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SUBSTANCE MISUSE ACROSS EARLY ADULTHOOD
Adolescents from upper middle class communities:
Substance misuse and addiction across early adulthood
Suniya S. Luthar, Phillip J. Small, & Lucia Ciciolla
Arizona State University
Development and Psychopathology, Online first
doi:10.1017/S0954579417000645
Note: This is the pre-publication version; for the published version, please email S. Luthar.
This paper is dedicated to the memory of Samuel H. Barkin, deeply cherished student and collaborator.
We are very grateful to the young adults who generously gave of their time and perspectives over the
many years of this study and for funding by the National Institutes of Health (R01DA014385,
R13MH082592). We are also thankful for the contributions of master’s and doctoral students in Dr.
Luthar’s prior lab at Teachers College, Columbia University, and to Nina L. Kumar.
Address correspondence to: Suniya S. Luthar, Department of Psychology, Arizona State University, 950
S. McAllister Ave. Tempe, Arizona, 85287-1104
Email: Suniya.Luthar@asu.edu
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SUBSTANCE MISUSE ACROSS EARLY ADULTHOOD
Adolescents from upper middle class communities:
Substance misuse and addiction across early adulthood
Abstract
In this prospective study of upper-middle class youth, we document frequency of alcohol and
drug use, as well as diagnoses of abuse and dependence, during early adulthood. Two cohorts were
assessed as high school seniors and then annually across four college years (NESSY-Y), and across ages
23 – 27 (NESSY-O; n’s 152 and 183 at final assessments respectively). Across gender and annual
assessments, results showed substantial elevations, relative to norms, for frequency of drunkenness and
using marijuana, stimulants, and cocaine. Of more concern were psychiatric diagnoses of alcohol/ drug
dependence: Among women and men respectively, lifetime rates ranged between 19%-24% and 23%-
40% among NESSY-O’s at age 26; and 11%-16% and 19%-27% among NESSY-Y’s at 22. Relative to
norms, these rates among NESSY-O women and men were 3 and 2 times as high respectively, and among
NESSY-Y, close to 1 among women but twice as high among men. Findings also showed the protective
power of parents’ containment (anticipated stringency of repercussions for substance use) at age 18; this
was inversely associated with frequency of drunkenness and marijuana and stimulant use in adulthood.
Results emphasize the need to take seriously the elevated rates of substance documented among
adolescents in affluent American school communities.
KEY WORDS: Substance use; Addiction; Affluence; Upper middle class; Emerging adulthood.
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Adolescents from upper middle class communities:
Substance misuse and addiction across early adulthood
The U.S. continues to experience an epidemic of drug overdose deaths. From 2000 to 2015 more
than half a million people died from drug overdoses, the majority (55 percent of these deaths)
occurring from 2009 to 2015…. Large suburban metro counties went from having the lowest to
the highest rate of premature death due to drug overdose within the past decade. Premature death
due to drug overdose was highest among whites (778 years of potential life lost per 100,000)
(Givens, Gennuso, Jovaag, & Van Dijk, J, 2017, p. 6).
Over the last two decades, studies have documented more frequent drug and alcohol use among
upper middle class teens than their less well-off counterparts, but what remains unclear is the degree to
which this might eventuate in serious problems of addiction. In this paper, we present adult data on two
cohorts from schools in predominantly affluent communities. Both cohorts were initially assessed as high
school seniors, with one subsequently assessed annually at ages 23-27, and the second across the four
years of college at ages 19-22 years. For women and men separately, we document both frequency of
using different substances, and psychiatric diagnoses of abuse as well as dependence, relative to national
normative data.
Substance misuse among upper middle class youth
In 2009, an Editorial in the Journal of the American Academy of Child & Adolescent Psychiatry,
declared affluent youth to be a “newly identified at-risk group,” (Koplewicz, Gurian, & Williams, 2009,
p. 1053) and over time, studies have confirmed that substance misuse is a problem of particular concern
(Botticello, 2009; Luthar & Barkin, 2012; Luthar & D’Avanzo, 1999; Patrick, Wightman, Schoeni, &
Schulenberg, 2012). Researchers have documented high binge-drinking and marijuana use in
neighborhoods with mostly well-educated, wealthy, White families (Reboussin, Preisser, Song, &
Wolfson, 2010; Song, Reboussin, Foley, Kaltenbach, Wagoner, & Wolfson, 2009). Similar patterns are
seen in highly achieving schools which serve mostly affluent students (as home prices typically rise with
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SUBSTANCE MISUSE ACROSS EARLY ADULTHOOD
schools' standardized test scores; Bui & Dougherty, 2017). Studies have recurrently shown elevated
substance use levels, compared to national norms, among students at high-achieving public and
independent schools, in the suburbs and cities, and across different parts of the country (for a review, see
Luthar, Barkin, & Crossman, 2013).
Consistent findings have been reported in analyses of large, national data sets. In a nationally
representative sample of over 13,000 US youth, Coley, Sims, Dearing, and Spielvogel (2017) found that
attendance at schools with a high proportion of affluent schoolmates was associated with significantly
higher likelihood of both intoxication and use of illicit drugs (marijuana, cocaine, and other illegal drugs).
In similar analyses of data in Norway, Lund, Dearing and Zachrisson (2017) established links between
school-level affluence and students’ drinking to intoxication: Boys and girls at the most affluent schools
were about two and half times more likely to report such abuse of alcohol than those at the poorest
schools, mirroring prior findings in the US (Luthar & D’Avanzo, 1999; Lyman & Luthar, 2014).
Maturing out?
Despite evidence of high substance use in affluent high schools, little is known about the
evolution of alcohol and drug use post high school graduation. In contemporary times, after the early or
“emerging” adulthood years roughly spanning ages 18-25 (Arnett, 2007), youth tend to mature out of
deviant behaviors linked with adolescence including misuse of drugs and alcohol (Arnett, 2005). To
illustrate, Schulenberg and Zarrett (2006) showed that binge-drinking and marijuana use declined at
approximately age 21 or 22, and Jackson, Sher, Gotham, and Wood (2001) documented maturation
toward less severe drinking by the age of 24 years.
Youth in relatively affluent communities may not show these patterns of maturing out for at least
two reasons. First, many in this demographic begin to use substances in pre-adolescence (Luthar &
Barkin, 2012; Luthar & Goldstein, 2008), and early substance use is a strong predictor of long term
continued use (Moss, Chen, & Yi, 2014). Longitudinal research has shown that the incidence of adult
alcohol dependence was over 10% among those who started drinking (more than two drinks per week) at
age 13, as compared to 2% who started at age 18 (Grant, Stinson, & Harford, 2001).
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SUBSTANCE MISUSE ACROSS EARLY ADULTHOOD
Second, almost all high school seniors in affluent settings go on to attend college (e.g., Drier,
2014; The Pell Institute, 2015) and among contemporary college students, mores in the peer culture often
actively support drinking and drug use (e.g., LaBrie, Hummer, & Pedersen, 2007; O’Hara, Armeli, &
Tennen, 2015). Across the college years, inebriation is not just normative at social gatherings, but it is
often desirable (Chase, 2008; Marano, 2005), and studies have revealed binge-drinking rates as high as
44% among undergraduates (Wechsler & Nelson, 2008; Wechsler, Lee, Kuo, Seibring, Nelson, & Lee,
2002). Moreover, harmful drinking habits have been found to be more pronounced in college students
from well-educated, affluent families, at least in part because they have more disposable income while at
the same time, living away from parent vigilance (Dantzer, Wardle, Fuller, Pampalone, & Steptoe, 2006;
Rose, Smith, & Segrist, 2010; see also Carrick, 2016; Hussey & Schlossberg, 2015; Schiffman, 2011).
Whereas these trends may apply across all four years of college, it is possible that there are at
least modest reductions in substance use around the time of graduation. During the first year or two of
college, experimentation with alcohol and drugs can be high given freedom from parental supervision
(Turrisi, Wiersma, & Hughes, 2000); some also drink or use drugs to conform to a new peer group and to
ease social anxiety (O’Hara et al., 2015; Turrisi et al., 2000; LaBrie et al., 2007; Reifman & Watson,
2003). Closer to the time of graduation, however, it is plausible that overall use levels are reduced given
attention, for example, to securing full-time employment (see Shulenberg & Zarrett, 2006; Steinman,
2003; Turrisi et al., 2000).
Following college graduation, similarly, it is possible that use levels may remain somewhat
elevated in the first year or so, and declines become apparent several years later as these youth approach
their late 20’s (Chen & Jacobson, 2013). Studies have shown that even after the college years, drinking to
get drunk is common in social gatherings (Maggs & Schulenberg, 2004), and hard drugs like cocaine are
used as well (White, Becker-Blease, & Grace-Bishop, 2006). Affiliation with deviant peers, who
routinely binge-drink and use drugs, can therefore reinforce substance misuse among these young people
through the mid 20’s (Andrews, Tildesley, Hops, & Li, 2002; Mason & Spoth, 2011; Schulenberg &
Zarrett, 2006). By the later 20’s, on the other hand, substance use should, in theory, be reduced as a result
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SUBSTANCE MISUSE ACROSS EARLY ADULTHOOD
of social role transitions including full time employment and family formation roles such as long-term,
committed relationships (Arnett, Bachman, Wadsworth, O'Malley, Johnston, & Schulenberg, 1997; Glatz,
Stattin, & Kerr, 2012).
