarette
smoking or e-cigarette use, they were excluded from the
analysis.
Predictors
Demographic and institutional-level factors. Consistent with
previous work,22 several demographic covariates were used in
this analysis. These demographic covariates included; age
groups (>20 (ref),20-24, 25-29, 30-34, ≥35 years old), gender
(man (ref), woman, non-binary person, two-spirit, I prefer not to
answer), ancestry (White (ref), Asian, Indigenous, other), trans
experience (no (ref), yes), sexual orientation (heterosexual (ref),
non-heterosexual, I prefer not to answer), and international
student status (domestic (ref) or international).
Additionally, food security was used as a proxy for socioeconomic status in our study. It was assessed through six items
from the Canadian Community Health Survey; Household
Food Security Survey Module.25 Each item asks about food
security in the last 12 months. Each question was coded affirmative or negative based on the response, scoring 1 for affirmative and 0 for negative, creating an integer from 0-6. One
example item is “The food that (I/we) brought just did not last, and
(I/we) did not have money to get more”. This score is then
categorized into three levels: food secure (scores <2), low food
security (2-4) and very low food security (5 or greater). This
measure has been used previously as a proxy for socioeconomic
status with the CCWS data.22
Finally, institutional-level factors were included in the data
analysis. More specifically, the location of the institution (Alberta (ref), Ontario, British Columbia, Atlantic provinces, Quebec/
Manitoba), college or university institution, and the institution
Fagan et al
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size in terms of enrollment (<5000 (ref), 5001-20000, 2000140000, >40000 students).
Physical activity. The amount of physical activity was captured
using the International Physical Activity Questionnaire
(IPAQ).26 The IPAQ assesses weekly moderate and vigorous
physical activity (MVPA) performed in bouts greater than 10
minutes. Following IPAQ truncation rules, each intensity of
physical activity was capped at 180 minutes a day. This amount
of MVPA was then used to create a variable to reflect meeting
the Canadian MVPA guidelines of 150 MVPA minutes/
week.27 When considering the psychometrics of the IPAQ, it
has a criterion validity similar to other physical activity questionnaires (r = .30) and a high test-retest reliability (r = .80).28
Sleep. The items used to assess sleep followed evidence-based
guidelines to measure self-reported sleep in Canadians through
population-based surveillance systems.29 Sleeping and waking
time were collected on a typical weekend and weekday (response
options are available in 30-minute increments). These four
items calculate total sleep for weekends and weekdays separately. The current study created a dichotomous variable of
meeting the sleep guidelines by identifying individuals who met
the sleep recommendations on both weekends and weekdays (79 hours;27).
Cannabis use. Cannabis use was measured by asking, “In the past
30 days, how often did you use cannabis?”. The response options
ranged from not in the past 30 days to daily. Consistent with
previous work with the CCWS,22 a dichotomous measure of
current use of cannabis was created to indicate use in the last
30 days.24
Binge drinking. Binge drinking was measured by asking,
“During the past 30 days, how often have you had 4 or more
drinks (female sex) OR 5 or more drinks (male sex) on one
occasion?”. The response options range from daily to I do not
drink alcohol. Consistent with previous work with the
CCWS,22 our analysis created a dichotomous variable of
current binge drinking, to reflect binge drinking at least once
in the last month.24
Mental distress. Consistent with previous work with the
CCWS,22 the Kessler Psychological Distress Scale (K10) was
used to collect a global measure of psychological distress.30,31
The K10 has shown acceptable reliability and validity across
sociodemographic subsamples.30 Each item within the K10 is
scored on a scale from 1-5. The ten items are then summed to
obtain a total score ranging from 10 to 50. After obtaining the
total score, categories were created to reflect the level of psychological distress; well (scores <19), mild mental distress
(scores 20-24), moderate mental distress (scores 25-29), and
severe mental distress (scores >29).
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Data analysis
Descriptive statistics were used to determine demographic and
mental health differences (Student’s t-test for continuous
variables and a chi-square test for categorical). All analyses were
conducted using R version 4.2.1 and R Studio. The mblogit
version .9.4.2 package was used to create a multi-level nominal
regression as the students are nested within their institutions.
The multi-level nature of the regression was included based on
the ICC and the design effects calculated.32 Using the mblogit
function, a multi-level nominal regression was created to assess
the correlates of the modality of nicotine use.
