Which psychosocial factors moderate or directly affect substance

use among inner-city adolescents?

Jennifer A. Epstein ⁎, Heejung Bang, Gilbert J. Botvin

Institute for Prevention Research, Department of Public Health, Cornell University, Weill Medical College,

411 East 69th Street, New York, NY 10021, United States

Abstract

Past etiology of adolescent substance use research concentrated on the main effects of various risk factors. The

purpose of this study was to also longitudinally predict interactions on poly-drug use intensity and future smoking

among inner-city adolescents. A panel sample of baseline, 1-year and 2-year follow-ups (N=1459) from the control

group of a longitudinal smoking prevention trial participated. We focused on the main effects, as well as, interaction

effects between psychosocial protective factors and various risk factors, including perceived norms of friends, peers and

adults to use drugs. Significant effects were identified for intensity of poly-drug use and future smoking. The analysis of

the poly-drug use outcome indicated that refusal assertiveness undermined perceived friends' drug use and siblings'

smoking, and that low risk-taking undermined perceived friends' drug use. There was a main effect for low

psychological wellness. The significant interactions between perceived friends' drug use with refusal assertiveness and

decision-making skillswere observed for future smoking.Moreover, perceived peer smoking norms, siblings' smoking,

and high risk-taking also showed significant main effects for increasing future smoking.

© 2006 Elsevier Ltd. All rights reserved.

Keywords: Adolescence; Substance use; Inner-city populations; Psychosocial factors; Peer and friends' norms; Perceived family

smoking

Research relevant to adolescent drug use has been shifting from a focus on risk factors to one that also

incorporates the skills individuals need to meet environmental challenges (Norman, 1994). Based on a

pure risk factor approach, the aim of drug abuse prevention is to eliminate, reduce or mitigate risk factors.

A resiliency approach, on the other hand, emphasizes prevention by enhancing behavioural factors that

Addictive Behaviors 32 (2007) 700–713

⁎ Corresponding author. Tel.: +1 212 746 1270; fax: +1 212 746 8390.

E-mail address: jepstein@mail.med.cornell.edu (J.A. Epstein).

0306-4603/$ - see front matter © 2006 Elsevier Ltd. All rights reserved.

doi:10.1016/j.addbeh.2006.06.011

protect against vulnerability. Accordingly, such an approach posits that both risk factors and vulnerability

significantly contribute to a model of adolescent drug use and furthermore that resilience moderates the

effects of individual vulnerabilities and environmental hazards. Akers' criminology model of Social

Structure and Social Learning model of crime and deviance posits that social learning is the chief process

by which the social structural causes of crime and deviance have an impact on individual behavior (1998).

A criminology study found that variations in the behavioral and cognitive variables specified in the social

learning process accounted for substantial portions of the variations in adolescent substance use and

mediate substantial or in some cases nearly all the effects of gender, SES, age, family structure and

community size on these forms of deviance (Lee, Akers, & Borg, 2004).

Perceived drug norms and family use play a tremendous role in adolescent drug use. Psychological

social learning theory states that individuals learn new behaviors (including drug use) from observation,

modeling and imitation of important others, such as peers and family (Bandura, 1977). Another

psychological theory, the theory of planned behavior describes behavior as being determined by

intentions, attitudes, and normative beliefs (Ajzen & Fishbein, 2004). Ethnographic research indicated

that drinking is useful for manipulating social relationships in many places including the United States

and is a social act that is part of virtually every social gathering (Myers & Stolberg, 2003). Moreover,

according to this same ethnographic review article the ritual importance of drinking is shown by the fact

that declining a drink is seen as disrespectful and unfriendly. Ethnography has documented problem

drinking in communities suffering from deprivation, economic and social stagnation and scarce resources

(Myers & Stolberg, 2003). This suggests that inner-city adolescents are an important group to study.

An ethnographic study of the need to smoke cigarettes found that a major reason that adolescents

smoke is not because they crave or desire nicotine, but rather because of their perceived need to use

cigarettes to manage social situations and maintain their social connections (Johnson et al., 2003).

