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Substance use disparities in adolescents of lower socioeconomic status: emerging trends in electronic cigarettes, alternative tobacco products, marijuana, and prescription drug use

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Substance use disparities in adolescents of lower socioeconomic status: emerging trends in electronic cigarettes, alternative tobacco products, marijuana, and prescription drug use

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Content Running Head: SUBSTANCE USE DISPARITIES IN LOWER SES ADOLESCENTS

1

Substance Use Disparities in Adolescents of Lower Socioeconomic Status: Emerging Trends in
Electronic Cigarettes, Alternative Tobacco Products, Marijuana, and Prescription Drug Use
Mariel S. Bello
University of Southern California
Department of Psychology

Submitted to the Faculty of the USC Graduate School
of the University of Southern California in fulfillment
of the requirements for the Degree of
Master of Arts in Psychology
December 2017

SUBSTANCE USE DISPARITIES IN LOWER SES ADOLESCENTS 2
Table of Contents
Abstract 3
Introduction 4
Methods 8
Participants and Procedures 8
Measures 9
Analytic Approach 11

Results 12
Preliminary Analyses 12
Primary Analyses 12

Discussion 13
Conclusions 18
References 20
Tables 29
SUBSTANCE USE DISPARITIES IN LOWER SES ADOLESCENTS 3
Abstract
Objective markers of socioeconomic status (SES), such as parental education, have demonstrated
inverse associations with adolescent cigarette, alcohol, and marijuana use. However, the extent to
which these associations generalize to new substances that are gaining popularity in youth (e.g.,
electronic cigarettes) are unknown. How someone subjectively perceives their social status
relative to a larger group may also provide incremental information about SES not captured by
objective measures. The current study examined associations of objective SES and subjective
social status (SSS) with use of a wide range of substances (i.e., alcohol, combustible cigarettes,
hookah, cigars, electronic cigarettes, marijuana, and prescription drugs) in a socioeconomically
diverse sample of 11
th
grade students in Los Angeles, California. Participants (N = 2,166)
completed semi-annual surveys assessing parental education, free or subsidized lunch, SSS
(societal- and school-level), and substance use over one’s lifetime, in the past 6 months, and in
the past 30 days. Utilizing polytomous logistic regression models that adjusted for covariates and
SES variables, we found that lower objective SES was incrementally associated with increased
odds of being a lifetime or recent user. Lower school-level SSS was incrementally associated
with greater odds of being a current user of majority of the substances. Our findings provide
evidence for varying patterns of association across objective SES and SSS dimensions with
adolescent drug use. Policy-making, interventions, and educational efforts to increase prevention
campaigns that reduce exposure to new, emerging drugs and increase access to substance-free
activities may effectively reduce substance-related health disparities among socioeconomically
disadvantaged adolescent populations.
Keywords: health disparities, socioeconomic status, subjective social status, substance use,
adolescents
SUBSTANCE USE DISPARITIES IN LOWER SES ADOLESCENTS 4
Introduction
Despite the recent implementation of prevention programs and interventions to reduce
addiction-related health disparities in diverse populations (Mechanic, 2012; Substance Abuse
and Mental Health Services Administration [SAMHSA], 2014), greater prevalence rates of
substance use, abuse, and dependence remain persistent among socioeconomically disadvantaged
adults (Steptoe & Marmot, 2004; van Lenthe, Martikainen, & Mackenbach, 2007; Williams,
Mohammed, Leavell, & Collins, 2010) and adolescent populations in the U.S. (Andrabi,
Khoddam, & Leventhal, 2017; Bachman, O'Malley, Johnston, Schulenberg, & Wallace, 2011;
Lemstra et al., 2008; Leventhal et al., 2015a; Unger, Sun, & Johnson, 2007). Lower levels of
socioeconomic status (SES) have been consistently linked with increased likelihood of substance
use initiation and frequency of adolescent substance use (Andrabi et al., 2017; Bachman et al.,
2011; Unger et al., 2007), and recent evidence suggests that SES may also be inversely
correlated with susceptibility to substance use in youth who have never tried substances
(Leventhal et al., 2015a). Given that adolescence is a critical developmental stage marked by
increased vulnerability to health risk behaviors (Bonomo et al., 2004; Kulig, 2005; Windle et al.,
2008) and considering that early onset of substance use can result in more severe long-term
substance use problems, morbidity, and early mortality in adulthood (King & Chassin, 2007;
Mathers et al., 2006; Riggs et al., 2007; Trenz et al., 2012), understanding the impact of SES on
adolescent substance use is of vital importance for advancing substance use epidemiology,
etiology, prevention, and intervention efforts to effectively reduce the public health burden of
substance use disparities among lower SES adolescent populations.
SES is a multidimensional construct that can be parsed into two domains (Adler, Epel,
Castellazzo, & Ickovics, 2000). Objective markers of SES in youth (i.e., parental education level,
SUBSTANCE USE DISPARITIES IN LOWER SES ADOLESCENTS 5
family income, free or subsidized lunch [the number of students receiving free or reduced cost
lunch from the federal free lunch program], and parental occupation) serve as proxy measures to
indicate an individual’s social capital (Oakes & Rossi, 2003) and access to goods and services
(Ritterman et al., 2009). By contrast, subjective measures of SES can include factors that
evaluate an individual’s perceived socioeconomic status relative to others, perceptions of a
family’s ability to afford basic necessities, or subjective perceptions about their social standing
on the SES hierarchy relative to others in American society (i.e., societal-level SSS) and within
their school community (i.e., school-level SSS; Goodman et al., 2001).
Although the aforementioned objective SES measures have been linked to a variety of
adolescent health and substance use outcomes (Chen, Matthews, & Boyce, 2002; Newacheck et
al., 2003; Evans & Kim, 2007; Starfield et al., 2002; Walker et al., 2007), findings among these
studies have been highly inconsistent (Starfield et al., 2002; Torsheim et al., 2004; West, 1997;
West & Sweeting, 2004). Majority of studies elucidated inverse associations of adolescent
substance use with parental education (Andrabi et al., 2017; Bachman et al., 2011; Leventhal et
al., 2015a; Ritterman et al., 2009; Unger et al., 2007), parental income (Conwell et al., 2003;
West et al., 2007), parental occupation (Legleye et al., 2013), and free or subsidized lunch (Riggs
& Pentz, 2016). Contrary to these findings, some U.S.-based studies found that greater levels of
both parental education and family income were positively associated with adolescent cigarette
smoking (Hanson & Chen, 2007a), drinking (Goodman & Huang, 2002; Hanson & Chen, 2007a;
Piko & Fitzpatrick, 2007), and marijuana use (Dornbusch et al., 2001). More research is needed
to clarify the discrepancies of relations between objective SES markers and adolescent substance
use. Extant literature illuminates that while parental SES may be useful for determining
individual differences among younger children, parental SES may decrease in impact once
SUBSTANCE USE DISPARITIES IN LOWER SES ADOLESCENTS 6
adolescents establish greater independence and spend less time in the home (Hanson & Chen,
2007b; West & Sweeting, 2004). Growing evidence suggests that an adolescent’s substance use
may be more strongly influenced by their perceptions about their social standing relative to
others within their school community (i.e., school-level SSS), as opposed to traditional SES
indicators, given that adolescents spend more time with their peers in school (Goodman et al.,
2001; Hanson & Chen, 2007b; West & Sweeting, 2004). Therefore, the assessment of an
adolescent’s school-level SSS, may prove to be a useful construct for providing interpretations
for substance-related disparities amongst youth populations.
Research examining associations between societal- and school-level SSS and adolescent
substance use remain remarkably scant. Few U.S.-based studies have demonstrated that students
with lower school-level SSS were at increased risk for cigarette smoking initiation and current
smoking (Finkelstein, Kubzansky, & Goodman, 2006), and alcohol use in contrast to students
with higher school-level SSS (Wilkinson et al., 2009; Wilkinson et al., 2011). Research utilizing
a sample of socioeconomically disadvantaged youth from Mexico discovered that societal- and
school-level SSS were differentially related to adolescent substance use, such that youth with
higher school-level SSS were more likely to be current smokers and drinkers (vs. lower school-
level SSS), while youth with lower societal-level SSS reported current smoking and drinking (vs.
those with greater societal-level SSS; Ritterman et al., 2009). Lastly, a recent study assessing
multiple dimensions of SSS (i.e., sports, school performance, popularity with peers) among
Scotland youth aged 13-15 years determined that rates of lifetime smoking and heavier drinking
were higher in youth who reported lower scholastic-SSS, sports-SSS, and higher peer-SSS
(Sweeting & Hunt, 2015). As such, these few studies provide initial evidence illuminating unique
and complex relationships between an adolescent’s subjective perceptions of their social status
SUBSTANCE USE DISPARITIES IN LOWER SES ADOLESCENTS 7
and their drug use behaviors, and it is evident that future work is needed to further delineate how
distinct dimensions of SSS relate to distinct types of substances, especially for new, emerging
drugs which are becoming increasingly popular among adolescents in the U.S.
Recent evidence indicates that electronic cigarettes (e-cigarettes) and alternative tobacco
products (i.e., cigars, hookah) are becoming increasingly attractive to adolescent populations
(Johnston et al., 2016) and may be linked to subsequent combustible cigarette smoking
(Leventhal et al., 2015b; Leventhal et al., 2016). Additionally, current prevalence estimates for
adolescent use of prescription drugs for non-medical reasons have increased since 1976 and
recently declined from 2013-2015 (12.9% in 2015; McCabe et al., 2017), yet still warrant
attention since adolescents also report lower perceived risk about the dangers of using
prescription drugs outside of medical reasons (Johnston et al., 2016). Currently, there are no
published findings that have investigated relations of SSS with e-cigarettes, alternative tobacco
products, and prescription drugs among U.S. adolescents aged 16 years old, leaving unclear
whether SES-substance use relationships found in prior work generalize to SSS dimensions and
these new, emerging substances amongst high risk youth populations.
The present cross-sectional study of 11
th
grade high school students in Los Angeles, CA
expands upon the literature by comparing associations between SSS (societal- and school-level),
objective SES (parental education, free or subsidized lunch), and adolescent alcohol, combustible
cigarettes, hookah, cigars, electronic cigarettes (with or without nicotine), marijuana (edibles,
vaping), and prescription drug use. Specifically, we are interested in determining whether the
relationship between objective SES and adolescent substance use generalizes across the two
dimensions of SSS, and whether these distinct dimensions of SSS may be incrementally
associated with specific substance use outcomes over and above their covariance with objective
SUBSTANCE USE DISPARITIES IN LOWER SES ADOLESCENTS 8
SES indicators. As supported by prior work, we hypothesized that societal- and school-level SSS
would each be inversely associated with combustible cigarettes, alcohol, and marijuana use.
Given the paucity of prior work examining relations between SSS and new, emerging
substances, we do not put forth hypotheses regarding how SSS dimensions may relate to these
specific types of substances.
Methods
Participants and Procedures
The present study utilized cross-sectional data from a longitudinal study of mental health
and substance use among adolescents enrolled in 10 public high schools in the greater Los
Angeles, CA, USA metropolitan area (NIH Grant R01-DA033296; PI: Adam Leventhal). All
students who were not enrolled in English as a Second Language Programs or special education
courses (i.e., students with learning disabilities) were eligible to partake in the study (N = 4,100).
Among the 4,100 students who were eligible, 3,874 (94.5%) provided active written or verbal
assent, and 3,395 (82.8%) provided active written or verbal parental consent. Each participating
school was selected based on its adequate representation of diverse demographic characteristics;
the percent of students eligible for free lunch within each school (i.e., student’s parental income
< 185% of the national poverty level) on average across the ten schools was 31.1% (SD = 19.7,
range: 8.0% - 68.2%). Paper-and-pencil surveys were distributed once every 6 months beginning
at the start of 9
th
grade in Fall 2013 through the start of 11
th
grade: Baseline (Wave 1; Fall of 9
th

