TITLE PAGE
Environmental Tobacco Smoke Exposure among Electronic Cigarette Users
Hai V. Nguyen, PhDa*
Aziz Sheikh, MDb,c
a Memorial University of Newfoundland, Canada
b Director, Asthma UK Centre for Applied Research, Usher Institute of Population Health
Sciences and Informatics, University of Edinburgh, UK
c Visiting Professor of Medicine, Harvard Medical School, Boston MA, USA
Corresponding author:
Hai V. Nguyen, Ph.D
School of Pharmacy
Memorial University of Newfoundland
Canada
Phone number: 1 (709) 777 6536
Email: [email protected]
Word Count: 3,325
Number of Tables: 3
Number of Figures: 0
Abstract (233 words)
IntroductionExposure to environmental tobacco smoke (ETS) from combustible tobacco products causes various diseases and makes quitting smoking more difficult. However, little is known about exposure of e-cigarette users to ETS from combustible tobacco products. This study aimed to investigate e-cigarette users’ exposure to ETS from tobacco smokers.
MethodsThe association between ETS exposure frequency and different types of smokers including e-cigarette users was examined using ordered logistic regression analysis and nationally representative survey data on 28,765 individuals who were interviewed in the Canadian Tobacco, Alcohol and Drugs Surveys conducted during 2013 and 2015. Survey respondents were classified into one of five smoker types: smokers of tobacco only, dual users of tobacco and e-cigarettes, users of e-cigarette only, former smokers and never smokers. The analyses were conducted using the entire sample and by age group. ResultsYoung to mid-age (15-54) dual users of both regular cigarettes and e-cigarettes have higher ETS exposure than even tobacco smokers. Young to mid-age single users of e-cigarettes are less exposed to ETS than tobacco smokers, but still have higher ETS than never smokers. At older age (55+), both dual and single e-cigarette users face similar risks of ETS exposure as tobacco smokers. ConclusionsE-cigarette users are at high risk of ETS exposure. Policies that target the behaviour of e-cigarette users as well as the environments surrounding them to address their high ETS exposure risk would be beneficial.
Key words: e-cigarettes, environmental tobacco smoke exposure.
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1. Introduction
Use of electronic cigarettes or e-cigarettes has increased rapidly, both among young
people (Arrazola et al., 2015; Reid, Rynard, Czoli, & Hammond, 2015) and adults (Caraballo,
Jamal, Nguyen, Kuiper, & Arrazola, 2016; King, Patel, Nguyen, & Dube, 2015). E-cigarettes
are advocated as a less harmful way than combustible tobacco to deliver nicotine without
creating harmful environmental tobacco smoke (ETS) for non-smokers and as a smoking-
cessation aid for tobacco smokers (Glynn, 2014; Goniewicz et al., 2014). Notably, two recent
reports from Public Health England and the Royal College of Physicians suggested that e-
cigarettes are far safer than traditional tobacco and called for smokers to switch to e-cigarettes
(McNeill et al., 2014; Public Health England, 2015; Royal College of Physicians, 2016).
However, there is not yet a scientific consensus on the long-term health effects of e-cigarettes
(Callahan-Lyon, 2014; Dinakar & O’Connor, 2016; Hajek, Etter, Benowitz, Eissenberg, &
McRobbie, 2014). A recent review raised concerns about the long-term health effects of e-
cigarettes and discouraged its use (Grana, Benowitz, & Glantz, 2014). Further, evidence
concerning e-cigarette’s role as a smoking-cessation aid is mixed (Dinakar & O’Connor, 2016;
Lindson‐Hawley et al., 2016; Malas et al., 2016).
