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. Author manuscript; available in PMC: 2016 Nov 22.
Published in final edited form as: Addict Behav. 2016 Mar 11;59:7–11. doi: 10.1016/j.addbeh.2016.03.003

Social Support as a Buffer Between Discrimination and Cigarette Use in Juvenile Offenders

Alexandra Hershberger 1, Tamika Zapolski 1, Matthew C Aalsma 2
PMCID: PMC5118820  NIHMSID: NIHMS825175  PMID: 27010849

Abstract

Cigarette use is a prominent problem in juvenile offenders, leading to negative health outcomes and substance use. One interesting precipitator of cigarette use in this population is discrimination. Social support could potentially buffer the positive relationship between cigarette use and discrimination in juvenile offenders, which could be dependent on the context in which the discrimination is experienced, such as peer, institutional (e.g., stores, restaurants), or educational contexts. The present study explored the relationship between three types of discrimination, social support, and smoking outcomes among 112 detained and probated juvenile offenders (mean age = 16.24, SD = 2.11, 29.2% female, 54.9% Caucasian, 40.4% detention, 53.8% smokers). Results indicated that the relationship between institutional discrimination (OR = −0.10, p = 0.005) and peer discrimination (OR = −0.11, p = 0.01) were significantly moderated by social support, with a higher likelihood of being a smoker, compared to a non-smoker at higher levels of peer and institutional discrimination. Further, based on a moderated regression analysis, results indicated that youth who experienced greater educational discrimination and lower levels of social support, they were at higher risk of nicotine addiction (b = −0.09, p = 0.03). Overall, results indicate that varying avenues of social support, such as parent, peer, and teacher support, can mitigate negative effects of discrimination on juvenile offenders, particularly cigarette use. Addressing discrimination in smoking treatment and prevention in juvenile offenders may be of great utility. Future studies should examine the potential mechanisms underlying the discrimination and cigarette use connection in juvenile offenders.

Keywords: juvenile offenders, cigarettes, discrimination, social support

1. Introduction

Cigarette use is a prominent health problem among juvenile offenders (Chassin, 2008), and both heightens risk for and leads to other social, behavioral, and psychological issues, such as other substance use and related problems and externalizing disorders (Helstrom, Bryan, Hutchison, Riggs & Clechman, 2004). Additionally, given limited access to healthcare within the justice system (Morris, Harrison, Knox, Tromanhauser, Marquis & Watts, 1995; Thornberry, Huizinga & Loeber, 2004), the health consequences associated with nicotine use among this population may be more severe than youth in the general population (Laub & Vaillant, 2000). Thus, not only are juvenile offenders at greater risk for cigarette use, but also are likely to experience more severe long-term health consequences due to limited access to treatment resources.

There is, however, the possibility for positive health outcomes that could be achieved through properly addressing smoking in juvenile offenders. In fact, smoking cessation treatment in youth reduces other substance use (Myers, Doran & Brown, 2007). Recent studies indicate that parental or family factors on cessation efforts are important (Curry, Mermelstein, Emery, Sporer, Berbaum, Camplbell & Warnecke, 2013), and research outside of the juvenile justice literature have found that youth who report receiving support from family, teachers and peers are at reduced risk for internalizing behavior and poor health status (e.g. sleep difficulties, physical issues; Solberg, Carlstom, Howard & Jones, 2007).

Youths’ support networks not only directly reduce risk for behavioral and health outcomes, but may also work as a protective factor by buffering the effect of various other risk factors on these behavioral outcomes (Smokowski, Guo, Rose, Evans, Cotter & Cacallao, 2014). For example, perceived discrimination serves as a stressor (e.g. Clark, Anderson, Clark & Williams, 1999) that is associated with several negative health youth outcomes, including conduct problems (Brody, Chen, Murry, De, Simmons, Gibbons & Cutrona, 2006), violence (Choi, Harachi, Gillmore & Catalano, 2006), and depression (Gibbons, Yeh, Gerrard, Cleveland, Cutrona, Simons, & Brody, 2007), cigarette use (Wiehe, Aalsma, Liu & Fortenberry, 2010). Though studies are limited, findings suggest that parental support may play an integral protective role in reducing the impact of discrimination and negative outcomes and behavior. For example, Simons and colleagues (2006) found that parental social support reduced the probability that discrimination will lead to violence in male youth. Similarly, studies have shown that emotional support can reduce the impact of discrimination on psychological distress among youth populations (Ajrouch, Reisine, Lim, Sohn, & Ismail, 2010; Gonzalez, Stein, Kiang, & Cupito, 2014). However, to date, no study has been conducted examining whether parental support buffers the effect of racial discrimination on these health outcomes.