grade, 2013; N = 3,383), 6-month (Wave 2; Spring 9
th
grade, 2014; N = 3,293), 12-month (Wave
3; Fall 10
th
grade, 2014; N = 3,288), 18-month (Wave 4; Spring 10th grade, 2015; N = 3,262),
and 24-month (Wave 5; Fall 11
th
grade, 2015; N = 3,235) follow-ups. Students who were absent
during data collection periods completed surveys by phone, internet, or mail (Wave 2: N = 51,
SUBSTANCE USE DISPARITIES IN LOWER SES ADOLESCENTS 9
Wave 3: N = 153, Wave 4: N = 215, Wave 5: N = 268). Students were informed that their
responses would be confidential and not shared with their teachers, parents, or school staff. Each
participating school was compensated $2,500 for their general activity fund; students were not
individually compensated, but were given small incentives (e.g., pens, keychains). The study is
approved by the University of Southern California Institutional Review Board.
Measures
Parental Education. Highest level of parental education was assessed during Wave 1
using ordinal forced choice item for each parent (1 = 8
th
grade or less, 2 = some high school, 3 =
high school graduate, 4 = some college, 5 = college graduate, 6 = advanced degree). Based on
prior work (Andrabi et al., 2016; Leventhal et al., 2015a; Unger et al., 2007), we utilized the
highest education level across the two parents in our analyses; if data is available for only one
parent, that parent’s score was used. Extensive research examining associations between SES
and adolescent substance use have utilized parental education as the key objective marker for
adolescent SES (Andrabi et al., 2016; Bachman et al., 2011; Conwell et al., 2003; Finkelstein,
Kubzansky, & Goodman, 2006; Leventhal et al., 2015a; Lintonen et al., 2000; Lowry et al.,
1996; Unger et al., 2007).
Free or Subsidized Lunch. Free or subsidized lunch was determined in Wave 5 by
asking participants to report whether they receive a free or reduced cost lunch at school (0 = No,
1 = Yes, reduced cost lunch, 2 = Yes, free lunch). Free or subsidized lunch has been widely used
in education and health research as a proxy for adolescent SES, and previous data assessing the
validity of free or subsidized lunch showed strong correlations with other measures of SES (e.g.,
family poverty, median household income; Nicholson et al., 2014).
SUBSTANCE USE DISPARITIES IN LOWER SES ADOLESCENTS 10
MacArthur Scale of Subjective Social Status (SSS). Societal- and school-level SSS
was assessed during Wave 5 via the adolescent version of the MacArthur Scale of Subjective
Social Status (Goodman et al., 2001). The 10-rung ladder image that assessed societal-level SSS
included the following instructions: “Imagine that this ladder pictures how American society is
set up. Now think about your family. Please tell us where you think your family would be on this
ladder.” The top rung was labeled “the best off people in America—they have the most money,
the highest amount of schooling, and the jobs that bring the most respect” and the bottom rung
represented “the worst-off people in America—they have the least money, little or no education,
no jobs or jobs that no one wants or respect”.
The 10-rung ladder image that assessed school-level SSS included the following
instructions: “Now assume that the ladder is a way of picturing all the students in your school.
Where would you place yourself on this ladder?” The top rung represented “the people in your
school with the most respect, the highest grades, and the highest standing” while the bottom rung
was labeled as “the people who no one respects, no one wants to hang around with, and have the
worst grades”. The MacArthur Scale has been shown to be a reliable and valid measure of
subjective perceptions of social status (Goodman et al., 2001; Singh-Manoux, Marmot, & Adler,
2005).
Lifetime, Past 6 months, and Past 30 Days Substance Use. Substance use was
assessed via standard validated items used in epidemiologic surveys of adolescents (Johnston et
al., 2016). For both lifetime and past 6 months’ use, students were asked whether they had ever
used any of the following substances for recreational purposes or to get “high”: cigarettes, e-
cigarettes, smokeless tobacco, cigars, hookah water pipe, other forms of tobacco, marijuana,
marijuana edibles, marijuana vaping, synthetic marijuana, one full drink of alcohol, inhalants,
SUBSTANCE USE DISPARITIES IN LOWER SES ADOLESCENTS 11
cocaine, methamphetamines, LSD/mushrooms/psychedelics, ecstasy, heroin, salvia, prescription
painkillers, tranquilizers or sedatives, diet pills, prescription stimulant pills, bath salts, and other
substances. These responses were coded for the lifetime and past 6 months’ use outcomes
(yes/no) for: alcohol, combustible cigarettes, hookah, cigars, e-cigarettes (with or without
nicotine), marijuana, marijuana edibles, marijuana vaping, and prescription drugs (use of
prescription painkillers or prescription stimulant pills). Frequency of recreational use in the past
30 days was assessed for each substance with 9 response options (0, 1-2, 3-5, 6-9, 10-14, 15-19,
20-24, 25-29, 30 days), which was coded as a binary outcome (0 days = No, 1-30 days = Yes).
Analytic Approach
Data were analyzed using IBM SPSS Version 24 (IBM, 2016). The first step in data
analyses involved calculating descriptive statistics for parental education, free or subsidized lunch,
subjective social status, substance use outcomes, and covariates, as well as examining
intercorrelations among SES measures. Polytomous logistic regression models were then
conducted to evaluate both objective and subjective SES dimensions as predictors of current,
recent, or past substance use with the use of 4 outcome categories: never users (reference group),
past users (lifetime substance use, but no use in the past 6 months), recent users (substance use in
the past 6 months, but no use in the past 30 days), and current users (substance use in the past 30
days). Separate sets of models were tested for each respective substance use outcome variable (i.e.,
alcohol, cigarettes, hookah, cigars, e-cigarettes [with or without nicotine], marijuana, marijuana
edibles, marijuana vaping, prescription drugs). Odds ratios (ORs) and 95% confidence intervals
(CI) were computed to estimate the risk of current, recent, or past use of each substance relative to
never use. Individual models adjusted for demographic characteristics (age, gender, ethnicity) and
school (as a random effect) hypothesized a priori to be associated with both SES markers and
SUBSTANCE USE DISPARITIES IN LOWER SES ADOLESCENTS 12
substance use. Combined models then simultaneously included all SES variables after adjusting
for demographic characteristics and school effect. Significance was set at p < .05 for all statistical
analyses.
Results
Preliminary Analyses
Descriptive statistics for sociodemographic characteristics and substance use outcomes
are reported in Table 1 and 2. There was approximately an equal number of male and female
adolescents in the overall sample (54.8% Female, 45.2% Male), and a large percentage of
Hispanic adolescents (44.1%) relative to other racial/ethnic groups (0.8% American Indian or
Alaskan Native, 17.8% Asian, 4.5% Black or African American, 17.4% White, 5.8% Multiracial,
5.9% Other; see Table 1). Approximately 72% of participants reported having parents who have
completed either some college, graduated college, or having obtained an advanced degree, and
41.9% of the sample endorsed receiving free or subsidized lunch (Table 1).
Intercorrelations among study variables are illustrated in Table 3. Age did not
