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Journal of Studies on Alcohol and Drugs logoLink to Journal of Studies on Alcohol and Drugs
. 2010 Sep;71(5):640–651. doi: 10.15288/jsad.2010.71.640

Racial/Ethnic Differences in Adolescent Substance Use: Mediation by Individual, Family, and School Factors*

Regina A Shih 1,†, Jeremy N V Miles 1,†, Joan S Tucker 1,†, Annie J Zhou 1,†, Elizabeth J D'Amico 1,†
PMCID: PMC2930496  PMID: 20731969

Abstract

Objective:

This study examined racial/ethnic differences in alcohol, cigarette, and marijuana use among a diverse sample of approximately 5,500 seventh and eighth graders. We also evaluated the extent to which individual, family, and school factors mediated racial/ ethnic disparities in use.

Method:

Students (49% male) from 16 participating middle schools in southern California reported on lifetime and past-month substance use, individual factors (expectancies and resistance self-efficacy), family factors (familism, parental respect, and adult and older sibling use), and school factors (school-grade use and perceived peer use). We used generalized estimating equations to examine the odds of consumption for each racial/ethnic group adjusting for sex, grade, and family structure. Path analysis models tested mediation of racial/ethnic differences through individual, family, and school factors.

Results:

After adjusting for sex, grade, and family structure, Hispanics reported higher and Asians reported lower lifetime and past-month substance use, compared with non-Hispanic Caucasians. Rates of substance use did not differ between non-Hispanic African Americans and Caucasians. Several individual factors mediated the relationship between Hispanic ethnicity and substance use, including negative expectancies and resistance self-efficacy. Higher use among Hispanics was generally not explained by family or school factors. By contrast, several factors mediated the relationship between Asian race and lower alcohol use, including individual, family (parental respect, adult and older sibling use), and school (perceived peer use, school-grade use) factors.

Conclusions:

Results highlight the importance of targeting specific individual, family, and school factors in tailored intervention efforts to reduce substance use among young minority adolescents.

Alcohol, cigarette, and marijuana use during adolescence presents a significant public health burden. Substance use during adolescence has been associated with an increased likelihood of substance-use disorders in young adulthood (Agrawal et al., 2008; D'Amico et al., 2005a; DeWit et al., 2000; Englund et al., 2008; Kandel and Logan, 1984; Segal, 1991), violent and aggressive behavior (DuRant et al., 1999a), early initiation of sexual intercourse (Paul et al., 2000), and carrying weapons (DuRant et al., 1999b). Some research has suggested that experimentation with alcohol and/or drugs is a normative behavior during adolescence (Guilamo-Ramos et al., 2004; Hurrelmann, 1990; Newcomb and Bentler, 1988). However, other evidence has shown that, compared with youths who experiment with substances during adolescence, abstaining youths have better health outcomes; have fewer alcohol and drug problems; and are at lower risk for criminal behavior, academic difficulties, employment problems, and less satisfaction with peer relationships in young adulthood (Ellickson et al., 2003a; Tucker et al., 2006).

Three large national surveys—the Youth Risk Behavior Survey, Monitoring the Future, and the National Survey on Drug Use and Health—have found considerable racial/ ethnic differences in alcohol, cigarette, and marijuana consumption rates among adolescents and adults (Caetano and Clark, 1998; Grunbaum et al., 2002; Johnston et al., 2009; Substance Abuse and Mental Health Services Administration [SAMHSA], 2002a, 2002b, 2002c; Wallace et al., 2002). For example, Asian and non-Hispanic African American adolescents ages 11-16 report lower rates of lifetime (Best et al., 2001; SAMHSA, 2000) and past-week alcohol use (Rodham et al., 2005) in comparison with their non-Hispanic Caucasian and Hispanic counterparts, and lifetime cigarette use was higher for non-Hispanic Caucasians (40%), compared with non-Hispanic African Americans (28%), Asians (24%), and Hispanics (34%) ages 12-17 (SAMHSA, 2000). In addition, among youths ages 12-17, Hispanics (19%) and Caucasians (17%) reported a higher lifetime prevalence of marijuana use, compared with African Americans (15%) (SAMHSA, 2002c).

A number of different mechanisms have been hypothesized to explain racial/ethnic differences in substance use, such as racial/ethnic variation in genetic polymorphisms and physiology (Luczak et al., 2001; Takeshita et al., 2001; Wall et al., 1997), individual beliefs, family or cultural factors (Li and Rosenblood, 1994; Oei and Jardim, 2007; Unger et al., 2002), and peer use at school (Ellickson et al., 2003b; Gillmore et al., 1990; Rountree and Clayton, 1999). Of these factors, individual, family, and school factors are modifiable, and have been targeted in intervention efforts (Dunn et al., 2000; Eisen et al., 2003; Faggiano et al., 2008; Liu and Flay, 2009; Orlando et al., 2005). The current study adds to the literature by examining individual, peer, and family factors that may be associated with alcohol, cigarette, and marijuana use, and assessing whether these factors may differ by racial/ ethnic groups among a racially/ethnically diverse sample of young adolescents.

Studies on individual factors such as positive and negative expectancies and resistance self-efficacy (RSE) are equivocal. Some studies report that African American adolescents have higher rates of both negative and positive alcohol expectancies, compared with Caucasians (Hipwell et al., 2005; Meier et al., 2007), whereas others report no differences (Rinehart et al., 2006). Oei and Jardim (2007) found that, compared with Asians, Caucasian college students reported more positive expectancies and less RSE for alcohol. However, only alcohol RSE explained a portion of the racial differences in alcohol consumption among Asians versus Caucasians. Research on younger adolescents is needed to understand whether alcohol expectancies and RSE may mediate potential racial/ethnic disparities in drinking behavior. Furthermore, few studies have assessed racial/ ethnic variation in expectancies and RSE for cigarette and marijuana use. Research with middle school youths has typically grouped all substances together (Barkin et al., 2002) or exclusively studied Hispanic adolescents (Elder et al., 2000). Overall, findings indicate that higher positive expectancies about alcohol, cigarettes, or marijuana are associated with greater risk of any lifetime use of these substances (Barkin et al., 2002; Elder et al., 2000; Spruijt-Metz et al., 2005). Yet, no studies to our knowledge have addressed whether cigarette or marijuana expectancies and RSE mediate racial/ ethnic disparities in use.

In addition to individual factors, family factors are often related to alcohol, cigarette, and marijuana use among adolescents (Bauman et al., 1990; Kaplan et al., 2001; Moreno et al., 1994; Unger et al., 2002). Two potentially important indicators of family relationships, familism and parental respect, have been investigated as protective factors for substance use by Unger et al. (2002). High levels of familism, or strength of family relationships, is a traditionally Hispanic value, and high parental respect is a traditionally Asian value (Kaplan et al., 2001; Unger et al., 2002). Hispanic adolescents with higher familism have decreased risk for cigarette initiation (Kaplan et al., 2001) and heavy drinking (Wahl and Eitle, 2010), and Asians may be less likely to disobey their parents' rules related to substance use (Unger et al., 2002).

Other family factors such as substance use by parents or older siblings may also play a role in adolescent substance use. For example, when young adolescents live with adults and siblings who smoke cigarettes, those adolescents are more likely to report lifetime (Bauman et al., 1990; Moreno et al., 1994) and past-month (Komro et al., 2003) cigarette, alcohol, and marijuana use (Ary et al., 1993; Li et al., 2002; van der Vorst et al., 2005; Windle, 2000). To date, no studies have assessed the contribution of multiple family factors to racial/ethnic disparities in adolescent alcohol, cigarette, or marijuana consumption.

School factors, such as availability of alcohol, cigarettes, and marijuana, and the proportion of students who use these substances in school settings may also affect adolescent substance use (Hanson et al., 2003; Kim and McCarthy, 2006; Maes and Lievens, 2003; Rountree and Clayton, 1999; Skager and Fisher, 1989). Moreover, Ellickson et al. (2003b) found that, above and beyond objectively measured prevalence of peer cigarette use, perceived peer cigarette use was independently related to smoking frequency. Similar associations have been reported between substance use and perceived alcohol, cigarette, and marijuana use among high school (D'Amico et al., 2001), middle school (D'Amico and McCarthy, 2006; Henry et al., 2005), and elementary school students (Iannotti et al., 1996). Some racial/ethnic groups may be more vulnerable to greater rates of peer smoking if they are more likely to use substances in social situations. For example, Asian American college students report that cigarette smoking facilitates a sense of belonging and peer acceptance (Hsia and Spruijt-Metz, 2003; Otsuki et al., 2008).

Overall, the majority of prior work on racial/ethnic disparities in alcohol, cigarette, and marijuana use is limited by small sample sizes and insufficient racial and ethnic heterogeneity to adequately assess differences in the patterns of alcohol, cigarette, and marijuana consumption. Moreover, most studies have focused on only one substance and assessed differences in consumption rates among older adolescents and young adults. The current study adds to the existing literature by examining racial/ethnic differences in alcohol, cigarette, and marijuana use among a racially/ ethnically diverse sample of approximately 5,500 seventh and eighth graders in southern California. We also address whether any racial/ethnic differences may be partially mediated by key individual, family, and school factors.

