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Socioeconomic Status, Depressive Symptoms, and Adolescent Substance Use
Elizabeth Goodman, MD;
Bin Huang, PhD
Arch Pediatr Adolesc Med. 2002;156:448-453.
ABSTRACT
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Objective To determine the relationships among socioeconomic status (SES), depression,
and substance use among teenagers. We hypothesized that, among teenagers,
substance use was associated with SES in a graded fashion and that depression
is a mechanism through which SES affects substance use behaviors.
Design Linear regression analyses of cross-sectional data from Wave I of the
National Longitudinal Study of Adolescent Health (1995).
Participants Fifteen thousand one hundred twelve adolescents whose parents answered
questions assessing household income and parental education.
Main Outcome Measures Use of cigarettes, alcohol, marijuana, and cocaine.
Results For all 4 substances, frequency of use varied by SES. In the total population,
inverse SES gradients were present for cigarette use (education, mean change= -0.052;
95% confidence interval [CI], –0.081 to –0.023; income, mean change=
–0.038; 95% CI, –0.069 to –0.007) and alcohol use (income,
mean change= 0.044; 95% CI, 0.016-0.071). The relationship between marijuana
use and education was also significant but inverse-U-shaped, not linear. This
relationship was only present among nonwhite teenagers. Race/ethnicity also
moderated the relationships between SES and cigarette use and SES and cocaine
use. For cigarette use, stratification by race/ethnicity revealed an inverse
graded relationship among white non-Hispanic teenagers and a direct, graded
relationship among nonwhite teenagers (ie, mean change for education among
white non-Hispanic teenagers, -0.012; 95% CI, –0.016 to –0.075;
mean change for education among nonwhite teenagers, 0.040; 95% CI, 0.014-0.072).
For cocaine use, a weak, inverse linear relationship existed only between
education and cocaine use among white non-Hispanic teenagers (mean change
for education, -0.013; 95% CI, –0.026 to -0.0004). The relationship
between the SES indicator and substance use weakened when depressive symptoms
were entered into the model for the SES–cigarette use relationship (23%
decrease in mean change associated with a 1-unit change in both education
and income) and for the association between education and cocaine use among
white non-Hispanic teenagers (31% decrease).
Conclusions Socioeconomic status is associated with substance use among teenagers
but the nature of the relationship is not consistent across SES indicators
or across race/ethnicity groups. Depressive symptoms are a mechanism through
which SES affects cigarette and cocaine use behaviors among teenagers. However,
these data indicate that interventions targeted toward decreasing depressive
symptoms will not have a strong impact on the effects of SES on teenage substance
use.
INTRODUCTION
ALTHOUGH RESEARCH on infants, young children, and adults indicates that
socioeconomic status (SES) is related to health outcomes in an inverse graded
fashion,1-10
research on the sociostructural determinants of adolescent health is sparse.11 This may be in part because adolescents are generally
considered a healthy population. However, serious morbidity, such as depression,
exists in this age group. Estimates of the prevalence of depression during
adolescence range from 15% to 20%.12 Depression
puts adolescents at increased risk for multiple sequelae that can have long-term
effects on many areas of adult functioning.12
Substance use and abuse are examples of such sequelae. In 1999, 81.0% of US
high school students had at least 1 drink containing alcohol and 70.4% had
tried cigarettes.13 Nearly half (47.2%) had
tried marijuana and 9.5% had used cocaine.13
Like depression, lower SES has also been associated with increased substance
use among adolescents.14-15
The theory of relative deprivation and the work of Wilkinson16-17 suggest a model that links the associations
among SES, depression, and substance use (Figure 1). Wilkinson has argued that psychosocial factors may be
part of the causal pathway linking SES to health outcomes.18
Such intermediary factors that link exposure and outcome variables are referred
to as mediators.19 Depression has been identified
as one of these potential mediating factors in the SES-health relationship.3, 20 However, studies examining these associations,
especially those focusing on the critical developmental period of adolescence,
are rare. The objective of this study, which uses data from a recent nationally
representative study, was to determine the relationships among SES, depressive
symptoms, and use of 4 commonly used substances—cigarettes, alcohol,
marijuana, and cocaine. We hypothesized that, among teenagers, substance use
would be associated with SES in a graded, linear fashion and that increased
depressive symptoms would be an intermediary factor in the casual pathway
linking SES to teenage substance use behaviors (Figure 1).