significantly correlate with SES variables, however, gender was significantly associated with
free or subsidized lunch and school-level SSS (rs = -.05-.05, ps < .05). Additionally, there were
strong intercorrelations between SES variables (rs = .10-.39, ps < .0001).
Primary Analyses
Individual models adjusting for demographics and school effects demonstrated that lower
levels of parental education, free or subsidized lunch, societal-level SSS, and school-level SSS
were significantly associated with greater likelihood of past (ORs: 1.10 [95% CI: 1.02-1.17] to
1.35 [95% CI: 1.21-1.51]), recent (ORs: 1.12 [95% CI: 1.02-1.24] to 1.45 [95% CI: 1.09-1.94]),
and current use (ORs: 1.15 [95% CI: 1.05-1.26] to 1.93 [95% CI: 1.48-2.52]) of most substances
SUBSTANCE USE DISPARITIES IN LOWER SES ADOLESCENTS 13
(i.e., alcohol, cigarettes, hookah, cigars, e-cigarettes [with or without nicotine], marijuana
[edibles and vaping], and prescription drugs; see Supplementary Tables 1 and 2) relative to never
use. These results can be interpreted as, per the example that, each 1 SD unit decrease reported
on the 10-point school-level SSS ladder was significantly associated with a 93% (95% CI: 1.48-
2.52) increased odds of being a current cigar user (vs. never user; see Supplementary Table 2).
Majority of associations of SES variables with substance use remained significant in
combined models that simultaneously included all SES variables as regressors after adjusting for
covariates and school effects (ORs: 1.12 [95% CI: 1.02-1.24] to 2.04 [95% CI: 1.62-2.58]).
Findings illustrated that lower levels of objective SES (i.e., parental education, free or subsidized
lunch) were incrementally related to increased odds of lifetime or adolescent substance use in the
past 6 months (ORs: 1.12 [95% CI: 1.02-1.24] to 1.34 [95% CI: 1.06-1.68]; see Table 4), while
lower school-level SSS was incrementally associated with 1.17 (95% CI: 1.04-1.31) to 2.04
(95% CI: 1.62-2.58) greater odds of using majority of substances within the past 30 days (see
Table 5). Moreover, higher levels of parental education were significantly related to increased
marijuana use in the past 6 months (OR: 0.75; 95% CI: 0.59-0.95) and greater societal-level SSS
was associated with higher likelihood of current alcohol use (OR: 0.87; 95% CI: 0.78-0.97) and
past cigar use (OR: 0.80; 95% CI: 0.71-0.91).
Discussion
The current study demonstrated differential relations of objective and subjective markers
of SES with odds of being a past, recent, or current user of distinct substances in a large,
socioeconomically diverse sample of 11
th
grade adolescents in the U.S. Consistent with prior
work, adolescents with lower levels of objective SES (parental education, free or subsidized
lunch) and lower subjective social status relative to U.S. society (societal-level SSS) and to their
SUBSTANCE USE DISPARITIES IN LOWER SES ADOLESCENTS 14
peers (school-level SSS) significantly reported lifetime, past 6 months, or current use of alcohol,
combustible cigarettes, and marijuana. Furthermore, our study expands upon the literature by
providing initial evidence for incremental associations between SSS dimensions and adolescent
use of new, emerging substances (electronic cigarettes, cigars, hookah, marijuana edibles,
marijuana vaping, and prescription drugs). Specifically, we determined that adolescents of lower
school-level SSS were 1.24 to 2.04 times more likely to use electronic cigarettes, cigars,
marijuana edibles, marijuana vaping, and prescription drugs within the past 30 days, even when
controlling for objective SES (i.e., parental education, free or subsidized lunch) and societal-
level SSS. Thus, our findings extend prior work examining use of e-cigarettes, cigars, and
marijuana vaping in lower SES adults (Huang, Kim, Vera, & Emery, 2016; Jones, Hill, Pardini,
& Meier, 2016; Nyman et al., 2016), by suggesting that adolescents with lower subjective
perceptions of their social standing relative to their peers within their school community may
also be at increased risk for being a current user of new, emerging substances that are gaining
popularity in youth.
There are some plausible theoretical interpretations for why objective SES characteristics
such as parental education and free or subsidized lunch may be associated with youth substance
use. Fothergill & Ensminger (2006) suggests that parents who have lower educational attainment
may have less prestigious occupations (e.g., manual-class jobs) that require them to work longer
hours or take on more shift work, which may give lower SES adolescents more opportunity to
experiment with drugs without adult supervision, as compared to adolescents with parents who
have greater education levels and higher status jobs. Another theory is that lower parental
income may indirectly affect adolescent substance use problems by increasing parental stress and
therefore decreasing the quality of parenting (Conger et al., 2002). Prior work suggests that
SUBSTANCE USE DISPARITIES IN LOWER SES ADOLESCENTS 15
adolescents with parents with lower education levels and less prestigious jobs may have greater
job instability and psychological stress, which may result in greater stress among their children
(Masten et al., 1988). This may be further supported by research on parenting which has shown
that both higher levels of parental education and family income are related to a warmer, social
climate in the home and greater parental warmth, nurturing, and affection during parent-child
interactions (Davis-Kean, 2005). Lastly, another theoretical interpretation is that the effects of
parental education on adolescent substance use may be mediated by involvement in
extracurricular activities, such that diminished access to and engagement in substance-free,
enjoyable activities may be an underlying mechanism linking lower parental education with
greater adolescent substance use risk (Andrabi et al., 2017; Leventhal et al., 2015a).
Our results also showed that higher parental education levels were significantly
associated with recent marijuana use, which may be supported by some research suggesting that
teens with higher family SES may have increased access to financial resources and spending
money, which may allow them to easily purchase substances (vs. teens with lower family
income; Hanson & Chen, 2007a; Luthar & Latendresse, 2005). Furthermore, higher SES
adolescents may also be at risk for engaging in drug use in order to cope with the stress, anxiety,
and depression they experience from over-scheduling of extracurricular activities and greater
academic achievement pressure, as well as greater isolation from their parents due to the
demands of more prestigious occupations (Luthar & Latendresse, 2005). It is important to note
that the inconsistencies and trends of association between objective SES and lifetime adolescent
substance use may be reflective of literature suggesting that parental SES markers may be more
sensitive to individual differences among younger adolescents, but may decrease in impact once
adolescents begin to spend less time at home with their parents and establish greater
SUBSTANCE USE DISPARITIES IN LOWER SES ADOLESCENTS 16
independence as they spend more time with their peers in their transition to adulthood (Hanson
& Chen, 2007b; West & Sweeting, 2004). Hence, as an adolescent’s self-conceptualization and
independence matures during this transitional period, their sense of social stratification may be
more based on their perceptions of their standing relative to their peers than on parental SES
(Goodman et al., 2001). This theoretical notion is supported by some evidence demonstrating