Method

Participants and procedure

Seventh- and eighth-grade students (N = 10,199) in 16 participating middle schools across three school districts in southern California received consent forms to participate in the study, and 91.8% of parents returned this form (n = 9,360). Seventy-one percent of parents gave permission for their child to participate in the study (n = 6,646), and 90.9% of students completed the survey (n = 6,038). These numbers are comparable to other school-based survey completion rates with youths (Johnson and Hoffmann, 2000; Johnston et al., 2008; Kandel et al., 2004). Surveys at all 16 schools were completed within a 4-week time frame from late Sep-tember to early October 2008. Spanish-speaking staff were available at each administration, and survey booklets were available in Spanish and Korean. Responses were protected by a Certificate of Confidentiality from the National Institutes of Health. All materials and procedures were approved by the institution's internal review board, school districts, and individual schools.

Study measures

Sociodemographic characteristics.

The survey asked students about their age, sex, grade level, and race/ethnicity. Respondents initially classified themselves by ethnicity and then by race (U.S. Department of Health and Human Services, 2001), which included categories of Caucasian, African American, and Asian or Pacific Islander. Hereafter, we refer to non-Hispanic Caucasians as Caucasians, non-Hispanic African Americans as African Americans, and Asians or Pacific Islanders as Asians. Individuals of other race or of mixed race were not included in the analytic sample (n = 552). The survey also asked about which adults the student lived with, and a nuclear family structure was defined as the student living with both his/her mother and father (Ellickson et al., 2001).

Alcohol, cigarette, and marijuana use.

Lifetime and past-month frequency of alcohol, cigarette, and marijuana use were assessed using well-established measures from the California Healthy Kids Survey (WestEd, 2008) and Project ALERT (Ellickson et al., 2003c). A “drink” was defined as one whole drink of alcohol (not including a few sips of wine for religious purposes). For lifetime use, cigarette use was defined as “a cigarette, even one or two puffs.” Variability in responses for higher frequency of consumption in this young population was low. For analytic purposes, we used dichotomous categories of any use (one time or more) and no use (never used the substance).

Individual factors.

For each of the three substances, we assessed positive and negative expectancies with six-item scales (Ellickson et al., 2003c). For example, one item each for alcohol and marijuana positive expectancies asked whether students thought that using the substance would relax them or let them have more fun; one item each for alcohol and marijuana negative expectancies asked whether students thought that using the substance would make them do poorly in school. Cigarette expectancies asked, for example, whether students thought cigarettes would make other people not want to be around them or would help them stay thin. Students indicated their extent of agreement with each statement (1 = strongly agree to 4 = strongly disagree) and responses were averaged (α's ranged from .75 to .87). Scores for negative expectancies were reverse coded, with higher scores indicating higher expectancies.

RSE for each substance was assessed by asking students: “Suppose you are offered alcohol (or marijuana or cigarettes) and you do not want to use it (them). What would you do in these situations (e.g., your best friend is drinking alcohol [smoking cigarettes/marijuana])?” (Ellickson et al., 2003c). Three items were rated on a scale ranging from 1 = “I would definitely drink/smoke cigarettes/use marijuana” to 4 = “I would definitely not drink/smoke cigarettes/use marijuana,” and scores were reverse coded and then averaged to develop a single RSE scale for each of the three substances (α's for the three scales ranged from .93 to .96). Higher scores indicated higher RSE.

Family factors.

Two scales from Unger et al.'s (2002) work measured familism (Cuellar et al., 1995) and parental respect (Ho, 1994). Both scales were assessed with four items (sample item for familism: “If anyone in my family needed help, we would all be there to help them,” sample item for parental respect: “It is important to honor my parents”) that were rated on a 4-point scale and averaged (1 = strongly agree to 4 = strongly disagree; α = .77 for familism and .89 for parental respect). Higher scores indicated greater familism and parental respect. For family substance use, students reported how often the adult most important to them and whom they spend time with used alcohol, cigarettes, or marijuana (1 = never, 2 = less than once/week, 3 = 1-3 days/ week, and 4 = 4-7 days/week). Students also reported if their older siblings used each substance (yes/no). If students did not have older siblings, this was classified as “no.”

School factors.

We calculated the proportion of respondents in each student's school and grade that reported lifetime or past-month alcohol, cigarette, or marijuana use to obtain the mean level of consumption for each school-grade combination. Perceived use among peers at school was a continuous variable that was measured by asking approximately how many students in 100 have smoked cigarettes/drank alcohol/ever tried marijuana at least once a month (WestEd, 2008). Responses were recorded on a scale of 1 to 11, where 0 or no students of 100 was coded as “1,” 10 students of 100 was coded as “2,” and so forth.

Analyses.

To adjust for potential bias caused by missing data within the survey, we used a multiple imputation-based approach to impute 15 datasets using a sequence of regression models with IVEware (Raghunathan et al., 2002). Imputations were conducted separately for males and females in the sample to preserve any information about interaction effects (Allison, 2001), and clustering at the school level was taken into account. The 15 imputed datasets were analyzed separately, and the results were combined (Little and Rubin, 1987) with the MIAnalyze procedure within SAS 9.2 (SAS Institute Inc., Cary, NC). Across all variables of interest (except age, sex, and race), missing data ranged from 0.2% (past-month alcohol use) to 24.6% (respect) and were imputed. Where age, sex, and race/ethnicity variables were missing (<0.1%), the case was excluded from the analysis (n = 24) for a total of 5,462 adolescents included in the analyses.

To examine the association between racial/ethnic groups and substance use, we estimated the odds of reporting use of each of the six outcomes (three substances: alcohol, cigarettes, and marijuana; two usage periods: past month and lifetime) using generalized estimating equation (GEE; Zeger et al., 1988) regressions with SAS 9.2. This GEE model served as the base model (Model 1), which adjusted for sex, grade, and nuclear family structure. It also accounted for clustering of the 16 schools and provided corrected standard errors. These analyses were conducted for each imputed dataset, and the results were combined.

We then examined the extent to which racial/ethnic differences in substance use were explained by or reduced when individual, family, or school factors were added to the model. We used path analysis modeling (Baron and Kenny, 1986; Fritz and MacKinnon, 2007) to examine the mediating pathways between race and consumption through three different sets of potential mediating factors grouped as (a) individual, (b) family, and (c) school factors. For each of the three path analysis models, African American, Asian, and Hispanic students were compared with Caucasian students using three dummy variables. Each path analysis model (Models 2-4) adjusted for the sex, grade, and family structure covariates from the base model (GEE Model 1) and a new set of mediating individual, family, or school variables. Model 2 included individual factors to determine whether racial/ethnic differences in Model 1 were mediated by this group of individual factors. Model 3 included all family factors (familism, respect, and adult and older sibling use), and Model 4 included all school factors (average school and grade consumption for each student and perceived peer use). In all models, we matched the version of the independent variable (e.g., alcohol expectancies, older sibling alcohol use) to the particular outcome variable (e.g., lifetime alcohol use). Models were estimated using Mplus 5.1 (Muthén and Muthén, 1998-2007) using weighted least squares with mean and variance correction estimation, with standard errors corrected via the complex samples procedure. We estimated standardized direct and indirect effects of race/ethnicity based on a polychoric correlation matrix, and calculated their standard errors using the delta method (see, e.g., MacKinnon, 2008), using the Model Indirect command in Mplus. The Model Indirect command provides the statistical significance of indirect effects and is considered to be a test of significant mediation between race/ethnicity and consumption.

Results

Table 1 provides demographic, and individual, family, and school factors by each racial/ethnic group. Approximately 22% of seventh and eighth graders reported lifetime alcohol use, 10% reported any lifetime cigarette smoking, and 7% reported lifetime marijuana use. Students reported relatively low levels of positive expectancies and high levels of negative expectancies and RSE. Students reported low frequencies of substance use by adults, and approximately 18%, 9%, and 7% reported having older siblings who drank alcohol, smoked cigarettes, or used marijuana, respectively.

Table 1.