For the current study we will fill the gaps in the literature by examining the buffering effect of discrimination of cigarette use among a juvenile offender population. It is also important to consider the context in which discrimination is experienced, as suggested by Williams and Mohammed (2009), it is likely that varying domains of discrimination differentially produce such negative outcomes. Previous work has identified several domains of discrimination which may pose a particular threat to the emotion health of youth: Institutional discrimination (e.g. stores, restaurants), educational discrimination (e.g. teachers), and peer discrimination (Fisher, Wallace & Fenton, 2000). For juvenile offenders specifically, delineating which domains of discrimination may be linked to negative externalizing behaviors (e.g. cigarette use) would both inform areas of intervention for smoking cessation in this population as well as serve as targets for preventative strategies. No research to date has examined how varying domains of discrimination are differentially related to cigarette use and addiction severity, or the positive role social support could play in buffering this relationship among juvenile offenders. Such information is integral in designing effective smoking cessation programs, particularly in identifying targets in treatment, such as coping with varying types of discrimination.

The present study aimed to examine the relationship between overall social support, and experiences of discrimination (institutional, peer, and educational) as related to cigarette use in juvenile offenders. Specifically, we hypothesized that, in juvenile offenders, 1) discrimination is related to cigarette use and addiction severity, 2) overall social support is related to cigarette use and addiction severity, and 3) social support moderates the relationships between discrimination and cigarette use, as well as discrimination and addiction severity.

2. Methods

2.1 Participants and Procedures

Data collection commenced after receiving approval from the University Institutional Review Board. Juvenile offenders were recruited, as part of a larger study, through which court records were collected for every case referred to county juvenile courts across 92 counties over a 5-year period (2005–2009). The current study is based on follow-up data gathered by researchers who worked directly with local justice actors to recruit system-involved youth on probation and in detention centers in three counties, which were selected based on their geographic and population variability. Data collections occurred over a 2 to 3 days in each jurisdiction, thus data collection was time limited.

Juvenile offenders on probation and in detention in the three target counties were eligible to participate if they met the following inclusion criteria: (1) the juvenile was present at the detention center or at their scheduled appointment with a probation officer during the days of data collection, (2) parental consent was received prior to the data collection, and (3) the juvenile voluntarily agreed to participate in the study after reading informed consent information. The study questionnaire was programmed into a web-based survey tool, Qualtrics, and was administered via a WiFi-enabled iPad. Research staff informed the participants that the normal procedure was to read the questions aloud, but participants could choose to “opt-out” if they preferred to complete the questionnaire on their own. Upon completion of the survey, participants received a $10 Wal-Mart gift card (given immediately to those on probation and placed in the personal belongings of those in detention).

Of the potential participants recruited for the study, a total of 112 juvenile offenders between the ages of 10 to 18 met inclusion criteria and completed the survey (a 53% response rate). Reasons for non-participation included: disconnected phone numbers; subjects were not present at the probation offices or in detention centers during the span of time that the data was collected; and parent(s) or youth refused to participate.

2.2 Materials

Demographics

Juveniles were asked to provide their age, gender, and ethnicity (Caucasian, African American, American Indiana or Alaska Native, Asian, Native Hawaiian or other Pacific Islander, Hispanic, Other), and responded to 4 items with the latent construct of socioeconomic status (SES; “Does your family own a car, van or truck?”; “Do you have a bedroom for yourself?”; “During the past 12 months, how many times did you travel away on holiday with your family?”; “How many computers does your family own?”) which were created for the present study. A composite SES score was created by summing responses. Participant’s setting (probation or detention) was also recorded.