that 8
th
and 10
th
grade White adolescents showed strong negative relations between parental
education and substance use, whereas by 12
th
grade, their heavy drinking and marijuana use were
no longer related to their parent’s education levels (Bachman et al., 2011). Considering that our
sample is of 11
th
grade older adolescents, parental SES may not be accurate indicators of this
population’s status and may only capture initial risks for drug use (being a lifetime user), but
may not be sensitive to detecting individual differences in escalation after experimentation and
maintenance of drug use (being a current user) in our sample population.
Data from the current study indicated differential patterns of relations among the two
dimensions of SSS, with lower school-level SSS exhibiting the strongest associations for risks of
being a current user across majority of the substances. Prior work indicates that lower school-
level SSS adolescents may be more motivated to initiate and experiment with drugs than their
higher school-level SSS peers because of their potentially differing beliefs about using
substances (e.g., beliefs that smoking will yield socially beneficial outcomes; Wilkinson et al.,
2009). As such, lower school-level SSS adolescents may hold greater positive expectations about
substance use, which may motivate them to smoke cigarettes as a means of achieving higher
school-level SSS and increasing their popularity and peer social standing (e.g., smoking to look
more mature and cool or obtain more friends; Wilkinson et al., 2009). Another study found that
higher school-level SSS was protective against smoking in both cross-sectional and longitudinal
SUBSTANCE USE DISPARITIES IN LOWER SES ADOLESCENTS 17
analyses, even after controlling for stress and objective SES (i.e., parental education; Finkelstein,
Kubzansky, & Goodman, 2006). Given that the school-level SSS ladder ranking captures various
dimensions of an adolescent’s social status (i.e., academic standing, popularity, and respect from
others), it is plausible that higher school-level SSS may deter substance use, as students with
higher school-level SSS may have higher academic performance and higher academic
aspirations, be involved in more substance-free, extracurricular activities that may be highly
regarded by their peers, or greater popularity status, which have all been shown to be protective
against substance use (Alexander et al., 2001; Andrabi et al., 2017; Leventhal et al., 2015a; Tyas
& Pederson, 1998).
There are also several explanations for the discrepancies in findings between societal-
level SSS and school-level SSS. Although the two ladders were correlated with each other, the
association was modest (r = .27). Previous literature denotes that hierarchical ranking is
contingent upon numerous factors (e.g., the given reference population, the language used to
describe the hierarchy, scale and proximity of hierarchy, and stability of hierarchy; Goodman et
al., 2001) and that the determinants that adolescents use to rank themselves within societal and
peer hierarchies differ from each other (Brown et al., 2008). Given that the societal-level SSS
ladder was designed to reflect more traditional SES indicators and given that the school-level
SSS ladder represents a more proximate, local, and less stable hierarchy (vs. societal-level SSS;
Goodman et al., 2001; Goodman et al., 2007), it is plausible that school-level SSS may be a more
precise predictor of older youth substance use outcomes and may be more sensitive to
progression and maintenance of adolescent substance use, particularly detecting use within the
past 30 days. This was evident in our findings as we found consistent associations between
school-level SSS and odds of being a current user, with results remaining significant in
SUBSTANCE USE DISPARITIES IN LOWER SES ADOLESCENTS 18
combined models that simultaneously included all SES variables, covariates, and school effects.
Hence, our findings are consistent with a growing body of research suggesting that school-level
SSS may be a powerful predictor of older adolescent health outcomes (Goodman et al., 2003;
Singh-Manoux, Marmot, & Adler, 2005) beyond the effects of objective SES indicators. Future
longitudinal work should continue to explore the differential effects of objective and subjective
SES dimensions on adolescent drug outcomes to further clarify which distinct SES markers may
be more strongly associated with lifetime, recent, or current substance use over time in
underserved older youth populations.
The current study is subject to several limitations. Firstly, the use of a cross-sectional,
correlational design hinders inferences regarding causality, such that we are unable to determine
the effects of objective SES and SSS on adolescent substance use over time. Therefore, future
prospective studies should examine these associations utilizing longitudinal data as trajectories
of substance use may be more informative in further understanding sociocontextual factors
related to adolescent substance use. Secondly, associations between SSS dimensions and
adolescent substance use may be bidirectional in nature, such that adolescent substance use
initiation and frequency of use may impact SSS markers such as perceived social standing in
society and school community. Thus, future work should test for potential bidirectional
relationships. Additionally, participants were from a single age group (16 years old) and sampled
from a restricted geographic region, which may limit generalizability of findings to populations
in other regions of the U.S. Lastly, data from this ongoing study utilized self-report measures to
assess SES and substance use. Hence, additional work should consider using alternate methods
to assess these variables (e.g., biomarkers of substance exposures or clinical interviews).
Conclusions
SUBSTANCE USE DISPARITIES IN LOWER SES ADOLESCENTS 19
The current study provides initial evidence for varying patterns of association between
traditional indicators of SES, SSS (societal- and school-level), and adolescent alcohol,
combustible cigarettes, hookah, cigars, electronic cigarettes (with or without nicotine), marijuana
(edibles, vaping), and prescription drug use. Results of this study may inform public health
agencies and health care organizations to develop and implement public education, prevention,
and intervention programs targeting substance-using lower SES adolescents. Specifically,
prevention and community intervention efforts that provide and improve access to substance-free
activities and resources among socioeconomically disadvantaged youth may have broad
implications for preventing initiation and progression of adolescent substance use. Additionally,
recent individual-level interventions such as personalized normative feedback (Lewis &
Neighbors, 2006), may be beneficial for reducing adolescent drug use by addressing lower
school-level SSS adolescents’ misperceptions and beliefs about social norms regarding substance
use. Lastly, our findings may provide implications for policy-making efforts to increase mass
media prevention campaigns in order to reduce exposure to e-cigarettes, alternative tobacco
products, and marijuana in vulnerable youth groups, which may aid in the reduction of substance
use disparities among socioeconomically disadvantaged adolescent populations.
SUBSTANCE USE DISPARITIES IN LOWER SES ADOLESCENTS 20
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SUBSTANCE USE DISPARITIES IN LOWER SES ADOLESCENTS 29