Imputed sample characteristics of seventh and eighth grade participants in Project Choice

Variable Caucasian (n = 954) African American (n = 212) Hispanic (n = 3,270) Asian (n= 1,026) Total (N= 5,462)
Male, n (%) 498 (55.20%) 102(48.11%) 1,558(47.65%) 496 (48.34%) 2,654 (48.59%)
Age, in years, M (SD) 12.50(0.66) 12.58 (0.73) 12.58 (0.72) 12.48 (0.68) 12.50(0.70)
Nuclear family structure, n (%) 353 (0.37%) 112(0.53%) 2,289 (0.70%) 718 (0.70%) 3,168(0.58%)
Consumption rates, n (%)
 Lifetime use
  Alcohol 175(18.37%) 44 (20.88%) 866 (26.50%) 98 (9.57%) 1,183(21.65%)
  Cigarettes 64 (6.72%) 20 (9.43%) 413 (12.64%) 49 (4.82%) 546(10.00%)
  Marijuana 38 (3.99%) 12(5.66%) 316(9.66%) 15 (1.42%) 381 (6.97%)
 Past-month use
  Alcohol 68(7.13%) 16(7.54%) 359 (10.98%) 32(3.15%) 475 (8.69%)
  Cigarettes 15(1.57%) 6 (2.83%) 117(3.58%) 5 (0.50%) 144 (2.63%)
  Marijuana 17 (1.79%) 4(1.89%) 151 (4.62%) 4 (0.43%) 176(3.23%)
Individual variables, M (SD)
 Positive expectancy scorea
  Alcohol 1.60(0.83) 1.46(0.72) 1.69(0.92) 1.45(0.72) 1.62(0.86)
  Cigarettes 1.51 (0.77) 1.55(0.80) 1.53(0.81) 1.42(0.68) 1.50(0.78)
  Marijuana 1.55(0.86) 1.54(0.89) 1.62(0.95) 1.37(0.72) 1.55(0.90)
 Negative expectancy scorea
  Alcohol 3.23 (0.98) 3.01 (1.09) 2.95 (1.04) 3.29 (0.91) 3.07(1.02)
  Cigarettes 3.20(1.02) 3.05(1.14) 2.91 (1.15) 3.30(1.00) 3.04(1.12)
  Marijuana 3.25 (1.03) 3.09(1.17) 2.96(1.16) 3.30 (0.99) 3.09(1.12)
 Resistance self-efficacy scorea
  Alcohol 3.56 (0.74) 3.61 (0.71) 3.41 (0.86) 3.71 (0.60) 3.50 (0.80)
  Cigarettes 3.80 (0.58) 3.73 (0.75) 3.72 (0.69) 3.86 (0.47) 3.76 (0.64)
  Marijuana 3.77 (0.60) 3.71 (0.73) 3.65 (0.78) 3.86 (0.48) 3.71 (0.71)
Family variables
 Familism score, M (SD)a 3.63 (0.47) 3.48 (0.60) 3.58 (0.50) 3.58 (0.50) 3.59 (0.57)
 Parental respect score, M (SD)a 3.77 (0.50) 3.72 (0.65) 3.80 (0.50) 3.85 (0.40) 3.80 (0.49)
 Any older sibling use, n (%)b
  Alcohol 172(18.01%) 34(15.91%) 667 (20.41%) 81 (7.92%) 954(17.47%)
  Cigarettes 86 (9.04%) 22 (10.57%) 324 (9.92%) 39 (3.79%) 472 (8.64%)
  Marijuana 63 (6.62%) 19(8.77%) 263 (8.03%) 23 (2.26%) 368 (6.73%)
 Frequency of adult use, M (SD)c
  Alcohol 1.92(1.95) 1.59(1.66) 1.70(1.73) 1.60(1.63) 1.72(1.74)
  Cigarettes 1.46(1.49) 1.47(1.55) 1.54(1.57) 1.49(1.52) 1.52(1.54)
  Marijuana 1.09(1.10) 1.13(1.18) 1.12(1.15) 1.02(1.04) 1.10(1.12)
School variable, M (SD)
 Perceived peer used
  Alcohol 2.16(1.78) 2.29(1.12) 2.30 (2.05) 1.67(1.32) 2.14(1.91)
  Cigarettes 1.79(1.38) 1.82(1.47) 1.88(1.56) 1.43(1.00) 1.77(1.45)
  Marijuana 1.97(1.76) 2.15 (2.03) 2.14(1.99) 1.47(1.20) 1.99(1.84)
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Notes: Subgroup statistics do not always add up to exact total because of averaging of statistics across imputed data sets and rounding.

a

Scores range from 1 to 4, where higher scores indicate higher expectancies, resistance self-efficacy, familism, and parental respect;

b

any older sibling use is defined as “yes” only if a student has an older sibling and if that older sibling uses substances; any older sibling use is categorized as “no use” if a student reported not having an older sibling, or if her or his older sibling does not use substances;

c

frequency of adult use ranges from 1 to 4 where 1 = “never,” 2 = “less than once/week,” 3 = “1-3 days/week,” 4 = “4-7 days/week”;

d

perceived peer use ranges from 1 to 11 where “1” represents a student perceiving 0 of 100 students using each type of substance, 2 represents 10 of 100, 3 represents 20 of 100, etc.

Table 2 shows the odds of substance use in Model 1, adjusting for sex, grade, and family structure. The overall pattern for all six outcomes was similar: Across all substances, Hispanic adolescents had odds of past-year or past-month consumption that were 1.58-1.97 times higher than that of Caucasians, indicating statistically significant higher risk of reporting consumption for all three substances. The odds of consumption for Asians were 0.25-0.73 times the odds of consumption for Caucasian adolescents, indicating a statistically significant lower risk of consumption of all substances among Asians. There were no statistically significant differences (all ps > .05) between African American and Caucasian students in lifetime or past-month use.

Table 2.

Model 1 parameter, odds ratio, and predicted probability estimates for consumption associated with race/ethnicity (N= 5,462)

Lifetime
 Past month
Variable Parameter (SE) OR [95% CI] Predicted probability for a male eighth grader without nuclear family Parameter (SE) OR [95% CI] Predicted probability for a male eighth grader without nuclear family
Alcohol
 African American 0.05 1.05 .29 0.01 1.01 .10
(0.21) [0.69, 1.60] (0.29) [0.57, 1.77]
 Hispanic 0.46** 1.58 .38 0.47** 1.60 .15
(0.10) [1.31, 1.92] (0.14) [1.22, 2.12]
 Asian −0.60** 0.55 .18 −0.66* 0.52 .05
(0.17) [0.40, 0.76] (0.27) [0.30, 0.88]
 Caucasiana .28 .10
Cigarettes
 African American 0.07 1.07 .17 0.22 1.25 .05
(0.33) [0.56, 2.05] (0.42) [0.55, 2.80]
 Hispanic 0.49** 1.63 .23 0.58** 1.79 .07
(0.14) [1.23, 2.16] (0.20) [1.21, 2.64]
 Asian −0.32* 0.73 .12 −1.15** 0.32 .01
(0.14) [0.55, 0.94] (0.42) [0.14, 0.72]
 Caucasiana .16 .04
Marijuana
 African American 0.03 0.97 .11 −0.35 0.70 .04
(0.26) [0.58, 1.62] (0.49) [0.27, 1.86]
 Hispanic 0.67** 1.95 .20 0.68** 1.97 .10
(0.13) [1.51, 2.53] (0.16) [1.43, 2.69]
 Asian −1.03** 0.36 .04 −1.39** 0.25 .01
(0.21) [0.23, 0.54] (0.52) [0.09, 0.68]
 Caucasiana .11 .05
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Notes: Model 1 adjusts for grade, sex, and nuclear family structure.

a

Reference group.

*

p ≤ .05;

**

p ≤ .01.

Odds ratios estimate the increased or decreased odds of reporting use for each racial/ethnic subgroup in comparison with Caucasians. However, odds ratios can be misleading if the risk in the reference group is high (Long, 1997). To aid interpretation, we also provide the sex-, age-, and family structure-adjusted probability of reporting lifetime use for each racial/ethnic group as predicted by Model 1 (Table 2). We arbitrarily selected the reference categories, male eighth graders who were not in a nuclear family, to calculate predicted probabilities. Thus, an adjusted odds ratio of 1.58 for lifetime alcohol consumption for Hispanics, compared with Caucasians, translates into the probability of lifetime alcohol consumption of 38% for eighth-grade Hispanic males with a nonnuclear family structure, compared with 29% for African Americans, 28% for Caucasians, and 18% for Asians (Table 2).

Model 1 results showed that the total race effect was not significant for African Americans because they did not differ from Caucasians in their rates of use. Thus, for the path analysis models, we do not report parameter estimates for this group, although African American race was retained in the model as an indicator. Table 3 provides parameter estimates for the indirect race effects for lifetime consumption. If the total indirect effect through all individual, or family, or school factors was not significant (p > .05), we did not further examine the indirect effects through specific factors in Table 3.

Table 3.