Discrimination

Discrimination Distress During Adolescence (DDDS; Fisher, et al. 2000) is a 15-item measure that assesses juvenile’s perceptions of discrimination in three life domains (response options 1-Never, 2-A few times, 3-A lot): Institutional Discrimination (e.g. “You were hassled by a store clerk or store guard”; 6 items; alpha=0.67), Educational Discrimination (e.g. “You were discouraged from joining an advanced level course”; 4 items; alpha=0.45), and Peer Discrimination (e.g. “Others your age did not include you in their activities”; 5 items; alpha=0.63). The DDDS has been validated for use in 9th to 12th graders of varying ethnic backgrounds (Fisher, et al., 2000). One item on the DDDS refers to race, while the remaining items are left up to participant interpretation.

Social Support

Vaux Social Support Record (VSSR; Vaux, 1988) is a nine-item measure that assesses juvenile’s satisfaction with perceived emotional advice‚ guidance‚ and practical social support from both adults and peers (e.g. “At school, there are adults I can talk to, who care about my feelings and what happens to me.”) on a 3-point Likert scale (0-Not at all, 1-Some, 2- A lot). Reliability was good in the present sample (alpha=0.81).

Cigarette Use

Cigarette use was conceptualized in two ways for the present study.

Smoking Status

Participants’ responded to one dichotomous face-valid cigarette use item (“Do you currently smoke cigarettes?).

Addiction Severity

Participants’ response to one item from the CDS-5 scale (“Please rate your addiction to cigarettes on a scale of 1–100”). Higher scores indicate higher addiction severity. Individual items on the CDS-5 have shown good test-retest reliability (Etter, Houezec & Perneger, 2003).

3. Results

3.1 Preliminary Analyses

A total of 112 juveniles either detained or on probation participated in the present study (mean age = 16.24, SD = 2.11, 29.2% female, 54.9% Caucasian, 40.4% detention). Just over 50% of the sample (53.8%) endorsed using cigarettes. Smokers (n = 62) and non-smokers (n = 50) did not vary in gender, setting, or age. However, groups did vary by Caucasian race (χ2 = 18.33, p < .001), with 69.4% of Caucasians endorsing smoking. Additionally, those of African American race differed from the sample in smoking (χ2 = 7.58, p = 0.01), with 62.8% of African Americans endorsing smoking.

All study variables were normally distributed: DDDS scores on the overall scale (mean=24.15, SD=4.41); institutional discrimination (mean=10.25, SD=2.35); education discrimination (mean=6.58, SD=1.50); peer discrimination (mean=7.32, SD=1.87); social support (mean=12.82, SD=3.45). For smokers, addiction severity (mean=51.28, SD=29.25) was approximately normally distributed.

Smoking status was significantly related to addiction severity (point biserial correlation = 0.76, p < .001). Addiction severity was significantly related to social support (r = −0.21, p=0.03), institutional discrimination (r = 0.30, p = 0.001), and peer discrimination (r = 0.19, p = 0.05). DDDS scales were all significantly and moderately intercorrelated (r’s 0.30 – 0.42, p’s < .01).

3.2 Hypothesis Testing

Moderated regression was conducted to examine the interactive effects of social support on the relationship between discrimination domains (institutional, peer, and educational; each run in a separate model) on both smoking status (0-non-smoker, 1-smoker) and nicotine addiction severity using Andrew Hayes’ PROCESS (Hayes, 2012) controlling for ethnicity, age, gender (0-male, 1-female), setting (0-detention, 1-probation), and SES.

Cigarette Use

Results indicated that the relationship between institutional discrimination and smoking status was significantly moderated by social support, OR=−0.10, p=.008. The significant interaction was probed, revealing a higher likelihood of being a smoker, compared to a non-smoker at higher levels of institutional discrimination and lower levels of social support, while at higher levels of social support the odds of being a smoker or a non-smoker were similar (Figure 1). The relationship between peer discrimination and smoking status was also significantly moderated by social support (OR = −0.11, p =. 01). The significant interaction was probed, revealing a higher likelihood of being a smoker at higher levels of peer discrimination and lower levels of social support, a similar likelihood of being a smoker or non-smoker at medium levels, and a higher likelihood of being a non-smoker at high levels of social support (Figure 1). There was no significant interaction for educational discrimination and social support as related to smoking status (OR = −.02, p = .66). See Table 1 further details.