The New England Study of Suburban Youth (NESSY)
With a focus on youth who grew up in affluent suburbs, we report on patterns of substance use
across a period of 10 adult years in a prospective, longitudinal design with two cohorts. The first cohort
was assessed from sixth grade through high school (Luthar & Barkin, 2012), and here we present data at
Grade 12 and then across the five years after college graduation, through the ages of 23 to 27 years. The
second cohort was first assessed in Grade 12 and then followed through their four years of college, across
the ages of 18-22. In this report, we refer to these two cohorts respectively as NESSY-O (Older) and
NESSY-Y (Younger). Both schools sampled were in communities with a high concentration of well-
educated, white-collar professionals, with median incomes in the top 5% of the country, from two
different states in the Northeastern United States.
Based on annual assessments in adulthood, we expected to see elevated frequencies, compared to
national norms, for at least four use indices: Drunkenness, and the use of marijuana, stimulants such as
Adderall or Ritalin, and cocaine. As noted earlier, elevated alcohol and marijuana use have already been
documented among high school youth in affluent communities, and past use is a good predictor of later
use. Studies have increasingly reported high misuse among college students of stimulant drugs such as
Ritalin and Adderall, both as study aids and for recreational use, with reported rates as high as 20-35%,
and acquisition easy either from friends or through black market sales on college campuses (Gendaszek &
Low, 2002; Moore, Burgard, Larson, & Ferm, 2014; Vrecko, 2015). Finally, cocaine is also more
commonly used by young adults from high SES families than others, whereas in general, use of cigarettes
and other drugs (e.g., inhalants, methamphetamines, and heroin), are more used among lower SES
individuals (Humensky, 2010; Lee, McClernon, Kollins, Prybol, & Fuemmeler, 2013).
Psychiatric diagnoses: Does frequent use imply impairment?
Percentages reporting some use of a substance per year do not necessarily imply serious
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SUBSTANCE MISUSE ACROSS EARLY ADULTHOOD
disorders; even if twice as many upper middle class youth report using a substance as their less wealthy
counterparts, this may not entail functional impairment (see Uestuen & Kennedy, 2009). In this paper,
therefore, we go beyond reporting on the percentages using substances relative to national norms (per
data from Monitoring the Future; Johnston, O’Malley, Bachman, & Schulenberg, 2012). Here we also
report on the proportions of the sample that met criteria for psychiatric diagnoses, based on structured
interviews, of substance abuse and dependence. Broadly speaking, diagnoses of abuse imply levels of use
that lead to failure to fulfill major role obligations, or problems with the law, without meeting criteria for
dependence. The latter is the more serious diagnosis, encompassing the medical term for what is
commonly referred to as alcoholism or addiction, involving physical tolerance, craving, and withdrawal
symptoms when use is reduced.
For both NESSY-Y and NESSY-O, diagnoses were made based on criteria in the Diagnostic and
Statistical Manual (4th ed.; DSM–IV; American Psychiatric Association, 1994), the version of the
diagnostic manual current at the time participants were enrolled in the study.
Alongside, we present national normative rates for individuals of the same age using data from the
National Comorbidity Study Replication (NCS-R), a nationally representative survey implemented
between 2001 and 2003 with individuals 18 years and older, where rates of diagnoses were also based on
structured interviews (Kessler et al., 2004).
In this paper, we report on lifetime rates in the NESSY cohorts using two sets of estimates. The
first set is based on diagnostic interviews conducted at a single assessment time as was done in the NCS-
R. This was the last interview with each cohort, at age 26 for NESSY-O (age 27 assessments
encompassed the questionnaires but not interviews; see Methods) and age 22 and NESSY-Y. The second
set of rates is based on lifetime diagnoses obtained cumulatively, across any of the annual NESSY
interviews conducted. Several studies have established that single, cross-sectional assessments of mental
disorders underestimate true lifetime rates, given limitations of retrospective recall across several years
(Moffitt et al., 2010; Olino et al., 2012; Takayanagi, Spira, Roth, Gallo, Eaton, & Mojtabai, 2014). Thus,
for both cohorts, we also present lifetime rates based on all four annual assessments, such that a positive
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SUBSTANCE MISUSE ACROSS EARLY ADULTHOOD
diagnosis at any of these indicated a lifetime diagnosis.
Parent containment
A final aim in this study was to examine the protective power of adolescents’ perceived parents’
“containment” in relation to long-term substance misuse. Containment represents views of the
seriousness of repercussions from parents if they were to discover the youth’s use of drugs or alcohol.
Cross-sectionally and across multiple affluent school samples, low perceived parent containment has been
found to have robust links with teenagers’ actual use, over and above conventionally examined aspects of
parents’ supervision or monitoring (Luthar & Barkin, 2012). As this single construct explained much
more variance than other parent predictors, researchers have underscored the need for upper middle class
parents to avoid laxness when detecting teen substance use, in order to mitigate high levels of misuse in
the future (Luthar et al., 2013).
Whereas such cautions may be reasonable, there is only limited evidence that high school parent
containment has any ramifications for youths’ substance use past the time that they no longer live at
home. In a prospective study of 339 high school students in the South West, parental containment and
parent-child relationship quality were examined in relation to alcohol use over the college transition
(Hartman et al., 2016). Again, given overlap between the constructs of parental monitoring and
containment, parental monitoring was included as a covariate. Results showed that higher parental
containment was associated with less alcohol use in college, especially in the context of more positive,
supportive parent-child relationships, mirroring findings that authoritative parenting is generally
beneficial whereas authoritarian parenting is not (Cohen & Rice, 1997; Hoffman & Bahr, 2013; Visser et
al., 2013). The authors concluded that the construct of containment can have “important implications for
parental efforts to reduce risk for alcohol use and related problems prior to the important transition to
college, during which rates of heavy drinking and related problems often dramatically increase” (Hartman
et al., 2016).
In this study, therefore, we examined, using longitudinal data, whether parents’ containment in
high school might have any prognostic significance for frequency of substance use well into adulthood.
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SUBSTANCE MISUSE ACROSS EARLY ADULTHOOD
In these prospective analyses (as in prior cross-sectional work), we controlled for overall parental
monitoring in high school, toward illuminating links largely specific to containment. Outcomes examined
were frequencies on three use indices: Drunkenness, marijuana, and stimulants. This choice was based on
anticipated range of scores, wherein enough individuals would likely report using at least once, and some
with high frequency scores, such that floor effects (with mostly zeros) could be averted. These three
indicators were considered, again, in the last year of adult assessments. Additionally, assuming these
associations were identified, we sought to determine whether they might be mediated by overall substance
use during high school, while these young people had still been living at home.
Summary
In this cross-lagged longitudinal study, the central goal was to provide in-depth data on adult
substance use among a population recently identified as being “at–risk”, that is, youth who grow up in
relatively affluent communities. We document levels of alcohol and drug use in two cohorts both
assessed as high school seniors, and then annually across four college years (NESSY-Y), and across five
years of young adulthood from ages 23 – 27 years (NESSY-O). In comparison with national norms, we
expected to see (a) high rates of drinking to intoxication, and the use of marijuana, stimulants, and
cocaine; and (b) modest reductions in rates, relative to prior use, by the senior year of college in NESSY-
Y and by age 27 years in NESSY-O. A second goal was to compare rates of lifetime diagnoses of abuse
or dependence relative to national norms, at ages 22 and 26 for NESSY-Y and NESSY-O respectively.
Finally, we sought to examine whether parental containment of substance use in high school was related
to frequency of substance use several years later in adulthood, with high school use as a potential
mediator. All analyses were conducted separately by gender, as prior work with affluent youth has shown
gender differences in specific areas of maladjustment as well as in salient risk and protective processes
(e.g., substance use shows robust links with high peer status among males but not among females; Luthar
et al., 2013).
Methods
Sample
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Originally encompassing only one cohort of students in a relatively affluent Northeastern suburb,
this study evolved into a two-cohort design. The first cohort, NESSY-O, was followed annually from
Grade 6 through freshman year of college (eight assessments, between 1998 and 2005; see Luthar &
Barkin, 2012), and then again, post college graduation, between the ages of 23 and 27 (five assessments,
between 2009 and 2013). Because data collection with NESSY-O was unavoidably stopped between
2006 and 2009, the second cohort (NESSY-Y) was recruited for this study to (a) capture trends among
suburban youth across the critical college years of ages 19-22, and (b) potentially, to establish
generalizability of young adult findings across two sites.
This report is the first to present on any data past the high school years on the NESSY-O cohort,
and the first to present any data at all on the NESSY-Y cohort. In high school assessments of both
samples, participation had been voluntary, and 71% and 70% of the graduating classes participated in the
senior year assessments, respectively, with n’s of 255 and 272. Approximately half of the participants
were female (48% and 54% in NESSY-O and NESSY-Y, respectively). Most students were white (88%
and 80%) and the majority of parents had a college degree (83% and 90% for fathers, and 83% and 88%
for mothers, respectively, in NESSY-O and NESSY-Y). Median family incomes were well over three
times the national average in 2014 of $52,250, at $151,771 and $241,453 in the two towns, and median
home prices in 2015 were both over one million dollars (United States Bureau of the Census, 2015).
With regard to retention, the number of participants assessed at each wave is shown for both
cohorts, in Table 1. Note that at each wave, students were invited to join even if they had not participated
in preceding waves of data collection. As shown in Table 1, at the final wave of data collection, we
assessed 72% of NESSY-O youth who participated as high school seniors (age 27 years: 183 of 255
participants), and 56% of NESSY-Y youth (age 22 years: 152 of 272 participants). Whereas retention
rates were lower for NESSY-Y, they compare favorably to other follow-up studies of substance use
among high school students. For example, the Monitoring the Future’s (MTF) reported retention rates for
high school seniors ranged from 50% to 54% for the first year post high school through the fifth year post
high school (Johnston, O'Malley, Bachman, Schulenberg, & Miech, 2014).