Mediation of race/ethnicity disparities in lifetime consumption: Standardized parameter estimates for individual, family, and school factors

Hispanics vs. Caucasians
 Asians vs. Caucasians
Variable Estimate SE P Estimate SE P
Model 2: Individual factors
 Alcohol
 Total indirect race effect .11 .04 <.01 −.11 .04 <.01
  Positive expectancies — — — −.04 .01 .02
  Negative expectancies .03 .01 <.01 −.01 .01 .08
  Resistance self-efficacy .06 .02 .01 −.07 .02 <.01
 Cigarettes
 Total indirect race effect .07 .03 <.01 −.06 .03 .03
  Positive expectancies — — — — — —
  Negative expectancies .03 .01 <.01 −.02 .01 .02
  Resistance self-efficacy .03 .01 .02 −.03 .02 .08
 Marijuana
 Total indirect race effect .09 .04 .03 −.12 .04 <.01
  Positive expectancies — — — −.07 .03 .01
  Negative expectancies .03 .01 <.01 −.02 .01 .08
  Resistance self-efficacy .04 .02 <.01 −.03 .01 .01
Model 3: Family factors
 Alcohol
 Total indirect race effect — — — −.11 .02 <.01
  Familism — — — — — —
  Parental respect — — — −.02 .01 <.01
  Adult use — — — −.04 .01 <.01
  Older sibling use — — — −.05 .03 .09
 Cigarettes
 Total indirect race effect — — — −.05 .02 <.01
  Familism — — — — — —
  Parental respect — — — −.02 .01 <.01
  Adult use — — — — — —
  Older sibling use — — — −.03 .01 .02
 Marijuana
 Total indirect race effect — — — −.07 .02 <.01
  Familism — — — — — —
  Parental respect — — — −.02 .01 <.01
  Adult use — — — — — —
  Older sibling use — — — −.03 .02 .05
Model 4: School factors
 Alcohol
 Total indirect race effect .09 .05 .04 −.14 .04 <.01
  Perceived peer use — — — −.08 .03 <.01
  School-grade consumption .07 .03 .02 −.06 .02 .02
 Cigarettes
 Total indirect race effect — — — −.07 .02 <.01
  Perceived peer use — — — −.06 .02 <.01
  School-grade consumption — — — — — —
 Marijuana
 Total indirect race effect — — — −.09 .02 <.01
  Perceived peer use — — — −.11 .02 <.01
  School-grade consumption — — — — — —
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Notes: Parameters for race effects are reported for Hispanic and Asian dummy variables compared with Caucasian as the reference group. The African American total race effect compared with C aucasians was not significant, and therefore indirect parameters are not reported for African Americans. A dash (—) indicates a nonsignificant indirect effect (p > .10). Model 2 adjusts for grade, sex, nuclear family structure, positive and negative expectancies, and resistance self-efficacy. Model 3 adjusts for grade, sex, nuclear family structure, familism, parental respect, adult use, and older sibling use. Model 4 adjusts for grade, sex, nuclear family structure, school-grade consumption, and perceived peer use.

Model 2 (individual factors) results showed that higher lifetime use of alcohol, cigarettes, and marijuana among Hispanics (as indicated in Model 1) was significantly mediated by RSE and negative expectancies (all p values <.05). None of the family factors (Model 3) or school factors (Model 4) significantly explained higher rates of lifetime alcohol, cigarette, or marijuana use among Hispanics (p > .05), with one exception: Higher school-grade consumption (in Model 4) did mediate the relationship between higher use in Hispanics, compared with Caucasian students, but only for alcohol.The pattern of mediating relationships for past-month use in Models 2-4 among Hispanics was the same as for lifetime use of alcohol, cigarettes, and marijuana (data not shown).

Lower Asian lifetime use of alcohol and marijuana, compared with Caucasians, was significantly mediated by RSE (p < .05) and positive (p < .05) and negative expectancies (p < .10) (Model 2). Lower Asian lifetime use of cigarettes was mediated by RSE (p < .10) and negative expectancies only (p < .05). Model 3 showed that lower alcohol, cigarette, and marijuana use was mediated, in general, by family factors, such as parental respect, older sibling use, and adult use, although this was most consistent across all substances for parental respect and older sibling use. In contrast to Hispanics, perceived peer use consistently mediated lower Asian use of all substances (Model 4). School-grade use additionally explained lower alcohol use among Asians.

Overall, patterns of mediating relationships were very similar for lifetime and past-month substance use in Asians. The only difference between the models for lifetime and past-month use was that adult use additionally explained differences in marijuana use between Asians and Caucasians in Model 3 (data not shown).

Discussion

The current study addresses several gaps in the literature by examining racial/ethnic differences in alcohol, cigarette, and marijuana consumption among a racially/ethnically diverse sample of young adolescents. We also examined whether individual, family, and school factors partially explained racial/ethnic differences in consumption. These findings may inform public health intervention efforts that aim to reduce consumption during the period of greatest risk for substance-use initiation and escalation. Furthermore, findings from the mediation analyses clarify the types of factors that may explain racial/ethnic disparities and serve as targets for interventions.

It is worth noting that, in this sample of seventh and eighth graders, rates of lifetime and past-month substance use were comparable to national samples (SAMHSA, 2008). For example, in the 2007 National Survey on Drug Use and Health (SAMHSA, 2008), 28.2% of eighth graders reported lifetime alcohol use, compared with 29.2% in our sample of eighth graders.

Overall, we found substantial racial/ethnic differences in both lifetime and past-month alcohol, cigarette, and marijuana use. After adjusting for sex, grade, and family structure, Hispanics reported significantly higher rates of lifetime and past-month use, and Asians reported significantly lower rates of lifetime and past-month use, compared with Caucasians. We found no evidence that African American students differed from Caucasians in their rates of lifetime or past-month alcohol, cigarette, and marijuana use.

Our findings support previous research that showed that Asians ages 12-17 report lower use of alcohol, cigarettes, and marijuana (SAMHSA, 2000), compared with Caucasians. Research findings that compare prevalence rates of substance use among Hispanic versus Caucasian youths have been mixed (French et al., 2002; Herd, 1990; Kandel, 1995; SAMHSA, 2000; Watt, 2004). Survey research from the Monitoring the Future study suggests that Hispanics may have higher rates of substance use than Caucasians in eighth grade; however, by 12th grade, Caucasians may have a slightly higher rate of use, compared with Hispanics (Johnston et al., 2009). This may be a result of higher dropout rates later in high school for Hispanics who are more likely to have initiated substance use (Johnston et al., 2009). Given that our sample involved middle school adolescents, those at higher risk for substance use may still be in school. In addition, the Hispanics in the current sample may have been more likely to have been born in the United States or were more acculturated than Hispanics in other samples, thereby contributing to the higher rates of use among Hispanics observed in this study (Brook et al., 1998; Chen et al., 2002; De La Rosa, 2002; Epstein et al., 2001; Litrownik et al., 2000; Love et al., 2006; McQueen et al., 2003; Unger et al., 2000).

We did not find any differences in consumption between African American and Caucasian youths, which is inconsistent with other research that has generally found lower rates of alcohol and drug use in African American compared with Caucasian youths ages 12 and older (Chen and Unger, 1999; French et al., 2002; Herd, 1990; Kandel, 1995). Our null findings for any difference between African Americans and Caucasians could be explained in part, by the smaller sample of African American adolescents, which somewhat reduced the power of the study to detect such differences.

Findings from the mediation analyses suggest that different individual, family, and school factors may partially explain higher Hispanic and lower Asian use of substances, compared with Caucasians. Individual factors consistently explained higher alcohol, cigarette, and marijuana use among Hispanics. By contrast, family and school factors generally did not play a role in the higher alcohol, cigarette, and marijuana use among Hispanic adolescents. Other research in middle schools has also shown that some family factors, such as familism, are not associated with risk for substance use (Unger et al., 2002). Although high familism is typically linked with Hispanic ethnicity, familism may not have been an important predictor of substance use in this sample, because the Hispanic adolescents in our sample could have been more acculturated to U.S. culture and, therefore, have lower familism, compared with more recent immigrants for example. In addition, the average familism scores were high, and there was limited variability, perhaps contributing to the inability to detect a significant association due to low power.

Results for Hispanics in this sample highlight the importance of targeting individual factors in prevention efforts. For example, providing skills training to strengthen RSE and discussing the effects that expectancies can have on alcohol and drug use are important intervention components that have been used with younger adolescents. Specifically, programs to change outcome expectancies and RSE in adolescents ages 12-18 have been associated with marked reductions in alcohol, cigarette, and marijuana intentions (D'Amico and Edelen, 2007; Orlando et al., 2005; Skenderian et al., 2008; Trudeau et al., 2003; Winkleby et al., 2004). Thus, universal prevention efforts that focus on individual-level factors, and especially negative expectancies and RSE, may be particularly important for young Hispanic adolescents.

For Asians, individual, family, and school factors partially accounted for lower use. The observation that RSE mediated differences in alcohol use among Asians concurs with findings for college students (Oei and Jardim, 2007). However, we found that, in this younger sample, not only RSE but also positive alcohol expectancies were mediators, suggesting that targeting positive expectancies during earlier adolescence may be especially useful to maintaining the lower rates of alcohol use in Asians.

Lower use in Asians was also influenced by parental respect and older sibling use but not by familism. This is consistent with prior research that has found that parental respect is a characteristic frequently associated with Asian culture (Unger et al., 2002), and is also associated with lower substance use (Unger et al., 2002, 2006). Thus, prior interventions that have successfully targeted individual factors to reduce substance use in adolescents could be particularly important for Asians to maintain low positive expectancies and promote RSE. In addition, interventions that aim to provide a more realistic estimation of perceived peer use may be particularly salient to reduce substance use in younger Asian adolescents. Parental respect also appears to be protective for Asians in this younger age group; thus, interventions that specifically encourage positive parent-child communication, cohesion, and interaction—and also strengthen adolescents' respect for their obligations and responsibilities to their parents (Unger et al., 2002; Velleman et al., 2005; Weiss et al., 2006)—may be especially successful in maintaining lower alcohol, cigarette, and marijuana use among some Asians.