Data inspection and data exclusion
To be included in the analysis, a participant must have been
classified as currently smoking cigarettes, using an e-cigarette or
dual using [11.45% of the total sample (3105/27,164)]. Missing
data on the included variables ranged from 0 to ∼19%. There
was an indication the data were not missing at random. For
example, individuals missing gender or sexual orientation were
more likely to be missing other data (e.g., cigarette smoking or
e-cigarette use). As the CCWS sample is not representative and
data were not missing at random, our analysis only includes
complete cases.33
Results
Descriptive statistics
Forty-nine percent of participants (n = 3105) identified as
White, 57% as women, and 80% as domestic students. Of the
3105 nicotine users, 22%, 58%, and 20% of the sample were
smoking cigarettes, e-cigarette users or dual users, respectively
(see Table 1 for a breakdown of demographics by nicotine
modality).
Correlates of nicotine modality
Several correlates of nicotine modality were identified in our
analyses. For ease of interpretation from the multi-level nominal
regression, a breakdown of individuals smoking cigarettes vs ecigarette users will be presented, followed by individuals
smoking cigarettes vs dual users (see Table 2).
Smoking cigarettes vs e-cigarette users
Compared to men, women had 55% higher odds of reporting
current e-cigarette use than smoking. Compared to <20 years,
individuals 20-24, 25-29, 30-34 or over 35 years old had approximately 65%, 86%, 93% and 96% lower odds of being an ecigarette user, respectively. Compared to individuals who
identified as heterosexual, non-heterosexuals were at 38% lower
odds of being an e-cigarette user than smoking cigarettes.
Individuals who were current cannabis users had 65% higher
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Table 1. Demographic and Descriptive Statistics.
CHARACTERISTIC
SMOKING CIGARETTES, N = 682A
E-CIGARETTE USE, N = 1,789A
DUAL USE, N = 634A
P-VALUEB
Gender
Man (ref)
286 (42%)
551 (31%)
229 (37%)
Woman
344 (51%)
1155 (65%)
349 (56%)
Non-binary
29 (4.3%)
51 (2.9%)
23 (3.7%)
Two-spirit
5 (.7%)
3 (.2%)
6 (1.0%)
Prefer not to answer
15 (2.2%)
12 (.7%)
14 (2.3%)
3
17
13
No
635 (94%)
1703 (96%)
575 (93%)
Yes
28 (4.1%)
45 (2.5%)
23 (3.7%)
Prefer not to answer
16 (2.4%)
21 (1.2%)
23 (3.7%)
3
20
13
Under 20
70 (10%)
655 (37%)
186 (30%)
20-24
252 (37%)
889 (50%)
302 (48%)
25-29
145 (21%)
147 (8.2%)
76 (12%)
30-34
98 (14%)
48 (2.7%)
30 (4.8%)
Over 35
115 (17%)
43 (2.4%)
33 (5.3%)
Missing
2
7
7
Heterosexual/Straight
444 (66%)
1234 (70%)
397 (64%)
Non-heterosexual
202 (30%)
499 (28%)
194 (31%)
I prefer not to answer
29 (4.3%)
34 (1.9%)
29 (4.7%)
7
22
14
White
296 (43%)
1000 (56%)
285 (46%)
Asian
214 (31%)
346 (19%)
192 (31%)
Indigenous
27 (4.0%)
38 (2.1%)
12 (1.9%)
Other
144 (21%)
396 (22%)
133 (21%)
Missing
1
9
12
Food secure
286 (53%)
803 (58%)
253 (52%)
Low food security
123 (23%)
300 (22%)
117 (24%)
Very low food security
131 (24%)
272 (20%)
120 (24%)
142
414
144
No
261 (38%)
491 (28%)
221 (35%)
Yes
421 (62%)
1294 (72%)
409 (65%)
0
4
4
No
349 (53%)
988 (57%)
357 (59%)
Yes
308 (47%)
749 (43%)
249 (41%)
25
52
28
No
380 (56%)
663 (37%)
265 (42%)
Yes
298 (44%)
1111 (62%)
359 (57%)
I don’t know/Prefer not to answer
2 (.3%)
15 (.8%)
10 (1.6%)
2
0
0
No
261 (38%)
510 (29%)
141 (22%)
Yes
404 (60%)
1269 (71%)
480 (76%)
I don’t know/Prefer not to say
13 (1.9%)
10 (.6%)
13 (2.1%)
4
0
0
Daily or almost daily
0 (0%)
851 (48%)
352 (56%)
Less than daily, but at least once a week
0 (0%)
394 (22%)
148 (23%)
Less than weekly, but at least once in the past 30 days
0 (0%)
544 (30%)
134 (21%)
Never
495 (73%)
0 (0%)
0 (0%)
Not in the past 30 days, but from time to time
187 (27%)
0 (0%)
0 (0%)
Missing
Trans experience
Missing
<.001
Age group
<.001
Sexual orientation
Missing
<.001
Ethnicity
<.001
Food security
Missing
.034
Met MVPA
Missing
<.001
Met sleep
Missing
.10
Current cannabis use
Missing
<.001
Current binge drinking
Missing
<.001
E-cigarette use
<.001
(Continued)
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Table 1. Continued.