Overlooked in the debate on the benefits of e-cigarettes is the extent of e-cigarette
users’ exposure to ETS generated by tobacco smokers. This is, however, an important issue
because ETS exposure can affect the extent of e-cigarettes’ health benefits and its effectiveness
as a smoking-cessation aid. The effects of ETS on non-smokers are nearly as large as that of
smoking on smokers (Centers for Disease Control and Prevention, 2017). Specifically, ETS
exposure causes various heart and lung diseases, low birth weight, sudden infant death
syndrome and respiratory distress syndrome (Royal College of Physicians, 2010), as well as
makes it hard for smokers to quit (Eng et al., 2014; Prugger et al., 2014). This is because 3
biologically, ETS exposure can increase nicotine acetylcholine receptors in the brain, and thus
nicotine dependence (Brody et al., 2011). It may also be difficult to quit smoking around other
smokers because of peer pressure (Powell, Tauras, & Ross, 2005), imitation of smoking
behaviour (Harakeh, Engels, Van Baaren, & Scholte, 2007) and smoking cues (Erblich &
Montgomery, 2012).
In this study, we investigated exposure of e-cigarette users to ETS from tobacco
smokers. Our analysis first quantified the extent of ETS exposure by e-cigarette users and
compared it with that of tobacco smokers as well as former smokers and never-smokers. We
distinguished between two types of e-cigarette users: dual users who used both e-cigarettes and
regular cigarettes versus single users who used only e-cigarettes. We then examined predictors
of ETS exposure focusing on the role of e-cigarette use. We hypothesized that e-cigarette
users’ exposure to ETS depends on whether they are single or dual users. Since smokers tend to
connect primarily to other smokers, and smokers and non-smokers cluster into discrete
subgroups that share similar characteristics (Christakis & Fowler, 2008; Poland et al., 2000),
we anticipated that single e-cigarette users likely interact mostly with their peer single e-
cigarette users and have low ETS exposure, while dual users likely interact mostly with their
fellow tobacco smokers and have substantial SHS exposure. We also hypothesized that e-
cigarette users’ exposure to ETS varied by age as younger smokers have different smoking
patterns and different levels of social interactions than older ones (Clark & Loheac, 2007;
Miething, Rostila, Edling, & Rydgren, 2016).
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2. Methods
2.1 Data and variables
We pooled individual-level data from the two most recent cycles of Canadian Tobacco,
Alcohol and Drugs Survey (CTADS) 2013 and 2015 conducted by Statistics Canada. The
CTADS biennially interviewed a nationally representative sample of nearly 15,000 Canadians
aged 15 years and older on their use of tobacco, alcohol and illicit drugs. All survey
participants were asked the following question about their ETS exposure to smoke from
combustible cigarettes: ‘Overall, [excluding your own smoking] in the past month how often
were you exposed to second-hand smoke?’ This is the standard question used by Statistics
Canada to extract information about exposure to environmental tobacco smoke from
combustible cigarettes. Based on responses to this question, we constructed our study outcome
of ETS exposure frequency. It was an ordered discrete variable coded as 1 if the person
reported being never exposed, 2 if exposed at least once in the past month, 3 if exposed at least
once a week, 4 if exposed almost every day, and 5 if exposed every day.
The survey contained conventional classification of tobacco smoking status, i.e. current
smokers, former smokers and never smokers (Statistics Canada, 2012). In addition, all
respondents were asked about their use of e-cigarettes. Specifically, they were asked whether
they ever used e-cigarette in their life, and among those who reported ever using e-cigarettes,
whether they used it over past 30 days. Based on their responses, we created two indicators of
e-cigarette use: ever use and past 30-day use. The ‘ever use of e-cigarettes’ thus included both
past 30-day users and those who ever used but did not use past 30 days. Combining the
information on whether a person smoked regular cigarettes and/or e-cigarettes, we classified
survey respondents into five groups: smokers of tobacco only, dual users of tobacco and e-
cigarettes, single e-cigarette users (users of e-cigarettes only), former smokers (those who are
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former smokers of tobacco and never used any e-cigarettes before) and never smokers (those
who never used tobacco or e-cigarettes). The classification below was based on the ‘ever use’
definition of e-cigarette use and was used to derive the main results for this paper. As ‘ever’
users may be experimenters and do not necessarily become regular users, we also used ‘past
30-day use of e-cigarettes’ to define e-cigarette use. The results using this e-cigarette use
definition were similar and reported in the Online Supplemental.