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-- Insert Table 1 about here ---
To test for attrition biases, we compared Grade 12 scores on substance use between participants at
the final year of data collection, with their counterparts who had dropped out after high school
assessments. In both NESSY-O and NESSY-Y, final year participants versus non-participants were not
significantly different on overall substance use as high school seniors (F(1, 249) = 0.40, n.s.), and (F(1,
264) = 0.63, n.s.). Further analyses showed, in addition, that there were no significant differences for
specific substance use categories, participant race, parents’ marital status, mother and father education
levels, mother and father employment status, and number of hours/week parents worked outside the
home. Thus, the adult prospective samples appear to be generally representative of the original NESSY-O
and NESSY-Y cohorts as high school seniors, with no evidence to suggest differential attrition.
Procedure
In the high school senior year, data collection occurred during the month of May for both cohorts.
Students’ participation was voluntary and based on passive consent procedures, as the study was part of
school-based initiatives promoting positive youth development. Participants completed a packet of
questionnaires administered in a group setting, with research assistants available to answer any questions
that arose. In Grade 12 assessments, participants received monetary incentives of a $30 gift card for
NESSY-O (who had already participated in prior school-based assessments) and a $10 gift card for
NESSY-Y (for whom this was the first assessment).
In subsequent annual adult assessments, data were collected via two processes: phone interviews
for psychiatric diagnoses, and online questionnaires in an extensive battery of self-report measures
including those on substance use. Of the five adult assessments of NESSY-O, the first four (ages 23, 24,
25, and 26 years) involved the complete battery with psychiatric interviews for diagnoses as well as all
questionnaires, whereas in the last assessment (age 27), project funds were available only for the
questionnaire part of the study. Thus, diagnostic data are presented through age 26 but use data are
available at age 27 as well. The second cohort, NESSY-Y, received the complete battery of assessments
across four annual assessments, so that data on both diagnoses and use are available for ages 19, 20, 21,
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SUBSTANCE MISUSE ACROSS EARLY ADULTHOOD
and 22. For NESSY-O adult assessments, incentives for completing both interview and online
assessments were $150 at each wave; for their final wave where only questionnaires were completed,
participants received $75. On completion of both parts of the study, NESSY-Y participants received $125
for participation during college freshman year, and $150 in subsequent years.
Measures
Substance Use: Prevalence rates and frequencies. Participants completed questions from the
Monitoring the Future study (MTF; Johnston et al., 2012) about the use of alcohol and different
substances. The reliability and validity of this type of self-report have been amply documented
(www.monitoringthefuture.org). For each substance, participants indicated use both for the past year and
the past 30 days, and in this paper, we present rates of any past-year use because for some substances (i.e.
those rarely used), normative data for the past 30 days are not available. In addition, we considered past-
year rates more reliable than those in the past month, as participants were assessed at different times in
the year, and events (e.g., final exams or football season in college) could have skewed some 30-day use
patterns.
Clinical Diagnoses. Trained research assistants with Bachelor’s degrees administered the
Computerized Diagnostic Interview Schedule for the DSM-IV (CDIS-IV; Robins, Cottler, Compton,
Bucholz, North, & Rourke, 2000) to subjects via telephone. The C-DIS-IV is a structured interview that
can be administered by lay interviewers and assesses for lifetime and past-year symptoms according to
the DSM-IV. All responses are pre-coded, and the measure has good reliability and criterion validity
(Robins et al., 2000). In this study, we report on lifetime DSM-IV diagnoses of both abuse and
dependence for alcohol and drugs. Criteria for drug abuse and dependence are parallel to those for
alcohol abuse and dependence.
As noted earlier, lifetime diagnoses in both NESSY cohorts are compared here to normative rates
from the National Comorbidity Study Replication (NCS-R) Survey (Kessler et al., 2004), wherein DSM-
IV diagnoses were obtained via a structured interview also administered by trained lay interviewers, using
laptop computers. The interview used in the NCS-R was the Composite International Diagnostic
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SUBSTANCE MISUSE ACROSS EARLY ADULTHOOD
Interview (CIDI), which is similar to the Diagnostic Interview Schedule (DIS); the CIDI was designed to
produce diagnoses based on criteria of both DSM-IV and ICD-10 (National Comorbidity Study, 2005).
To ensure comparability with our sample, we used the DSM-IV rates.
In the national NCS-R study, completed from 2001-2003, researchers had recruited participants
from US households in different geographic regions, and had chosen one adult member of the household
randomly, for a total of 9836 interviews of English-speaking adults to determine diagnosis. Rates were
then appropriately weighted to adjust for differential probabilities of selection for the national survey, as
some groups of respondents were undersampled, e.g., based sociodemographic and geographic variables
(see Kessler et al., 2004). Accordingly, the NCS-R prevalence rates we used, shown in the present tables,
were calculated using the same weighting criteria as were used in the NCS-R study, to derive national
lifetime rates, by gender, and at the specific ages that corresponded to our NESSY-Y (age 22) and
NESSY-O (age 26) samples. These conversions were done by using SAS survey procedures (Version
9.1.5; SAS Institute Inc).
As previously indicated, we report on two sets of lifetime diagnoses in our NESSY cohorts. The
first is based on the last assessment point only for each sample, as NCS-R rates were based on a single
interview. The second set of NESSY lifetime rates are based on diagnoses received across all annual
interviews, a method that both corrects for underestimation due to retrospective recall spanning several
years, and takes into account data from all participants, whether or not they were interviewed in the last
year of the study, specifically.
Parent containment of substance use: 12th grade. With responses rated on a 5 point scale,
students were asked, “How serious would the repercussions from your parents be if they found out that
you… Attended a keg or drinking party without permission; Got drunk; Went to a party where no adults
were present without permission; Were smoking marijuana.” (Luthar & Goldstein, 2008). Alpha
coefficients among females and males respectively were .89 and .87, among NESSY-O, and .85 for both
genders among NESSY-Y.
Parent Monitoring: 12th grade. Paralleling measures used by Fletcher et al. (2004), participants
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SUBSTANCE MISUSE ACROSS EARLY ADULTHOOD
were asked about how much their parents know about their activities via a five-item, 5-point scale (Luthar
& Goldstein, 2008). Illustrative items include “My parents know where I am after school,” “My parents
know how I spend my money”, and “My parents know who my friends are.” Alpha coefficients among
females and males respectively were .75 and .78 among NESSY-O, and .75 and .78 among NESSY-Y.
Results
Comparability of the two cohorts in adolescence
As noted earlier, the two cohorts were similar in socio-demographics; to ascertain overall
comparability on substance use, we examined past-year rates of use as high school seniors vs. rates in
MTF norms also assessed in the 12th grade and during the same calendar year (i.e. 2005 and 2010, to
compare with NESSY-O and NESSY-Y, respectively). Results for both cohorts are shown in Table 2.
Note that in this and subsequent tables (Tables 3 and 4), comparisons in which the category encompasses
more than one particular substance within MTF norms, the MTF value that we report is that of the
substance with the highest rate in the norms. Thus, in the interest of stringency, we compare to MTF rates
of Adderall rather than Ritalin which are lower in MTF; Tranquilizers rather than Barbiturates; and
Ecstasy rather than Ketamine.
To ease interpretation of the array of values, we present not just rates in our two cohorts and
those in MTF norms during the same calendar year but also the relative risk, calculated as a simple ratio.
Thus, as 84.5% of NESSY-Y girls reported alcohol use in Grade 12 versus 65.3% in MTF norms, the
relative ratio was 84.5/65.3 = 1.29 (the first row in Table 2). Additionally, we conducted, significance
testing to compare the proportions of use in the NESSY samples versus the MTF sample using two-tailed
z-tests, appropriately weighted for sample size differences. Where NESSY values were significantly
higher than those in MTF, ratios and z scores are shown in bold face in the tables.
As shown in Table 2, elevations were in fact apparent on the use dimensions we had expected, a
priori, based on prior high school assessments. For females and males in the older and younger cohorts,
NESSY values were significantly higher than those in MTF on 11 of the 12 comparisons: frequency of
alcohol use, drunkenness, and marijuana use among all subgroups with the exception of marijuana use
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SUBSTANCE MISUSE ACROSS EARLY ADULTHOOD
among NESSY-O males. In addition, slight elevations in the use of tranquilizers only among NESSY-Y
males and NESSY-O females, with the latter also showing elevations in Adderall use. Other rates of use
were below norms in both high school cohorts.
-- Insert Tables 2 and 3 about here ---
Substance use relative to national norms
In Table 3, we present prevalence rates of substance use across each of the four college years for
the NESSY-Y cohort, as compared to MTF normative rates (Johnston et al., 2014) corresponding to ages
19-22 years during the same calendar year. For comparisons in Table 3, again, two-tailed z-tests weighted
for sample size were conducted to test for differences in the proportions between the NESSY and MTF
samples.
Unfortunately, MTF prevalence rates for college students specifically are not available separately
by both age and gender, but rather are reported for the overall age bracket 19-22 years -- encompassing
typical ages during college attendance -- separately for males and females. Because (a) it is well known
that substance use is higher among college students than others (SAMHSA, 2012), and (b) of central
interest to us, a priori, were gender-specific use patterns among NESSY youth across young adulthood,
we used these MTF rates in central analyses of relative risk among NESSY-Y. (Note that comparisons by
specific ages 19, 20, 21, and 22, relative to overall MTF rates for these ages – not just for college students
and not separated by gender -- are depicted in Figure 1.)
