Topic: effect of Vaping among teenagers
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Journal of Youth and Adolescence (2019) 48:1899–1911
Schools Influence Adolescent E-Cigarette use, but when? Examining
the Interdependent Association between School Context and Teen
Vaping over time
Adam M. Lippert 1 ● Daniel J. Corsi2 ● Grace E. Venechuk3
Received: 12 June 2019 / Accepted: 2 August 2019 / Published online: 24 August 2019
© Springer Science+Business Media, LLC, part of Springer Nature 2019
Schools are important contexts for adolescent health and health-risk behaviors, but how stable is this relationship? We
develop a conceptual model based on Ecological Systems Theory describing the changing role of schools for adolescent
health outcomes—in this case, teen e-cigarette use. To examine this change, we fit Bayesian multilevel regression models to
two-year intervals of pooled cross-sectional data from the 2011–2017 U.S. National Youth Tobacco Survey, a school-based
study of the nicotine use behaviors of roughly 65,000 middle and high school students (49.5% female; 41.1% nonwhite; x̄
age of 14.6 ranging from 9 to 18) from over 700 schools. We hypothesized that school-level associations with student e-
cigarette use diminished over time as the broader popularity of e-cigarettes increased. Year-specific variance partitioning
coefficients (VPC) derived from the multilevel models indicated a general decrease in the extent to which e-cigarette use
clusters within specific schools, suggesting that students across schools became more uniform in their propensity to vape
over the study period. This is above and beyond adjustments for personal characteristics and vicarious exposure to smoking
via friends and family. Across all years, model coefficients indicate a positive association between attending schools where
vaping is more versus less common and student-level odds of using e-cigarettes, suggesting that school contexts are still
consequential to student vaping, but less so than when e-cigarettes were first introduced to the US market. These findings
highlight how the health implications of multiply-embedded ecological systems like schools shift over time with
concomitant changes in other ecological features including those related to policy, culture, and broader health practices
within society. Though not uniformly reported in multilevel studies, variance partitioning coefficients could be used more
thoughtfully to empirically illustrate how the influence of multiple developmentally-relevant contexts shift in their influence
on teen health over time.
Keywords Adolescence ● Schools ● E-Cigarettes ● Multilevel
Use of electronic cigarettes (“e-cigarettes”) among adoles-
cents has rapidly proliferated. Between 2011 and 2015, the
prevalence of current e-cigarette use climbed from 0.6% to
5.3% among US middle schoolers, and from 1.5% to 16%
among high schoolers (Singh et al. 2016a). More recent
estimates suggest continued increases from 2015-2018 with
the widening popularity of novel “vaping” modalities
including Juul devices (Gentzke et al. 2019). These patterns
have emerged alongside historic lows in conventional
tobacco use among teens who now favor vaping over
smoking (Johnston et al. 2016). Conventional smoking
remains among the most powerful correlates of adolescent
e-cigarette use (Lippert 2018), though evidence suggests a
These authors contributed equally: Adam M. Lippert and Daniel
J. Corsi
* Adam M. Lippert
[email protected]
1 University of Colorado Denver, Sociology Department, 1380
Lawrence Street, Suite 420, Denver, CO 80204, USA
2 Ottawa Hospital Research Institute, Clinical Epidemiology
Program, 501 Smyth Box 241, Ottawa, ON KIH 8L6, USA
3 University of Wisconsin-Madison, Sociology Department, 1180
Observatory Dr, Madison, WI 53706, USA
Supplementary information The online version of this article (https:// contains supplementary
material, which is available to authorized users.
mailto:[email protected]
bi-directional relationship with e-cigarette use being linked
to a higher probability of future smoking, even among
tobacco-abstinent teens (Bunnell et al. 2015; Coleman et al.
2014; Leventhal et al. 2015; Wills et al. 2015). In addition
to the health risks associated with vaping (Chun et al. 2017;
El-Hellani et al. 2018; Larcombe et al. 2017; Moheimani
et al. 2017), the link between vaping and conversion to
smoking has driven interest in how the traits of adolescents
and the contexts they engage with most are associated with
Schools have long been recognized as critical contexts
that shape youth health-risk behaviors (Alexander et al.
2001; Bonell et al. 2013a; Bonell et al. 2013b; Ellickson
et al. 2003), including vaping (Corsi and Lippert 2016;
Lippert 2018). However, recent population-wide trends call
to question whether adolescent e-cigarette use has remained
concentrated in particular schools or if the likelihood of
vaping has become more uniform among students across
schools. Responding to this question, the current study
draws on Ecological Systems Theory (Bronfenbrenner
1977; Bronfenbrenner and Morris 1998) to investigate two
research questions: (1) How has the school clustering of
vaping—a measure of the extent to which schools differ
based on their prevalence of student e-cigarette use—
changed between 2011 and 2017 as the population-wide
prevalence of youth vaping increased? (2) How is the pre-
valence of school-level e-cigarette use associated with a
student’s odds of vaping net of key influences from one’s
family and peer group? To answer these questions, pooled
cross-sectional data from the 2011 to 2017 National Youth
Tobacco Surveys (NYTS) are used to decompose variance
partitioning coefficients from multiyear multilevel regres-
sion models of students nested within schools across the
US. Variance partitioning components (VPC) provide an
empirical estimate of school influence and a measure of the
similarity of e-cigarette use or abstinence between students
within schools. Thus, year-specific VPCs allow an assess-
ment of change in the school clustering of teen vaping over
a period when adolescent e-cigarette use emerged as a
population health concern.
The Ecology of Youth Vaping
According to Ecological Systems Theory, youth develop-
ment is guided by influences across multiply-embedded
systems. These include features within the exosystem and
macrosystem – broad societal influences such as public
policy, culture, and population-wide trends—and the
microsystem, or those institutions and relationships nearest
to youth such as schools, families, and peers. Connections
between and within the two systems are made via the
mesosystem, a “system of systems” comprised of the reci-
procal relationships among ecological features, such as
federal policies in the exosystem that alter family- or
school-based processes in the microsystem. A fifth dimen-
sion of the ecological model is the chronosystem, which
reflects the developmental implications of both an indivi-
dual’s stage in the life course and the sociohistorical context
within which ecological systems are embedded and
experienced. This aspect of ecological theory is critical for
the purposes of this study, though the chronosystem has
received less attention than features within the microsystem
(Tudge et al. 2016).