2.2 Descriptive analysis
We reported the means of demographic characterises for the entire sample and by
smoking status. We also estimated the prevalence of various smoking statuses by different age
groups. To test whether there was a statistically significant difference in composition of
smoking status across different age groups, we used the Chi-square test of independence
between two categorical variables: smoking status (5 categories: tobacco smokers, single e-
cigarette users, dual users, former smokers and never smokers) and age group (3 categories:
age<25, age 25-44, and age 45 and above). The null hypothesis was that the two variables are
independent.
2.3 Regression model
The association between ETS exposure frequency and different types of smokers was
studied using ordered logistic regression analysis. The covariates of primary interest were
indicators of the five groups (i.e. dual users, single e-cigarette users, former smokers and
never-smokers, with tobacco smokers as reference category). The regression models included
age (continuous variable), sex (male/female), household size (continuous variable for the
number of persons living in the respondent’s household), marital status (indicator for single or
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married), urban status (indicator for whether the respondent resides in an urban or rural area),
and province fixed effects (dummy variables for each province to capture time-invariant
province-specific factors that affect the outcomes). We also controlled for presence of
smoking at home (defined as having at least one cigarette smoked at home a day) which is a
source of ETS, illicit drug use (indicator for whether the respondent used any opioids) which
correlates with smoking, employment status (indicator for whether the respondent worked at a
job or business in the past week) to account for the fact that most workplaces are smoke free
nowadays which affects ETS exposure, and drinking status (indicator for being a current
drinker, former drinker or abstainer) as drinking and smoking are often correlated affecting
ETS exposure (Dee, 1999)). The analyses were conducted for the entire sample and repeated
for age subgroups (less than 25, between 25 and 54, and 55 or above). All analyses were
conducted using Stata 15 (StataCorp, 2017).
3. Results
3.1 Descriptive analysis
Table 1 shows the prevalence of the five groups. In column 1, nearly one-fifth of the
sample reported using either regular cigarettes or electronic cigarettes or both, with a
breakdown of tobacco smokers (7.7%), dual users (6.0%), and single e-cigarette users (4.9%).
Never-smokers accounted for 57.2% and former smokers for 24.2%. The next three columns 2-
4 display the prevalence by age group. For the group aged less than 25 years, the prevalence of
only e-cigarette use and of dual use each was three times as high as that of tobacco use (13.2%
and 10.7% vs. 3.7%). This indicated that e-cigarettes have become very popular among the
youth group. For the group aged 25-54, the pattern was reversed: tobacco smokers accounted
for the highest percentage (8.8%), followed by dual users (6.7%) and single e-cigarette users
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(4.7%). Among people aged 55 and above, the prevalence of dual use was higher than that of
single use (2.8% vs. 1.4%) but substantially lower than that of regular cigarette use (2.8% vs.
7.8%).
Table 2 displays ETS exposure and demographic characteristics of the whole sample
and the five groups. 13.1% of sample was exposed to ETS every day or almost every day, and
up to 1/3 of the sample exposed at least once a week. Among the 5 groups, dual users had the
highest ETS exposure with 45.3% reporting exposure almost every day or more, and more than
two-third of this group (70.8%) was exposed at least once a week. Tobacco smokers are the
second highest exposed group, with 34.2% exposed almost every day or more and 56.2%
exposed at least once a week. Former smokers and never-smokers were least exposed with
10.5% and 7.4% respectively reporting ETS exposure almost every day or more. Compared
with tobacco smokers, dual users were younger (37 vs. 47 years), more likely to be current
drinkers (85.7% vs. 78.9%) and to use illicit drugs (20.7% vs. 15.7%). Single e-cigarette users
were the youngest with an average age of 32. Nearly one-third of tobacco smokers and one-
third of dual users reported the presence of smoking in their home while 5% of single e-
cigarette users and 2% of former smokers and never-smokers reported the same.