As shown in Table 3, for the four use indices in which we expected to see elevations (i.e. drunk,
marijuana, stimulants, and cocaine), and across all 4 years of college and among both males and females,
prevalence rates were significantly higher than norms in 27 of the 32 instances. Of the other five ratios
that were not statistically significant, four were those for cocaine, which in fact were 1.5 - 2.4 times
higher in NESSY than MTF, but actual incidence rates were too low to allow for statistical significance
(n’s of 3, 5, 6, and 8 for values in order, across Table 3). The 27 significantly elevated ratios ranged from
1.27 to 5.33, with a median value of 1.81. Overall, therefore, findings were striking in showing that
across all 8 observations (four years and both genders), almost every ratio for drunk, marijuana,
15
SUBSTANCE MISUSE ACROSS EARLY ADULTHOOD
stimulants, and cocaine was above norms (97%), and relative elevations were statistically significant in
85% of comparisons.
-- Insert Table 3 and Table 4 about here ---
In Table 4, we present parallel values for NESSY-O for five assessments between the ages of 23
in 2010, and 27 in 2014, compared with MTF normative rates for adults (ages 19-30, during the same
calendar years), separately by males and females. Unfortunately, MTF normative rates for adult men and
women are not broken down by specific age but are presented across the span of 19-30 years. Again, as
our central priori goal was to illuminate NESSY use patterns separately by gender, our primary analyses
entailed comparisons of adult MTF men and women in Table 4. Subsequently, we also show descriptive
rates for specific NESSY ages 23, 24, 25, 26, and 27, relative to each of these ages in MTF (not split by
gender), in Figure 1.
As in preceding analyses, two-tailed z-tests weighted for sample size were conducted to test for
differences in the proportions between the NESSY and MTF samples. Rates were significantly different
in 36 of the 40 comparisons involving elevations hypothesized a priori: drunkenness, marijuana,
stimulants, and cocaine, among females and males across five observations. The four non-significant
ratios were all above one, e.g., 1.25 for marijuana use by females at 25; 1.62 for Adderall use by males at
age 25; 1.31 for marijuana use by females, age 27; and 1.27 for Adderall use by males, age 27. The 36
significantly elevated ratios ranged from 1.25 to 5.25, with a median value of 2.1.
Aside from the indices we had hypothesized to be elevated, findings also revealed distinct
elevations on club drugs such as ecstasy, with nine of the ten values statistically significant among
NESSY-O, ranging from 2.30 to 3.69 (Table 4). Tranquilizer use rates were somewhat higher then norms,
with 12 of the 18 ratios across Tables 3 and 4 being greater than 1.5 (although only statistically significant
differences in five of these, possibly due in part to low incidence of tranquilizer use overall). Finally,
cigarette use, surprisingly, was significantly elevated in nine of the ten comparisons among NESSY-O,
with z scores ranging from 1.34 to 1.82.
Trends displayed over time
16
SUBSTANCE MISUSE ACROSS EARLY ADULTHOOD
In Figure 1, we descriptively display patterns of use over time for both cohorts, side by side,
across the four annual assessments during college (NESSY-Y) and five adult assessments (NESSY-O),
along with national norms by individual age. As noted before, previously discussed comparisons in Table
3 referenced national rates for 19-22 year old college students, rather than separate rates for 19, 20, 21,
and 22 year olds. In Figure 1, we present comparisons of NESSY-Y with MTF rates for each age (19, 20,
21 and 22 years), acknowledging that the latter normative data included both college students and others,
and combined for men and women. As a reference point for patterns during each of the adult years, we
include Grade 12 rates. In these figures, NESSY-Y and NESSY-O rates are shown by the lines across the
annual assessments, and the bars show parallel rates for the MTF normative samples at the same age and
during the same calendar year. These comparative rates are shown not just for the four indices for which
we had expected elevations a priori – drunk, marijuana, stimulants, and cocaine -- but also for ecstasy
and downers (e.g. tranquilizers) that we found, in this study, to show elevations (Table 3 and Table 4).
---- Insert Figure 1 about here ---
With regard to possible “maturing out”, across the college years, prevalence rates did seem to
decrease for some indices by the senior year of college, for example, marijuana and ecstasy. At the same
time, elevations relative to norms appeared somewhat elevated among NESSY-Y on drunk (83% versus
66% in norms), stimulant use (21% vs. 8% in norms), and cocaine use (9% versus 5%; percentages
averaged across men and women, in Table 3). Among NESSY-O individuals, we did not see any marked
reductions in rates of use at the last assessment relative to all prior years, ages 23 to 27 years.
Diagnoses
Presented in Table 5 are lifetime rates of DSM-IV substance use diagnoses in the NESSY-Y (age
22 years) and NESSY–O (age 26 years) cohorts, compared with rates of diagnoses estimated in the NCS-
R study for adults of the same age (Kessler et al., 2004), separately by gender. Values in the first set of
columns are based on lifetime diagnoses as reported in only the last assessment of NESSY-Y or NESSY-
O along with parallel values in the NCS-R (also based on a single assessment), and their ratio,
NESSY/NCS-R. Again, two-tailed z-tests weighted for sample size were conducted to test for differences
17
SUBSTANCE MISUSE ACROSS EARLY ADULTHOOD
in the proportions between the NESSY and MTF samples. Incidence rates in the last set of columns for
women and men represent diagnoses based cumulative assessments, that is, if criteria were met at any of
the annual assessments conducted, for each cohort.
The first two rows of Table 5 depict the diagnostic values of greatest concern, i.e., signifying
dependence on, or addiction, to any substance. As shown there, values were significantly elevated for
three of the four subgroups, with relative ratios of 2.37, 3.30, and 2.05 for NESSY-Y men and NESSY-O
women and men respectively. Among NESSY-Y women, rates of dependence were close to those in
national norms.
Percentages of participants meeting diagnostic criteria for dependence based on cumulative
assessments in the last column, versus a single assessment (first column) were 15.8% versus 11.4% for
NESSY-Y women; 26.9% versus 19.2% for NESSY-Y men; 24.2% versus 18.5% for NESSY-O women;
and were substantially different among NESSY-O men, at 40.0% versus 22.6%.
On diagnoses of any substance abuse, NESSY-Y men and women were both below norms. Their
older counterparts were above norms (ratios 1.4 and 1.33) but these differences were not statistically
significant.
-- Insert Table 5 about here ---
Predicting substance use in adulthood
For the prospective examination of associations between high school containment and adult use
levels, regression and mediation analyses were conducted using MPlus 7.11 (Muthén & Muthén, 2013)
with full information maximum likelihood (FIML) unless otherwise specified. Maximum likelihood
missing data handling was employed to account for the existence of missing data (Enders, 2010), whether
due to attrition or a result of incomplete measurement. Maximum likelihood missing data handling
utilizes all of the available observations for each case to compute the likelihood function (Enders &
Bandalos, 2001), and subsequently provides unbiased estimates with minimal standard errors when data
are missing at random (Schafer & Graham, 2002). To account for the existence of missing data and ensure
the use of all available observations, maximum likelihood (ML) missing data handling requires that the
18
SUBSTANCE MISUSE ACROSS EARLY ADULTHOOD
model specify the estimation of means, variances and covariances among the predictors.
In examining long-term effects of perceived parents’ containment, we first examined whether
containment in high school was linked with use frequencies at the last assessment for three indices on
which we expected adequate variability: drunk, marijuana, and stimulants. Regression analyses showed
that after controlling for gender and parental monitoring, containment was significantly associated with
lower levels of drunkenness and marijuana use among NESSY-Y, and for all three outcomes among
NESSY-O; see Table 6. Once Grade 12 levels of overall substance use (summed across all substances)
were also considered in Model 2, three of the five associations became non-significant, suggestive of
mediated effects. (Correlations between containment and Grade 12 use were r = -.40 for NESSY-Y, and r
= -.46 for NESSY-O, respectively; between containment and gender were r = -.05 for NESSY-Y, and r =
-.02 for NESSY-O, respectively; and between Grade 12 use and gender were r = -.03 for NESSY-Y, and r
= -.19 for NESSY-O, respectively).
-- Insert Tables 6 and 7 about here ---
In follow up analyses, we estimated mediated effects and 95% confidence intervals for the same
three substance use indices for which we ran regressions, using MODEL INDIRECT in Mplus 7.1
(Muthén & Muthén, 2013) with bias corrected bootstrap resampling (5,000 samples) for greater accuracy
in the estimation of the standard errors (MacKinnon, 2008). A mediated effect was considered statistically
significant when the confidence interval did not contain zero (MacKinnon, 2008). Results indicated that
Grade 12 use significantly mediated the influence of parental containment on drunkenness and marijuana
use for both NESSY-O and NESSY-Y cohorts, and on stimulant use for the NESSY-Y cohort only (see
Table 7).
Discussion
Our findings suggest that it is probably unwise to treat lightly the elevated rates of substance use
previously documented among upper middle class teenagers; a troubling proportion of these youth met
criteria for diagnoses of substance dependence in their late 20’s. In the older NESSY cohort assessed at
age 26, lifetime diagnoses of addiction to any substance – drug or alcohol – were over two and three
19
SUBSTANCE MISUSE ACROSS EARLY ADULTHOOD
times those of national rates, for men and women respectively. Data on the younger NESSY cohort
showed that by age 22 years, lifetime rates of dependence on any substance were 2.4 times national rates
for men, although rates among the younger women were closer to normative rates (.95).