Schools and Youth Health Behaviors
The association between school contexts and youth vaping
is made clearer by the conceptual model developed by
Frohlich and colleagues (Abel and Frohlich 2012; Frohlich
et al. 2002; Frohlich, Corin and Potvin 2001), a complement
to ecological theory. The model describes the interplay
between contextual and agentic factors that produce—and
are produced by—community features. According to this
model, health behaviors are viewed as “generated prac-
tices,” or products of both structural conditions and self-
determination. This perspective is rooted in Weber’s dis-
tinction between “life chances”—features of social structure
that free or constrain human agency, and “life choices”—
the health behaviors enacted by individuals responding to
structural demands and opportunities (Hays 1994). Abel and
Frohlich (2012) elaborate on self-determination by
describing it in terms of its contextual embeddedness, or the
“socially-structured development, acquisition, and applica-
tion of structural and personal resources by individuals in a
given context,” (p. 239). Under this view, the consequences
of human agency can be understood as both the healthy and
unhealthy practices among individuals responding to con-
textual demands and opportunities, which reinforces the
“collective health lifestyles” practiced among community
members (Frohlich, Corin and Potvin 2001).
Like ecological theory, this model acknowledges the
developmental importance of school environments and the
reciprocal nature of relationships among features within and
between ecological systems. Evidence from countless stu-
dies lends empirical support to these claims. Given the
recency of vaping as a population health concern much of
this research is focused on other related outcomes, such as
conventional tobacco use. These studies show that students
—even low-risk students—attending schools with high
rates of smoking are themselves more likely to smoke
(Alexander et al. 2001; Leatherdale and Manske 2005). The
association between school-level factors and adolescent
smoking has proven robust to a range of confounders at the
individual, family, and neighborhood levels (Dunn et al.
2015). Evidence from a limited number of studies on
schools and e-cigarette use also show that youth attending
1900 Journal of Youth and Adolescence (2019) 48:1899–1911
schools where vaping is common are more likely to vape
(Corsi and Lippert 2016).
The mechanisms linking schools to youth health-risk
behaviors are not entirely clear, but may include norms and
the provision of both social and material resources. Norms
include the health beliefs, attitudes, and policies that inhere
within school communities. While school-based policies
have shown inconsistent and often null associations with
adolescent health behaviors, especially smoking (Coppo
et al. 2014; Galanti et al. 2014), the beliefs and attitudes
held among one’s schoolmates have been shown to corre-
late with one’s own beliefs, attitudes, and behaviors. For
instance, there is a persistent and inverse association
between average school-level student attainment and
attendance and student-level substance use (Bonell et al.
2013b), suggesting that school environments supporting
norms that emphasize student achievement discourage
behaviors incompatible with academic success. Conversely,
youth attending schools with lenient norms risk developing
beliefs and attitudes compatible with substance use. For
example, youth attending schools with high versus low rates
of vaping are more likely to believe that e-cigarettes are
harm-free and less addictive than combustible cigarettes,
irrespective of their actual e-cigarette use (Lippert 2018).
In schools with lenient norms that fail to correct students’
misconceptions of the risks associated with e-cigarettes,
pupils are more likely to have access to social and material
resources needed to vape. This includes peer modeling of
vaping behaviors and use of peers’ vaping devices. As a
learned behavior, initiating e-cigarette use is facilitated by
peer modeling and demonstrations. Indeed, a recent mixed-
methods study of adolescents and young adults found that
one-third of the sample first began experimenting with e-
cigarettes because an e-cigarette using friend introduced
them to the practice (Kong et al. 2015). Analysis of quali-
tative data from the same study suggests that when one
member of a peer group acquires an e-cigarette, offers to
use the device are soon made to other group members.
Other learned behaviors associated with e-cigarettes include
performative aspects of use, such as “vaping tricks”
demonstrated by friends or a desire to “look cool,” espe-
cially for younger adolescents (Roditis et al. 2016). Given
the social functions of e-cigarette use it is not surprising that
a recent study showed leisure time activities among e-
cigarette using teens is often centered on a mutual interest in
vaping activities (Evans-Polce et al. 2018).
A Model for Changing School-Level Influence
The temporal stability of the link between school features
and youth health behaviors has not been widely investi-
gated. Ecological Systems Theory acknowledges that the
influence of such systems on youth development is
conditioned by the sociohistorical context in which they are
experienced (Bronfenbrenner and Morris 2006). In the case
of adolescent e-cigarette use, recent changes in the exo and
macrosystems warrant closer inspection of how the role of
schools has changed for teen vaping. These cross-system
relationships are described in Fig. 1.
Following the ecological model, adolescent e-cigarette
use is considered a product of factors found across multiply-
embedded systems, namely the microsystem (school,
family, peer, and individual factors) and exo or macro-
systems (policy, media, culture, and population-wide trends
in e-cigarette use). The conceptual model in Fig. 1 under-
scores the influence that features within the exo and mac-
rosystems have on both schools as well as individual
behaviors. At the level of the exosystem, state and federal
policies on e-cigarettes have a bearing on both individual
consumption (e.g., new minimum age restrictions for pur-
chase) and school functionings (e.g., regulations on e-
cigarette sales near schools). At the level of the macro-
system, cultural shifts—the population-wide rise in e-
cigarette use; youth-targeted marketing campaigns—not
only have direct influences on youth behaviors, but they
also modify the association between school features and
youth behaviors. For instance, the role that peers play in
disseminating beliefs and attitudes about the safety and
benefits of vaping (e.g., looking cool or demonstrating
adult-like behavior) could be augmented, or even sup-
planted by, messages circulating throughout social media
within the macrosystem. Indeed, recent evidence shows that
advertising of e-cigarettes to adolescents has climbed dra-
matically over recent years (Duke et al. 2014; McCarthy
2016), and exposure to such advertising, including celebrity
endorsements via social media, is linked to higher odds of
Fig. 1 Conceptual model of the interdependent association between
schools and e-cigarette use
Journal of Youth and Adolescence (2019) 48:1899–1911 1901
teen use (Camenga et al. 2018; Hammig, Daniel-Dobbs and
Blunt-Vinti 2017; Pasch et al. 2018; Phua, Jin and Hahm
2018; Singh et al. 2016b). Under this scenario, the impor-
tance of schools to teen vaping could have diminished over
time as analogous influences from other ecological systems
became more pronounced—a phenomenon we refer to as
system drift.