SMOKING CIGARETTES, N = 682A
E-CIGARETTE USE, N = 1,789A
DUAL USE, N = 634A
0 (0%)
301 (17%)
0 (0%)
I have never been a smoker (i.e. smoked for a year or more)
0 (0%)
1175 (66%)
0 (0%)
I have stopped smoking completely in the last year
0 (0%)
131 (7.3%)
0 (0%)
I smoke cigarettes (including hand-rolled) every day
283 (41%)
0 (0%)
208 (33%)
I smoke cigarettes (including hand-rolled), but not every day
399 (59%)
0 (0%)
426 (67%)
0 (0%)
182 (10%)
0 (0%)
Well
111 (16%)
193 (11%)
58 (9.3%)
Mild mental distress
123 (18%)
322 (18%)
102 (16%)
Moderate mental distress
143 (21%)
350 (20%)
125 (20%)
Severe mental distress
298 (44%)
907 (51%)
341 (54%)
7
17
8
Alberta
55 (8.1%)
169 (9.4%)
53 (8.4%)
Atlantic
15 (2.2%)
117 (6.5%)
36 (5.7%)
British Columbia
84 (12%)
228 (13%)
82 (13%)
Manitoba/Quebec
92 (13%)
260 (15%)
71 (11%)
Ontario
436 (64%)
1015 (57%)
392 (62%)
Domestic
487 (71%)
1604 (90%)
495 (78%)
International
195 (29%)
185 (10%)
139 (22%)
<=5000
30 (4.4%)
97 (5.4%)
26 (4.1%)
5001-20000
161 (24%)
523 (29%)
141 (22%)
20001-40000
181 (27%)
556 (31%)
174 (27%)
>40000
310 (45%)
613 (34%)
293 (46%)
CHARACTERISTIC
Tobacco use
I do not smoke cigarettes at all, but I do smoke tobacco of some kind (e.g. Pipe or shisha)
I stopped smoking completely more than a year ago
<.001
Mental distress
Missing
<.001
Province
<.001
Student status
<.001
Size of institution
a
b
P-VALUEB
<.001
n (%).
Pearson’s Chi-squared test.
odds of being an e-cigarette user compared to smoking cigarettes. Finally, international students were at 65% lower odds of
being e-cigarette users when compared to smoking cigarettes.
Smoking cigarettes vs dual-users
Compared to <20 years, individuals 20-24, 25-29, 30-34 or over
35 years old had approximately 51%, 78%, 84%, and 88% lower
odds of being a dual-user, respectively. Individuals who were
current cannabis users were at 61% higher odds of being a dual
user than smoking cigarettes. Individuals who were current
binge drinkers had 89% higher odds of being a dual user
compared to smoking cigarettes. Individuals attending postsecondary institutions in Ontario were at 60% lower odds of
being dual users when compared to smoking cigarettes. Finally,
international students were at 31% lower odds of being dual
users when compared to smoking cigarettes.
Discussion
This paper aims to provide insights into the factors associated
with the modality of nicotine use in a large sample of Canadian post-secondary students. Given that nicotine use,
particularly cigarette smoking, remains a leading cause of
morbidity and economic burden in Canada,34,35 it is crucial
to gain a better understanding of usage patterns. Our study
found approximately 11.5% of the total sample were using
nicotine. Of the nicotine users, 22% were currently smoking
cigarettes, 58% were current e-cigarette users, and 20% were
dual users. A similar prevalence has been found in the Canadian Tobacco and Nicotine Survey (CTNS) in 2020, as the
prevalence of smoking in young adults (age 20-24) was
approximately 8% and 11% among adults (older than 25).36
However, in a post-secondary context, the Canadian Postsecondary Education Alcohol and Drug Use Survey (CPDS)
found a higher prevalence of daily and occasional cigarette
smoking (10%) than that of current e-cigarette users
(∼6%).37 Broadly, these findings indicate that a minority of
Canadians smoke or vape, yet continued tobacco control
interventions are required.