3.2 Regression estimates
Table 3 reports the regression estimates for the relationship between ETS exposure and
different types of smokers. To explore possible confounding effects of age, drinking and illicit
drug use suggested by Table 2, we first estimated a baseline regression that excluded these
three covariates. The results reported in column 1 indicate that dual users had a higher risk of
ETS exposure than tobacco smokers (OR = 1.78; 95% CI = 1.35 – 2.36; p<0.01). Meanwhile,
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Table 1. Smoking status prevalence, all sample and by age group, CTADS 2013 & 2015 (pooled)
All sample N Age<25 N Age 25-54 N Age 55+ N p-value
(1) (2) (3) (4)
Tobacco smoker (%)
7.7 2,142 3.7 439 8.8 888 7.8 815 <0.001
(7.4 - 8.0) (3.3 - 4.0) (8.2 - 9.4) (7.3 - 8.4)
Dual user (%) 6.0 2,279 10.7 1,217 6.7 743 2.8 319(5.7 - 6.3) (10.1 - 11.2) (6.2 - 7.3) (2.4 - 3.1)
Single e-cigarette user (%)
4.9 2,203 13.2 1,519 4.7 473 1.4 139
(4.7 - 5.2) (12.6 - 13.9) (4.2 - 5.1) (1.2 - 1.7)
Former smoker (%)
24.2 5,958 1.5 168 20.1 1,878 40.8 3,912
(23.7 - 24.7) (1.3 - 1.7) (19.2 - 20.9) (39.8 - 41.8)
Never smoker (%) 57.2 16,183 70.9 7,483 59.7 4,668 47.2 4,032(56.6 - 57.8) (70.1 - 71.8) (58.7 - 60.8) (46.1 - 48.2)
Total N 28,765 10,898 8,650 9,217Note: Weighted percentages for indicator variables are presented; 95% confidence intervals are in parentheses. Smoking classification is based on e-cigarette use defined as "ever use of e-cigarettes"
Table 2. Descriptive statistics (ETS exposure level and demographic characteristics), all sample and by smoking status; CTADS 2013 & 2015
Variable All sample (N=28,609)
Tobacco smoker
(N=2,110)
Dual user (N=2,256)
Single e-cigarette
user (N=2,200)
Former smoker
(N=5,923)
Never smoker
(N=16,120)
ETS Exposure frequency Everyday
(%)6.9 22.0 30.7 8.7 5.6 2.8
(6.6 - 7.2) (20.3 - 23.9) (28.8 - 32.6) (7.5 - 9.8) (5.0 - 6.2) (2.6 - 3.1)
Almost 6.2 12.2 14.6 10.8 4.9 4.6everyday
(%)(5.9 - 6.5) (10.8 - 13.6) (13.2 - 16.1) (9.5 - 12.1) (4.4 - 5.5) (4.3 - 5.0)
>=Once a 18.4 22.0 25.5 28.3 18.1 16.6week (%) (18.0 - 18.9) (20.2 - 23.7) (23.6 - 27.2) (26.4 - 30.2) (17.1 - 19.1) (16.0 - 17.1)
>=Once a 26.9 15.8 12.6 29.0 23.5 31.2month (%) (26.4 - 27.4) (14.2 - 17.3) (11.2 - 14.0) (27.1 - 30.8) (22.4 - 24.6) (30.4 - 31.9)
Never (%) 41.6 28.0 16.6 23.2 47.9 44.8(41.0 - 42.1) (26.1 - 29.9) (15.1 - 18.2) (21.5 - 25.1) (46.7 - 49.2) (44.0 - 45.6)
Age (years) 45.9 47.2 36.9 31.9 56.6 43.3(45.7 – 46.1) (46.6 - 47.9) (36.3 – 37.5) (31.3 - 32.5) (56.2 – 56.9) (43.0 – 43.6)