Conversely, other changes in the exo and macrosystems
could have enhanced the relevance of schools to teen vaping
via the mechanisms described earlier. Recently expanded
regulations on e-cigarettes by the FDA (U.S. Food and Drug
Administration 2016) imposed mandatory minimum age
requirements on e-cigarette sales, even though research
shows that sales of e-cigarettes to minors is still common
(Levinson 2018). Should e-cigarettes become more difficult
for youth to purchase, adolescents will rely more on peer-
mediated access organized within schools, raising the
importance of schools to adolescent vaping. Additionally,
important shifts in how teens vape could have strengthened
the role that schools played in facilitating the initial emer-
gence of adolescent e-cigarette use. Manufacturers of
technologically-novel “next-gen” e-cigarette devices have
targeted teenagers in their marketing (Chu et al. 2018) and
to great effect, as devices like Juul have become popular
among youth (Krishnan-Sarin et al. 2019). The evolution of
vaping devices over time could have required localized
peer-to-peer demonstrations of use, peer-mediated access to
vaping materials, and flexible school-based cultural norms
that permit vaping, bringing schools back into focus as the
broader prevalence of teen vaping climbed.
Current Study
While either scenario—diminishing or increasing school-
level influences—is possible, no empirical attention has
been given to how the clustering of adolescent e-cigarette
use within schools has shifted over recent years. Respond-
ing to the lack of attention to shifting school influences on
teen health, Ecological Systems Theory and conceptual
models of school environments and adolescent health (Abel
and Frohlich 2012; Frohlich et al. 2002; Frohlich, Corin and
Potvin 2001) are used to address the following research
questions: (1) How has the school clustering of vaping
changed between 2011 and 2017 as the population-wide
prevalence of youth vaping increased? (2) How is the pre-
valence of school-level e-cigarette use associated with a
student’s odds of vaping net of key influences from one’s
family and peer group? The analyses presented here
emphasize the importance of schools over time in distin-
guishing risk for teen vaping while simultaneously adjust-
ing for other features of the microsystem known to correlate
with adolescent e-cigarette use including one’s own
conventional smoking status and that of their friends or
family members. Poor academic achievement is also taken
into account given the association this shares with nicotine
use in prior studies. Additionally, as extant research has
shown higher rates of vaping among older versus younger
adolescents, males versus females, and among non-Hispanic
Whites versus other racial/ethnic groups (Lippert 2018),
students’ demographic characteristics are assessed. With
adjustments made for student demographics, low scholastic
achievement, personal smoking status and vicarious expo-
sure to tobacco use among friends or family, the multilevel
models presented here provide an estimate of the impor-
tance of school context to teen vaping over time.
In addressing these questions, three key hypotheses are
tested. The first is that the extent of school clustering of
adolescent e-cigarette use declined from 2011 to 2015 as
vaping became more normative across society and alter-
native sources of social and material resources needed to
vape became available to teens across a variety of schools
(Hypothesis 1). This hypothesis is tested by comparing the
year-specific variance components from multilevel models
where the null hypothesis is that there were not significant
differences in the variance components across years, and the
alternative is that school-level variance in student vaping
declined between 2011 and 2015. The second is that
between 2015 and 2017 this pattern reversed as novel
methods of vaping again necessitated context-mediated
access to vaping resources, peers, and permissive school
norms (Hypothesis 2). Again, this hypothesis is evaluated
by comparing year-specific variance components and via
Wald tests evaluating whether the variance components
from 2015 and 2017 are statistically identical (Ho) or if the
variance components from 2017 were larger than those from
2015 (Ha). The third hypothesis is that net of the changing
school clustering of e-cigarette use between 2011 and 2017,
students attending a school with a higher versus lower
prevalence of e-cigarette use will present a higher risk of
vaping (Hypothesis 3). We test these hypotheses by using
repeated cross-sectional data from the CDC-sponsored
2011–2017 National Youth Tobacco Surveys (NYTS) and
by decomposing variance partitioning coefficients (VPC)
from multiyear multilevel regression models of students
nested within schools across the US.
Data are from four repeated cross-sections of the NYTS
conducted in 2011, 2013, 2015, and 2017. The NYTS is a
self-administered survey based on a national sample of US
middle- and high-school students in public and private
1902 Journal of Youth and Adolescence (2019) 48:1899–1911
schools across all 50 states and DC. The 2011 survey—the
first to include e-cigarette measures—covered 178 schools
and 18,866 students (overall response rate, defined as the
product of school- and student-level participation, was
73.2%); the 2013 sample covered 187 schools and
18,406 students (68.4% overall response rate); the 2015 sam-
ple covered 183 schools and 17,711 students (63.4% overall
response rate); the 2017 sample covered 185 schools and
17,872 students (68.1% overall response rate).
NYTS used a 3-stage cluster-based sampling design
where primary sampling units (counties, several small
counties, or portions of larger counties) were selected
without replacement followed by schools within counties
and students within schools. Purposeful oversampling was
done for specific groups including Blacks and Hispanic
students. The pooled sample size across all three rounds
of NYTS was 72,855. Item missingness was rare and no
single item exceeded 5% missing, a common threshold for
judging the necessity of multiple imputation (Jakobsen
et al. 2017). Thus, complete-case data were used by
excluding those with any missing data across all items (n
= 7788, 10.7%), arriving at a final analytic sample of
65,067 students.
E-cigarette use
Both lifetime and current (past-month) e-cigarette use were
examined. In the 2011 and 2013 survey years, this was
assessed dichotomously across two questions with those
students who indicated use of “electronic cigarettes or e-
cigarettes, such as Ruyan or NJOY” to the question:
“Which of the following tobacco products have you ever
tried?” Students reporting any lifetime use of e-cigarettes
were coded “1” for the lifetime use measure (“0” other-
wise), while students reporting any use of e-cigarettes in the
month prior to the interview were coded “1” for the current
use measure (“0” otherwise). A minor revision to these
survey items was instituted in the 2015 and 2017 ques-
tionnaires, where example brands were not provided and
youth were simply asked if they had used e-cigarettes.
School prevalence of e-cigarette use
School-level e-cigarette usage was captured by aggregating
student-level reports of lifetime e-cigarette use to the school
level and was categorized in three groups—low, moderate,
high—based on tertiles in regression models. This approach
aligns with that of the CDC, which in 2017 began providing
a similar aggregate measure of school-level lifetime e-
cigarette use in data files released to the public.