Specifically, these youth described requiring cigarettes to function socially (to party, to connect and to fit

in). Aside from the social aspect of smoking, adolescents identified an empowering aspect helping them to

gain a sense of identity and independence: they could exert control over others either by sharing or by

selectively withholding cigarettes. According to this study, some forms of dependence may exist among

young smokers who might be classified as light or irregular smokers. A study conducted by medical

anthropologists regarding nicotine dependence among adolescents found that dependency does not mean

that one smokes all day; cigarettes were used to make them feel better from stress, peak smoking occurred

on Friday and Saturday nights at social events or hanging out with friends (Nichter, Nichter, Thompson,

Shiffman, & Moscicki, 2002).

Adolescents become less reliant on parental influences in making drug use decisions and turn instead to

friends and peers (Miller, Alberts, Hecht, & Krizek, 2000; Newcomb, 1997). The psychological peer

cluster theory also views the peer group as critical in adolescent drug use (Oetting & Beauvais, 1986,

1987). However, family members still play a role in modeling. Siblings' role in adolescent substance use

has identified them as influential role models who are of the same generation and may serve as a bridge

between family and peers literature review by a social worker (Vakalahi, 2001). Furthermore, this same

review reported that parents do shape adolescents' personality and environment by the length and

intensity of their relationship and significant relationships exist with parental substance use/attitudes and

adolescent substance use. One sociological study found that the relationship between older siblings' self-

reported tobacco and alcohol use remained significant with younger siblings' tobacco and alcohol use

controlling for numerous shared family experiences (Fagan & Najman, 2005). In this same study,

maternal tobacco and alcohol use was also related to their younger children's tobacco and alcohol use.

701J.A. Epstein et al. / Addictive Behaviors 32 (2007) 700–713

Family exposure to drugs (cigarettes, alcohol and other drugs) also increased the likelihood that urban

black youth would use drugs according to a study conducted by a pediatrics group (Feigelman, Li, &

Stanton, 1995).

Reviews of research on the development of drug use all report that drug use of peers and friends is a

major risk factor for adolescent drug use (Belcher & Shinitzky, 1998; Copans & Kinney, 1996; Hawkins,

Donovan, & Miller, 1992). In a longitudinal analysis of friendships and substance use conducted by child

clinical psychologists, the strongest proximal correlate of adolescent substance use is the tendency to

cluster into peer groups that use substances and that the power of drugs connects individuals (Dishion &

Owen, 2002). Furthermore, reduced levels of substance use were undermined by exposure to deviant

peers (Dishion & Skaggs, 2000). These studies confirm the ethnographic and other research about

adolescent substance use and the peer group. Perceived peer norms tend to be the strongest predictor of

gateway drug use in middle school and high school (Jenkins, 1996). Much of this research was conducted

with primarily white middle class samples.

Research conducted with predominantly black and Hispanic youth residing in the inner-city also found

that friends' and peers' drug use and attitudes were related to smoking (Epstein, Botvin, & Diaz, 1999),

alcohol use (Epstein, Botvin, Baker, & Diaz, 1999), marijuana use (Epstein, Botvin, Diaz, Toth, &

Schnike, 1995) and for all three substances separately (Walter, Vaughan, & Cohall, 1993). Interestingly,

the child's perception of friends' use was found to be more important than actual friends' behavior among

a sample of young black urban fourth and fifth graders in the public health literature (Iannotti & Bush,

1992). Factors meant to protect against vulnerability to drug use include competence skills, such as

assertiveness (Belcher & Shinitzky, 1998; Miller et al., 2000). Among a sample of inner-city adolescents,

more frequent use of refusal assertiveness skills prospectively predicted less smoking (Epstein, Griffin, &

Botvin, 2000a) and less drinking (Epstein, Griffin, & Botvin, 2000b). Susceptibility to peer pressure to

misbehave (whose items resembled a risk-taking tendency, e.g., “If your best friend is skipping school,

would you skip school too?”) contributed to drunkenness (Schulenberg et al., 1999).

The vast majority of etiology research concentrates on testing main effects of models of drug use. A far

smaller number of studies examined interactions between predictors of substance use (e.g., Brook,

Whiteman, Balka, Win, & Gursen, 1997; Brook, Whiteman, Gordon, & Cohen, 1986, 1989; Cooper,

Peirce, & Tidwell, 1995; Curran, White, & Hansell, 1997). In one longitudinal study, a number of

personality variables (liberalism, self-acceptance, and extraversion) moderated the effect of social

influences to use drugs on individual drug use measures of marijuana use and cocaine use (Stacy,

Newcomb, & Bentler, 1992). In another study, decision-making and self-reinforcement diminished the

impact of peer drinking on alcohol use among rural youth (Botvin, Malgady, Griffin, Scheier, & Epstein,