Tables
Table 1. Sample Characteristics
Note. Wave 1 data from 9th grade students in Los Angeles, California, USA collected in Fall 2013. Wave 5 data from 11th grade
students collected in Fall 2015. MacArthur Scale of Subjective Social Status (range 1-10).
a
Wave 1 data.
b
Wave 5 data.

Variable
Overall Sample
(N = 2,166)
Lifetime Substance
Users
(N = 1,375)
Never Users
(N = 762)
Age,
a
M (SD) 14.06 (0.40) 14.09 (0.41) 14.02 (0.39)
Gender,
a
n (%)
Female 1,186 (54.8%) 769 (55.9%) 402 (52.8%)
Male 980 (45.2%) 606 (44.1%) 360 (47.2%)
Race/Ethnicity,
a
n (%)
American Indian or Alaskan Native 17 (0.8%) 12 (0.9%) 5 (0.7%)
Asian 381 (17.8%) 161 (11.9%) 216 (28.7%)
Black or African American 97 (4.5%) 63 (4.6%) 34 (4.5%)
Hispanic or Latino 942 (44.1%) 680 (50.1%) 246 (32.7%)
Native Hawaiian or Pacific Islander 79 (3.7%) 57 (4.2%) 22 (2.9%)
White 371 (17.4%) 232 (17.1%) 136 (17.8%)
Multiracial 124 (5.8%) 70 (5.2%) 51 (6.8%)
Other 125 (5.9%) 81 (6.0%) 43 (5.7%)
Highest Parental Education,
a
n (%)
8
th
grade or less 84 (3.9%) 57 (4.1%) 25 (3.3%)
Some high school 192 (8.9%) 148 (10.8%) 43 (5.6%)
High school graduate 329 (15.2%) 232 (16.9%) 94 (12.3%)
Some college 422 (19.5%) 291 (21.2%) 121 (15.9%)
College graduate 709 (32.7%) 418 (30.4%) 284 (37.3%)
Advanced degree 430 (19.9%) 229 (16.7%) 195 (25.6%)
Free or Subsidized Lunch,
b
n (%) 907 (41.9%) 636 (46.3%) 255 (33.5%)
MacArthur Scale of Subjective Social Status (SSS),
b
M (SD)