Student, family, and peer smoking
Student-level conventional cigarette smoking was cap-
tured in a three-category variable with categories for
‘never’, ‘experimenter’, and ‘current’. Never smokers had
never smoked or tried cigarettes; experimenters had tried
smoking at least 1-2 puffs but had lifetime usage of less
than 100 cigarettes (5 packs) or no past-month smoking
occurrences; current smokers had smoked at least 100
cigarettes in their lifetime and reported smoking at least
once in the past 30 days. Vicarious exposure to smoking
of family members was assessed via student reports of any
household members who had smoked tobacco products in
the previous week while the respondent was home (coded
“1” for affirmative responses, “0” otherwise). Second-
hand smoking exposure from peers was based on a
question asking respondents whether in the past month
they had breathed tobacco smoke from a smoker in places
such as school buildings or grounds, indoors or outside, or
other public places (coded “1” for affirmative responses,
“0” otherwise).
Student demographics
Respondent age is a continuous variable and centred
around its mean (14.6 years). Age appropriate-for-grade
was calculated as a proxy for poor academic performance
which may have resulted in students repeating a grade. A
dichotomous indicator was created to indicate high age for
grade (“1”) or appropriate age for grade (“0”) according to
the following criteria: age >12 y in grade 6; >13 y in grade
7; >14 y in grade 8; >15 y in grade 9; >16 y in grade 10;
>17 y in grade 11; and >18 y in grade 12. Race is self-
reported and grouped into the following: non-Hispanic
White (reference), non-Hispanic Black, Hispanic, or other
race/ethnicity. Student gender is based on self-reports and
dichotomized with “female” serving as the reference
group. Finally, survey year is used to estimate year-
specific variance components and VPCs from multilevel
models and as a covariate in bivariate as well as multi-
variate multilevel models, where 2011 is treated as the
reference survey year.
To assess the relationship between lifetime and past-
month e-cigarette usage and survey year, a series of 2-
level logistic regression models were fit to the response of
lifetime and past-month e-cigarette usage (y, used vs had
not used) for individual i in school j. The general mod-
elling approach was as follows: e-cigarette usage, Pr(yij =
1), assumed to be binomially distributed yij ~ Binomial (1,
πij) with probability πij, was related to indicators for all
Journal of Youth and Adolescence (2019) 48:1899–1911 1903
survey years (β0j, β1j, β2j) and other covariates X and a
random effect for each survey year by a logit link function
using the following specification:
Logit πij
� �
¼ β0j2011 þ β1j2013 þ β2j2015 þ β3j2017
þβXij þ u0j þ u1j þ u2j þ u3j
� �
The terms u0j, u1j, u2j, and u3j, in brackets represent
school-level random effects which are allowed to be esti-
mated separately for each survey year using a diagonal
matrix for the variance-covariance matrix. These random
effects are assumed to be independently and identically
distributed with variances σ2u0, σ
u1, σ
u2, and σ
u3. The var-
iance parameters quantify heterogeneity in the log odds of
e-cigarette usage between schools in 2011, 2013, 2015, and
2017, respectively. We expressed the year-specific school-
level variance as a percentage of the total variance for that
period using the variance partitioning coefficient (VPC).
The VPC (for 2011) is calculated as,
VPC ¼ σ2u0= σ2u0 þ π
� �
The first series of models were specified with indica-
tors for survey year only. The second set included
student-level characteristics (age, sex, age-for-grade,
race) and school-level e-cigarette usage. A final set of
models included individual smoking behaviour along
with all of the covariates from the previous model. All
models used Bayesian estimation procedures imple-
mented via Monte Carlo Markov Chain (MCMC) meth-
ods and Metropolis Hastings algorithm available in
MLwiN 2.3. MCMC simulations were run for 50,000 to
75,000 iterations following a burn-in of 1000 iterations.
MCMC diagnostics were examined for all parameters to
ensure model convergence. The random variances and
their associated credible intervals were obtained directly
from summarizing the simulated posterior distributions of
model parameters. This methodology has been demon-
strated to reduce bias in estimated random effects in
binomial multilevel models which can occur when using
maximum-likelihood procedures (Browne et al. 2005).
Further, the MCMC procedure provides the Deviance
Information Criterion (DIC) which is an overall measure
of model goodness-of-fit. A small difference in DIC
between models indicates that they are empirically
equivalent, whereas differences larger than 10 are taken
to suggest support for the model with lower DIC value.
Multivariate results were similar for both lifetime and
past-month e-cigarette use. Thus, results only from mod-
els predicting current e-cigarette use are reported, though
in Table 3 year-specific variance components from all
models for lifetime and current use are provided.
Table 1 indicates that the unweighted prevalence of lifetime
and current e-cigarette usage increased in the NYTS from
3.0% and 1.0% in 2011, respectively, to 20.8% and 7.8% by
2017. The sample descriptives also reveal an unstandar-
dized average age of 14.6 years across all years, a sample
well-balanced by gender (50.3% male), and one that gen-
erally follows population-wide racial and ethnic composi-
tional attributes for younger cohorts, though deliberate
oversampling led to a higher share of Hispanic students in
the NYTS samples than is observed in the general popula-
tion. …
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Tobacco Use Insights
Volume 13: 1–15
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DOI: 10.1177/1179173X20945695
Current (past 30-day) vaping among U.S. adolescents has
increased dramatically in recent years.1,2 Rates almost doubled
from 2017 (11.0% of 12th graders) to 2018 (20.9%), the largest
substance use increase ever observed in the 44-year history of
the national Monitoring the Future study.1 Vapes have been
the most commonly used tobacco product among adolescents
since 2014,2 and more than 5 million middle and high school
students were current vape users in 2019.3 These dramatic
increases have offset reductions in cigarette smoking, fueling an
overall increase in adolescent current tobacco use.1,4
This explosion of vaping is concerning because of the risks
associated with adolescent vape use. Adolescents who vape are
more likely than non-users to initiate cigarette smoking and
escalate smoking among those who have already experimented
with cigarettes,5-11 though this association may be due to
shared risk factors for vaping and smoking.12 Researchers are
beginning to understand the chemical constituents and health
implications of vape juice and aerosols, which include carcino-
gens and irritants.8,13-17 Although long-term health effects are
unknown, vaping may be associated with short-term risks
including respiratory symptoms, asthma, and bronchitis among
adolescents.8,18,19 In addition, nicotine exposure affects adoles-
cent brain development, leading to long-term cognitive issues
including memory and attention impairment.20-22
Despite the alarming increase, teens who vape remain a
minority of the adolescent population.3 Little is known about
which youth are at the greatest risk beyond demographic
descriptions, leaving public health interventionists with a lim-
ited understanding of who should be prioritized in prevention
efforts. Current vaping is more prevalent among male, non-
Hispanic White, higher socioeconomic status, and lesbian, gay,
and bisexual adolescents and young adults.23-27 In addition,
young current vape users often have friends and family mem-
bers who vape or who accept vaping,28 and use other substances
including cigarettes and marijuana.29-31
The Vaping Teenager: Understanding the
Psychographics and Interests of Adolescent Vape
Users to Inform Health Communication Campaigns
Carolyn Ann Stalgaitis , Mayo Djakaria
and Jeffrey Washington Jordan
Research Department, Rescue Agency, San Diego, CA, USA.