1998). Another study found that high risk-taking tendency and low refusal assertiveness each increased

the effect of friends' drinking among an inner-city adolescent sample (Epstein & Botvin, 2002). Since the

influence of protective factors (refusal assertiveness, decision-making skills, high efficacy, psychological

wellness) comes to light in interaction models, this points to the importance of developing more complex

models. Risk-taking tendency also appears to be another independent risk factor, in addition to norms for

substance use and perceptions of use among friends and family, for substance use that should be examined

in interactions with protective factors. Both of these previously cited studies were cross-sectional so that

causality cannot be drawn and focused on only one drug (alcohol). Moreover, no one has examined poly-

drug in adolescent urban youth, which is regarded as a more serious problem.

Over the past 10 years, the rates of single-risk behaviors have declined, but the rates of multiple-risk

behaviors have remained stable among adolescents (Lindberg et al., 2000). Adolescence has been

702 J.A. Epstein et al. / Addictive Behaviors 32 (2007) 700–713

identified as a time when developmental changes increase vulnerability to risky behaviors including

potentially health-damaging behaviours like drug use and the presence of multiple-risk behaviors can

substantially worsen health outcomes (Irwin, Burg, & Cart, 2002).

A measure of poly-drug use more accurately captures overall health risk. Unfortunately studies of

adolescent drug use often examine the drug use singly. Some work that developed poly-drug measures

(including one that combined stage-intensity of involvement measure) found that adolescent protective

factors weakened the effect of childhood risk factors resulting in lower drug involvement (Brook et al.,

1989).

While prior research that focused on the etiology of specific drugs (cigarettes, alcohol or

marijuana) in adolescence is informative, such work overlooks the more general process of drug

initiation and progression among inner-city adolescents that could take combination of multiple

substances and future use into account. Also research does not often focus on intentions to use in the

future, which is another important outcome according to theory of planned behavior (Ajzen &

Fishbein, 2004). Few studies have focused on social competence factors as predictors of substance

use among inner-city minority youth and none that we know of have investigated the moderating role

of these protective factors for friends' use, peer norms and adults norms for substance use, perceived

family smoking, including older sibling smoking and parental smoking (other measures of drug use

were unavailable because the original parent project was a smoking prevention trial) and risk taking

tendency on poly-drug use. This study will test the moderating role of protective factors (refusal

assertiveness, sound decision-making skills, high efficacy, psychological wellness, and low risk-taking

tendency) in the relationship between risk factors (such as perceived norms for drug use – friends'

use, peer norms, adult norms, siblings' smoking, mother's smoking, father's smoking) and poly-drug

use, while controlling for sociodemographic and background characteristics (ethnicity, gender, age,

grades, and family composition). This protective factor (refusal assertiveness) is of importance as a

primary role player as well as a moderator because it serves as a major component of many of current

prevention programs including refusal skill programs and competence enhancement programs (see

review by Botvin, 1998). The study also predicted future smoking intentions with many of the same

predictors, substituting smoking versions of variables relevant to substance use, such as friends' use,

peer norms and adult norms.

1. Method

1.1. Overview

Data for these analyses are from the control group of a longitudinal smoking prevention trial described

in detail in previous work (Botvin et al., 1992). Participants are from 22 predominantly Hispanic middle

and junior high schools in New York City. The majority of the schools served inner-city youth from

families with average incomes well below the Federal poverty level. Bilingual and special education

classes were not included in the original study and all surveys were conducted in English. At baseline,

2400 students completed questionnaires. The panel sample across baseline, 1-year, and 2-year follow-up

consisted of 1459 students (61% of baseline participants). The retention rate over the course of the 2-year

follow-up compared favorably with school-based studies whose 2-year follow-up rates ranged around

60% in our work with inner-city samples (e.g., Botvin, Schinke, Epstein, Diaz, & Botvin, 1995). For

703J.A. Epstein et al. / Addictive Behaviors 32 (2007) 700–713

example, research with Project ALERT conducted in Oregon and California has shown retention rates

ranging from 60% to 64% (Ellickson, Bell, & Harrison, 1994; Hays & Ellickson, 1990). A smoking

prevention evaluation with a shorter follow-up conducted with an urban Los Angeles sample of African-

American and Latino youth had 60% retention rate (Sussman et al., 1995). Our retention rate is similar to

other studies. We restricted our analyses to the 1459 participants, who completed baseline, 1-year and 2-

year follow-up merged into a final data set.