There are several limitations to note in our study. First, our sample consists of a very diverse group of adolescents in southern California and is not necessarily generalizable to the larger population of middle school adolescents in the United States. Second, we were not able to assess other community or family factors that have been linked to racial disparities in substance use among adolescents, including low neighborhood socioeconomic status, which may influence access to substances; parental attitudes toward alcohol, cigarette, and marijuana use; or other psychosocial correlates (e.g., social support, violence in the home) that have been linked to racial/ethnic disparities in substance use among adolescents (Bossarte and Swahn, 2008; Watt, 2004; Watt and Rogers, 2007). Third, our goal was to examine the variation in substance-use rates by broad categories of race/ethnicity, and therefore we did not examine variation in substance use by Asian or Hispanic subgroups. Fourth, given the young age of this sample and low rates of use, we did not have enough variability to examine age of initiation and escalation in the level of use over time. Finally, we used self-reported substance use, of which the limitations are well documented (Chan, 2008), although our protocol discussed confidentiality, used scan forms, and had study staff rather than teachers conduct the administration and collect forms (D'Amico and McCarthy, 2006; Dennis et al., 2002; Shillington and Clapp, 2000). Despite these limitations, this study is an important first step to estimate racial/ethnic differences in substance use across a diverse sample of young adolescents.

In sum, our findings provide valuable information on racial/ethnic disparities in alcohol, cigarette, and marijuana use, and add substantial knowledge about the importance of individual, family and school factors in contributing to racial/ethnic disparities in alcohol and drug use among a very large sample of younger adolescents. Few existing intervention programs are culturally grounded for the ethnic culture of students (Gosin et al., 2003) or the stage of development of adolescents (Brown, 2001; D'Amico et al., 2005b). We identified consistent individual, family, and school factors that may serve as important targets for future intervention research that is more culturally appropriate for younger adolescents of different racial/ethnic groups. Future research is needed to examine other individual, family, and school factors that may contribute to racial/ethnic differences in alcohol, cigarette, and marijuana use, including sensation-seeking behavior, parental attitudes toward substance use, parental monitoring, acculturation, and racial/ethnic make-up of school populations, which have been previously linked with early adolescent substance use (Beyers et al., 2004; Botvin et al., 1998; Chilcoat and Anthony, 1996; Fosados et al., 2007; Kim and McCarthy, 2006; Kopstein et al., 2001; Steinberg et al., 1994; Unger et al., 2009). In addition, studies with prospective data should examine whether individual, family, and school factors contribute to both age of initiation and escalation of alcohol and other drug use among younger adolescents over time. This future work could help identify additional targets for intervention, with the ultimate goal of reducing the incidence of alcohol, cigarette, and marijuana use among middle school adolescents.

Acknowledgments

We thank the 16 schools that provided us the opportunity to conduct our surveys. We also thank our survey directors, Kirsten Becker and Megan Zander-Cotugno, for overseeing the survey administration process in these 16 schools.

Footnotes

*

This research was supported by National Institute on Alcohol Abuse and Alcoholism grant R01AA016577 awarded to Elizabeth J. D'Amico.