Several demographic and health behaviours were associated
with the odds of different nicotine modalities. Regarding the
factors associated with cigarette smoking and e-cigarette use,
several factors played a significant role. Women were more
likely to use e-cigarettes than to smoke cigarettes. Our gender
finding aligns with the results of the CTNS, which showed a
higher prevalence of smoking among men compared to
women.6 Additionally, participants in the older age categories
were more likely to smoke cigarettes rather than use ecigarettes, consistent with the CTNS findings.6 However,
the association of age with modality should be examined
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Table 2. Results From the Multi-Nominal Regression.
SMOKING CIGARETTES (REF) VS E-CIGARETTE USER
OR
SMOKING CIGARETTES (REF) VS
DUAL USE
95%CI
OR
95%CI
Gender
Man (ref)
-
Woman
Non-binary
1.553
1.212
-
-
-
1.202, 2.006
.552, 2.662
1.194
.769
.885, 1.611
.312, 1.895
Two-spirit
.713
.119, 4.265
2.172
.406, 11.607
Prefer not to answer
.444
.223, 1.889
.649
.152, 1.295
Trans experience
No
-
-
-
-
Yes
.654
.305, 1.403
.974
.416, 2.279
Prefer not to answer
.783
.297, 2.063
1.502
.574, 3.928
Age group
Under 20
-
-
-
-
20-24
.448
.321, .625
.491
.337, .717
25-29
.140
.093, .212
.221
.137, .357
30-34
.076
.046, .125
.163
.091, .292
Over 35
.041
.024, .071
.122
.065, .230
Sexual orientation
Heterosexual
-
-
-
-
Non-heterosexual
.642
.485, .851
.916
.662, 1.267
Prefer not to say
.998
.474, 2.099
1.356
.609, 3.016
Ethnicity
White
-
-
-
-
Asian
.965
.694, 1.343
1.302
.892, 1.901
Indigenous
Other
.711
1.372
.338, 1.494
.999, 1.885
.728
1.126
.284, 1.866
.775, 1.637
Food security
Food secure
-
-
-
-
Low food security
.930
.688, 1.257
1.031
.726, 1.464
Very low food security
.781
.575, 1.061
1.003
.706, 1.425
1.141
.888, 1.464
.952
.713, 1.270
Met MVPA
No
Yes
Met sleep
No
-
-
-
-
Yes
1.006
.793, 1.277
.830
.629, 1.097
No
Yes
1.651
1.296, 2.104
1.608
1.209, 2.138
I don’t know/Prefer not to answer
2.831
.602, 13.311
4.457
.887, 22.394
Current cannabis use
(Continued)
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Table 2. Continued.
SMOKING CIGARETTES (REF) VS E-CIGARETTE USER
OR
SMOKING CIGARETTES (REF) VS
DUAL USE
95%CI
OR
95%CI
Current binge drinking
No
-
-
-
-
Yes
1.153
.895, 1.484
1.885
1.384, 2.568
I don’t know/Prefer not to say
.562
.174, 1.808
1.779
.597, 5.305
Mental distress
Well
-
-
-
-
Mild mental distress
.807
.525, 1.241
1.027
.593, 1.780
Moderate mental distress
.865
.564, 1.326
1.304
.763, 2.228
Severe mental distress
.999
.672, 1.486
1.638
.992, 2.704
College or university
College
-
University
.963
-
-
-
.557, 1.665
1.139
.565, 2.299
Province
Alberta
-
-
-
-
Atlantic
.982
.312, 3.093
.570
.148, 2.195
British Columbia
.738
.293, 1.859
.717
.242, 2.120
Manitoba/Quebec
.611
.262, 1.427
.446
.166, 1.195
Ontario
.628
.310, 1.273
.396
.180, .873
Student status
Domestic
-
International
.350
-
-
-
.251, .487
.689
.476, .996
Size of institution
<=5000
-
-
-
5001-20000
.943
.399, 2.228
1.218
-
20001-40000
1.241
.559, 2.756
1.149
.441, 2.991
>40000
.881
.368, 2.104
1.451
.505, 4.168
.419, 3.542
Notes: OR = Odds ratio; CI = confidence interval; Bold OR and 95%CI = P < .005.
longitudinally, considering recent regulations implemented to
deter youth from using e-cigarettes.38
Regarding substance use, current cannabis use was associated
with increased odds of e-cigarette use compared to cigarette
smoking. Other work has found cannabis use to be positively
associated with e-cigarette use among adolescents and young
adults.39 However, it has also been associated with cigarette
smoking.40 A possible explanation for our finding is the potential that young adults are choosing to use electronic devices
for the consumption of cannabis (vaporizers), leading young
adults to consume nicotine similarly. However, our results
cannot directly confirm this possibility. Future work could assess
this hypothesis.