1.2. Participants

The mean age at baseline for the panel sample was 12.4 (S.D.=0.75) and the sample was 46% boys. In

terms of ethnicity, this sample was 54% Hispanic, 20% black, 7% Asian, 16% white, and 3% other.

Approximately 70% of participants lived in two-parent households. Demographic and background

characteristics are summarized in Table 1.

1.3. Procedure

Students completed surveys containing measures of smoking, drinking, marijuana use and

psychological factors. Surveys were completed during a 40-min class period at baseline (in the fall)

and about 1- and 2-year intervals later (in the spring). To encourage full disclosure, data collectors were

members of the same ethnic groups as the participating students, school administrators and teachers were

not involved in data collection activities. Surveys were identified through numerical codes, not by

students' names.

1.4. Measures

1.4.1. Outcome measures

An 11-point smoking index assessed smoking frequency. Specifically, students responded to the

question, “How often do you currently smoke?” Response options ranged from “I have never smoked” (1)

to “A pack or more each day” (11). Students indicated how often (if ever) they drank alcoholic beverages

on a 9-point scale ranging from 1 (“never”) to 9 (“more than once a day”). They completed a similar 9-

point scale regarding their marijuana use. Finally, a 5-point future smoking item assessed intentions to be

a cigarette smoker in 2 years, “Do you think you'll be a cigarette smoker two years from now?” The scale

ranged from “I definitely will not” (1) to “I definitely will” (5).

A measure of multiple drug use created from the three drug frequency scales was used as one

outcome. Specifically, a composite poly-drug use index of the three behavioral indices (smoking,

drinking and marijuana) was created to take frequency of use into account. The smoking index was

recoded from an 11-point scale to a 9-point scale to correspond to the scales for alcohol and

marijuana use. Each drug index is weighted by the drug's position on stage of drug use (helping to

equalize the variance of the individual measures) and summed for a composite index following Brook

et al. (1989). Such measures take increased level of substance involvement into account based on a

stage like progression from one substance to more than one substance (Kandel, 1975). The same

sequence of drug initiation has been found among black, Hispanic and Asian adolescents (Brook,

Hamburg, Balka, & Wynn, 1992; Ellickson, Hays, & Bell, 1992). In the current study, the dependent

measures were: poly-drug use intensity and future smoking intentions. Prior research conducted with

704 J.A. Epstein et al. / Addictive Behaviors 32 (2007) 700–713

predominantly minority samples in this age group (Botvin, Epstein, Baker, Diaz, & Ifill-Williams,

1997; Catalano et al., 1992; Wells et al., 1992) has used such a poly-drug use measure and future

smoking.

1.4.2. Predictor measures

1.4.2.1. Background characteristics. Sociodemographic factors and other background characteristics

controlled for in the models are: ethnicity, gender, age, academic grades and family structure. Ethnicity

was categorized as African-American, Hispanic, White and Asian (with the largest group Hispanic, as the

reference group). Family composition was dichotomized by whether or not the participants were from

two-parent families, while age and grade were used as continuous scales.

Table 1

Baseline characteristics of participating students (N=1459)

Characteristics

Sociodemographic and background factors

Ethnicity (%)

Latino/Hispanic 54

Black/African-American 20

White/Caucasian 16

Oriental/Asian 7

Male (%) 46

Age at baseline (mean (S.D.)) 12.4 (0.75)

Grades (%)

Mostly or some A 39

Mostly B or some B 40

Mostly C or lower 21

Living with both parents (%) 70

Social influences and environment

Friends' smoking or drinking (%) 40

Peers' smoking or drinking (%) 39

Siblings' smoking a (%) 16

Adults' smoking b (%) 85

Psychosocial and behavioral factors c

High refusal assertiveness (%) 46

High risk-taking tendency (%) 43

Low psychological wellness (%) 42

High self-efficacy (%) 52

Low decision making skill (%) 49

Outcomes

Poly-drug use intensity (mean/median (S.D.)) 5.0/4 (1.83) with range=4–23

Future smoking (mean/median (S.D.)) 1.4/1 (0.75) with range=1–5

S.D.=standard deviation.a If there is no sibling, then the answer is coded as No.b Think at least half of the adults smoke cigarettes.c Dichotomized by median.