Societal-Level SSS 5.99 (1.66) 5.93 (1.69) 6.10 (1.60)
School-Level SSS 6.80 (1.81) 6.69 (1.85) 7.00 (1.73)
SUBSTANCE USE DISPARITIES IN LOWER SES ADOLESCENTS 30

Table 2. Descriptive Statistics for Substance Use Outcomes

Note. N = 2,125-2,166 due to missing data for specific substance use outcomes. Wave 5 data from 11th
grade students collected in Fall 2015.

Substance Use, n (%) Lifetime Use Past 6 months Use Past 30 days Use
Alcohol 1,112 (52.3%) 611 (28.7%) 439 (20.6%)
Cigarettes 308 (14.4%) 111 (5.2%) 80 (3.7%)
Hookah 591 (27.7%) 132 (6.2%) 73 (3.4%)
Cigars 252 (11.8%) 82 (3.8%) 33 (1.5%)
E-Cigarettes (with Nicotine) 774 (36.4%) 193 (9.1%) 118 (5.5%)
E-Cigarettes (No Nicotine) 394 (18.5%) 198 (9.3%) 125 (5.9%)
Marijuana 683 (32.1%) 355 (16.7%) 239 (11.2%)
Marijuana Edibles 498 (23.4%) 251 (11.8%) 138 (6.5%)
Marijuana Vaping 288 (13.5%) 158 (7.4%) 76 (3.6%)
Prescription Drugs 507 (23.4%) 169 (7.8%) 104 (4.8%)
SUBSTANCE USE DISPARITIES IN LOWER SES ADOLESCENTS 31

Table 3. Intercorrelations between Study Variables
Outcome Measures
Intercorrelations (r)
1. 2. 3. 4. 5. 6.
1. Age -

2. Gender .07
**
-

3. Parental Education -.02 .01 -

4. Free or Subsidized Lunch -.004 -.05
*
.39
****
-

5. Societal-Level SSS .03 .03 .23
****
.27
****

-
6. School-Level SSS .03 .05
*
.10
****
.02 .27
****
-
Note: N = 2,166; Correlations among continuous variables are Pearson correlation coefficients.
Correlations between continuous and dichotomous variables are point-biserial correlation
coefficients. SSS = MacArthur Scale of Subjective Social Status (range 1-10). Self-reported Free
Lunch (0 = Free Lunch; 1 = Reduced Lunch; 2 = No).
*
p< .05,
**
p< .01,
***
p< .001,
****
p< .0001.
SUBSTANCE USE DISPARITIES IN LOWER SES ADOLESCENTS 32

Table 4. Combined Models for Association of Objective SES with Substance Use Outcomes

Substance Use Outcome
Objective SES Indicators
a

Parental Education Free or Subsidized Lunch
Past vs. Never Recent vs. Never Current vs. Never Past vs. Never Recent vs. Never Current vs. Never
Alcohol 1.16 (1.05, 1.27)
**
0.92 (0.71, 1.19) 1.18 (0.99, 1.41) 1.21 (1.11, 1.32)† 1.14 (0.94, 1.38) 1.07 (0.92, 1.24)
Cigarettes 1.15 (0.94, 1.40) 1.34 (1.06, 1.68)
*
0.97 (0.78, 1.20) 1.18 (1.00, 1.41) 1.03 (0.72, 1.45) 1.04 (0.88, 1.23)
Hookah 1.15 (1.03, 1.29)
*
0.98 (0.70, 1.36) 1.08 (0.77, 1.50) 1.13 (1.00, 1.29) 1.19 (0.89, 1.58) 1.06 (0.75, 1.49)
Cigars 1.27 (1.00, 1.61) 1.03 (0.70, 1.51) 0.89 (0.74, 1.10) 1.15 (0.98, 1.33) 1.30 (1.11, 1.52)
**
1.07 (0.79, 1.45)
E-Cigarettes (with Nicotine) 1.15 (1.03, 1.28)
*
1.16 (0.91, 1.50) 1.22 (0.98, 1.51) 1.12 (0.99, 1.26) 1.02 (0.73, 1.42) 1.06 (0.87, 1.30)
E-Cigarettes (No Nicotine) 1.01 (0.84, 1.22) 1.27 (0.93, 1.74) 0.97 (0.76, 1.25) 1.06 (0.90, 1.25) 0.88 (0.74, 1.04) 1.11 (0.84, 1.47)
Marijuana 1.32 (1.16, 1.49)† 0.75 (0.59, 0.95)
*
1.13 (0.93, 1.38) 1.27 (1.12, 1.43)
***
0.95 (0.75, 1.20) 0.97 (0.87, 1.09)
Marijuana Edibles 1.33 (1.15, 1.54)
***
1.24 (0.93, 1.66) 0.96 (0.74, 1.25) 1.16 (1.06, 1.26)
**
0.83 (0.68, 1.00) 1.12 (1.02, 1.24)
*

Marijuana Vaping 1.34 (1.15, 1.57)
***
1.16 (0.76, 1.77) 0.96 (0.75, 1.24) 1.03 (0.89, 1.18) 1.08 (0.93, 1.26) 1.01 (0.79, 1.29)
Prescription Drugs 1.01 (0.83, 1.23) 0.98 (0.68, 1.42) 0.87 (0.71, 1.06) 1.09 (0.94, 1.27) 1.24 (0.79, 1.96) 1.05 (0.76, 1.43)
Note: N = 2,166; Free or Subsidized Lunch (0 = No; 1 = Free or Subsidized Lunch). Past users = Lifetime substance use. Recent users = Past 6 months substance use. Current users =
Past 30 days substance use.
a
Data are expressed as OR (95% CI). Combined models simultaneously included all SES variables after adjusting for age, gender, ethnicity, and school
effects. SES variables were reverse scored for primary analyses to facilitate ease of interpretation (Higher SES à Lower SES).
*
p< .05,
**
p< .01,
***
p< .001, †p< .0001.
SUBSTANCE USE DISPARITIES IN LOWER SES ADOLESCENTS 33