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Mediational model of the socioeconomic status (SES)–substance
use relationship by depression. Paths A and B represent the indirect effects
of SES on substance use that are mediated by depression. Path C represents
the residual direct effect of SES on substance use.
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METHODS
DATABASE AND SAMPLE DESCRIPTION
The National Longitudinal Study of Adolescent Health (Add Health) provided
the data for this study. Add Health is a recent nationally representative
study of in-school teenagers in grades 7 through 12.21
Wave I in-home data were collected between April and December 1995. This study
included 15 112 adolescents whose parents completed information on parental
education, household income, or both during the Wave I in-home interview.
DESCRIPTION OF VARIABLES INCLUDED IN ANALYSES
Measures of SES
Income.
Parental respondents were asked to report, in thousands of dollars,
total 1994 household income before taxes. The ratio of the reported household
income to the 1994 federal poverty thresholds (FPT) adjusted for household
size was determined. An ordinal 5-level variable was then created using these
ratios and 1994 US census data for total household incomes. Categories included
less than 1.5 times the FPT, a conservative estimate of poverty; 1.5 to less
than 2.5 times the FPT; 2.5 to less than 4 times the FPT; greater than 4 times
the FPT but not in the top 5% of US household incomes; and in the top 5% of
US household incomes.11
Education.
A parent reported self and spouse educational attainment. The higher
of these was used to categorize parent education as less than a high school
degree; a high school degree or equivalent or vocational training instead
of high school; vocational training after high school or some college; college
graduate; and professional training beyond college.
Depressive Symptoms
Depressive symptoms were measured with 18 of the 20 items from the Center
for Epidemiologic Studies Depression Scale (CES-D).22
Two of these items had been modified slightly by Add Health. Full CES-D scores
were imputed from the 18 available items by using the mean of the scored items
to adjust for missing items. Subjects had to complete at least 80% of the
available items to be scored. The CES-D was developed to measure symptoms
of depression within the community. It is a valid and reliable measure with
good internal consistency and test-retest reliability that has been commonly
used in the literature as a measure of adolescent mental health.23-24
The scores of the CES-D can range from 0 to 60. Among adolescents, scores
of 22 or higher among boys and 24 or higher among girls are felt to be predictive
of major depressive disorder.24
Substance Use
Measures of alcohol, cigarette, marijuana, and cocaine use were included
in this study. Five-point, Likert-type scales were created from the items
assessing alcohol, cigarette, and marijuana use from the in-home survey. Cocaine
use was assessed on a 4-point scale since use of this substance was limited.
Categories assessing frequency of alcohol use in the past 12 months ranged
from never to daily. Cigarette, marijuana, and cocaine use scales focused
on current use (use in the past 30 days). Measures to assess current alcohol
use were not available in Add Health. For cigarette use, items were combined
to create the following categories: never used, experimented (has never smoked
a whole cigarette or has smoked a whole cigarette but not in the past 30 days),
smoked in the past 30 days but less than 1 pack per week, smoked at least
1 pack per week but less than 1 pack per day in the past 30 days, and smoked
at least 1 pack per day in the past 30 days. For marijuana use, categories
ranged from never used to used 6 or more times in the past 30 days. For cocaine
use, categories ranged from never to used more than once in the past 30 days.
ANALYTIC STRATEGY
Linear regression was used to assess the relationships between indicators
of SES and individual substance use measures, and to determine how adjusting
for depressive symptoms would affect that relationship. Socioeconomic status
indicators were not run together in a single model because these constructs
are currently thought to measure different domains of social status. The literature
suggests that these different components of SES act through different pathways
to create health differentials. Thus, current recommendations state that these
indicators should be modeled separately.25-27
First, linear regression models were run to determine the association between
the 5-level SES indicator of interest and the individual substance use measure.
Socioeconomic status, the exposure of interest in this study, was modeled
as both a first-order (linear) and second-order (quadratic) term to ensure
that a linear gradient rather than a curvilinear effect best described the
noted relationship. We next tested for potential interactions between SES
and depressive symptoms, age, sex, and race/ethnicity. Significant interactions
were found for race/ethnicity only. Analyses stratified by race/ethnicity
were performed in these instances.