705J.A. Epstein et al. / Addictive Behaviors 32 (2007) 700–713

1.4.2.2. Perceptions of friends' use, family smoking and normative beliefs for peers and adults. To

measure smoking and drinking among friends, respondents were asked how many friends smoked

cigarettes/drank alcohol with responses ranging from 1=“none” to 5=“all/nearly all.” Friends are defined

as someone to hang out with or talk to in school, after-school organizations, the neighborhood or religious

organizations, according to focus groups. This definition would also be offered if a child asked about the

meaning of friend while completing the survey. A perceived use of friends measure was then created and

then categorized as high, if the respondent reported that any friends smoked or drank and low if the

student reported that none of his/her friends smoked or drank. Similarly, adult and peer norms for smoking

and drinking were measured by items asking “In your opinion, how many adults/people your age smoke

cigarettes/drink alcoholic beverages?” Thus what we refer to as peers are simply people of the

respondents' age as indicated on the survey, though not necessarily friends. Adults are clearly understood

to be individuals over 18 years old. The scales for these variables ranged from “none” (1) to “almost all”

(5). Categorization of these variables was the same as for friends' use.

1.4.2.3. Risk-taking tendency. Seven items (α=.66) taken from the Eysenck Personality Inventory

(Eysenck & Eysenck, 1975) assessed impulsive and daring behavior. Items included: “I would do almost

anything on a dare” and “I enjoy taking risks.” Students indicated responses on a scale ranging from

“really not true for me” (1) to “really true for me” (5).

1.4.2.4. Refusal assertiveness. Refusal assertiveness was measured with three items (α=.77) derived

from the Gambrill-Richey Assertion Inventory (1975). The three items from the refusal assertiveness

scale are: “Say no when someone asks you to do something you do not want to do,” “ Say no when

someone tries to ask you to smoke,” “Say no when someone tries to get you to drink.” Each item had

response options ranging from “never” (1) to “almost always” (5).

1.4.2.5. Self-efficacy. Five items from the personal efficacy subscale of the Spheres of Control Scale

(Paulhus, 1983) assessed Self-Efficacy (α=.75). This scale measured the extent to which respondents

believed they could achieve personal goals through their own efforts (e.g., “When I get what I want it's

because I worked hard for it,” “I can learn almost anything if I set my mind to it”). Responses were scored

on a 5-point Likert scales ranging from “strongly disagree” (1) to “strongly agree” (5).

1.4.2.6. Psychological wellness. Four items (α=.77) from the Mental Health Inventory (Veit and Ware,

1983) assessed psychological wellness (e.g., “I generally enjoyed the things that I did,” “I felt that I was a

happy person”). Each item had response options on a 5-point Likert scale ranging from “None of the time”

(1) to “Most of the time” (5) over the last month.

1.4.2.7. Decision-making. Five items derived from a subscale of the Coping Inventory (Wills, 1986)

related to problem-solving and direct action measured decision-making skills (α=.80). These items

assessed sound decision-making skills (e.g., “When I have a problem I think about which of the alternatives

is best”). Responses were rated on a 5-point scale which ranged from “never” (1) to “almost always” (5).

1.4.3. Statistical analysis

Our primary goal is to examine the interactions effects between protective psychosocial and risk

factors, as well as their main effects, on poly-drug use intensity and future smoking intentions. To make all

706 J.A. Epstein et al. / Addictive Behaviors 32 (2007) 700–713

interaction effects have positive estimates and to render more straightforward interpretations, we defined

the level of each protective psychosocial factor that incurs lower risk as referent (coded as 0) in our

regression models (e.g., low refusal=1 and high risk taking=1, etc.). Both outcomes were analyzed as

continuous variables by the generalized estimating equations approach (Diggle, Heagerty, Liang, &

Zeger, 2002). Both methods were implemented by the PROC GENMOD procedure in SAS 9.1 (SAS,

2002) with unstructured covariance and normality assumptions. For sensitive checking, we replicated the

same set of analyses using different distributional assumptions such as the gamma distribution.

In the multivariate regression models for longitudinal data, time (in years since baseline) and the main

effects of individual variables listed in Table 1 along with the two-way interactions among behavioral and

psychosocial measures were entered as covariates. For the analysis of the students' perspective about

smoking in their later life, we used smoking-related information for covariates, not drinking. However, for

the poly-drug outcome, we tried to combine smoking and drinking statuses together (siblings' drinking

status was not available). The final parsimonious model was established with all the significant factors

with p-value less than 0.05. If the effect modification was significant, the associated main effects

remained in the model regardless of their statistical significance. For robust inference, empirical standard

errors were employed, and two-sided hypothesis and 5% type I error were assumed.