BACkgRoUnd: Adolescent vaping continues to rise, yet little is known about teen vape users beyond demographics. Effective intervention
requires a deeper understanding of the psychographics and interests of adolescent vape users to facilitate targeted communication
MeTHodS: We analyzed the 2017-2018 weighted cross-sectional online survey data from Virginia high school students (N = 1594) to iden-
tify and describe subgroups of adolescents who vaped. Participants reported 30-day vape use, identification with 5 peer crowds (Alterna-
tive, Country, Hip Hop, Mainstream, Popular), social prioritization, agreement with personal values statements, social media and smartphone
use, and television and event preferences. We compared vaping rates and frequency by peer crowd using a chi-square analysis with follow-
up testing to identify higher-risk crowds and confirmed associations using binary and multinomial logistic regression models with peer crowd
scores predicting vaping, controlling for demographics. We then used chi-square and t tests to describe the psychographics, media use,
and interests of higher-risk peer crowds and current vape users within those crowds.
ReSUlTS: Any current vaping was the highest among those with Hip Hop peer crowd identification (25.4%), then Popular (21.3%). Stronger
peer crowd identification was associated with increased odds of any current vaping for both crowds, vaping on 1 to 19 days for both crowds,
and vaping on 20 to 30 days for Hip Hop only. Compared with other peer crowds and non-users, Hip Hop and Popular youth and current
vape users reported greater social prioritization and agreement with values related to being social and fashionable. Hip Hop and Popular
youth and current vape users reported heavy Instagram and Snapchat use, as well as unique television show and event preferences.
ConClUSIonS: Hip Hop and Popular adolescents are most likely to vape and should be priority audiences for vaping prevention cam-
paigns. Findings should guide the development of targeted health communication campaigns delivered via carefully designed media
keywoRdS: E-cigarette, vaping, adolescent, psychographics, peer crowd, personal value, social media, health communication
ReCeIVed: November 26, 2019. ACCePTed: June 17, 2020.
TyPe: Original Research
FUndIng: The author(s) received no financial support for the research, authorship, and/or
publication of this article.
deClARATIon oF ConFlICTIng InTeReSTS: The author(s) declared no potential
conflicts of interest with respect to the research, authorship, and/or publication of this
CoRReSPondIng AUTHoR: Carolyn Ann Stalgaitis, Rescue Agency, 2437 Morena
Blvd., San Diego, CA 92110, USA. Email: [email protected]
945695TUI0010.1177/1179173X20945695Tobacco Use InsightsStalgaitis et al
mailto:[email protected]
2 Tobacco Use Insights
Audience psychographics move beyond demographics to
provide health communicators with critical insights about val-
ues, identities, and interests that can inform effective messag-
ing and campaign strategies.32-35 In addition, these insights are
critical for the effective planning and execution of modern
digital media campaigns that rely on interest-based targeting
to deliver digital advertisements to the intended audience.36
Past studies describing ever and current vape users have typi-
cally focused on vaping attitudes and beliefs,37-40 or have used
psychographics and motivations to segment adult, but not ado-
lescent, vape users into discrete subgroups.41,42 Only a few
studies have examined the psychographics of adolescent or
young adult vape users, revealing that novelty-seeking, sensa-
tion-seeking, and lower social conservatism are generally asso-
ciated with ever and current vaping in these populations.27,43,44
From this basis, we seek to expand health communicators’
understanding of the psychographics, identities, media use, and
interests of adolescent current vape users to inform the devel-
opment of effective vaping prevention campaigns.
Knowing which adolescents vape, what other substances
they use, what they care about, and what influences them is
crucial to addressing adolescent vaping. Commercial market-
ing, including vape marketing, relies on audience segmentation
to identify population subgroups with shared desires and needs
for whom a tailored brand can be built and marketed via tar-
geted media channels.45 In health communications, a similar
approach is necessary to counter industry marketing by identi-
fying adolescent subgroups at the greatest risk for vaping,
developing targeted campaigns that appeal to their shared val-
ues, beliefs, and interests, and delivering campaign content via
specific media channels and strategies to ensure the target
audience is reached.45 Health campaigns designed around the
psychographics of their target audiences are effective,45-47 but
this approach requires a clearly defined audience with unique
characteristics for whom appealing content can be tailored.
Importantly, campaigns must both tailor messaging (by select-
ing messaging that caters to audience preferences, values, and
interests to capture attention and increase persuasion) and tar-
get media delivery (by selecting highly specialized media chan-
nels and using state-of-the-art ad-targeting technologies) to
effectively reach their target audiences in the modern, cluttered
media environment.47 Although much is known about the
demographics of adolescent vape users, health educators lack
crucial information about their values, influences, and interests
that is necessary to define an audience and deliver effective,
targeted communications.