Table 5. Combined Models for Association of Subjective Social Status with Substance Use Outcomes
Substance Use Outcome
MacArthur Scale of Subjective Social Status (SSS)
a

Societal-Level SSS School-Level SSS
Past vs. Never Recent vs. Never Current vs. Never Past vs. Never Recent vs. Never Current vs. Never
Alcohol 1.02 (0.91, 1.14) 1.10 (0.95, 1.27) 0.87 (0.78, 0.97)
*
1.22 (1.07, 1.38)
**
0.97 (0.82, 1.15) 1.17 (1.04, 1.31)
**

Cigarettes 0.92 (0.80, 1.06) 0.95 (0.71, 1.29) 1.19 (0.90, 1.57) 1.03 (0.93, 1.15) 1.29 (1.02, 1.63)
*
1.33 (1.05, 1.69)
**

Hookah 0.98 (0.88, 1.09) 1.11 (0.88, 1.39) 1.03 (0.80, 1.34) 1.07 (1.00, 1.14) 0.99 (0.81, 1.19) 1.17 (0.84, 1.61)
Cigars 0.80 (0.71, 0.91)
***
1.06 (0.66, 1.70) 0.83 (0.60, 1.14) 1.07 (0.90, 1.26) 1.05 (0.80, 1.39) 2.04 (1.62, 2.58)†
E-Cigarettes (with Nicotine) 0.97 (0.83, 1.12) 0.98 (0.85, 1.12) 0.89 (0.77, 1.04) 1.09 (0.99, 1.19) 1.16 (0.90, 1.49) 1.24 (1.08, 1.43)
**

E-Cigarettes (No Nicotine) 1.13 (0.94, 1.37) 0.96 (0.78, 1.18) 0.92 (0.80, 1.07) 1.10 (0.99, 1.22) 1.04 (0.81, 1.33) 1.14 (0.97, 1.33)
Marijuana 0.92 (0.82, 1.03) 1.23 (1.01, 1.49)
*
0.90 (0.78, 1.05) 1.06 (0.94, 1.19) 1.05 (0.83, 1.33) 1.21 (0.98, 1.49)
Marijuana Edibles 0.94 (0.81, 1.10) 0.91 (0.65, 1.27) 0.95 (0.82, 1.10) 1.14 (0.96, 1.35) 1.08 (0.86, 1.35) 1.30 (1.11, 1.52)
**

Marijuana Vaping 0.98 (0.79, 1.22) 0.97 (0.75, 1.26) 0.92 (0.77, 1.10) 1.05 (0.86, 1.28) 1.04 (0.83, 1.29) 1.24 (1.02, 1.51)
*

Prescription Drugs 0.99 (0.86, 1.13) 1.22 (0.95, 1.56) 0.95 (0.71, 1.27) 1.23 (1.11, 1.36)
***
1.37 (0.96, 1.95) 1.37 (1.15, 1.65)
***

Note: N = 2,166; MacArthur Scale of Subjective Social Status (range 1-10). Past users = Lifetime substance use. Recent users = Past 6 months substance use. Current users = Past 30
days substance use.
a
Data are expressed as OR (95% CI). Combined models simultaneously included all SES variables after adjusting for age, gender, ethnicity, and school effects.
SES variables were reverse scored for primary analyses to facilitate ease of interpretation (Higher SES à Lower SES).
*
p< .05,
**
p< .01,
***
p< .001, †p< .0001.
SUBSTANCE USE DISPARITIES IN LOWER SES ADOLESCENTS 34

Supplementary Table 1. Individual Models for Association of Objective SES with Substance Use Outcomes

Substance Use Outcome
Objective SES Indicators
a

Parental Education Free or Subsidized Lunch
Past vs. Never Recent vs. Never Current vs. Never Past vs. Never Recent vs. Never Current vs. Never
Alcohol 1.23 (1.12, 1.35)† 0.96 (0.76, 1.23) 1.21 (1.04, 1.40)
*
1.25 (1.13, 1.38)† 1.15 (0.98, 1.35) 1.07 (0.95, 1.20)
Cigarettes 1.17 (0.97, 1.42) 1.37 (1.00, 1.87)
*
1.07 (0.84, 1.36) 1.20 (1.02, 1.42)
*
1.09 (0.72, 1.67) 1.07 (0.91, 1.27)
Hookah 1.17 (1.05, 1.31)
**
1.02 (0.75, 1.39) 1.12 (0.86, 1.45) 1.16 (1.03, 1.31)
*
1.21 (0.90, 1.64) 1.09 (0.83, 1.43)
Cigars 1.25 (1.01, 1.55)
**
1.11 (0.79, 1.56) 0.94 (0.74, 1.20) 1.15 (1.01, 1.30)
*
1.33 (1.13, 1.56)
***
1.00 (0.71, 1.40)
E-Cigarettes (with Nicotine) 1.18 (1.08, 1.29)
***
1.18 (0.96, 1.46)
*
1.25 (1.03, 1.52)
*
1.14 (1.00, 1.28)
*
1.05 (0.77, 1.43) 1.08 (0.90, 1.29)
E-Cigarettes (No Nicotine) 1.08 (0.90, 1.29) 1.21 (0.94, 1.57) 1.00 (0.80, 1.24) 1.10 (0.92, 1.31) 0.93 (0.80, 1.08) 1.08 (0.84, 1.40)
Marijuana 1.35 (1.21, 1.51)† 0.79 (0.60, 1.04) 1.13 (0.94, 1.36) 1.31 (1.16, 1.48)† 0.94 (0.73, 1.22) 0.97 (0.87, 1.08)
Marijuana Edibles 1.34 (1.17, 1.55)† 1.15 (0.89, 1.49) 0.99 (0.73, 1.34) 1.22 (1.09, 1.36)
***
0.85 (0.69, 1.05) 1.09 (0.93, 1.29)
Marijuana Vaping 1.34 (1.14, 1.57)
***
1.19 (0.81, 1.75) 0.98 (0.78, 1.22) 1.08 (0.94, 1.25) 1.11 (0.97, 1.27) 0.98 (0.78, 1.23)
Prescription Drugs 1.01 (0.84, 1.22) 1.08 (0.82, 1.41) 0.97 (0.82, 1.15) 1.09 (0.93, 1.27) 1.29 (0.90, 1.84) 0.99 (0.75, 1.31)
Note: N = 2,166; Free or Subsidized Lunch (0 = No; 1 = Free or Subsidized Lunch). Past users = Lifetime substance use. Recent users = Past 6 months substance use. Current users =
Past 30 days substance use.
a
Data are expressed as OR (95% CI). Individual models adjusted for age, gender, ethnicity, and school effects. SES variables were reverse scored for
primary analyses to facilitate ease of interpretation (Higher SES à Lower SES).
*
p< .05,
**
p< .01,
***
p< .001, †p< .0001.
SUBSTANCE USE DISPARITIES IN LOWER SES ADOLESCENTS 35