Household size
2.9 2.7 2.8 3.1 2.5 3.0
(persons) (2.9 – 2.9) (2.6 – 2.7) (2.8 – 2.9) (3.0 – 3.1) (2.5 – 2.5) (3.0 – 3.1)
Single (%) 23.5 24.7 37.4 42.1 11.2 25.4(23.0 - 23.9) (22.8 - 26.5) (35.4 - 39.4) (40.0 - 44.2) (10.4 - 12.0) (24.8 - 26.1)
Male (%) 49.1 57.6 55.2 57.8 54.9 44.2(48.6 - 49.7) (55.5 - 59.7) (53.2 - 57.3) (55.7 - 59.8) (53.6 - 56.2) (43.4 - 45.0)
Urban status (%)
80.3 77.5 80.1 82.0 77.4 81.8
(79.9 - 80.8) (75.8 - 79.3) (78.4 - 81.7) (80.4 - 83.6) (76.3 - 78.5) (81.2 - 82.4)
Employ (%)
51.8 53.5 60.1 60.8 44.1 53.2
(51.2 - 52.4) (51.4 - 55.6) (58.1 - 62.1) (58.7 - 62.8) (42.8 - 45.4) (52.4 - 54.0)
Home smoking
5.7 29.4 28.9 5.1 2.2 1.6
(%) (5.5 - 6.0) (27.5 - 31.4) (27.1 - 30.8) (4.2 - 6.1) (1.9 - 2.6) (1.4 - 1.8)
Drinking status
Current drinker
76.7 78.9 85.7 89.3 81.7 72.2
(%) (76.2 - 77.1) (77.1 - 80.6) (84.3 - 87.2) (88.0 - 90.6) (80.7 - 82.7) (71.5 - 72.9)
Former drinker
14.1 18.6 13.0 7.9 16.2 13.2
(%) (13.7 - 14.5) (17.0 - 20.3) (11.7 - 14.4) (6.7 - 9.0) (15.3 - 17.2) (12.7 - 13.7)
Non-drinker (%)
9.3 2.5 1.2 2.8 2.1 14.6
(8.9 - 9.6) (1.8 - 3.2) (0.8 - 1.7) (2.1 - 3.5) (1.7 - 2.4) (14.0 - 15.1)
Illegal drug use
13.6 15.7 20.7 18.0 15.7 11.3
(%) (13.2 - 14.0) (14.2 - 17.3) (19.0 - 22.3) (16.4 - 19.6) (14.8 - 16.6) (10.8 - 11.7)Note: Weighted percentages for indicator variables and weighted means for continuous variables are presented; 95% confidence intervals are in parentheses;; Smoking classification is based on e-cigarette use defined as "ever use of e-cigarettes"; “Single” refers to those who were single or never married; “Household size” refers to number of persons in the respondent’s household; “Urban status” refers to whether the respondent resides in an urban area.
single e-cigarette users were exposed to ETS less than tobacco smokers (OR = 0.72; 95% CI =
0.64 – 0.80; p<0.01) but still more than former smokers and never-smokers. When the
regression was augmented to include age, drinking and illicit drug use (column 2), the
magnitude of the dual user and single e-cigarette user coefficients decreased. This suggests that
these three factors confounded the relationship between ETS exposure and e-cigarette use.
Notably, after controlling for these factors, dual users still had a higher risk of ETS exposure
(OR = 1.39; 95% CI = 1.10 – 1.75; p=0.01), suggesting that some other factors were also
driving the high ETS exposure of dual users. Single e-cigarette users still had lower ETS
exposure risk than tobacco smokers (OR = 0.50; 95% CI = 0.44 – 0.57; p<0.01).