To fill this gap, we used online survey data to describe the
risk profile, psychographic characteristics, and interests of ado-
lescent current vape users in a single U.S. state. We had 2 pri-
mary objectives: to identify potential target audiences for
adolescent vaping prevention campaigns and to describe the
psychographics, media use, and interests of these higher-risk
youth to inform campaign planning. First, we sought to define
potential target audiences by applying a peer crowd audience
segmentation approach. Peer crowds are macro-level subcul-
tures with shared interests, values, and norms47,48 which
are associated with adolescent and young adult health
behaviors49-57 and have served as the basis for targeted health
interventions.58-64 For example, the Commune campaign target-
ing Hipster peer crowd young adults resulted in reductions in
cigarette smoking associated with stronger anti-tobacco atti-
tudes among those recalling the campaign,58,62 whereas engage-
ment with the Down and Dirty campaign was associated with
stronger anti-chewing tobacco attitudes and lower odds of cur-
rent use among Country peer crowd teens.61 In this study, we
examined vaping behavior for 5 adolescent peer crowds previ-
ously established in the literature: Alternative (counterculture,
value creativity and uniqueness), Country (patriotic, value hard
work and being outdoors), Hip Hop (confident, value over-
coming struggles and proving themselves), Mainstream
(future-oriented, value organization and stability), and Popular
(extroverted, value socializing and excitement).47,49,50,52,54,55,61
After identifying the highest risk peer crowds, we sought to
create a profile of these audiences by examining their broader
health risk profiles, psychographics (social prioritization and
personal values), digital behaviors (social media and smart-
phone use), and interests (television shows and events). With
this information, we aimed to identify and describe segments
of adolescents most in need of targeted vaping interventions to
provide clear guidance for health message development and
media targeting.
Sample and design
We collected cross-sectional online survey data from high
school students ages 13 to 19 living in the U.S. state of Virginia
(N = 1594). Participants were recruited from November 2017
to January 2018 using paid Instagram and Facebook advertise-
ments that directed interested individuals to a screener to
determine eligibility (13-19 years old, current high school stu-
dent, and Virginia resident). Eligible youth were invited to par-
ticipate in the full survey and provided electronic assent (ages
13-17) or consent (ages 18-19). We delivered a parental opt-
out form via email for participants ages 13 to 17. Qualified
participants who completed the full survey received a US$10
electronic gift card incentive. We implemented numerous fraud
prevention and detection measures to maximize data integrity,
including concealing eligibility criteria during screening, col-
lecting email addresses to prevent duplicate completions, and
reviewing responses for inconsistencies. Chesapeake IRB
approved the study (No. Pro00023204).
To address our research objectives, we examined participant
demographics; current vaping, tobacco, and other substance
use; peer crowd identification; 2 psychographic measures,
namely, social prioritization65 and personal values; social media
Stalgaitis et al 3
and smartphone use; and television show and event
Demographics. Participants provided their birthdate, from
which we calculated their age. Participants also indicated their
gender (male, female) and race/ethnicity (Hispanic, non-His-
panic White, non-Hispanic Black, non-Hispanic Asian-Pacific
Islander, and non-Hispanic other including multiracial and
American Indian or Alaska Native).
Past 30-day vape use. Participants reported the number of days
in the past 30 days on which they used e-cigarettes or vapes,
with response options of 0, 1 or 2, 3 to 5, 6 to 9, 10 to 19, 20 to
29, and all 30 days. To mirror commonly reported statistics, we
examined both any current vaping (1-30 days) and frequency of
vaping defined as occasional use (1-19 days) or frequent use
(20-30 days).66
Past 30-day tobacco and substance use. Participants also reported
the number of days in the past 30 days on which they used
cigarettes; cigars, cigarillos, and little cigars (cigar products);
smokeless tobacco; hookah; alcohol; marijuana; and prescrip-
tion medication without a prescription. Those who reported
any past 30-day use were considered current users of that item.
Peer crowd identif ication. Participants completed Rescue
Agency’s I-Base Survey®, a photo-based tool that measured
identification with 5 peer crowds: Alternative, Country, Hip
Hop, Mainstream, and Popular. The I-Base Survey has identi-
fied consistent patterns of peer crowd prevalence and health
risks in adolescents across the United States.49-52,55,57,61,64 In
brief, participants viewed a grid of 40 photos of unknown
female adolescents and selected 3 who would best and 3 who
would least fit with their main group of friends; they then
repeated the process with male photos. Photos were presented
in random order to each participant to reduce order effects, and
represented a mix of races/ethnicities and peer crowds deter-
mined through prior qualitative research. Participants earned
positive points for the peer crowds of photos selected as the
best fit and negative points for those selected as the least fit,
resulting in a score ranging from –12 to 12 for each of the 5
crowds. For analyses, we assigned participants to each crowd
with which they had at least some identification, defined as a
score of 1 or more on the I-Base Survey for that crowd. Partici-
pants could be assigned to more than 1 peer crowd as they
could score positively for multiple crowds.
Social prioritization index. Participants completed the social
prioritization index (SPI), a validated measure of the degree to
which an individual places importance on their social life that is
associated with young adult cigarette use.58,59,65 The SPI
included 13 questions: 8 items wherein participants selected 1
response that best described them from a pair (up for anything/
pick and choose what to do, outgoing/low-key, center of
attention/lay low, street smart/book smart, partier/studier, wing
it/plan it out, the carefree one/the responsible one, in a picture I
. . . strike a pose/smile big); 3 true or false items (In groups of
people, I am rarely the center of attention; I have considered
being an entertainer or actor; I can look anyone in the eye and
tell a lie with a straight face); 1 item asking how many nights
they went out for fun in the past week (0-1, 2-3, 4-5, 6-7 nights);
and 1 item asking how late they typically stayed out when they
went out for fun (9:59-10:59 pm, 11:00 pm-12:59 am, 1:00-
2:59 am, 3:00 am or later). To calculate the SPI score (range:
0-17), participants received 1 point for each socially oriented
selection for the 8 descriptive pairs and 3 true/false questions,
and received 0 points for selecting 0-1 nights per week or 9:59-
10:59 pm, 1 point for 2-3 nights per week or 11:00 pm-12:59 am,
2 points for 4-5 nights per week or 1:00-2:59 am, and 3 points
for 6-7 nights per week or 3:00 am or later.
Personal values. Participants viewed 26 personal values state-
ments (e.g., I think it is more important to live in the moment
than focus on the future) and rated each on a 5-point Likert-
type scale from 1 (strongly disagree) to 5 (strongly agree).
Past 7-day social media use. Participants reported if they had
consumed or created content on 6 social media platforms in the
past 7 days: Facebook, Instagram, Twitter, Tumblr, Snapchat,
and Pinterest.
Lifetime smartphone use. Participants were asked if they had a
smartphone, and if so, if they had ever used their smartphone
to engage in 9 different activities (e.g., listen to an online radio
or a music service such as Pandora or Spotify; watch movies or
TV shows through a paid subscription service like Netflix).