Figure 1.

Figure 1

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Effect of social support (SS) on the relationship between institutional discrimination (left) and peer discrimination (right) on cigarette smoking status.

Table 1.

The effect of social support on the relationship between domains of discrimination and smoking status.

OR (95% CI Lower bound to Upper Bound) z p
Race (ref: Caucasian)
 African American −1.67 (−2.65 to −0.68) −3.32 .0009
 American Indian/Native Alaskan −1.78 (−5.48 to 1.92) −0.94 .35
 Hispanic 0.10 (−1.69 to 1.88) 0.11 .92
 Other/Multiracial −0.78 (−3.42 to 1.82) −0.58 .56
Gender (ref: male) −0.90 (−1.92 to 0.15) −1.72 .08
Age 0.54 (0.19 to 0.88) 3.03 .002
Setting (ref: detention) −0.29 (−1.24 to 0.66) −0.59 .55
SES −0.08 (−0.28 to 0.12) −0.76 .45
Institutional Discrimination 1.49 (0.15 to 2.52) 2.82 .005
Social Support 0.99 (0.22 to 1.75) 2.53 .01
Institutional Discrimination X Social Support −0.10 (−0.17 to −0.03) −2.65 .008
Race (ref: Caucasian)
 African American −1.53 (−2.46 to 0.59) −3.19 .001
 American Indian/Native Alaskan −0.87 (−3.84 to 2.11) −0.57 .57
 Hispanic 0.53 (−1.30 to 5.36) 0.57 .57
 Other/Multiracial −0.84 (−3.40 to 1.72) −0.64 .52
Gender (ref: male) −0.81 (−1.80 to 0.18) −1.61 .11
Age 0.51 (0.14 to 0.88) 2.68 .007
Setting (ref: detention) −0.41 (−1.33 to 0.51) −0.88 .38
SES −0.05 (−0.24 to 0.14) −0.48 .63
Education Discrimination 0.35 (−0.84 to 1.54) 0.57 .57
Social Support 0.11 (−0.51 to 0.73) 0.34 .74
Education Discrimination X Social Support −0.02 (−0.11 to 0.07) −0.45 .66
Race (ref: Caucasian)
 African American 1.69 (−2.66 to −0.71) −3.38 .0007
 American Indian/Native Alaskan −0.71 (−3.59 to 2.16) −0.49 .63
 Hispanic 0.49 (−1.33 to 2.31) 0.53 .60
 Other/Multiracial −1.28 (−3.96 to 1.40) −0.94 .35
Gender (ref: male) −0.59 (−1.64 to 0.46) −1.10 .27
Age 0.49 (0.10 to 0.87) 2.48 .01
Setting (ref: detention) −0.55 (−1.50 to 0.39) −1.15 .25
SES −0.08 (−0.28 to 0.12) −0.83 .41
Peer Discrimination 1.59 (0.34 to 3.84) 2.49 .01
Social Support 0.80 (0.14 to 1.47) 2.35 .02
Peer Discrimination X Social Support −0.11 (−.20 to −0.02) −2.47 0.01
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Note. Cigarettes smoking status (Yes-1, No-0) was the outcome variable for each model. Significance was evaluated on a p<.05 level. Nagelkerke R2: Institutional Discrimination=0.37, Educational Discrimination=.29, Peer Discrimination=0.35

Addiction severity

Results indicated that the relationship between educational discrimination and addiction severity was significantly moderated by social support (b = −0.09, p = .03). The significant interaction was probed using the Johnsons-Neyman technique, which revealed that at low levels of social support (social support = 6–7), higher educational discrimination is associated with higher levels of addiction severity (b = 0.34, p = .05). This effect disappears at medium levels of social support (social support = 8–12; b=0.11, ns), but at high levels of social support (social support =13–18) higher educational discrimination is associated with lower addiction severity (b = −0.72, p =.05). See Table 2 for further details.

Table 2.