It should be noted that the estimates discussed here are on the conservative side, being based on a
single diagnostic interview (the last in the follow ups of each cohort). Retrospective recall could have
diminished reporting of some serious use in past years (Moffitt et al., 2010; Olino et al., 2012; Takayanagi
et al., 2014). As shown in the tables, when overall lifetime rates were computed on the basis of repeated
annual interviews, rates of diagnoses were substantially higher.
Further suggesting the seriousness of the issues are data on the actual frequency of using different
substances over time, including not just drunkenness and marijuana use (elevated even in high school),
but also the use of stimulants and cocaine as hypothesized. Across multiple waves, stimulant use rates for
NESSY-O ranged between 15% and 20%, at least twice as high as in MTF norms. Among NESSY-Y
across the college years, one in five of NESSY-Y on average reported misusing stimulants, rates more
than twice as high as in normative MTF samples. Similar trends were seen on cocaine: In both cohorts,
rates of cocaine use were again, at least twice as high as in norms.
One of the four subgroups in this study did not show significant elevations in diagnoses of any
substance dependence compared to NCS-R rates, that is, NESSY-Y women. However, even among these
young women, a disturbingly high proportion were not just getting drunk frequently, but also misusing
other substances. Rates of intoxication were around 1.5 those of MTF norms across the four assessment
waves as rates for both stimulants and cocaine were 1.5 to more than twice normative values. The
consistency of elevated frequencies across time is cause for concern (notwithstanding that some frequent
users did not meet criteria for addiction by age 22). It is also possible that increasing proportions of these
young women would meet diagnostic criteria in the years post college graduation, approaching the clearly
elevated rates of NESSY-O women by age 26.
Conservatively estimating, therefore, for three of the four subgroups in this study, findings
resonate with reports on the growing problem of abuse in segments of the population that thus far have
20
SUBSTANCE MISUSE ACROSS EARLY ADULTHOOD
not been thought of as being “at-risk”. Recent studies have suggested that in their adult years, adolescents
from high SES families have higher rates of binge-drinking as well as misuse of marijuana, stimulants,
prescription drugs, and cocaine, with these elevations seen even among those with full-time employment,
well after college graduation (Arria, Bugbee, Caldeira, & Vincent, 2014; Humensky, 2010). These past
reports have been based on survey data; to our knowledge, ours is the first study to report on interview-
based DSM-IV diagnoses, with rates reported for both substance dependence and abuse. It is worrisome
to learn that before their 27th birthdays, lifetime diagnoses of any substance dependence could be seen
among men and women from upper middle class communities in as many as 23-40% of men, and 19-24%
of women.
Potentially mitigating concerns, on the other hand, it should be noted that with further
developmental maturity, diagnoses of past year dependence could be reduced somewhat. Whereas life
transitions such as marriage or having children are typically associated with marked reductions in
substance use (Bachman et al., 1997; Glatz et al., 2012), in the upper middle class cohorts in this study,
neither event had yet occurred at their final assessments for any participant. In future work, it will be
important to ascertain whether diagnostic rates, as well as frequency of use, in fact decrease with
transition to adult roles such as marriage (which tends to occur later among the more affluent), or whether
they might remain elevated given high lifetime rates already documented.
Containment
Perceived parent containment for substance use is reportedly a robust predictor of concurrent use
levels among affluent adolescents (Luthar et al., 2013), and results of this study indicate potential
ramifications continuing well into adulthood (Hartman et al., 2016). Perceived parents’ containment at
age 18 was found to have direct associations with the frequency of marijuana use at age 22 among the
younger NESSY cohort, and with the frequency of stimulant use at age 27 among the older cohort.
Furthermore, containment was indirectly associated -- through substance use in Grade 12 -- with
drunkenness and marijuana use at both age 22 and age of 27 years, and with stimulant use at age 22.
Along with prior evidence that perceived parental approval of substance use influences substance
21
SUBSTANCE MISUSE ACROSS EARLY ADULTHOOD
use behavior (Boyle & Boekeloo, 2006; Messler, Quevillon, & Simons, 2014), findings from this study
highlight the need for upper middle class parents to revisit laissez-faire attitudes toward their high school
children’s substance use, with three caveats. The first is that the repercussions meted out are
consequential but at the same time, (a) are not draconian, (b) are mutually agreed upon (for “repeat
offenses”), and (c) are consistently enforced, within the context of a supportive parent-child relationship.
Overly severe punishments in the absence of support and nurturance will inevitably backfire (Luthar et
al., 2013). Second, along with such limit-setting, parents would do well to discuss these issues long
before the onset of high school, spelling out the risks associated. In this regard, Reyna and Mills’ (2014)
findings are useful, showing the benefits of sex education programs conveying the “bottom line” of the
risks involved, such as “it only takes once” to contract a sexually transmitted disease. With regard to
substance abuse, an analogous message for these highly achieving and ambitious youth might be that “it
takes only one arrest” for cocaine possession, or for injuring someone while driving intoxicated, to
disqualify them from future careers involving high-profile, senior positions of substantive leadership.
Limitations
There are questions about generalizability of our findings, as both schools from which cohorts
were originally sampled were located in the suburbs of the Northeastern U.S. (although in their adult
years, participants lived in varied geographic locations). At the same time, we should note that the 12th
grade elevations in use are consistent with high relative risk rates patterns across many other high school
samples, in suburbs and cites (see Botticello, 2009; Coley et al., 2017; Lund et al., 2017; Patrick et al.,
2012; Reboussin et al., 2010; Song et al., 2009).
A related limitation is small sample size; neither cohort was large, and each showed the kind of
attrition that is expected when high school students are followed into adulthood. As noted earlier, high
school assessments were conducted as part of the routine school day, whereas all subsequent interviews
required proactive participation. This said, our retention rates of 56% to 72% across adult assessments
compare reasonably with parallel Monitoring the Future’s (MTF) rates for high school seniors, ranging
from 50% to 54% for the first year through the fifth year post high school (Johnston et al., 2014),
22
SUBSTANCE MISUSE ACROSS EARLY ADULTHOOD
although for NESSY-Y, rates are lower than the approximately 65% retention in prospective studies that
used interviews rather than survey-based questionnaires (see Deng, Hillygus, Reiter, Si, & Zeng, 2013;
Rothman, 2009). Perhaps most importantly, there was no evidence of differential attrition in either the
NESSY-O or NESSY-Y cohort, in terms of high school levels of substance use, or on multiple
sociodemographic indices including parents’ education, employment status, race, or marital status.
With regard to findings on containment, the possibility of bidirectional effects must be
acknowledged. There is a considerable literature showing that children’s behaviors can affect those of
parents in addition to the reverse (see Abar, Jackson, & Wood, 2014; Kerr, Stattin, & Özdemir, 2012;
Pardini, 2008; Racz & McMahon, 2011). Thus, in some instances, it is plausible that adolescents were in
fact using frequently but without being caught, leading to beliefs that if use were to be detected, the teen
might minimize it as a one-time event and thus meet with few consequences from parents.
As we fully acknowledge the limitations of this work, we believe that it is worth considering our
findings, at the very least, as pointing to potentially serious public health issues warranting further
rigorous study. A defining feature of the field of developmental psychopathology, across the more than
three decades since its inception, is careful attentiveness to the implications of data for policy and practice
(Cicchetti, 1984). As colleagues in science weight the credibility of the rates we have described, to treat
them as probable “false positives” (e.g., as they are based on just two Northeast cohorts) could turn out to
be a disservice to many if, in fact, there are serious problems of drug and alcohol addiction in a
substantial proportion of youth growing up in affluent communities. Our own perspective is that from a
prevention standpoint, it might be more prudent to treat these elevated rates of addiction, as real
possibilities -- with future research systematically refuting or substantiating this postulate as the case may
be -- given the substantial costs of these problems to society: estimated at over $600 billion dollars in the
US (National Institute on Drug Abuse, 2014). Even among well-educated adults, frequent use of alcohol
and drugs is linked with lower yearly earnings as well as poorer functioning at work (Ellickson, Martino,
& Collins, 2004; Ellickson, Tucker, Klein, & McGuigan, 2001; Griffin, Samuolis, & Williams, 2011).
Finally, in weighing the possible seriousness of these issues, it is worth considering national data
23
SUBSTANCE MISUSE ACROSS EARLY ADULTHOOD
on the misuse of prescription drugs over time. Between 2006 and 2011, the non-medical use of the
stimulant Adderall reportedly rose 67 percent, and emergency room visits went up 156 percent (Chen et
al., 2016). Increasing misuse was particularly pronounced among 18-to-25-year-olds (often used as study
aids in college); these young adults usually procured the medications from family and friends (Chen et al.,
2016). In analyses of trends spanning 2004 – 2013, researchers documented significant increases in
emergency department visits for drug overdoses among children and adolescents, with most poisonings
resulting from unsupervised exposure to opioids (including pain medications such as oxycodone),
followed closely by benzodiazepines or tranquilizers (Lovegrove, Weidle, & Budnitz, 2015). Finally,
analyses of patterns between 1997 and 2012 showed significant increases in opioid poisonings among 15
to 19 year olds (Gaither, Leventhal, Ryan, & Camenga, 2016). Besides suicidal intent, poisonings
occurred because of recreational misuse and self-medication for depression or anxiety, and once again,
most teens obtained the drugs from friends or family (Gaither et al., 2016; Hirshman, 2016). Obviously,
the ability to obtain all these controlled substances is easiest for young adults who have ample
discretional spending money.