Television show preferences. Participants selected all television
shows they regularly watched from a list of 24 broadcast and
streaming shows popular with youth (e.g., 13 Reasons Why,
Event preferences. Participants selected all events they regularly
attended from a list of 25 leisure time events youth might
attend (e.g., sports games, high school dances).
Statistical analysis
Respondents were required to complete the survey, so no data
were missing. Data were weighted to the gender, race/ethnicity,
and urban/rural demographics of Virginia teens for all analy-
ses. As a first step, we ran weighted and unweighted frequen-
cies and means for demographic measures.
To address our first objective of identifying which adoles-
cents were at the greatest risk, we used chi-square tests to com-
pare the rates of current vaping and vaping frequency among
those who did and did not identify with each crowd, using
follow-up z tests with Bonferroni correction to identify specific
4 Tobacco Use Insights
significant differences. To confirm that associations persisted
while controlling for demographics, we ran separate binary and
multinomial logistic regression models for each peer crowd,
with a single peer crowd’s score (range: –12 to 12) predicting
odds of current vaping, or of occasional or frequent vaping,
while controlling for age, gender, and race/ethnicity. We also
ran binary logistic regression models for each crowd to predict
odds of any current cigarette, cigar product, smokeless tobacco,
hookah, alcohol, and marijuana use, and any current prescrip-
tion medication misuse, to understand the broader risk profile
of the peer crowds. We ran separate models for each peer crowd
to avoid multicollinearity associated with including all 5 scores
in a single model.
After identifying 2 peer crowds at elevated risk for vaping,
we addressed our second objective of developing interest-based
profiles of these potential target audiences by describing their
psychographics (SPI and personal values), social media and
smartphone use, and television and event preferences. We first
compared frequencies and means for those who did and did
not identify with the 2 crowds of interest, using chi-square tests
and t tests to identify significant differences. Then, within the
2 peer crowds, we compared frequencies and means between
current vape users and non-users, using chi-square tests and t
tests to identify significant differences. This approach allowed
us to identify the characteristics of the 2 peer crowds of interest
to inform campaign content and media targeting, as well as to
hone in on psychographics and interests that specifically char-
acterized current vape users within the higher-risk crowds.
Due to the relatively small subset of participants who were fre-
quent vape users, we focused on any current use to improve the
reliability of results. Tables present items that differed signifi-
cantly between groups in at least 1 analysis and had endorse-
ment rates above 5.0%.
The weighted mean age of the sample was 16.47 years, and
about half identified as female (50.8%) and as non-Hispanic
White (55.3%) (Table 1). The most common peer crowd iden-
tifications were Popular (63.1%) and Mainstream (62.6%).
Race/ethnicity and gender breakdowns differed by crowd
(Supplemental Appendix Table 1).
Consistent with 2018 National Youth Tobacco Survey
results,2 20.6% of Virginia high school students in our sample
currently vaped (Table 2). A significantly greater proportion of
those with any Hip Hop peer crowd identification currently
vaped (25.4%) than those with no Hip Hop identification
(18.0%, P < .001). In binary logistic regression models using each peer crowd score (–12 to 12) to predict odds of current vaping while controlling for demographics, a 1-point increase in the Popular score was associated with a 4% increase in odds of current vaping, whereas a 1-point increase in the Hip Hop score was associated with a 10% increase. Further differentiating current vape users in the sample, 17.0% were occasional vape users (1-19 days in the past 30 days) and 3.7% were frequent users (20-30 days). Those with any Hip Hop identification reported higher rates of occasional vaping (21.2%) than others (14.6%, P < .05). Although rates of frequent vaping did not differ significantly for any peer crowd, stronger Hip Hop identification was associated with greater odds of both occasional and frequent vaping. Stronger Popular identification was associated with greater odds of occasional vaping only. In addition, stronger Hip Hop identification was associated with greater odds of current cigarette, cigar product, hookah, alcohol, and marijuana use, whereas stronger Popular identification was associated with lower odds of use for many products. Based on the chi-square tests and logistic regression results, we identified the Hip Hop and Popular peer crowds as being at elevated risk for vaping. We then characterized the psycho- graphics (Table 3), social media and smartphone use (Table 4), and interests (Table 5) of Hip Hop and Popular youth in gen- eral, as well as Hip Hop and Popular current vape users in particular. Overall, Hip Hop participants were social, trendy individu- als interested in hip hop/rap music and sports. Compared with those with no Hip Hop identification, Hip Hop youth had higher SPI scores, in particular describing themselves as par- tiers, street smart, and carefree (Table 3). Hip Hop youth more often agreed that they make decisions quickly, are fashionable, are social people with lots of friends, and are tougher than most people. In contrast, they less often agreed that they are patri- otic, good students, care what others think about them, care about keeping their bodies free from toxins, and follow the rules. A greater proportion of Hip Hop youth used Snapchat in the past week and used their smartphones to look up sports scores or analyses than those with no Hip Hop identification (Table 4). Many TV shows more often endorsed by Hip Hop youth revolved around hip hop/rap musical interests, such