The effect of social support on the relationship between domains of discrimination and nicotine addiction severity.

b (95% CI Lower bound to Upper Bound) t p ΔR2 F
Race
 African American −0.59 (−1.37 to 0.20) −1.5 .14
 American Indian/Native Alaskan 0.96 (−1.79 to 3.72) 0.7 .49
 Hispanic −0.40 (−1.55 to 0.76) −0.69 .50
 Other/Multiracial 0.37 (−2.34 to 3.07) 0.27 .79
Gender 0.03 (−0.77 to 0.82 0.07 .95
Age 0.22 (−0.05 to 0.50) 1.62 .11
Setting 0.27 (−0.44 to 0.97) 0.76 .45
SES 0.02 (−0.15 to 0.19) 0.25 .81
Institutional Discrimination 0.39 (−0.30 to 1.07) 1.14 .27
Social Support 0.07 (−0.46 to 0.60) 0.26 .79
Institutional Discrimination X Social Support −0.02 (−0.07 to 0.03) −0.73 .47 0.007 0.54
Race
 African American −0.43 (−1.21 to 0.34) −1.11 .27
 American Indian/Native Alaskan 2.57 (0.06 to 5.08) 2.06 .05
 Hispanic −0.24 (−1.31 to 0.83) −0.45 .65
 Other/Multiracial −0.77 (−3.38 to 1.84) −0.59 .56
Gender 0.37 (−0.40 to 1.15) 0.97 .34
Age 0.35 (0.06 to 0.63) 247 .02
Setting 0.14 (−0.53 to 0.81) 0.42 .68
SES 0.14 (−0.03 to 0.32) 1.62 .11
Education Discrimination 0.94 (−0.08 to 1.96) 1.85 .07
Social Support 0.47 (−0.09 to 1.02) 1.69 .10
Education Discrimination X Social Support −0.09 (−0.17 to −0.01) −2.26 .03 0.06 5.12
Race
 African American −0.44 (−1.23 to 0.34) −1.14 .26
 American Indian/Native Alaskan 2.27 (−0.31 to 4.84) 1.77 .08
 Hispanic −0.21 (−1.37 to 0.94) −0.37 .71
 Other/Multiracial 0.43 (−2.31 to 3.17) 0.32 .75
Gender 0.15 (−0.66 to 0.96) 0.37 .72
Age 0.23 (−0.05 to 0.51) 1.68 .10
Setting 0.10 (−0.61 to 0.82) 0.29 .78
SES 0.06 (−0.11 to 0.23) 0.66 .51
Peer Discrimination 0.22 (0.56 to 1.00) 0.56 .58
Social Support −0.08 (−0.57 to 0.42) −0.62 .75
Peer Discrimination X Social Support −0.004 (−0.06 to 0.05) −0.13 .90 0.0002 0.02
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Note. Nicotine addiction severity was the outcome variable for each model. Race: dummy coded with Caucasian as reference group. Gender: male=0, female=1; setting: detention=0, probation=1. Significance was evaluated on a p<.05 level

4. Discussion

Juvenile offenders are at a disproportionate risk for negative health outcomes, exacerbated by their limited access to proper healthcare (Morris, et al., 1995; Thornberry, et al 2004). It is thus troubling that a large proportion of these juvenile’s report smoking cigarettes (Chassin, 2008), making smoking cessation treatment for juvenile offenders essential. One important aspect to address in cessation treatment with juvenile offenders, as highlighted in the present study, is discrimination. Discrimination is associated negative health outcomes and negative affect, and is therefore a likely trigger for substance use, particularly cigarette use. The study further highlights the importance of distinguishing how different types of discrimination may affect cigarette use, as well the positive effects social support may have on this relationship.

The relationship of institutional (e.g. being hassled by the police, hassled by a store clerk, receiving poor service at a restaurant) and peer discrimination (e.g. you were called racially insulting names, others your age did not include you in their activities, you were discouraged from joining a club) to cigarette use was buffered by social support. Thus addressing these types of discrimination in both preventative (both within the community and with juvenile offenders) and smoking cessation efforts is likely of great utility. Specifically, psychoeducation concerning the negative health impacts of discrimination may be targeted at students, in general, such as through programs in the classroom, as well as for those that frequently interact with these juveniles (e.g. police, teachers, parents), such as through training seminars, in aims of decreasing discriminatory practices. Further, preventative strategies targeted at detained and probated juvenile offenders, such as providing mandatory psychoeducation on discrimination as part of probation requirements, could potentially buffer negative health effects. Additionally, in cessation treatment, clinicians should assess for the potential of these discriminatory experiences and address them directly, particularly aiding these juveniles in managing negative affect and restructuring negative beliefs they may have developed as a result of discrimination.