References

  1. Agrawal A, Lynskey MT, Pergadia ML, Bucholz KK, Heath AC, Martin NG, Madden PA. Early cannabis use and DSM-IV nicotine dependence: A twin study. Addiction. 2008;103:1896–1904. doi: 10.1111/j.1360-0443.2008.02354.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Allison PD. Missing Data (Sage University Papers Series on Quantitative Applications in the Social Sciences, series no. 07-136) Thousand Oaks, CA: Sage; 2001. [Google Scholar]
  3. Ary DV, Tildesley E, Hops H, Andrews J. The influence of parent, sibling, and peer modeling and attitudes on adolescent use of alcohol. International Journal of the Addictions. 1993;28:853–880. doi: 10.3109/10826089309039661. [DOI] [PubMed] [Google Scholar]
  4. Barkin SL, Smith KS, DuRant RH. Social skills and attitudes associated with substance use behaviors among young adolescents. Journal of Adolescent Health. 2002;30:448–454. doi: 10.1016/s1054-139x(01)00405-0. [DOI] [PubMed] [Google Scholar]
  5. Baron RM, Kenny DA. The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology. 1986;51:1173–1182. doi: 10.1037//0022-3514.51.6.1173. [DOI] [PubMed] [Google Scholar]
  6. Bauman KL, Foshee VA, Linzer MA, Koch GC. Effect of parental smoking classification on the association between parental and adolescent smoking. Addictive Behaviors. 1990;15:414–422. doi: 10.1016/0306-4603(90)90027-u. [DOI] [PubMed] [Google Scholar]
  7. Best D, Rawaf S, Rowley J, Floyd K, Manning V, Strang J. Ethnic and gender differences in drinking and smoking among London adolescents. Ethnicity and Health. 2001;6:51–57. doi: 10.1080/13557850123660. [DOI] [PubMed] [Google Scholar]
  8. Beyers JM, Toumbourou JW, Catalano RF, Arthur MW, Hawkins JD. A cross-national comparison of risk and protective factors for adolescent substance use: The United States and Australia. Journal of Adolescent Health. 2004;35:3–16. doi: 10.1016/j.jadohealth.2003.08.015. [DOI] [PubMed] [Google Scholar]
  9. Bossarte RM, Swahn MH. Interactions between race/ethnicity and psychosocial correlates of preteen alcohol use initiation among seventh grade students in an urban setting. Journal of Studies on Alcohol and Drugs. 2008;69:660–665. doi: 10.15288/jsad.2008.69.660. [DOI] [PubMed] [Google Scholar]
  10. Botvin GJ, Malgady RG, Griffin KW, Scheier LM, Epstein JA. Alcohol and marijuana use among rural youth: Interaction of social and intrapersonal influences. Addictive Behaviors. 1998;23:379–387. doi: 10.1016/s0306-4603(98)00006-9. [DOI] [PubMed] [Google Scholar]
  11. Brook JS, Whiteman M, Balka EB, Win PT, Gursen MD. Drug use among Puerto Ricans: Ethnic identity as a protective factor. Hispanic Journal of Behavioral Sciences. 1998;20:241–254. [Google Scholar]
  12. Brown SA. Facilitating change for adolescent alcohol problems: A multiple options approach. In: Wagner EF, Waldron HB, editors. Innovations in adolescent substance abuse intervention. New York: Pergamon Press/Elsevier Science; 2001. pp. 169–187. [Google Scholar]
  13. Caetano R, Clark CL. Trends in alcohol consumption patterns among whites, blacks and Hispanics: 1984 and 1995. Journal of Studies on Alcohol. 1998;59:659–668. doi: 10.15288/jsa.1998.59.659. [DOI] [PubMed] [Google Scholar]
  14. Chan D. So why ask me? Are self-report data really that bad? In: Lance CE, Vandenberg RJ, editors. Statistical and methodological myths and urban legends: Doctrine, verity and fable in the organizational and social sciences. New York: Psychology Press; 2008. pp. 309–336. [Google Scholar]
  15. Chen X, Unger JB. Hazards of smoking initiation among Asian American and non-Asian adolescents in California: A survival model analysis. Preventive Medicine. 1999;28:589–599. doi: 10.1006/pmed.1999.0487. [DOI] [PubMed] [Google Scholar]
  16. Chen X, Unger JB, Palmer P, Weiner MD, Johnson CA, Wong MM, Austin G. Prior cigarette smoking initiation predicting current alcohol use: Evidence for a gateway drug effect among California adolescents from eleven ethnic groups. Addictive Behaviors. 2002;27:799–817. doi: 10.1016/s0306-4603(01)00211-8. [DOI] [PubMed] [Google Scholar]
  17. Chilcoat HD, Anthony JC. Impact of parent monitoring on initiation of drug use through late childhood. Journal of the American Academy of Child and Adolescent Psychiatry. 1996;35:91–100. doi: 10.1097/00004583-199601000-00017. [DOI] [PubMed] [Google Scholar]
  18. Cuellar I, Arnold B, Gonzalez G. Cognitive referents of ac-culuration: Assessment of cultural constructs in Mexican Americans. Journal of Community Psychology. 1995;23:339–356. [Google Scholar]
  19. D'Amico EJ, Edelen MO. Pilot test of Project CHOICE: A voluntary afterschool intervention for middle school youth. Psychology of Addictive Behaviors. 2007;21:592–598. doi: 10.1037/0893-164X.21.4.592. [DOI] [PubMed] [Google Scholar]
  20. D'Amico EJ, Ellickson PL, Collins RL, Martino SC, Klein DJ. Processes linking adolescent problems to substance use problems in late young adulthood. Journal of Studies on Alcohol. 2005a;66:766–775. doi: 10.15288/jsa.2005.66.766. [DOI] [PubMed] [Google Scholar]
  21. D'Amico EJ, Ellickson PL, Wagner EF, Turrisi R, Fromme K, Ghosh-Dastidar B, Wright D. Developmental considerations for substance use interventions from middle school through college. Alcoholism: Clinical and Experimental Research. 2005b;29:474–483. doi: 10.1097/01.alc.0000156081.04560.78. [DOI] [PubMed] [Google Scholar]
  22. D'Amico EJ, McCarthy DM. Escalation and initiation of younger adolescents' substance use: The impact of perceived peer use. Journal of Adolescent Health. 2006;39:481–487. doi: 10.1016/j.jadohealth.2006.02.010. [DOI] [PubMed] [Google Scholar]
  23. D'Amico EJ, Metrik J, McCarthy DM, Appelbaum M, Frissell KC, Brown SA. Progression into and out of binge drinking among high school students. Psychology of Addictive Behaviors. 2001;15:341–349. [PubMed] [Google Scholar]
  24. De La Rosa M. Acculturation and Latino adolescents' substance use: A research agenda for the future. Substance Use and Misuse. 2002;37:429–456. doi: 10.1081/ja-120002804. [DOI] [PubMed] [Google Scholar]
  25. Dennis M, Titus JC, Diamond G, Donaldson J, Godley SH, Tims FM the CYT Steering Committee. The Cannabis Youth Treatment (CYT) experiment: rationale, study design and analysis plans. Addiction. 2002;97(Suppl. 1):16–34. doi: 10.1046/j.1360-0443.97.s01.2.x. [DOI] [PubMed] [Google Scholar]
  26. DeWit DJ, Adlaf EM, Offord DR, Ogborne AC. Age at first alcohol use: A risk factor for the development of alcohol disorders. American Journal of Psychiatry. 2000;157:745–750. doi: 10.1176/appi.ajp.157.5.745. [DOI] [PubMed] [Google Scholar]
  27. Dunn ME, Lau HC, Cruz IY. Changes in activation of alcohol expectancies in memory in relation to changes in alcohol use after participation in an expectancy challenge program. Experimental and Clinical Psychopharmacology. 2000;8:566–575. doi: 10.1037//1064-1297.8.4.566. [DOI] [PubMed] [Google Scholar]
  28. DuRant RH, Krowchuk DP, Kreiter S, Sinal SH, Woods CR. Weapon carrying on school property among middle school students. Archives of Pediatrics and Adolescent Medicine. 1999b;153:21–26. doi: 10.1001/archpedi.153.1.21. [DOI] [PubMed] [Google Scholar]
  29. DuRant RH, Smith JA, Kreiter SR, Krowchuk DP. The relationship between early age of onset of initial substance use and engaging in multiple health risk behaviors among young adolescents. Archives of Pediatrics and Adolescent Medicine. 1999a;153:286–291. doi: 10.1001/archpedi.153.3.286. [DOI] [PubMed] [Google Scholar]
  30. Eisen M, Zellman GL, Murray DM. Evaluating the Lions-Quest "Skills for Adolescence" drug education program: Second-year behavior outcomes. Addictive Behaviors. 2003;28:883–897. doi: 10.1016/s0306-4603(01)00292-1. [DOI] [PubMed] [Google Scholar]
  31. Elder JP, Campbell NR, Litrownik AJ, Ayala GX, Slymen DJ, Parra-Medina D, Lovatro CY. Predictors of cigarette and alcohol susceptibility and use among Hispanic migrant adolescents. Preventive Medicine. 2000;31(2 Pt 1):115–123. doi: 10.1006/pmed.2000.0693. [DOI] [PubMed] [Google Scholar]
  32. Ellickson PL, Tucker JS, Klein DJ, McGuigan KA. Prospective risk factors for alcohol misuse in late adolescence. Journal of Studies on Alcohol. 2001;62:773–782. doi: 10.15288/jsa.2001.62.773. [DOI] [PubMed] [Google Scholar]
  33. Ellickson PL, Bird CE, Orlando M, Klein DJ, McCaffrey DF. Social context and adolescent health behavior: Does school-level smoking prevalence affect students' subsequent smoking behavior? Journal of Health Social Behavior. 2003b;44:525–535. [PubMed] [Google Scholar]
  34. Ellickson PL, McCaffrey DF, Ghosh-Dastidar B, Longshore DL. New inroads in preventing adolescent drug use: Results from a large-scale trial of project ALERT in middle schools. American Journal of Public Health. 2003c;93:1830–1836. doi: 10.2105/ajph.93.11.1830. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Ellickson PL, Tucker JS, Klein DJ. Ten-year prospective study of public health problems associated with early drinking. Pediatrics. 2003a;111(5 Pt 1):949–955. doi: 10.1542/peds.111.5.949. [DOI] [PubMed] [Google Scholar]
  36. Englund MM, Egeland B, Oliva EM, Collins WA. Childhood and adolescent predictors of heavy drinking and alcohol use disorders in early adulthood: A longitudinal developmental analysis. Addiction. 2008;103(Suppl. 1):23–35. doi: 10.1111/j.1360-0443.2008.02174.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Epstein JA, Botvin GJ, Diaz T. Linguistic acculturation associated with higher marijuana and polydrug use among Hispanic adolescents. Substance Use and Misuse. 2001;36:477–499. doi: 10.1081/ja-100102638. [DOI] [PubMed] [Google Scholar]
  38. Faggiano F, Vigna-Taglianti FD, Versino E, Zambon A, Borraccino A, Lemma P. School-based prevention for illicit drugs use: A systematic review. Preventive Medicine. 2008;46:385–396. doi: 10.1016/j.ypmed.2007.11.012. [DOI] [PubMed] [Google Scholar]