2. Results

Table 1 displays the prevalence of psychosocial and behavioral risk factors, which ranged from 43% to

52% based on median splits. About 40% of students thought their friend and peers smoke or drink. About

16% reported one or more brothers or sisters smoke cigarettes and only 15% believed that less than half of

adults smoke. Mean and median of drug use intensity measure were 5 and 4, with those for the future

smoking index were 1.4 and 1, where the level 1 in future smoking variable corresponds to “I definitely

will not.”

Multivariate regression analyses are presented in Tables 2 and 3. The parameter estimate can be

interpreted as the difference in the response variable for 1 unit change of the given covariate with all other

conditions fixed. White students engaged in higher intensity of poly-drug use than Hispanic students

(p=0.03) and Asian students tended to report they would not smoke in the future (p=0.01). All other

ethnicity-related differences compared to Hispanic were not significant. Drug use tended to increase as

time progressed (p-value<0.0001) and students' concepts about smoking in the future is pretty much

time-invariant, as would be anticipated. Lower grades were a strong linear predictor for both outcomes

(p<0.003). Girls showed more willingness to smoke in the future (p=0.002).

We found that the interaction effect of low refusal assertiveness with friends' smoking/drinking was

highly significant, incurring a 1 unit increase in the drug use outcome under the joint condition

(p<0.0001), as shown in Table 2. Similar multiplicative effects were observed for friends' smoking/

drinking status and risk-taking tendency, and siblings' smoking status and refusal skill with somewhat

reduced interactive effects. Low psychological wellness and peers' smoking/drinking demonstrated

significant direct effects (p<0.002) but those effects were not modified by other factors. When an

interaction term is significant, the two associated main effects should be understood jointly, not singly.

Table 3 presents the analysis of future smoking intentions, which showed two significant interactions

among psychosocial and relationship factors. Specifically, low refusal assertiveness and decision making

skills intensified the direct effect of having smoker friends towards smoking tendency in 2 years. Three

707J.A. Epstein et al. / Addictive Behaviors 32 (2007) 700–713

other factors (i.e., peers' smoking, siblings' smoking and high risk-taking) greatly increased the outcome

of future smoking intentions (p<0.0001), while high self-efficacy was significant (p=0.03). Results from

the analyses using different distributional assumptions for the outcomes also yielded consistent findings

(results not shown), which further supports the robustness of the results we reached. Students' perceptions

of adult smoking behavior did not predict either outcome.

Table 3

Longitudinal multivariate regression model for future smoking

Characteristics Estimate (95% CI) p-value

Years since baseline −0.005 (−0.03, 0.02) 0.72

Ethnicity

Black/African-American −0.06 (−0.15, 0.02) 0.11

White/Caucasian 0.01 (−0.07, 0.10) 0.80

Oriental/Asian −0.10 (−0.18, −0.02) 0.01

Sex (Girls) −0.10 (−0.16, −0.04) 0.002

Grade −0.06 (−0.08, −0.04) <0.0001

Friends' smoking 0.03 (−0.05, 0.10) 0.46

Peers' smoking 0.11 (0.06, 0.16) <0.0001

Siblings' smoking 0.29 (0.19, 0.38) <0.0001

Low refusal assertiveness 0.07 (0.02, 0.12) 0.01

High risk-taking 0.13 (0.08, 0.19) <0.0001

High self-efficacy 0.06 (0.01, 0.11) 0.03

Low decision-making skill 0.03 (−0.03, 0.09) 0.32

Friends' smoking*Low refusal assertiveness 0.31 (0.21, 0.40) <0.0001

Friends' smoking*Low decision-making skill 0.14 (0.04, 0.24) 0.006

For ethnicity, Latino/Hispanic is the reference and for all others, the absence of each condition is the reference.A*B denotes the interaction term for A and B.