Once a linear, graded relationship was established between an SES indicator
and use of a particular substance, the effect of depressive symptoms was assessed
by adding CES-D scores to the model. If depression were part of the causal
pathway linking SES with substance use, the addition of depressive symptoms
to the regression model would cause a weakening of the effect of the SES indicator.
Such a change reflects mediation. Mediation is commonly measured by the amount
(percentage) that the mean estimated change in the predictor variable moved
toward zero when the potential mediator (depressive symptoms) was added to
the regression model.19 A change of 10% or
more is considered indicative of partial mediation.28
For all regression models, we report the mean estimated change in average
substance use category that was found for each 1-unit increase in either category
of household income or parental education. Ninety-five percent confidence
intervals (CIs) are also reported for these estimates. A positive estimate
for the mean change represents direct relationships in which lower SES is
associated with decreased substance use and higher SES with increased use.
Negative estimates of mean change represent inverse relationships in which
lower SES is associated with increased substance use and higher SES with decreased
use. Analyses were performed using SUDAAN (Research Triangle Institute, Research
Triangle Park, NC) to account for design effects.29
Sample weights were used to account for the complex sampling frame of Add
Health. Analyses controlled for other determinants of depression and substance
use that could confound the relationships. These included race/ethnicity (white
non-Hispanic vs nonwhite teenagers, defined as teenagers belonging to all
other racial/ethnic categories), age (in years), family structure (2 parents
in the home vs other), alcoholism in the mother or father, and US acculturation
(immigrant, first generation, second generation or higher).12
Means are reported with SDs and all percentages are weighted.
RESULTS
A description of the study population and substance use behaviors among
these adolescents is presented in Table
1. Mean ± SD age was 15.9 ± 1.8 years. Mean ±
SD CES-D score was 11.8 ± 8.0. Substance use was quite common. Only
31% had never used 1 of the 4 substances. Use of cigarettes was reported most
frequently (57%), followed by alcohol use (55%). Few adolescents reported
cocaine use. Sixty-seven percent of subjects had tried more than one of these
substances and their use was highly correlated (Table 2). The correlations among cigarette, alcohol, and marijuana
use were nearly identical. Cocaine use was less well correlated with use of
these other substances and most highly correlated with marijuana use.
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Table 1. Description of the Study Sample
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Table 2. Spearman Rank Correlations Among Use of Cigarettes, Alcohol,
Marijuana, and Cocaine in Adolescents in Wave I Add Health*
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REGRESSION ANALYSES: ASSOCIATIONS BETWEEN SES AND SUBSTANCE USE
Table 3 presents the results
of linear regression analyses for the total population and race/ethnicity
stratified analyses where appropriate. Inverse gradients were present for
both SES indicators and cigarette use. Adolescent use of cigarettes decreased
as parental education increased: the mean change was –0.052 (95% CI,
–0.081 to –0.023) for each 1-unit increase in category of parental
education. Similarly, for each unit increase in household income, average
cigarette use decreased (mean change, -0.039; 95% CI, –0.070 to
–0.008). However, this inverse relationship was moderated by race/ethnicity.
The inverse gradient remained among white non-Hispanic teenagers but not among
nonwhite teenagers. Among nonwhite teenagers, a significant, albeit less powerful,
direct relationship was present. For these teenagers, an increase in 1 unit
of either SES indicator was associated with increased cigarette use (education
mean change, 0.040 [95% CI, 0.014-0.065]; income mean change, 0.062 [95% CI,
0.017-0.107]). Alcohol use was associated only with income in a direct, positive
fashion. Marijuana use was significantly associated with education only. This
relationship was not linear but inverse-U-shaped, with those in the middle
of the income gradient showing the highest rates of use. In stratified analyses,
this inverse-U-shaped relationship was present only for nonwhite teenagers.