Table 3. Predictors of ETS exposure frequency, all sample and by age group; Ordered logistic models; CTADS 2013 & 2015; Smoking classification is based on e-cigarette use defined as "ever use of e-cigarettes"
Baseline(N=28,609)
Augmented (N=28,516)
Age<25 (N=10,853)
Age 25-44 (N=8,575)
Age 45+ (N=9,088)
(1) (2) (3) (4) (5)
Type of smokerDual user 1.782** 1.385** 1.282** 1.421* 1.269
(1.345 - 2.361) (1.099 - 1.745) (1.083 - 1.519) (1.056 - 1.911) (0.943 - 1.706)Single e-cigarette
user 0.716** 0.504** 0.420** 0.455** 0.841
(0.642 - 0.799) (0.444 - 0.572) (0.375 - 0.471) (0.329 - 0.629) (0.507 - 1.394)Never smoker 0.331** 0.323** 0.247** 0.292** 0.433**
(0.307 - 0.356) (0.305 - 0.341) (0.206 - 0.295) (0.260 - 0.328) (0.376 - 0.498)Former smoker 0.416** 0.497** 0.331** 0.433** 0.684**
(0.367 - 0.471) (0.439 - 0.562) (0.167 - 0.656) (0.368 - 0.509) (0.569 - 0.821)Home smoking 3.163** 3.412** 5.138** 2.957** 3.472**
(2.767 - 3.616) (2.975 - 3.912) (4.616 - 5.719) (2.540 - 3.442) (2.374 - 5.078)Employ 1.776** 1.340** 1.217** 1.406** 1.254*
(1.662 - 1.897) (1.255 - 1.431) (1.071 - 1.382) (1.166 - 1.696) (1.046 - 1.504)Male 1.330** 1.339** 1.083* 1.420** 1.338**
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(1.222 - 1.448) (1.228 - 1.459) (1.012 - 1.158) (1.241 - 1.626) (1.236 - 1.449)Single 1.904** 1.203** 1.044 1.157* 1.308**
(1.777 - 2.040) (1.139 - 1.271) (0.856 - 1.272) (1.014 - 1.319) (1.068 - 1.602)Household size 1.115** 0.938** 0.963 0.905** 0.986
(1.094 - 1.137) (0.900 - 0.978) (0.914 - 1.014) (0.844 - 0.970) (0.862 - 1.128)Urban status 0.947 0.973 1.173* 0.874* 1.078**
(0.878 - 1.021) (0.918 - 1.031) (1.025 - 1.342) (0.772 - 0.990) (1.030 - 1.128)Age 0.967** 0.989 0.974** 0.966**
(0.962 - 0.973) (0.963 - 1.017) (0.969 - 0.979) (0.959 - 0.972)Drinking status
Current drinker 1.714** 1.737** 2.194** 1.299*(1.503 - 1.954) (1.438 - 2.099) (1.681 - 2.863) (1.063 - 1.588)
Former drinker 1.570** 1.595** 2.098** 1.152**(1.359 - 1.814) (1.172 - 2.171) (1.439 - 3.060) (1.044 - 1.270)
Illegal drug use 1.364** 1.222** 1.421** 1.354*(1.108 - 1.679) (1.144 - 1.305) (1.125 - 1.795) (1.041 - 1.761)
Note: Odds ratios from ordered logistic models are reported; 95% confidence intervals are in parentheses; Five-point scale for ordered ETS exposure frequency outcome: 1 (never exposed), 2 (exposed at least once a month), 3 (exposed at least once a week), 4 (exposed almost every day), and 5(exposed every day). All models include province fixed effects. Reference categories are female, non-drinker, tobacco smoker, married, non-user of illicit drugs and unemployed. Robust standard errors throughout are clustered at the province level and estimates are weighted. Smoking classification is based on e-cigarette use defined as "ever use of e-cigarettes"; Significance levels are **p<0.01; *p<0.05.