as Love & Hip Hop, The Rap Game, and Wild ’N Out (Table 5). Similarly, Hip Hop youth more often indicated that they regu- larly attend hip hop concerts and dance clubs than others, as well as basketball and football games. Characteristics of vape users within the Hip Hop peer crowd largely reflected an amplification of the broader crowd’s profile. Hip Hop vape users had higher SPI scores than non- users within the crowd, and they described themselves as par- tiers, street smart, carefree, and up for anything (Table 3). They more often agreed that they are fashionable, use their clothes to express their identity, and are tough, and less often agreed that they follow the rules, follow tradition, and care about keeping their bodies free from toxins than non-users. A greater propor- tion of Hip Hop vape users reported using Snapchat, Instagram, and Twitter in the past week than non-users (Table 4). Hip Hop vape users also more often reported using their smart- phones to look up sports scores and analyses, stream music, and make video calls than non-users. Hip Hop vape users more often reported watching 2 cartoon shows, The Boondocks and Bob’s Burgers, than non-users (Table 5). Similar to the overall Stalgaitis et al 5 crowd, a greater proportion of Hip Hop vape users indicated that they attend dance clubs, hip hop concerts, basketball games, and football games than non-users. Popular youth shared some characteristics with Hip Hop youth, but also differed in key ways. Although Popular and Hip Hop youth both reported higher SPI scores than others, the specific SPI items they endorsed often differed (Table 3). Though both Hip Hop and Popular youth described them- selves as partiers, Popular youth also described themselves as the center of attention, outgoing, and up for anything, which were not significant in Hip Hop analyses. Similar to Hip Hop youth, Popular youth more often agreed that they are fashion- able and are social people with lots of friends. However, Popular youth also more often agreed that they care about being good students, keeping their bodies free from toxins, and being patriotic, items with which Hip Hop youth less often agreed. Popular youth also more often agreed that family is important, that they try to follow tradition, and that they are religious than other youth. Popular youth more often reported using Instagram, Snapchat, and Twitter than other youth and more often used their smartphones to look up sports scores or analy- ses and to stream music or video content (Table 4). Compared with others, Popular youth more often reported watching teen dramas, including 13 Reasons Why, Jane the Virgin, Pretty Little Liars, and Riverdale (Table 5). Sports were favored by Popular youth, as they more often reported attending basketball, foot- ball, baseball, and soccer games than others. They also more often reported attending church events, community service events, high school dances, and pop and country music concerts. Popular vape users shared many traits with the broader Popular crowd as well as with Hip Hop vape users. Similar to Hip Hop vape users, Popular vape users reported higher SPI scores than non-users, describing themselves as outgoing, Table 1. Unweighted and weighted sample descriptive statistics. UNWEIghTED WEIghTED PERCENTAgE N PERCENTAgE N Age, mean (SD) 16.45 (1.17) 16.47 (1.19) Female 62.4 994 50.8 810 Race/ethnicity hispanic 10.5 167 11.8 188 Non-hispanic White 56.8 906 55.3 881 Non-hispanic Black 11.3 180 21.0 335 Non-hispanic Asian-Pacific Islander 11.6 185 5.1 81 Non-hispanic Other 9.8 156 6.8 108 Alternative peer crowd In crowd 42.4 676 43.2 689 Not in crowd 57.6 918 56.8 905 Country peer crowd In crowd 48.8 778 46.9 748 Not in crowd 51.2 816 53.1 846 hip hop peer crowd In crowd 32.2 514 35.5 566 Not in crowd 67.8 1080 64.5 1028 Mainstream peer crowd In crowd 64.6 1029 62.6 997 Not in crowd 35.4 565 37.4 597 Popular peer crowd In crowd 64.5 1028 63.1 1006 Not in crowd 35.5 566 36.9 588 6 Tobacco Use Insights Ta b le 2 . W e ig h te d f re q u e n ci e s a n d a d ju st e d o d d s ra tio s fo r p e e r cr o w d r is k b e h a vi o rs . P A S T 3 0 -D A y v A P E U S E P A S T 3 0 -D A y O T h E R R IS k B E h A v IO R A N y U S E 1- 19 D A y S 2 0 -3 0 D A y S C Ig A R E T T E S C Ig A R P R O D U C T S S M O k E l E S S T O B A C C O h O O k A h A l C O h O l M A R IJ U A N A P R E S C R IP T IO N M E D IC A T IO N F u ll sa m p le ( % ) 2 0 .6 17 .0 3 .7 11 .6 6 .9 3 .5 3 .8 3 2 .7 2 0 .3 6 .7 A lte rn a ti ve In c ro w d ( % ) 19 .4 16 .0 3 .5 15 .4 ** * 8 .0 4 .1 4 .5 3 2 .2 2 3 .9 ** 8 .3 * N o t in c ro w d ( % ) 2 1. 5 17 .8 3 .9 8 .6 6 .1 3 .1 3 .3 3 3 .0 17 .6 5 .5 A O R 0 .9 7 0 .9 7 0 .9 8 1. 12 ** * 1. 0 6 1. 0 0 1. 0 5 1. 0 1 1. 0 7 ** * 1. 0 8 ** C o u n tr y In c ro w d ( % ) 18 .7 15 .3 3 .5 8 .7 ** * 4 .6 ** * 4 .0 2 .7 * 2 8 .5 ** * 16 .3 ** * 6 .3 N o t in c ro w d ( % ) 2 2 .2 18 .4 3 .8 14 .1 8 .9 3 .1 4 .7 3 6 .3 2 3 .8 7. 1 A O R 0 .9 4 ** * 0 .9 5 ** 0 .9 3 * 0 .9 1* ** 0 .9 6 1. 11 ** 0 .9 7 0 .9 2 ** * 0 .9 1* ** 0 .9 5 * h ip h o p In c ro w d ( % ) 2 5 .4 ** * 2 1. 2 * 4 .2 14 .0 * 9 .5 ** 3 .7 4 .8 3 5 .0 2 7. 2 ** * 7. 4 N o t in c ro w d ( % ) 18 .0 14 .6 3 .4 10 .2 5 .4 3 .3 3 .2 3 1. 3 16 .5 6 .3 A O R 1. 10 ** * 1. 10 ** * 1. 11 ** 1. 0 7 ** * 1. 0 8 ** 1. 0 0 1. 0 7 * 1. 0 6 ** * 1. 11 ** * 1. 0 3 M a in st re a m In c ro w d ( % ) 18 .2 ** 14 .9 * 3 .3 8 .7 ** * 5 .2 ** * 2 .2 ** * 2 .8 ** 3 2 .0 16 .9 ** * 6 .0 N o t in c ro w d ( % ) 2 4 .7 2 0 .3 4 .4 16 .2 9 .6 5 .7 5 .4 3 3 .7 2 6 .1 7. 9 A O R 0 .9 5 ** 0 .9 5 * 0 .9 4 0 .8 9 ** * 0 .9 1* * 0 .8 4 ** * 0 .9 0 * 0 .9 9 0 .9 2 ** * 0 .9 4 P o p u la r In c ro w d ( % ) 2 1. 3 17 .3 3 .9 10 .1 * 6 .1 3 .0 3 .3 3 2 .5 18 .7 * 5 .2 ** N o t in c ro w d ( % ) 19 .6 16 .3 3 .2 13 .9 8 .3 4 .4 4 .6 3 2 .8 2 3 .1 9 .3 A O R 1. 0 4 * 1. 0 4 * 1. 0 4 0 .9 4 ** 0 .9 5 * 0 .9 1* 0 .9 6 1. 0 0 0 .9 8 0 .9 4 * A b b re vi a tio n : A O R , a d ju st e d o d d s ra tio . P e rc e n ta g e s …

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