Among white non-Hispanic teenagers, no significant relationship between education
and marijuana use was found. Cocaine use was not significantly associated
with either SES indicator in the total population. However, a significant
interaction between education and race/ethnicity was present. In stratified
analyses, a weak, inverse relationship was demonstrated for white non-Hispanic
teenagers only, which suggested that for each category increase in parental
education, cocaine use decreased on average by –0.013 categories of
use (95% CI, –0.026 to –0.0004).
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Table 3. Linear Regression Analyses Assessing the Associations Between
SES and Substance Use Among Adolescents in the National Longitudinal Study
of Adolescent Health, Stratified by Race/Ethnicity Where Appropriate*
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MEDIATIONAL ROLE OF DEPRESSIVE SYMPTOMS IN DEMONSTRATED SES–SUBSTANCE
USE GRADIENTS
Table 4 presents results
of regression models that included depressive symptoms. These models were
run only for the significant linear associations presented in Table 3. In the total population, these analyses suggested partial
mediation for the association between education and cigarette use. The effect
of parental education on cigarette use decreased 23.1% with the addition of
depressive symptoms to the model. Partial mediation was also present for the
income–cigarette use relationship, which showed a 23.7% decrease in
the mean change effect. In addition, among white non-Hispanic teenagers, the
association between parental education and cocaine use seemed to be partially
mediated by depressive symptoms. A 30.7% weakening of the effect of education
on cocaine use among white non-Hispanic teenagers was demonstrated.
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Table 4. Linear Regression Analyses Including Depressive Symptoms in
the Models That Assess the Associations Between SES and Substance Use Among
Adolescents in the National Longitudinal Study of Adolescent Health*
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COMMENT
The purpose of this study was 2-fold. We sought to determine if SES
gradients in cigarette, alcohol, marijuana, and cocaine use were present among
US adolescents and to assess whether depressive symptoms played a mediating
role in these relationships. Socioeconomic status was associated with use
of all 4 substances but the relationship was not consistent across SES indicators
or across race/ethnicity groups, and the direction and shape of the relationship
changed among substances and among SES indicators. Prior work has suggested
that marijuana is related to SES in a nonlinear fashion.30
Our data confirm this finding. The relationship between parental education
and level of marijuana use, present only among nonwhite teenagers, was an
inverse U shape. All other relationships between SES level and substance use
categories were linear, as hypothesized.
The linear relationship between level of SES and use of a particular
substance was most consistent for cigarette smoking, which was related to
both income and education. However, the direction of the effect of SES on
cigarette use differed between white non-Hispanic teenagers and nonwhite teenagers.
Among white non-Hispanic teenagers, an inverse SES gradient was present, which
was consistent with the prior reports.14-15
In contrast, among nonwhite teenagers, a direct relationship was demonstrated,
indicating that higher SES was associated with increased cigarette use. This
finding contradicts that of the Third National Health and Nutrition Examination
Survey, which indicated that increased education was associated with decreased
cigarette use among both Mexican American and black youth. We also found a
direct relationship between income and alcohol use in contrast to data from
the 1992 National Health Interview Survey.14
Some of these contradictions may be due to different means of measuring SES
and use of these substances. In this study, we examined a wider spectrum of
behaviors related to the use of each substance and a broader gradient in education.
We also used a measure of household income that was adjusted for household
size, which may better reflect disposable income.
Parental education was more consistently associated with use of these
substances than household income. These inconsistencies support the idea that
SES is a multidimensional construct.26, 31
Many studies include only a single measure of SES and generalize these effects.
These data highlight the importance of separating the effects of income, education,
and other components of SES, such as occupation and perceived social status.32
There are limitations that must be acknowledged. Although mostly drawn
from existing surveys, such as the Youth Risk Behavior Survey, many measures
in Add Health have not been validated. Because it is a school-based study,
teenagers who are no longer in school—a group that is at high risk for
increased substance use and increased depressive symptoms and likely to be
of lower SES—would not be eligible for the in-home survey. Although
the literature suggests that depression is causally related to the outcomes
assessed here, these data are cross sectional and cannot determine whether
the substance use led to the increased depressive symptoms in these adolescents
or whether prior episodes of depression were important in the genesis both
current depressive symptoms and substance use. In addition, no physiologic
markers of substance use were collected.
These data suggest that depressive symptoms act as a partial mediator
for some relationships between SES and level of substance use among teenagers.