We next explored the relationship between ETS exposure and e-cigarette use by age
group. The results are reported in columns 3-5 of Table 3. Compared with the reference group
of tobacco smokers, young to mid-age dual users face higher risks of ETS than tobacco
smokers (OR = 1.28; 95% CI = 1.08 – 1.52; p<0.01 for the age group less than 25, and OR =
1.42; 95% CI = 1.06 – 1.91; p=0.02 for the age group 25-54). For dual users aged 55 and
above, the odds ratio is greater than 1 but not statistically significant (OR = 1.27; 95% CI =
0.94 – 1.71; p=0.11). Meanwhile, the pattern of ETS exposure risk for young to mid-age single
e-cigarette users was reversed: compared with tobacco smokers, ETS exposure risk was lower
for the youth aged less than 25 (OR = 0.42; 95% CI = 0.38 – 0.47; p<0.01) and for adults aged
25-54 (OR = 0.46; 95% CI = 0.33 – 0.63; p<0.01). As with older age dual users, older age
single e-cigarette users faced similar risks of ETS exposure as tobacco smokers (OR = 0.84;
CI=0.51– 1.39; p=0.50).
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Regression estimates for other covariates, also reported in Table 3, indicated that
presence of smoking at home raised the risk of ETS exposure significantly. Males were at
higher risk of ETS exposure than females while respondents who were single had a higher risk
of ETS exposure than married respondents. Risk of ETS exposure also increased with the size
of household. Those who used illicit drugs were more exposed to ETS than those who did not.
Similarly, both current and former drinkers were more exposed to ETS than non-drinkers.
Despite the fact that most workplaces have adopted smoking restrictions during the study
period of 2013-2015 (Statistics Canada), those who were employed had a higher ETS exposure
risk than the unemployed. It is possible that employed people have higher level of social
interactions that exposed them to more ETS than those who are not. This also suggests that
most of ETS exposure for employed people must have occurred outside their workplaces.
4. Discussion
Our study provides the first comparison of self-reported ETS exposure among different
types of tobacco and e-cigarette users. We found that ETS exposure risk was higher for dual
users than for those only smoking tobacco. This finding was driven by a high ETS exposure of
young to mid-age dual users. In contrast, young to mid-age single e-cigarette users faced a
lower risk of ETS exposure than tobacco smokers.
Why did young to mid-age dual users (age 15-54) face a higher risk of ETS exposure
than older dual users (age 55 and above)? It is possible that younger dual users had a more
socially active lifestyle and interacted more often with tobacco smokers than older ones. This
hypothesis is consistent with the finding that drinking alcohol was associated with higher ETS
exposure. Meanwhile, it is likely that single e-cigarette users interacted mostly with other
single e-cigarette users in their social network (Christakis & Fowler, 2008) and as a result,
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were not exposed to ETS as frequently as tobacco smokers. The lower ETS exposure for single
e-cigarette users (compared with what their ETS exposure would be if they take up tobacco
smoking) can be seen as another benefit of using e-cigarettes. This, however, does not
necessarily mean that early use of e-cigarettes is not a gateway to initiation of regular cigarette
smoking (Leventhal et al., 2015; Rigotti, 2015).
Our findings have a number of policy implications. First, to maximize potential benefits
associated with e-cigarette use (relative to regular cigarette smoking), policymakers should
target young to mid-age dual users who had higher level of ETS exposure than tobacco
smokers. Understanding the smoking behaviour of e-cigarette users (e.g. where and when they
smoke e-cigarettes) and the environments around them (e.g. whom they often smoke with)
would provide useful information to help design interventions for reducing their ETS exposure.