Our study contributes to the existing literature by identifying
that individuals who identify as non-heterosexual have higher
odds of using cigarettes compared to e-cigarettes. Previous
studies have identified that members of the 2SLGBTQ +
community are at higher risk of smoking and e-cigarette use.41–43
However, the differentiation of modality of use in our sample
could help inform targeted approaches for diverse populations to
reduce nicotine use. Further research is needed to confirm this
association. Lastly, it was found that international students had
lower odds of using e-cigarettes compared to smoking cigarettes.
Limited evidence exists on the association of international student status and smoking or vaping although certain countries
have a higher prevalence of cigarette smoking than Canada.44
One study showed that international students exhibited higher
noncompliance with smoke-free policies at a large university.45
This finding should be particularly relevant to post-secondary
institutions with a significant number of international students, as
targeted smoking cessation programs may be needed to address
rates of use.
When considering factors associated with the odds of cigarette smoking vs dual use, many factors were similar to those
Tobacco Use Insights
8
n
associated with cigarette smoking vs e-cigarette use. For instance, being an older student and an international student were
associated with lower odds of being a dual user. Additionally,
cannabis use was found to increase the odds of dual use.
However, one factor that did not differentiate cigarette smoking
vs e-cigarette use but did for dual use was binge drinking.
Specifically, current binge drinking was associated with higher
odds of dual use compared to cigarette smoking. A recent
systematic review and meta-analysis found a positive association
between e-cigarette use and alcohol use, including binge
drinking, among adolescents.46
Furthermore, alcohol use and cigarette smoking have been
found to be positively associated, and alcohol consumption has
been negatively associated with smoking cessation.47,48 However,
it is interesting to note that binge drinking did not emerge as a
significant factor when considering cigarettes vs e-cigarettes.
Speculatively, one possible explanation could be the concept of a
“social user”49 for whom behaviour is triggered by alcohol use.
The introduction and popularity of e-cigarettes may have created
a phenomenon where individuals who binge drink are susceptible
to consuming various nicotine products in social settings.
Several strengths and limitations of this study should be
acknowledged. Firstly, our study provides insights into factors
associated with the dual use of cigarettes and e-cigarettes among
at-risk populations. Secondly, we utilized a large sample of postsecondary students across Canada. However, the CCWS is a
cross-sectional data surveillance system, and therefore, causality
cannot be assumed. The overall response rate for the CCWS is
relatively low and could introduce bias to our results. Additionally, the sample collected may not be representative of the
Canadian post-secondary population. Therefore, caution is
necessary when generalizing the results. Using self-reported
measures in the study introduces various types of bias (e.g.,
desirability bias). While self-reported data on cigarette smoking
status has been reported as providing a valid estimate of smoking
prevalence in Canada,50 it may be underestimated in this
sample, given reporting biases for socially undesirable behaviours.51 Finally, the item used to collect vaping “During the past
30 days, how often did you use an e-cigarette or vaping device?”
does not explicitly mention that the e-cigarette or vaping device
contains nicotine.
Our results have implications for policymakers and decisionmakers at post-secondary institutions. Firstly, e-cigarette use is
the preferred nicotine delivery method among young adults,
making campaigns addressing the harms of this behaviour
important.52 Secondly, international students may be a specific
population that could benefit from cigarette cessation interventions. Targeting this subgroup may be the most effective way
to reduce the harm associated with cigarette smoking in the
post-secondary context in Canada. Lastly, further research is
needed to understand the temporal nature of the dual-use
category, as it is unclear from our study whether individuals
initiated cigarette smoking or e-cigarette use and then transitioned to the other modality.
n
In conclusion, our findings indicate e-cigarette use is the
most common modality for nicotine consumption among postsecondary students. Continued monitoring and health campaigns addressing the harms of e-cigarette use may be a starting
point for post-secondary institutions to consider. Moreover,
international students may require specific attention regarding
cigarette smoking, and smoking cessation efforts could be
tailored to this sub-population in Canada. Further research is
needed to understand the dynamics of dual use and the transition between modalities.
Acknowledgements
The Rossy Foundation and the University of British Columbia
provided direct funding, and the University of British Columbia
and the University of Toronto were collaborating partners on
the development of the CCWS.
ORCID iD
Guy Faulkner  https://orcid.org/0000-0001-8898-2536
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