Future directions
In the years ahead, our findings suggest the need for more focused research on substance use in
teens in upper middle class communities. It has been noted that acquiring these samples in developmental
research can be difficult given the high emphasis on privacy of students (some of whom have well-known
parents); furthermore, even when school-based assessments are obtained, following them over time can
be complicated as monetary incentives for participation are not adequate as many are well off (Luthar et
al., 2013). As was the case with the accelerated scientific attention to youth in poverty when they were
recognized as being at risk for adjustment problems (see Huston, McLoyd, & Garcia Coll, 1994), we
would suggest that there is value in future initiatives seeking focused research on this subgroup of youth.
Useful, for example, could be requests for proposals with funds allocated to studies on the onset,
ontogenesis, and potential mitigation of drug and alcohol use among teens growing up in relatively
affluent schools and communities.
24
SUBSTANCE MISUSE ACROSS EARLY ADULTHOOD
In future prospective research and with large enough samples, it will be important to tease apart
long-term effects, on youth, of demographic indices not examined here, with appropriate significance
testing. As suggested at the outset of this paper, for example, parents’ income or education levels might
contribute less unique variance to high substance use than does growing up in schools and communities
with mostly high SES families (Coley et al., 2017; Jensen, Chassin, & Gonzales, 2017; Lund et al., 2017;
Odgers, Donley, Caspi, Bates, & Moffitt, 2015; Trim & Chassin, 2008). Also important will be
disentangling effects of ethnicity (Crosnoe, 2009). In this study as in the general population, families
with high SES disproportionately included Caucasians. Finally, with larger samples (and thus greater
power to detect significance given relatively low-incidence problems), there is value in exploring if there
are any long-term associations between high school parent containment and later psychiatric diagnoses of
dependence on different substances, and if they are, whether these too might be mediated by levels of use
during the high school years.
In conclusion, results of this study suggest the value of more systematic, longitudinal studies on a
population that is quite possibly at considerable risk for problems of addiction: youth raised in relatively
well-to-do, school and community settings. Although by no means decisive, the patterns documented are
troubling given their consistency across two independent cohorts, different sets of measures
(questionnaires and interviews), and across ten annual assessments, considered cumulatively. Prospective
studies of any at-risk groups are rarely pursued without some initial evidence of long-term problems. As
results of this study show little evidence that participants matured out of serious alcohol and drug misuse
well into their twenties, we hope that this work will, at the least, serve as “preliminary data” for long-
term, contextually sensitive research on problems of substance misuse among children in upwardly
mobile communities. Over time, such research could prove invaluable for a sizeable group of youth
whose vulnerability is profound, but remains largely unacknowledged thus far in science and public
policy.
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Table 1.
The New England Study of Suburban Youth (NESSY): Two cohort longitudinal design
NESSY-O (Older) NESSY-Y (Younger)
Academic
Year
Median Age
(years)
nAcademic
Year
Median Age
(years)
n
High School - Grade 12 2004-05 18 255 2009-10 18 272
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SUBSTANCE MISUSE ACROSS EARLY ADULTHOOD
College: Year 1 2005-06 19 - 2010-11 19 136
College: Year 2 2006-07 20 - 2011-12 20 154
College: Year 3 2007-08 21 - 2012-13 21 160
College - Year 4 2008-09 22 - 2013-14 22 152
Post-College - Year 1 2009-10 23 147
Post-College Year 2 2010-11 24 160
Post-College Year 3 2011-12 25 169
Post-College Year 4 2012-13 26 175
Post-College Year 5 2013-14 27 183
Table 2.
Comparability of NESSY-Y and –O cohorts: Rates of past year use in Grade 12 split by gender, with ratios to MTF Norms a in the same chronological year.
FEMALES IN 2010 MALES IN 2010 FEMALES IN 2005
N-Y bMTF N-Y b MTF N-O cMTF N-O
N123 7,100 146 6,700 121 7,300 131
% % Ratio z% % Ratio z% % Ratio z%
Alcohol 84.5 65.3 1.29 4.40 480.2 65.0 1.23 3.80 483.3 67.5 1.23 3.69 380.9
Drunk 71.5 40.8 1.75 6.9 469.8 46.8 1.49 5.51 471.1 44.0 1.61 5.95 466.4
Marijuana 39.0 30.7 1.27 1.99 158.2 38.3 1.52 4.88 453.6 29.6 1.81 5.71 444.6
Cigarettes 21.8 - - - 27.5 - - -48.8 - - - 43.1
Adderalld7.4 5.5 0.60 .91 7.7 7.5 1.02 0.09 9.9 3.3 3.00 3.97 46.9
Cocaine 0.8 1.9 0.42 -.09 0.7 4.0 0.17 -2.03 15.9 4.2 1.40 0.92 3.1
Tranquilizersd1.6 5.2 0.31 -1.79 9.7 5.9 1.64 1.92 110.7 6.2 1.73 2.02 17.0
Inhalants 0.8 2.5 0.32 -1.20 1.4 4.7 0.29 -1.88 2.5 4.1 0.61 -0.88 5.4
Hallucinogen 0.0 2.9 0.00 -1.92 8.2 7.9 1.04 0.13 1.6 3.4 0.47 -1.09 4.6
Ecstasyd0.8 3.6 0.23 -.17 2.1 5.3 0.39 -1.72 3.3 2.7 1.22 0.40 1.6
Amphetamines 0.0 6.4 0.00 -1.88 2.8 8.3 0.34 -2.40 17.5 7.9 0.19 -0.16 1.5
Steroids 0.0 0.3 0.00 -0.61 1.4 2.5 0.56 -0.85 0.0 0.4 0.00 -0.70 0.0
Note. Shaded rows reflect substances on which NESSY adult elevations were expected, a priori.
aJohnston, et al., 2012; b N-Y = Younger NESSY cohort; c N-O is Older NESSY cohort; d Comparisons for which MTF features multiple substances, ratios are based
37
SUBSTANCE MISUSE ACROSS EARLY ADULTHOOD
on the MTF substance with the highest rate, i.e., Adderall > Ritalin. Ratio = %NESSY / %MTF; Bolded values indicate ratios where NESSY rates are significantly
higher than MTF; Comparisons of population proportions using z scores were weighted for sample size, and significance indicated by:
1p < .05; 2 p < .01; 3 p < .001; 4 p < .0001.
38
SUBSTANCE MISUSE ACROSS EARLY ADULTHOOD
Table 3.
NESSY-Y (Younger cohort) College: Past-year substance use, with ratios of rates to MTF normsa for College 19-22 year olds, by
FEMAL
ES IN
2011
MALES
IN
2011
FEMAL
ES IN
2012 MALES IN 2012
N-Y MTF N-Y MTF N-Y MTF
AGE 19 19-22 19 19-22 20 19-22
N77 750 58 480 78 670
% % Ratio z% % Ratio z% % Ratio z
Drunk 83.9 58.1 1.44 4.40 480.9 63.4 1.28 2.64 293.6 62.5 1.50 5.48
Marijua
na 53.3 29.0 1.84 4.38 456.8 39.9 1.42 2.46 161.1 32 1.91 5.10
Addera
ll b16.9 7.7 2.19 2.75 227.5 13.2 2.08 2.90 216.9 7.6 2.22 2.78
Cocain
e3.9 2.6 1.50 0.67 12.1 4.5 2.69 2.44 16.4 2.7 2.37 1.79
Ecstas
y b2.6 3.8 0.68 -0.53 5.1 4.7 1.09 0.14 5.1 5.6 0.91 -0.18
Tranqui
l b2.6 3.8 0.68 -0.53 8.5 4.9 1.73 1.16 3.9 3.1 1.26 0.38
Inhalan
ts 1.4 0.7 2.00 0.67 5.1 1.1 4.64 2.34 11.3 1 1.3 0.25
Heroin 0.0 0.1 0.00 -0.28 1.7 0.1 17.00 2.21 10.0 0.1 0.00 -0.28
Halluci
nogens 2.6 1.9 1.37 0.42 3.5 7.5 0.47 -1.12 1.3 3.5 0.37 -1.03
Amphe
tamine
s
0.0 8.2 0.00 -2.61 1.7 11.1 0.15 -2.25 12.6 10.1 0.26 -2.16
Alcohol 93.5 78.1 1.20 3.19 391.2 76.2 1.20 2.60 294.9 80.3 1.18 3.16
Cigaret
tes 19.7 23.4 0.84 -0.73 39.6 29.5 1.34 1.58 25.7 21.3 1.21 0.89
Steroid
s0.0 - 0.00 - 1.7 0.7 2.43 0.80 1.3 0.5 2.60 0.88
Note. Shaded rows reflect substances on which NESSY adult elevations were expected, a priori.
aJohnston, et al., 2012; b N-Y = Younger NESSY cohort; c N-O is Older NESSY cohort; d Comparisons for
which MTF features multiple substances, ratios are based on the MTF substance with the highest rate, i.e.,
Adderall > Ritalin. Ratio = %NESSY / %MTF; Bolded values indicate ratios where NESSY rates are
significantly higher than MTF; Comparisons of population proportions using z scores were weighted for
sample size, and significance indicated by:
1p < .05; 2 p < .01; 3 p < .001; 4 p < .0001.
39
SUBSTANCE MISUSE ACROSS EARLY ADULTHOOD
Table 3 (continued).