Second, given that ETS exposure can act as a barrier to smoking cessation, high ETS exposure
of dual users might explain the mixed findings in the literature on the role of e-cigarettes in
helping its users to quit smoking. Tobacco control efforts should promote awareness of the
risk of ETS exposure among e-cigarette users. Further, as e-cigarette smokers are increasingly
prohibited from smoking in public places and thus have to smoke e-cigarettes in the same areas
where combustible cigarettes are allowed, separate designated smoking areas for e-cigarette
users might be needed to prevent them being exposed to ETS from combustible cigarette
smokers. Third, our finding of high ETS exposure of e-cigarette users draws attention to a
potential impact of the current policy trend of banning e-cigarette use in public places. If
banning e-cigarettes in public places induces e-cigarette users to switch their e-cigarette use to
the same places as tobacco smokers, this may expose e-cigarette users to harmful ETS from
tobacco smokers. There has been evidence of displacement effect of public smoking bans. For
example, it was reported that public-place smoking laws significantly increased non-smokers'
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exposure to ETS at building entrances (Carpenter, Postolek, & Warman, 2011). While this
potential unintended consequence is unlikely to outweigh the obvious and significant benefits
of bans including protecting non-smokers from e-cigarette aerosol and de-normalizing smoking
behaviour, it is nevertheless should not be ignored. Fourth, our results indicate that the risk of
ETS exposure substantially increased with the presence of smoking at home. This highlights
the need to implement measures to curb home smoking.
It is worth noting that we used the term ‘Environmental Tobacco Smoke (ETS)’ instead
of ‘Second-Hand Smoke (SHS)’ to refer to passive smoking in this study (Protano & Vitali,
2011). ETS includes not only SHS (the combination of smoke emitted from the burning end of
a cigarette or other tobacco products and smoke exhaled by the smoke) but also the thirdhand
smoke (the residue from tobacco smoke that persists on the clothing and hair of smokers, on
environmental surfaces and in dust long after a cigarette has been extinguished). As such, the
term ETS captures passive smoking more accurately than the term SHS.
Our study has a number of limitations. First, our finding of the association between
ETS exposure and e-cigarette use was based on cross-sectional data and does not mean that
smoking e-cigarettes causes higher ETS exposure. The high ETS exposure experienced by
young to mid-age dual users is likely to have been driven by self-selection issues. That is,
people who self-selected into using e-cigarettes have certain characteristics that make them
more likely to be exposed to ETS rather than the use of e-cigarettes itself. We have shown that
age, drinking and illicit drug use status of dual users may contribute to their high ETS
exposure. But the fact that dual users still have higher ETS exposure even after controlling for
these factors suggests that there may be other factors that drive ETS exposure, such as e-
cigarette users’ social networks. Unfortunately, there is no information in the survey on e-
cigarette users’ social network to explore this issue further. This is a topic for future research.
15
Second, we used an alternative definition of e-cigarette use based on “past 30-day use” to
account for experimental use of e-cigarettes. While this measure excludes users who only
experimented with e-cigarettes in the distant past, these may still include respondents who
experimented with e-cigarettes in the past 30 days. Another limitation of our study is that there
was no objective data on respondents’ actual exposure to ETS. Next, the survey we used for
this study contains no information on second hand exposure to e-cigarette vapor. Future
surveys and research should explicitly address the issue of exposure to e-cigarette vapor. This
will be important as second-hand exposure to vapor from e-cigarettes is not harmless,
particularly among infants and children (Protano, Manigrasso, Avino, Sernia, & Vitali, 2016;
Protano, Manigrasso, Avino, & Vitali, 2017). Cotinine levels in non-smokers exposed to e-
cigarette vapor have been found to be similar to those exposed to ETS from combustible
cigarettes (Ballbè et al., 2014; Flouris et al., 2013). Finally, there is no information on specific
places of ETS (e.g., workplace, bars, building entrance, bus stop, park, etc.) and our findings
could not shed light on specific locations where interventions should be targeted.
5. Conclusions
Studying ETS exposure of e-cigarette users provides a new perspective with which to
assess and inform potential benefits and risks of using e-cigarettes. Our analysis demonstrated
that health benefits associated with using e-cigarettes should not be taken for granted when e-
cigarette users are at high risk of ETS exposure. Policies that target the behaviour of e-cigarette
users as well as the environments surrounding them to address their high ETS exposure risk
would be beneficial.
16
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