However, this mediating role was neither consistent nor powerful. Thus, our
findings indicate that interventions targeted toward improving depressive
symptoms among adolescents may not have a strong impact on decreasing the
effects of SES on adolescent substance use.
One of the more perplexing findings in this study was the change in
the direction of the association between SES and cigarette use between white
non-Hispanic and nonwhite teenagers. Among white non-Hispanic teenagers, higher
SES was associated with decreased cigarette use, while the reverse was true
among nonwhite teenagers. Race/ethnicity is often confounded with SES. However,
the relationships among race/ethnicity, SES, and health are highly complex.
In fact, the association between SES and the use of 3 of the 4 substances
studied here was different between white non-Hispanic and nonwhite teenagers.
Our analyses suggest that the effect of parental education and household income
on adolescent substance use varies by racial/ethnic group. Studies assessing
racial/ethnic differences in health as well as those addressing socioeconomic
inequalities in health will need to assess for potential interactions between
these 2 critical social forces to improve understanding of how sociostructural
factors create health disparities.
| What This Study Adds
The SES gradient in health is well established but not well understood.
Little research has focused on the effects of SES on health during adolescence,
a critical developmental period when important biological, psychological,
and social transitions occur.
Indicators of SES were associated with cigarette, alcohol, marijuana,
and cocaine use but the associations differed across SES indicators. Parental
education was more consistently associated with substance use than household
income. Race/ethnicity was an important moderating factor for many of these
associations. This study provides evidence that depressive symptoms are part
of the causal pathway linking SES to adolescent substance use behaviors but
suggests that the mediating effects of depressive symptoms are not strong.
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AUTHOR INFORMATION
Accepted for publication January 31, 2001.
This research was funded in part by the Jacob's Institute of Women's
Health and Ortho-MacNeil Pharmaceuticals Scholar in Women's Health Award,
and a grant from the Jacob's Institute of Women's Health (Washington, DC)
and Ortho-MacNeil Pharmaceutical (Raritan, NJ). This research is based on
data from the Add Health project, a program project designed by J. Richard
Udry and Peter Bearman, and funded by grant P01-HD31921 from the National
Institute of Child Health and Human Development, to the Carolina Population
Center, University of North Carolina at Chapel Hill, with cooperative funding
participation by the National Cancer Institute; the National Institute of
Alcohol Abuse and Alcoholism; the National Institute on Deafness and Other
Communication Disorders; the National Institute of Drug Abuse; the National
Institute of General Medical Sciences; the National Institute of Mental Health;
the National Institute of Nursing Research; the Office of AIDS Research, National
Institutes of Health (NIH); the Office of Behavior and Social Science Research,
NIH; the Office of the Director, NIH; the Office of Research on Women's Health,
NIH; the Office of Population Affairs, the US Department of Health and Human
Services (HHS); the National Center for Health Statistics, Centers for Disease
Control and Prevention, HHS; the Office of Minority Health, Centers for Disease
Control and Prevention, HHS; the Office of Minority Health, Office of the
Assistant Secretary of Health, HHS; the Office of Assistant Secretary of Planning
and Evaluation, HHS; and the National Science Foundation. These data are not
available from the author. Persons interested in obtaining data files from
the National Longitudinal Study of Adolescent Health should contact Francesca
Florey, Carolina Population Center, 123 W Franklin St, Chapel Hill, NC 27516-3997
(e-mail: fflorey{at}unc.edu).
This study was presented in part at the Society for Adolescent Medicine
meeting, San Diego, Calif, March 21, 2001.
We thank Gail B. Slap, MD, MSc, and the anonymous reviewers for comments
on earlier drafts of the manuscript.
Corresponding author: Elizabeth Goodman, MD, Division of Adolescent
Medicine, Children's Hospital Medical Center, 3333 Burnet Ave, Cincinnati,
OH 45229 (e-mail: goode0{at}chmcc.org).
From the Division of Adolescent Medicine (Dr Goodman) and the Center
for Epidemiology and Biostatistics (Dr Huang), Cincinnati Children's Hospital
Medical Center, and the Department of Pediatrics, University of Cincinnati
College of Medicine (Dr Goodman), Cincinnati, Ohio.
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