NESSY-Y (Younger cohort) College: Past-year substance use, with ratios of rates to MTF normsa for College 19-22 year olds, by gender
FEMALE
S IN 2013
MALES
IN 2013
FEMALE
S IN 2014 MALES IN 2014
N-Y bMTF N-Y MTF N-Y MTF
AGE 21 19-22 21 19-22 22 19-22
N84 660 76 430 78 590
% % Ratio z% % Ratio z% %
Drunk 91.5 56.4 1.62 6.19 478.9 60.3 1.31 3.10 287.1 61.2
Marijuana 58.4 32.6 1.79 4.65 454.1 40.1 1.35 2.28 132.1 32.3
Adderall c21.6 8.9 2.43 3.60 328.4 13.3 2.14 3.35 319.2 8.6
Cocaine 9.6 1.8 5.33 4.17 413.1 4.2 3.12 3.13 27.8 3.6
Ecstasy c10.8 3.9 2.77 2.82 218.4 7.5 2.45 3.04 25.2 4.3
Tranquiliz
ers c6.0 3.6 1.67 1.07 11.7 5.8 2.02 1.90 3.9 3.1
Inhalants 1.2 0.5 2.40 0.80 2.6 0.5 5.20 1.89 0.0 1.2
Heroin 0.0 0.1 0.00 -0.29 0.0 0.5 0.00 -0.62 1.3 -
Hallucino
gens 1.2 2.7 0.44 -0.82 6.6 7.3 0.90 -0.22 0.0 2.5
Ampheta
mines 2.4 9.4 0.26 -2.1516.5 12.4 0.52 -1.49 1.3 8.8
Alcohol 98.7 74.2 1.33 5.02 484.1 77.7 1.08 1.26 92.3 76.2
Cigarette
s23.9 20.7 1.15 0.68 40.7 27.1 1.50 2.40 115.4 21.3
Steroids 0.0 - - - 0.0 1.8 0.00 -1.18 0.0 0.2
Note. Shaded rows reflect substances on which NESSY adult elevations were expected, a priori.
aJohnston, et al., 2012; b N-Y is Younger NESSY cohort; c Comparisons for which MTF features multiple substances,
ratios are based on the MTF substance with the highest rate, i.e., Adderall > Ritalin. Ratio = %NESSY / %MTF;
Bolded values indicate ratios where NESSY rates are significantly higher than MTF; Comparisons of population
proportions using z scores were weighted for sample size, and significance indicated by: 1p < .05; 2 p < .01; 3 p < .001;
4 p < .0001.
Table 4.
NESSY-O (Older cohort) College: Past-year substance use, with ratios of rates to MTF normsa for College 19-30 year olds, by gender
FEMAL
ES IN
2010
MALES
IN 2010
FEMAL
ES IN
2011
MALES IN 2011
N-O bMTF N-O MTF N-O MTF
AGE 23 19-30 23 19-30 24 19-30
N73 3400 74 2400 79 3300
% % Ratio z% % Ratio z% % Ratio z
Drunk 90.4 62.4 1.45 4.90 486.6 66.4 1.30 3.64 381.5 59.9 1.36 3.88 4
Marijua
na 52.1 23.4 2.23 5.68 464.9 33.7 1.92 5.56 438.5 26.3 1.46 2.43 1
Adderal
l c23.3 5.4 4.31 6.49 429.9 7.9 3.78 6.66 412.6 5.2 2.42 2.88 2
Cocain
e15.1 3.5 4.31 5.17 419.0 6.3 3.01 4.31 410.2 3.3 3.27 3.32 3
40
SUBSTANCE MISUSE ACROSS EARLY ADULTHOOD
c9.6 2.6 3.69 3.62 313.5 3.9 3.46 4.06 47.6 3.1 2.45 2.24 1
Tranqui
lizers c8.2 6.1 1.34 0.74 13.6 6.6 2.06 2.35 15.1 5.4 0.94 -0.12
Inhalan
ts 1.4 0.7 2.00 0.70 0.0 1.9 0.00 -1.20 1.3 0.6 2.17 0.79
Heroin 0.0 0.3 0.00 -0.47 1.4 0.6 2.33 0.86 0.0 0.3 0.00 -0.49
Halluci
nogens 6.9 2.7 2.55 2.16 112.2 5.6 2.18 2.39 15.1 2.2 2.32 1.71
Amphet
amines 8.3 5.6 1.48 0.99 6.8 7.7 0.88 -0.29 3.9 5.8 0.67 -0.72
Alcohol 97.2 83.0 1.17 3.22 296.0 83.8 1.15 2.83 296.1 83.1 1.16 3.07 2
Cigarett
es 47.8 29.5 1.62 3.38 354.2 37.0 1.46 3.01 239.2 27.8 1.41 2.23 1
Steroid
s0.0 0.1 0.00 -0.27 0.0 1.6 0.00 -1.10 0.0 0.1 0.00 -0.28
Note. Shaded rows reflect substances on which NESSY adult elevations were expected, a priori.
aJohnston, et al., 2012; b N-O is Older NESSY cohort; c Comparisons for which MTF features multiple substances,
ratios are based on the MTF substance with the highest rate, i.e., Adderall > Ritalin. Ratio = %NESSY / %MTF;
Bolded values indicate ratios where NESSY rates are significantly higher than MTF; Comparisons of population
proportions using z scores were weighted for sample size, and significance indicated by: 1p < .05; 2 p < .01; 3 p < .001;
4 p < .0001.
Table 4. Continued
NESSY-O (Older cohort) College: Past-year substance use, with ratios of rates to MTF normsa for College 19-30 year olds, by gender
FEMALES IN 2012 MALES IN 2012 FEMALES IN 2013
N-O b MTF N-O MTF N-O MTF N-O
AGE 25 19-30 25 19-30 26 19-30 26
N79 3200 89 2200 82 3100 93
% Ratio z% Ratio z% Ratio z%
Drunk 83.6 63.6 1.31 3.66 388.6 66.2 1.34 4.41 485.4 59.7 1.43 4.70 491.4
Marijuana 31.4 25.2 1.25 1.25 61.8 33.5 1.84 5.50 439.1 28.0 1.40 2.20 164.6
Adderall c 14.8 5.8 2.55 3.32 313.3 8.2 1.62 1.70 22.1 5.3 4.17 6.46 415.2
Cocaine 8.6 3.0 2.87 2.82 219.0 5.3 3.58 5.41 414.7 2.8 5.25 6.13 419.5
Ecstasy c 7.4 3.1 2.39 2.14 19.0 4.8 1.87 1.79 10.9 3.2 3.40 3.80 317.3
Tranquilizers c 6.1 4.9 1.24 0.49 9.1 5.4 1.68 1.50 9.6 5.2 1.85 1.75 11.9
Inhalants 0.0 0.7 0.00 -0.75 2.2 1.5 1.47 0.53 4.8 0.3 16.00 6.25 41.1
Heroin 0.0 0.4 0.00 -0.56 0.0 0.5 0.00 -0.67 0.0 0.5 0.00 -0.64 0.0
Hallucinogens 4.9 2.7 1.81 1.18 9.0 4.4 2.04 2.04 14.8 2.2 2.18 1.56 14.0
Amphetamines 3.7 6.3 0.59 -0.94 2.2 8.6 0.26 -2.14 16.0 6.5 0.92 -0.18 4.4
Alcohol 96.4 83.4 1.16 3.09 294.4 81.8 1.15 3.05 297.5 82.5 1.18 3.56 399.0
Cigarettes 39.5 26.5 1.49 2.58 147.2 33.4 1.41 2.70 245.1 26.8 1.68 3.67 349.4
Steroids 0.0 0.1 0.00 -0.28 0.0 0.9 0.00 -0.90 0.0 0.1 0.00 -0.29 0.0
Note. Shaded rows reflect substances on which NESSY adult elevations were expected, a priori.
aJohnston, et al., 2012; b N-O is Older NESSY cohort; c Comparisons for which MTF features multiple substances,
ratios are based on the MTF substance with the highest rate, i.e., Adderall > Ritalin. Ratio = %NESSY / %MTF;
Bolded values indicate ratios where NESSY rates are significantly higher than MTF; Comparisons of population
41
SUBSTANCE MISUSE ACROSS EARLY ADULTHOOD
proportions using z scores were weighted for sample size, and significance indicated by: 1p < .05; 2 p < .01; 3 p < .001;
4 p < .0001.
Table 4 (continued)
NESSY-O (Older cohort) College: Past-year substance use, with ratios of rates to MTF normsa for College 19-22 year
olds, by gender
FEMALES IN 2014 MALES IN 2014
N-O b MTF N-O MTF
AGE 27 19-30 27 19-30
N82 3000 89 2000
% % Ratio z% % Ratio z
Drunk 89.0 61.8 1.44 5.02 483.1 66.3 1.25 3.30 2
Marijuana 36.3 27.8 1.31 1.69 55.8 34.2 1.63 4.18 4
Adderall c 22.0 6.5 3.38 5.46 411.2 8.8 1.27 0.78
Cocaine 10.9 3.6 3.03 3.41 318.9 6.9 2.74 4.23 4
Ecstasy c 9.9 3.4 2.91 3.13 212.4 5.4 2.30 2.79 2
Tranquilizers c 13.4 5.0 2.68 3.37 37.8 4.8 1.63 1.28
Inhalants 2.6 0.9 2.89 1.57 0.0 1.1 0.00 -0.99
Heroin 0.0 0.3 0.00 -0.50 0.0 0.4 0.00 -0.60
Hallucinogens 2.5 2.4 1.04 0.06 11.1 5.9 1.88 2.00 1
Amphetamines 6.0 6.8 0.88 -0.28 1.1 8.8 0.13 -2.55 1
Alcohol 97.6 82.9 1.18 3.52 389.9 83.1 1.08 1.69
Cigarettes 43.9 24.1 1.82 4.11 439.4 31.1 1.27 1.65
Steroids 0.0 0.3 0.00 -0.50 0.0 1.1 0.00 -3.31 3
42
SUBSTANCE MISUSE ACROSS EARLY ADULTHOOD
Note. Shaded rows reflect substances on which NESSY adult elevations were expected, a priori.
aJohnston, et al., 2012; b N-O is Older NESSY cohort; c Comparisons for which MTF features multiple
substances, ratios are based on the MTF substance with the highest rate, i.e., Adderall > Ritalin. Ratio =
%NESSY / %MTF; Bolded values indicate ratios where NESSY rates are significantly higher than MTF;
Comparisons of population proportions using z scores were weighted for sample size, and significance
indicated by: 1p < .05; 2 p < .01; 3 p < .001; 4 p < .0001
Table 5.