Table 2

Longitudinal multivariate regression model for poly-drug use intensity

Characteristics Estimate (95% CI) p-value

Years since baseline 0.37 (0.28, 0.47) <0.0001

Ethnicity

Black/African-American −0.21 (−0.47, 0.06) 0.13

White/Caucasian 0.33 (0.03, 0.63) 0.03

Oriental/Asian −0.21 (−0.59, 0.16) 0.27

Grade −0.15 (−0.25, −0.05) 0.003

Friends' smoking or drinking 0.26 (0.05, 0.47) 0.02

Peers' smoking or drinking 0.36 (0.15, 0.57) 0.001

Siblings' smoking 0.67 (0.41, 0.94) <0.0001

Low refusal assertiveness 0.09 (−0.09, 0.27) 0.32

Low psychological wellness 0.33 (0.12, 0.54) 0.002

High risk-taking tendency 0.10 (−0.07, 0.28) 0.24

Friends' smoking or drinking*Low refusal assertiveness 1.00 (0.70, 1.30) <0.0001

Friends' smoking or drinking*High risk taking tendency 0.53 (0.24, 0.83) 0.0004

Siblings' smoking*Low refusal assertiveness 0.51 (0.05, 0.96) 0.029

For ethnicity, Latino/Hispanic is the reference and for all others, the absence of each condition is the reference.A*B denotes the interaction term for A and B.

708 J.A. Epstein et al. / Addictive Behaviors 32 (2007) 700–713

3. Discussion

This study found that refusal assertiveness significantly mitigated the impact of friends' use on poly-

drug use and future smoking intentions among adolescents residing in inner-city regions. Conversely, the

high perceived friends' use combined with low refusal assertiveness showed the highest incidence of

poly-drug use and future smoking intentions. These findings were borne out in our prospective

longitudinal data analysis focusing on interaction effects between refusal assertiveness and friends' use

for two different outcomes: poly-drug use intensity and future smoking intentions. This indicates an

advance over past work that was cross-sectional, did not examine future intentions or only examined the

main effects of drugs individually rather than poly-drug use. Similarly, siblings' smoking and refusal

assertiveness interacted in predicting poly-drug use intensity. Another interaction showed that two risk

factors (friends' use and high risk-taking tendency) led to greater intensity of poly-drug use.

These findings show how critical it is to consider interactions between risk and protective factors in the

etiology of adolescent substance use. This study adds to the literature examining more complex

relationships in predicting substance use (Brook et al., 1997, 1986, 1989; Cooper et al., 1995; Stacy et al.,

1992). In this case, social risk factors like friends' smoking and friends' drinking can be migrated with

competence skills, such as refusal assertiveness and decision-making skills. This longitudinal work

expands on earlier cross-sectional research demonstrating that competence skills (refusal assertiveness,

decision-making and self-reinforcement) decreased the impact of friends or peers on alcohol use among

rural youth (Botvin et al., 1998) and inner-city youth (Epstein & Botvin, 2002). These studies confirm

various theories that suggest drug use develops from the interplay between social and personal factors

(Botvin, 1998).

Of note, students' belief about adults' smoking behaviors was not a significant predictor in either

analysis, nor was family smoking. Moreover, among friends, peers, siblings and adults, siblings' behavior

seemed to be most influential in the both analyses. Siblings do appear to be the most influential members

of the family for this age group, as suggested by earlier works (Fagan & Najman, 2005; Vakalahi, 2001).

High self-efficacy was positively associated with the future smoking, which seems counterintuitive.

However, it is possible that adolescents who believed that they would smoke in 2 years derived some

sense of self-efficacy from this belief. Perhaps, they believed they would be more popular, cool, happier

and part of their peer crowd if they smoked in the future. Such feelings might lead them to feel greater

self-efficacy. More attention to the role and validity of this measure will be desirable, as well as testing for

these other various possibilities, in subsequent studies on adolescent smoking behavior. Low

psychological wellness predicted poly-drug use intensity. This suggests that feeling unhappy may lead

to intense poly-drug use.

This study has a number of advantages. Performing longitudinal analyses showed advancement over

cross-sectional findings that are known to be an inferior design, especially for establishing causality.