  39. Fosados R, McClain A, Ritt-Olson A, Sussman S, Soto D, Baezconde-Garbanati L, Unger JB. The influence of acculturation on drug and alcohol use in a sample of adolescents. Addictive Behaviors. 2007;32:2990–3004. doi: 10.1016/j.addbeh.2007.06.015. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. French K, Finkbiner R, Duhamel L. Patterns of substance use among minority youth and adults in the United States: An overview and synthesis of national survey findings (National Evaluation Data Services [NEDS] Technical Report, Center for Substance Abuse Treatment, Substance Abuse and Mental Health Services Administration, contract no. 270-97-7016) Fairfax, VA: Caliber Associates; 2002. Retrieved from http://www.icpsr.umich.edu/samhda/nties/nties-pdf/reports/sub-stance_use_patterns.pdf. [Google Scholar]
  41. Fritz MS, MacKinnon DP. Required sample size to detect the mediated effect. Psychological Science. 2007;18:233–239. doi: 10.1111/j.1467-9280.2007.01882.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Gillmore MR, Catalano RF, Morrison DM, Wells EA, Iritani B, Hawkins JD. Racial differences in acceptability and availability of drugs and early initiation of substance use. American Journal of Drug and Alcohol Abuse. 1990;16:185–206. doi: 10.3109/00952999009001583. [DOI] [PubMed] [Google Scholar]
  43. Gosin M, Marsiglia FF, Hecht ML. Keepin' it R.E.A.L.: A drug resistance curriculum tailored to the strengths and needs of pre-adolescents of the southwest. Journal of Drug Education. 2003;33:119–142. doi: 10.2190/DXB9-1V2P-C27J-V69V. [DOI] [PubMed] [Google Scholar]
  44. Grunbaum JA, Kann L, Kinchen SA, Williams B, Ross JG, Lowry R, Kolbe L. Youth risk behavior surveillance—United States, 2001. Journal of School Health. 2002;72:313–328. doi: 10.1111/j.1746-1561.2002.tb07917.x. [DOI] [PubMed] [Google Scholar]
  45. Guilamo-Ramos V, Turrisi R, Jaccard J, Wood E, Gonzalez B. Progressing from light experimentation to heavy episodic drinking in early and middle adolescence. Journal of Studies on Alcohol. 2004;65:494–500. doi: 10.15288/jsa.2004.65.494. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Hanson TL, Austin G, Lee-Bayha J. Student health risk, resilience, and academic performance: Year 1 report. Los Alamitos, CA: West Ed; 2003. [Google Scholar]
  47. Henry KL, Slater MD, Oetting ER. Alcohol use in early adolescence: The effect of changes in risk taking, perceived harm and friends' alcohol use. Journal of Studies on Alcohol. 2005;66:275–283. doi: 10.15288/jsa.2005.66.275. [DOI] [PubMed] [Google Scholar]
  48. Herd D. Subgroup differences in drinking patterns among black and white men: Results from a national survey. Journal of Studies on Alcohol. 1990;51:221–232. doi: 10.15288/jsa.1990.51.221. [DOI] [PubMed] [Google Scholar]
  49. Hipwell AE, White HR, Loeber R, Stouthamer-Loeber M, Chung T, Sembower MA. Young girls' expectancies about the effects of alcohol, future intentions and patterns of use. Journal of Studies on Alcohol. 2005;66:630–639. doi: 10.15288/jsa.2005.66.630. [DOI] [PubMed] [Google Scholar]
  50. Ho DY. Filial piety, authoritarian moralism, and cognitive conservatism in Chinese societies. Genetic, Social, and General Psychology Monographs. 1994;120:349–365. [PubMed] [Google Scholar]
  51. Hsia F-N, Spruijt-Metz D. The meanings of smoking among Chinese American and Taiwanese American college students. Nicotine and Tobacco Research. 2003;5:837–849. doi: 10.1080/14622200310001615259. [DOI] [PubMed] [Google Scholar]
  52. Hurrelmann K. Health promotion for adolescents: preventive and corrective strategies against problem behavior. Journal of Adolescence. 1990;13:231–250. doi: 10.1016/0140-1971(90)90016-z. [DOI] [PubMed] [Google Scholar]
  53. Iannotti RJ, Bush PJ, Weinfurt KP. Perception of friends' use of alcohol, cigarettes, and marijuana among urban schoolchildren: A longitudinal analysis. Addictive Behaviors. 1996;21:615–632. doi: 10.1016/0306-4603(95)00086-0. [DOI] [PubMed] [Google Scholar]
  54. Johnson RA, Hoffmann JP. Adolescent cigarette smoking in U.S. racial/ethnic subgroups: Findings from the National Education Longitudinal Study. Journal of Health and Social Behavior. 2000;41:392–407. [PubMed] [Google Scholar]
  55. Johnston L, O'Malley PM, Bachman JG, Schulenberg JE. Monitoring the future national survey results on drug use, 1975-2007: Vol. I. Secondary school students (NIH Publication No. 08-6418A) Bethesda, MD: National Institute on Drug Abuse; 2008. [Google Scholar]
  56. Johnston L, O'Malley PM, Bachman JG, Schulenberg JE. Monitoring the future national results on adolescent drug use: Overview of key findings, 2008 (NIH Publication No. 09-7401) Bethesda, MD: National Institute of Drug Abuse; 2009. [Google Scholar]
  57. Kandel DB. Ethnic differences in drug use. In: Botvin GJ, Schinke S, Orlandi MA, editors. Drug abuse prevention with multiethnic youth. Thousand Oaks, CA: Sage; 1995. [Google Scholar]
  58. Kandel DB, Kiros GE, Schaffran C, Hu MC. Racial/ethnic differences in cigarette smoking initiation and progression to daily smoking: A multilevel analysis. American Journal of Public Health. 2004;94:128–135. doi: 10.2105/ajph.94.1.128. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Kandel DB, Logan JA. Patterns of drug use from adolescence to young adulthood: I. Periods of risk for initiation, continued use, and discontinuation. American Journal of Public Health. 1984;74:660–666. doi: 10.2105/ajph.74.7.660. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Kaplan CP, Nápoles-Springer A, Stewart SL, Pérez-Stable EJ. Smoking acquisition among adolescents and young Latinas: The role of socioenvironmental and personal factors. Addictive Behaviors. 2001;26:531–550. doi: 10.1016/s0306-4603(00)00143-x. [DOI] [PubMed] [Google Scholar]
  61. Kim J, McCarthy WJ. School-level contextual influences on smoking and drinking among Asian and Pacific Islander adolescents. Drug and Alcohol Dependence. 2006;84:56–68. doi: 10.1016/j.drugalcdep.2005.12.004. [DOI] [PubMed] [Google Scholar]
  62. Komro KA, McCarty MC, Forster JL, Blaine TM, Chen V. Parental, family, and home characteristics associated with cigarette smoking among adolescents. American Journal Health Promotion. 2003;17:291–299. doi: 10.4278/0890-1171-17.5.291. [DOI] [PubMed] [Google Scholar]
  63. Kopstein AN, Crum RM, Celentano DD, Martin SS. Sensation seeking needs among 8th and 11th graders: Characteristics associated with cigarette and marijuana use. Drug and Alcohol Dependence. 2001;62:195–203. doi: 10.1016/s0376-8716(00)00170-8. [DOI] [PubMed] [Google Scholar]
  64. Li C, Pentz MA, Chou CP. Parental substance use as a modifier of adolescent substance use risk. Addiction. 2002;97:1537–1550. doi: 10.1046/j.1360-0443.2002.00238.x. [DOI] [PubMed] [Google Scholar]
  65. Li HZ, Rosenblood L. Exploring factors influencing alcohol consumption patterns among Chinese and Caucasians. Journal of Studies on Alcohol. 1994;55:427–433. doi: 10.15288/jsa.1994.55.427. [DOI] [PubMed] [Google Scholar]
  66. Litrownik AJ, Elder JP, Campbell NR, Ayala GX, Slymen DJ, Parra-Medina D, Lovato CY. Evaluation of a tobacco and alcohol use prevention program for Hispanic migrant adolescents: Pro-moting the protective factor of parent-child communication. Preventive Medicine. 2000;31(2 Pt 1):124–133. doi: 10.1006/pmed.2000.0698. [DOI] [PubMed] [Google Scholar]
  67. Little R, Rubin DB. Statistical analysis with missing data. Hoboken, NJ: John Wiley & Sons; 1987. [Google Scholar]
  68. Liu LC, Flay BR. Evaluating mediation in longitudinal multivariate data: Mediation effects for the Aban Aya Youth Project drug prevention program. Prevention Science. 2009;10:197–207. doi: 10.1007/s11121-009-0125-1. [DOI] [PubMed] [Google Scholar]
  69. Long JS. Regression models for categorical and limited dependent variables (advanced quantitative techniques in the social sciences no. 7) Thousand Oaks, CA: Sage; 1997. [Google Scholar]
  70. Love AS, Yin Z, Codina E, Zapata JT. Ethnic identity and risky health behaviors in school-age Mexican-American children. Psychological Reports. 2006;98:735–744. doi: 10.2466/pr0.98.3.735-744. [DOI] [PubMed] [Google Scholar]
  71. Luczak SE, Wall TL, Shea SH, Byun SM, Carr LG. Binge drinking in Chinese, Korean, and white college students: Genetic and ethnic group differences. Psychology of Addictive Behaviors. 2001;15:306–309. doi: 10.1037//0893-164x.15.4.306. [DOI] [PubMed] [Google Scholar]
  72. MacKinnon DP. Introduction to statistical mediation analysis. Mahwah, NJ: Lawrence Erlbaum; 2008. [Google Scholar]
  73. Maes L, Lievens J. Can the school make a difference? A multilevel analysis of adolescent risk and health behaviour. Social Science and Medicine. 2003;56:517–529. doi: 10.1016/s0277-9536(02)00052-7. [DOI] [PubMed] [Google Scholar]
  74. McQueen A, Getz JG, Bray JH. Acculturation, substance use, and deviant behavior: Examining separation and family conflict as mediators. Child Development. 2003;74:1737–1750. doi: 10.1046/j.1467-8624.2003.00635.x. [DOI] [PubMed] [Google Scholar]
  75. Meier MH, Slutske WS, Arndt S, Cadoret RJ. Positive alcohol expectancies partially mediate the relation between delinquent behavior and alcohol use: Generalizability across age, sex, and race in a cohort of 85,000 Iowa schoolchildren. Psychology of Addictive Behaviors. 2007;21:25–34. doi: 10.1037/0893-164X.21.1.25. [DOI] [PubMed] [Google Scholar]
  76. Moreno C, Laniado-Laborin R, Sallis JF, Elder JP, de Moor C, Castro FG, Deosaransingh K. Parental influences to smoke in Latino youth. Preventive Medicine. 1994;23:48–53. doi: 10.1006/pmed.1994.1007. [DOI] [PubMed] [Google Scholar]
  77. Muthén LK, Muthén BO. Mplus User's Guide (Version 5) Los Angeles, CA: Muthén & Muthén; 1998-2007. [Google Scholar]
  78. Newcomb MD, Bentler PM. Consequences of Adolescent Drug Use: Impact on the Lives of Young Adults. Newbury Park, CA: Sage; 1988. [Google Scholar]
  79. Oei TP, Jardim CL. Alcohol expectancies, drinking refusal self-efficacy and drinking behaviour in Asian and Australian students. Drug and Alcohol Dependence. 2007;87:281–287. doi: 10.1016/j.drugalcdep.2006.08.019. [DOI] [PubMed] [Google Scholar]