The relationships between peer discrimination and institutional discrimination with addiction severity was not protected by social support. It is possible that, though initiation of cigarette use could be prevented through social support, the severity of cigarette use likely requires other interventions; if support does not aid the juvenile offender in managing these harmful discriminatory experiences, it may be necessary to introduce effective strategies to cope with these discriminatory experiences and reduce the potential internalization of such experiences. Educational discrimination and addiction severity was, however, buffered by social support. One potential reason for this could be that educational discrimination items may be perceived more subjectively (unfairly receiving a bad grade), while other forms of discrimination may be more objective (being excluded by peers, being hassled by police or in stores), thus subjective discrimination could be more malleable to positive support from others. These findings give promise to reshaping these negative discriminatory experiences in the process of cessation treatment, and may even suggest those experiencing these forms of discrimination may be more resilient when given support.

Though these findings make a great contribution to better understanding the link between discrimination and negative health behaviors, as well as potential protective factors for these behaviors, there are some limitations to discuss. First, the overall sample size of 112, with 62 juvenile offenders being smokers, may have made our analyses underpowered. Though some potential effects may not have been detected, it speaks to the likely strength of the effects that were detected using this sample, and the great potential that targeting these effects in smoking cessation treatment may have. Additionally, this study was cross-sectional in nature, thus no causal or directional inferences may be made. It could be that discrimination leads to smoking or that smoking leads to discrimination in the first place. It is likely that these juvenile offenders do experience discrimination due to their smoking; however there are likely other factors that preceded smoking related discrimination, such as race and SES, increasing confidence in a discrimination to smoking direction, although this is beyond the data of the present study. It is further possible that juvenile offenders are separately at risk for both discrimination and smoking and the observed relationships were spurious; however, given previous literature citing this relationship (e.g. Wiehe, et al., 2010) and the variability in our discrimination measures, it is likely there is a true relationship between discrimination and cigarette use. Future research should elucidate the direction of the discrimination-smoking relationship.

Juveniles in the present study completed self-report measures, which can be susceptible to some social desirability bias; however given the present results in light of this potential issue, it seems likely that youth generally answered cooperatively. Additionally, the discrimination measure used in this study only referenced race for one item, thus it is unclear what type of discrimination may be most important in these found relationships (e.g. race, gender, offender status). Also, approximately 45% of the sample was non-white, thus if discrimination is racially driven, there may have been an underrepresentation of minority groups to detect some relationships. Given the promising findings, future studies should examine if some of these types of discrimination are more predictive of negative health behaviors, particularly smoking. Cigarette use in the present study was assessed using two face-valid items. Though not uncommon in the smoking literature (e.g. Hershberger, et al., 2016), it is possible that other measures of cigarette use could yield different results.

The present study draws important attention to the far reaching negative consequences of discrimination experienced by juvenile offenders. This study also opens the door for a wide area of research examining the potential mechanisms driving the relationships between cigarette use and discrimination (e.g. negative affect, other psychological disorders, personality factors), clinical research examining the efficacy of targeting discrimination in smoking cessation treatment for juvenile offenders, and research on the relationship between distinct types of discrimination and other negative behaviors among juvenile offenders, such as drug and alcohol use, aggression, violence, and gang involvement. Smoking cessation treatment providers, particularly those working with juvenile offenders, should target beliefs associated with discrimination, and preventative strategies, particularly psychoeducation, should be disseminated to individuals in close contact with such juveniles, such as police, probation officers, teachers, and other school officials.

Acknowledgments

Research support was provided to Tamika Zapolski by the NIH Grant Number 5KL2TR001106-03 and Matthew Aaslma by the HRSA/MCHB R40MC08721, provided through the U.S. Department of Health and Human Services, Health Resources and Services Administration, Maternal and Child Health Research Program and from the Indiana Criminal Justice Institute.