Moreover, some results were consistent across the measures of poly-drug use and future smoking

intentions. Another strength is the focus on inner-city youth, a study population that is underrepresented

in the literature. Finally, examining interactive or joint effects rather than only main effects helped

highlight the complexity of the relationships in predicting poly-drug use in the regression context. The

proper detection of effect modifications with sufficient statistical power requires larger sample sizes and

we believe that the large number of participants that were longitudinally observed strengthens the

findings. The weaknesses of the study tend to concern the generalizability of the results. Since the study

was conducted in the schools, the findings cannot be extended to adolescents who are not in school. Yet, it

709J.A. Epstein et al. / Addictive Behaviors 32 (2007) 700–713

should be pointed out that the study was conducted with students in the early years of middle school when

dropout rates remain low for a mobile inner-city population (particularly relative to high school where the

drop-out rates would have been much greater) and absentees were pursued on at least one return data

collection. Our analyses only included successful completers and so they may be limited due to possible

selection bias. It is not certain that the results would apply to other inner-city regions outside of New York

City or to areas that are considered suburban or rural. Future research should test these models by studying

adolescents in urban areas besides New York City, as well as suburban and rural locations, but we do not

have any reason to suspect different findings in this particular model.

The findings of the current study support the inclusion of competence skills training in interventions

designed to prevent poly-drug use among inner-city adolescents. This is particularly important because

some have argued that social norm approaches that correct the norms for drug use are sufficient for

reducing drug use and that skills training may not be a critical ingredient. Our findings indicate, to the

contrary, that competence skills may be crucial to such programs and that the influence of such skills

needs to be further and more closely examined in at-risk populations. Some earlier research conducted

among inner-city youth has indicated the relevance of skills training; refusal assertiveness mediated the

effects of a competence enhancement prevention program (Botvin et al., 1995).

A competence enhancement approach to preventing drug use includes both a way for migrating

perceptions of friends, peers and siblings' use. This is accomplished through discussion of myths and

realities of substance use, including the actual prevalence rates for drug behavior and the lack of social

acceptability of using these substances. The myths and realities of substance use include: the majority

of teenagers and adults do not smoke cigarettes or marijuana; most adults drink only occasionally and

in moderation; smoking cigarettes, drinking alcohol, and using marijuana have immediate and long-

term effects on the body; provision of the actual figures for how many teenagers and adults use various

substances, which tend to be much lower than the students believed. For eighth graders, 82%

disapproved of occasional marijuana use (Johnston, O'Malley, & Bachman, 2005). Moreover, units

cover the importance of an a positive self-image and alternative to improving one's image rather than

using drugs with friends, as well as specific skills training relevant to refusal skills and broader skills

training (decision-making skills, goal setting skills, self-efficacy) meant to encourage refusal skills

further. Moreover, other studies using a competence enhancement approach have proven effective with

inner-city populations residing in New York City (Botvin et al., 1992, 1997; Botvin, Griffin, Diaz, &

Ifill-Williams, 2001). In summary, the results from these prior studies and the present study suggest

that refusal assertiveness should be included in prevention programs for inner-city adolescents. It

should be noted that future smoking is one of the determinants of smoking behavior (Ajzen &

Fishbein, 2004). And smoking appears related to increased rates of use of other substances. Moreover,

abuse starts with drug use early in life.

The National Household Survey on Drug Abuse (2003) reported that almost 60% of recent marijuana

initiates had used both cigarettes and alcohol prior to using marijuana. Moreover, this survey also reported

that earlier use of marijuana (prior to the age of 14 years which corresponds to the age of the current

sample) is associated with illicit use during adult years. Other research found that becoming cannabis-

dependent was linked with use of using three other drugs and cannabis onset before later adolescence

(18 years) is related to substantially increased risk of becoming cannabis-dependent soon after onset

(Chen, O'Brien, & Anthony, 2005). Cigarette smoking poses not only an immediate threat to the health of

the adolescents but is also a risk factor for marijuana use (Graves, Fernandez, Shelton, Frabutt, &

Williford, 2005).

710 J.A. Epstein et al. / Addictive Behaviors 32 (2007) 700–713

Therefore, if substance use can be stopped or delayed later substance use is less likely. Not

unsurprisingly, adolescent poly-drug use has been found to predict such behavior in adulthood;

specifically poly-drug use at the start of the study predicted poly-drug use at the 12-year follow-up in

adulthood for white, Hispanic and black individuals (Galaif & Newcomb, 1999). These authors conclude

that since early drug use was the only consistent predictor of future drug use for these three ethnic groups,

prevention programs should be aimed at reducing teenage drug use.

Acknowledgement

This study was supported by Grant 1 R03 DA 12432 from the National Institute on Drug Abuse to Dr.

Epstein. The smoking prevention trial research from which the data for this study was collected was

supported by Grant 1 R18 CA 39280 from the National Cancer Institute to Dr. Botvin.

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