  80. Orlando M, Ellickson PL, McCaffrey DF, Longshore DL. Mediation analysis of a school-based drug prevention program: Effects of Project ALERT. Prevention Science. 2005;6:35–46. doi: 10.1007/s11121-005-1251-z. [DOI] [PubMed] [Google Scholar]
  81. Otsuki M, Tinsley BJ, Chao RK, Unger JB. An ecological perspective on smoking among Asian American college students: The roles of social smoking and smoking motives. Psychology of Addictive Behaviors. 2008;22:514–523. doi: 10.1037/a0012964. [DOI] [PubMed] [Google Scholar]
  82. Paul C, Fitzjohn J, Herbison P, Dickson N. The determinants of sexual intercourse before age 16. Journal of Adolescent Health. 2000;27:136–147. doi: 10.1016/s1054-139x(99)00095-6. [DOI] [PubMed] [Google Scholar]
  83. Raghunathan TE, Solenberger PW, van Hoewyk J. IVEware: Imputation and variance estimation software: User guide. Ann Arbor, MI: Survey Research Center, Institute for Social Research, University of Michigan; 2002. [Google Scholar]
  84. Rinehart CS, Bridges LJ, Sigelman CK. Differences between Black and White elementary school children's orientations toward alcohol and cocaine: a three-study comparison. Journal of Ethnicity in Substance Abuse. 2006;5:75–102. doi: 10.1300/J233v05n03_05. [DOI] [PubMed] [Google Scholar]
  85. Rodham K, Hawton K, Evans E, Weatherall R. Ethnic and gender differences in drinking, smoking and drug taking among adolescents in England: A self-report school-based survey of 15 and 16 year olds. Journal of Adolescence. 2005;28:63–73. doi: 10.1016/j.adolescence.2004.07.005. [DOI] [PubMed] [Google Scholar]
  86. Rountree PW, Clayton RR. A contextual model of adolescent alcohol use across the rural-urban continuum. Substance Use and Misuse. 1999;34:495–519. doi: 10.3109/10826089909037228. [DOI] [PubMed] [Google Scholar]
  87. Segal B. Adolescent initiation into drug-taking behavior: Comparisons over a 5-year interval. International Journal of the Addictions. 1991;26:267–279. doi: 10.3109/10826089109058885. [DOI] [PubMed] [Google Scholar]
  88. Shillington AM, Clapp JD. Self-report stability of adolescent substance use: Are there differences for gender, ethnicity and age? Drug and Alcohol Dependence. 2000;60:19–27. doi: 10.1016/s0376-8716(99)00137-4. [DOI] [PubMed] [Google Scholar]
  89. Skager R, Fisher DG. Substance use among high school students in relation to school characteristics. Addictive Behaviors. 1989;14:129–138. doi: 10.1016/0306-4603(89)90042-7. [DOI] [PubMed] [Google Scholar]
  90. Skenderian JJ, Siegel JT, Crano WD, Alvaro EE, Lac A. Expectancy change and adolescents' intentions to use marijuana. Psychology of Addictive Behaviors. 2008;22:563–569. doi: 10.1037/a0013020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  91. Spruijt-Metz D, Gallaher P, Unger JB, Johnson CA. Unique contributions of meanings of smoking and outcome expectancies to understanding smoking initiation in middle school. Annals of Behavioral Medicine. 2005;30:104–111. doi: 10.1207/s15324796abm3002_2. [DOI] [PubMed] [Google Scholar]
  92. Steinberg L, Fletcher A, Darling N. Parental monitoring and peer influences on adolescent substance use. Pediatrics. 1994;93(6 Pt 2):1060–1064. [PubMed] [Google Scholar]
  93. Substance Abuse and Mental Health Services Administration (Office of Applied Studies) Summary of findings from the 1999 National Household Survey on Drug Abuse (DHHS Publication No. (SMA) 00-3466) Rockville, MD: Author; 2000. [Google Scholar]
  94. Substance Abuse and Mental Health Services Administration. Adolescent marijuana use by race/ethnicity in three national surveys (National Evaluation Data Services [NEDS] fact sheet 122) Rockville, MD: Center for Substance Abuse Treatment (CSAT); 2002a. [Google Scholar]
  95. Substance Abuse and Mental Health Services Administration. Alcohol and cigarette use by race/ethnicity and age in the NHSDA Survey (National Evaluation Data Services [NEDS] fact sheet 136) Rockville, MD: Center for Substance Abuse Treatment; 2002b. [Google Scholar]
  96. Substance Abuse and Mental Health Services Administration. Marijuana and cocaine powder use by age and race/ethnicity in the NHSDA Survey. (National Evaluation Data Services [NEDS] fact sheet 138) Rockville, MD: Center for Substance Abuse Treatment; 2002c. [Google Scholar]
  97. Substance Abuse and Mental Health Services Administration (Office of Applied Studies) Results from the 2007 National Survey on Drug Use and Health: National findings (DHHS Publication No. SMA 08-4343) Rockville, MD: Author; 2008. [Google Scholar]
  98. Takeshita T, Yang X, Morimoto K. Association of the ADH2 genotypes with skin responses after ethanol exposure in Japanese male university students. Alcoholism: Clinical and Experimental Research. 2001;25:1264–1269. [PubMed] [Google Scholar]
  99. Trudeau L, Spoth R, Lillehoj C, Redmond C, Wickrama KAS. Effects of a preventive intervention on adolescent substance use initiation, expectancies, and refusal intentions. Prevention Science. 2003;4:109–122. doi: 10.1023/a:1022926332514. [DOI] [PubMed] [Google Scholar]
  100. Tucker JS, Ellickson PL, Collins RL, Klein DJ. Are drug experimenters better adjusted than abstainers and users? A longitudinal study of adolescent marijuana use. Journal of Adolescent Health. 2006;39:488–494. doi: 10.1016/j.jadohealth.2006.03.012. [DOI] [PubMed] [Google Scholar]
  101. Unger JB, Cruz TB, Rohrbach LA, Ribisl KM, Baezconde-Gar-banati L, Chen X, Johnson CA. English language use as a risk factor for smoking initiation among Hispanic and Asian American adolescents: Evidence for mediation by tobacco-related beliefs and social norms. Health Psychology. 2000;19:403–410. doi: 10.1037//0278-6133.19.5.403. [DOI] [PubMed] [Google Scholar]
  102. Unger JB, Ritt-Olson A, Teran L, Huang T, Hoffman BR, Palmer P. Cultural values and substance use in a multiethnic sample of California adolescents. Addiction Research and Theory. 2002;10:257–279. [Google Scholar]
  103. Unger JB, Shakib S, Gallaher P, Ritt-Olson A, Mouttapa M, Palmer PH, Johnson CA. Cultural/interpersonal values and smoking in an ethnically diverse sample of Southern California adolescents. Journal of Cultural Diversity. 2006;13:55–63. [PubMed] [Google Scholar]
  104. Unger JB, Ritt-Olson A, Soto DW, Baezconde-Garbanati L. Parent-child acculturation discrepancies as a risk factor for substance use among Hispanic adolescents in Southern California. Journal of Immigrant and Minority Health. 2009;11:149–157. doi: 10.1007/s10903-007-9083-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  105. U.S. Department of Health and Human Services. NIH policy and guidelines on the inclusion of women and minorities as subjects in clinical research-amended, October, 2001. Bethesda, MD: Office of Extramural Research, National Institutes of Health; 2001. Retrieved from http://grants.nih.gov/grants/funding/women_min/guidelines_amended_10_2001.htm. [Google Scholar]
  106. van der Vorst H, Engels RC, Meeus W, Dekovic M, Van Leeuwe J. The role of alcohol-specific socialization in adolescents' drinking behaviour. Addiction. 2005;100:1464–1476. doi: 10.1111/j.1360-0443.2005.01193.x. [DOI] [PubMed] [Google Scholar]
  107. Velleman RD, Templeton LJ, Copello AG. The role of the family in preventing and intervening with substance use and misuse: A comprehensive review of family interventions, with a focus on young people. Drug and Alcohol Review. 2005;24:93–109. doi: 10.1080/09595230500167478. [DOI] [PubMed] [Google Scholar]
  108. Wahl A-MG, Eitle TM. Gender, acculturation and alcohol use among Latina/o adolescents: A multi-ethnic comparison. Journal of Immigrant and Minority Health. 2010;12:153–165. doi: 10.1007/s10903-008-9179-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  109. Wall TL, Peterson CM, Peterson KP, Johnson ML, Thomasson HR, Cole M, Ehlers CL. Alcohol metabolism in Asian-American men with genetic polymorphisms of aldehyde dehydrogenase. Annals of Internal Medicine. 1997;127:376–379. doi: 10.7326/0003-4819-127-5-199709010-00007. [DOI] [PubMed] [Google Scholar]
  110. Wallace JM, Bachman JG, O'Malley PM, Johnston LD, Schulenberg JE, Cooper SM. Tobacco, alcohol, and illicit drug use: racial and ethnic differences among U.S. high school seniors, 1976-2000. Public Health Reports. 2002;117(Suppl. 1):S67–S75. [PMC free article] [PubMed] [Google Scholar]
  111. Watt TT. Race/ethnic differences in alcohol abuse among youth: An examination of risk taking attitudes as a mediating factor. Journal of Ethnicity in Substance Abuse. 2005;3(3):33–47. doi: 10.1300/J233v03n03_03. [DOI] [PubMed] [Google Scholar]
  112. Watt TT, Rogers JM. Factors contributing to differences in substance use among black and white adolescents. Youth and Society. 2007;39:54–74. [Google Scholar]
  113. Weiss JW, Garbanati JA, Tanjasiri SP, Xie B, Palmer PH. Effects of family functioning and self-image on adolescent smoking initiation among Asian-American subgroups. Journal of Adolescent Health. 2006;39:221–228. doi: 10.1016/j.jadohealth.2005.12.005. [DOI] [PubMed] [Google Scholar]
  114. WestEd . California Healthy Kids Survey. Los Alamitos, CA: Author; 2008. Retrieved from http://www.wested.org/cs/chks/view/chks_s/17?x-layout=surveys. [Google Scholar]
  115. Windle M. Parental, sibling, and peer influences on adolescent substance use and alcohol problems. Applied Development Science. 2000;4:96–110. [Google Scholar]
  116. Winkleby MA, Feighery E, Dunn M, Kole S, Ahn D, Killen JD. Effects of an advocacy intervention to reduce smoking among teenagers. Archives of Pediatrics and Adolescent Medicine. 2004;158:269–275. doi: 10.1001/archpedi.158.3.269. [DOI] [PubMed] [Google Scholar]
  117. Zeger SL, Liang K-Y, Albert PS. Models for longitudinal data: A generalizing estimating equation Approach. Biometrics. 1988;44:1049–1060. [PubMed] [Google Scholar]

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