Supplementary Table 2. Individual Models for Association of Subjective Social Status with Substance Use Outcomes

Substance Use Outcome
MacArthur Scale of Subjective Social Status (SSS)
a

Societal-Level SSS School-Level SSS
Past vs. Never Recent vs. Never Current vs. Never Past vs. Never Recent vs. Never Current vs. Never
Alcohol 1.12 (1.02, 1.22)
*
1.12 (1.02, 1.24)
*
0.94 (0.85, 1.05) 1.23 (1.09, 1.38)
***
0.97 (0.84, 1.13) 1.15 (1.05, 1.26)
**

Cigarettes 0.97 (0.86, 1.10) 1.00 (0.67, 1.47) 1.26 (0.94, 1.68) 1.01 (0.92, 1.12) 1.28 (1.01, 1.63)
*
1.41 (1.12, 1.76)
**

Hookah 1.04 (0.94, 1.16) 1.11 (0.85, 1.44) 1.06 (0.81, 1.39) 1.10 (1.02, 1.17)
**
1.01 (0.86, 1.19) 1.19 (0.87, 1.63)
Cigars 0.86 (0.76, 0.98)
*
1.14 (0.81, 1.61) 1.02 (0.67, 1.54) 1.01 (0.85, 1.21) 1.06 (0.87, 1.29) 1.93 (1.48, 2.52)†
E-Cigarettes (with Nicotine) 1.02 (0.89, 1.17) 1.07 (0.97, 1.17) 0.97 (0.82, 1.15) 1.10 (1.02, 1.18)
*
1.17 (0.93, 1.48) 1.23 (1.06, 1.43)
**

E-Cigarettes (No Nicotine) 1.16 (0.97, 1.38) 1.02 (0.86, 1.21) 0.97 (0.83, 1.13) 1.15 (1.02, 1.30)
*
1.02 (0.80, 1.30) 1.11 (0.95, 1.30)
Marijuana 1.01 (0.92, 1.11) 1.16 (1.01, 1.34)
*
0.97 (0.87, 1.09) 1.07 (0.98, 1.17) 1.08 (0.90, 1.31) 1.20 (0.99, 1.44)
Marijuana Edibles 1.06 (0.94, 1.19) 0.96 (0.75, 1.22) 1.05 (0.88, 1.25) 1.16 (1.01, 1.34)
*
1.08 (0.91, 1.29) 1.26 (1.08, 1.46)
**

Marijuana Vaping 1.03 (0.85, 1.25) 1.00 (0.82, 1.21) 1.02 (0.85, 1.21) 1.10 (0.93, 1.30) 1.03 (0.86, 1.24) 1.20 (0.96, 1.52)
Prescription Drugs 1.05 (0.91, 1.23) 1.19 (0.87, 1.62) 1.01 (0.81, 1.25) 1.22 (1.12, 1.34)† 1.45 (1.09, 1.94)
*
1.37 (1.18, 1.59)†
Note: N = 2,166; MacArthur Scale of Subjective Social Status (range 1-10). Past users = Lifetime substance use. Recent users = Past 6 months substance use. Current users = Past 30
days substance use.
a
Data are expressed as OR (95% CI). Individual models adjusted for age, gender, ethnicity, and school effects. SES variables were reverse scored for primary
analyses to facilitate ease of interpretation (Higher SES à Lower SES).
*
p< .05,
**
p< .01,
***
p< .001, †p< .0001.

Abstract (if available)

Abstract Objective markers of socioeconomic status (SES), such as parental education, have demonstrated inverse associations with adolescent cigarette, alcohol, and marijuana use. However, the extent to which these associations generalize to new substances that are gaining popularity in youth (e.g., electronic cigarettes) are unknown. How someone subjectively perceives their social status relative to a larger group may also provide incremental information about SES not captured by objective measures. The current study examined associations of objective SES and subjective social status (SSS) with use of a wide range of substances (i.e., alcohol, combustible cigarettes, hookah, cigars, electronic cigarettes, marijuana, and prescription drugs) in a socioeconomically diverse sample of 11th grade students in Los Angeles, California. Participants (N = 2,166) completed semi-annual surveys assessing parental education, free or subsidized lunch, SSS (societal- and school-level), and substance use over one’s lifetime, in the past 6 months, and in the past 30 days. Utilizing polytomous logistic regression models that adjusted for covariates and SES variables, we found that lower objective SES was incrementally associated with increased odds of being a lifetime or recent user. Lower school-level SSS was incrementally associated with greater odds of being a current user of majority of the substances. Our findings provide evidence for varying patterns of association across objective SES and SSS dimensions with adolescent drug use. Policy-making, interventions, and educational efforts to increase prevention campaigns that reduce exposure to new, emerging drugs and increase access to substance-free activities may effectively reduce substance-related health disparities among socioeconomically disadvantaged adolescent populations.

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Asset Metadata

Creator Bello, Mariel Seanne Mercado (author)

Core Title Substance use disparities in adolescents of lower socioeconomic status: emerging trends in electronic cigarettes, alternative tobacco products, marijuana, and prescription drug use

School College of Letters, Arts and Sciences

Degree Master of Arts

Degree Program Psychology

Publication Date 05/25/2018

Defense Date 10/18/2017

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag Adolescents,health disparities,OAI-PMH Harvest,socioeconomic status,subjective social status,substance use

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Leventhal, Adam M. (committee chair), Davison, Gerald C. (committee member), Saxbe, Darby (committee member)

Creator Email mariel.bello003@gmail.com,marielbe@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c40-457664

Unique identifier UC11265676

Identifier etd-BelloMarie-5932.pdf (filename),usctheses-c40-457664 (legacy record id)

Legacy Identifier etd-BelloMarie-5932.pdf

Dmrecord 457664

Document Type Thesis

Rights Bello, Mariel Seanne Mercado

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

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