Lifetime DSM-IV substance use diagnoses in the NESSY-Y (age 22 years) and NESSY–O (age 26 years) cohorts vs. National Comorbidity Survey
Replication (NCS-R) for participants aged 22 years and 26 years, respectively.
Women
NESSYaNCS-RaRatioaNESSYbNESSYaNCS-R Ratio
% (n) % (n)z% (n) % (n) % (n)
Any substance
dependence NESSY-Y 11.4 (9) 12.0 (9) 0.95 -0.12 15.8 (15) 19.2 (14) 8.1 (5) 2.37
NESSY-O 18.5 (15) 5.6 (5) 3.3022.68124.2 (23) 22.6 (21) 11.0 (9) 2.05
Any substance
abuse NESSY-Y 10.1 (8) 21.0 (17) 0.481-1.94115.8 (15) 17.8(13) 23.7 (14) 0.75
NESSY-O 22.2 (18) 15.9 (13) 1.40 1.07 30.5 (29) 26.9 (25) 20.2 (14) 1.33
Alcohol
dependence NESSY-Y 8.9 (7) 9.9 (8) 0.89 -0.22 12.6 (12) 13.7 (10) 8.1 (5) 1.69
NESSY-O 14.8 (12) 4.1 (4) 3.6022.48222.1 (21) 17.4 (16) 10.9 (9) 1.60
Alcohol abuse
NESSY-Y 7.6 (6) 18.2 (15) 0.412-2.04112.6 (12) 15.1 (11) 20.5 (12) 0.74
NESSY-O 18.5 (15) 12.7 (11) 1.46 1.07 27.4 (26) 20.4 (19) 20.2 (14) 1.01
Drug
dependence
NESSY-Y 2.5 (2) 5.7 (4) 0.44 -1.01 4.2 (4) 9.7 (7) 1.8 (1) 5.39
NESSY-O 7.4 (7) 2.3 (2) 3.2211.61 8.4 (8) 8.6 (8) 4.8 (4) 1.79
Drug abuse NESSY-Y 3.8 (3) 11.2 (10) 0.341-1.8016.3 (6) 9.7 (7) 10.3 (7) 0.94
NESSY-O 11.1 (9) 12.9 (10) 0.86 -0.38 12.6 (12) 11.8 (11) 18.5 (12) 0.64
Note. NESSY-Y, New England Study of Suburban Youth younger cohort; NESSY-O, NESSY older cohort; NCS-R, National Comorbidity Survey Replication.
Sampling weights were used to calculate percentages for NCS-R data according to Kessler et al. (2004). NCS-R age 22 women: n . 95, men: n . 84; NCS-R age 26
women: n . 96, men: n . 71. Comparisons of population proportions using z scores were weighted for sample size. a NESSY rates based on final assessment point
only: ages 22 and 27 for NESSY-Y and NESSY-O, respectively. Ratio .%NESSY/%MTF. NESSY-Y: n.78 women, n.73 men; NESSY-O: n.82 women, n.89 men.
b NESSY rates based on all annual assessments. Cumulative NESSY-O: n . 95 women, n . 110 men; cumulative NESSY-Y: n . 95 women, n . 93 men. *z _ 1.6, p , .
05. **z _ 2.5, p , .011.
Table 6.
Linear regression analyses of Grade 12 perceived parents’ containment in relation to adult use frequencies
NESSY-Y College senior (age 22) substance use frequencies
43
SUBSTANCE MISUSE ACROSS EARLY ADULTHOOD
Drunk Marijuana Stimulants
Model 1 Model 2 Model 1 Model 2 Model 1
Predictors β b (SE)β b (SE)β b (SE)β b (SE)β b (SE
Grade 12
substance use - - .41** .13 (.03) - - .34** .10 (.03) - -
Gender .07 .24 (.32) .02 .06 (.30) .32** 1.17 (.30) .28** 1.0 (.29) .02 .05 (.24)
Containment -.23* -.09 (.04) -.07 -.03 (.04) -.29** -.12 (.04) -.17* -.07 (.04) -.01 -.00 (.03)
Parent
monitoring .04 .02 (.04) .10 .04 (.04) .03 .01 (.04) .07 .03 (.04) -.07 -.02 (.03)
Adjusted R2.03 .17** .16** .26** -.02
NESSY-O Age 26 substance use frequencies
Drunk Marijuana Stimulants
Model 1 Model 2 Model 1 Model 2 Model 1
Predictors β b (SE)β b (SE)β b (SE)β b (SE)β b (SE
Grade 12
substance use - - .28** .06 (.02) - - .23* .06 (.03) - -
Gender .02 .09 (.33) .03 .11 (.32) .20* .94 (.39) -.20* .96 (.39) -.14 -.47 (.28)
Containment -.23** -.10 (.04) -.11 -.05 (.04) -.25** -.13 (.04) -.16 -.08 (.05) -.32** -.12 (.03)
Parent
monitoring --.08 -.26 (.27) -.03 -.10 (.26) -.05 -.18 (.31) -.01 -.03 (.31) -.04 -.10 (.22)
Adjusted R2.05* .10** .10** .14** .10**
Note. Gender was coded 0 = female, 1 = male; G12 substance use is the sum of the frequencies of using all substances; Model 1 does not
control for Grade 12 substance use, whereas Model 2 does control for Grade 12 substance use. β = Standardized coefficient;
b = Unstandardized coefficient; SE = Standard error;
*p<.05, **p<.01.
44
SUBSTANCE MISUSE ACROSS EARLY ADULTHOOD
Table 7.
Grade 12 perceived parents’ containment in relation to adult use frequencies with estimates of
the mediating effects of Grade 12 substance use with bias-corrected 95% confidence limits.
Point Estimate
(ab)
BC 95% CLs
Mediated Pathway SE Lower Upper
NESSY-Y
ContainmentG12 Substance useDrunk Age 22 -0.08* 0.02 -0.13 -0.04
ContainmentG12 Substance use Marijuana Age 22 -0.07* 0.02 -0.13 -0.04
ContainmentG12 Substance useStimulants Age 22 -0.05* 0.02 -0.09 -0.02
NESSY-O
ContainmentG12 Substance useDrunk Age 26 -0.003* 0.001 -0.006 -0.001
ContainmentG12 Substance useMarijuana Age 26 -0.003* 0.001 -0.007 -0.001
ContainmentG12 Substance useStimulants Age 26 -0.000 0.001 0.000 0.001
Direct Pathway Estimate SE
BC 95% CLs
Lower Upper
NESSY-Y
Containment G12 Substance Use -0.72** 0.10 -0.85 -0.45
Containment Drunk Age 22 -0.04 0.04 -0.11 0.03
G12 Substance Use Drunk Age 22 0.11** 0.03 0.06 0.16
ContainmentMarijuana Age 22 -0.09* 0.04 -0.16 -0.01
G12 Substance Use Marijuana Age 22 0.10* 0.03 0.04 0.16
Containment Stimulants Age 22 0.05 0.03 -0.01 0.12
G12 Substance Use Stimulants Age 22 0.07** 0.02 0.03 0.12
NESSY-O
Containment G12 Substance Use -0.04** 0.02 -0.07 -0.001
Containment Drunk Age 26 -0.001 0.01 -0.02 0.02
G12 Substance Use Drunk Age 26 0.08** 0.02 0.04 0.11
ContainmentMarijuana Age 26 -0.005 0.01 -0.02 0.02
G12 Substance Use Marijuana Age 26 0.08** 0.02 0.03 0.12
Containment Simulants Age 26 -0.004** 0.001 -0.005 -0.003
G12 Substance Use Stimulants Age 26 -0.002 0.003 -0.008 0.004
Note. G12 = Grade 12; G12 substance use is the sum of the frequencies of using any substances.
Covariates not shown include gender and Parent Monitoring. SE = Standard error;
CLs = Confidence limits;
*p<.05; **p<.01
45
SUBSTANCE MISUSE ACROSS EARLY ADULTHOOD 46
SUBSTANCE MISUSE ACROSS EARLY ADULTHOOD
Figure 1. Rates of use at specific ages for NESSY-Y and NESSY-O compared to Monitoring the Future
(MTF; Johnston et al., 2012) norms during the same calendar years for: drunkenness, and use of
marijuana, Adderall, cocaine , ecstasy , and downers. Data for NESSY-Y and NESSY-O are represented
by the lines, and MTF norms are represented by the columns.
47
SUBSTANCE MISUSE ACROSS EARLY ADULTHOOD 48