References

  1. Ajrouch KJ, Reisine S, Lim S, Sohn W, Ismail A. Perceived everyday discrimination and psychological distress: Does social support matter? Ethnicity & Health. 2010;15(4):417–434. doi: 10.1080/13557858.2010.484050. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Brody GH, Chen Y, Murry VM, Ge X, Simons RL, Gibbons FX, Cutrona CE. Perceived Discrimination and the Adjustment of African American Youths: A Five-Year Longitudinal Analysis With Contextual Moderation Effects. Child Development. 2006;77(5):1170–1189. doi: 10.1111/j.1467-8624.2006.00927.x. [DOI] [PubMed] [Google Scholar]
  3. Brody GH, Lei M, Chae DH, Yu T, Kogan SM, Beach SH. Perceived discrimination among African American adolescents and allostatic load: A longitudinal analysis with buffering effects. Child Development. 2014;85(3):989–1002. doi: 10.1111/cdev.12213. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Chassin L. Juvenile justice and substance use. The future of children. 2008;18(2):165–183. doi: 10.1353/foc.0.0017. [DOI] [PubMed] [Google Scholar]
  5. Choi Y, Harachi TW, Gillmore MR, Catalano RF. Are multiracial adolescents at greater risk? Comparisons of rates, patterns, and correlates of substance use and violence between monoracial and multiracial adolescents. American Journal Of Orthopsychiatry. 2006;76(1):86–97. doi: 10.1037/0002-9432.76.1.86. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Clark R, Anderson NB, Clark VR, Williams DR. Racism as a stressor for African Americans: A biopsychosocial model. American Psychologist. 1999;54(10):805–816. doi: 10.1037//0003-066x.54.10.805. [DOI] [PubMed] [Google Scholar]
  7. Curry SJ, Mermelstein RJ, Emery SL, Sporer AK, Berbaum ML, Campbell RT, Warnecke RB. A national evaluation of community-based youth cessation programs: End of program and twelve-month outcomes. American Journal Of Community Psychology. 2013;51(1–2):15–29. doi: 10.1007/s10464-012-9496-8. [DOI] [PubMed] [Google Scholar]
  8. Etter JF, Le Houezec J, Perneger TV. A self-administered questionnaire to measure dependence on cigarettes: the cigarette dependence scale. Neuropsychopharmacology. 2003;28:359–70. doi: 10.1038/sj.npp.1300030. [DOI] [PubMed] [Google Scholar]
  9. Fisher CB, Wallace SA, Fenton RE. Discrimination distress during adolescence. Journal Of Youth And Adolescence. 2000;29(6):679–695. [Google Scholar]
  10. Gibbons FX, Yeh H, Gerrard M, Cleveland MJ, Cutrona C, Simons RL, Brody GH. Early experience with racial discrimination and conduct disorder as predictors of subsequent drug use: A critical period hypothesis. Drug and Alcohol Dependence. 2007;88:S27–S37. doi: 10.1016/j.drugalcdep.2006.12.015. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Gonzalez LM, Stein GL, Kiang L, Cupito AM. The impact of discrimination and support on developmental competencies in Latino adolescents. Journal of Latina/o Psychology. 2014;2(2):79–91. doi: 10.1037/lat0000014. [DOI] [Google Scholar]
  12. Hayes AF. PROCESS: A versatile computational tool for observed variable mediation, moderation, and conditional process modeling [White paper] 2012 Retrieved from http://www.afhayes.com/public/process2012.pdf.
  13. Helstrom A, Bryan A, Hutchison KE, Riggs PD, Blechman EA. Tobacco and alcohol use as an explanation for the association between externalizing behavior and illicit drug use among delinquent adolescents. Prevention Science: The Official Journal Of The Society For Prevention Research. 2004;5(4):267–277. doi: 10.1023/b:prev.0000045360.23290.8f. [DOI] [PubMed] [Google Scholar]
  14. Hershberger AR, Karyadi KA, VanderVeen JD, Cyders MA. Combined expectancies of alcohol and e-cigarette use relate to higher alcohol use. Addictive behaviors. 2016;52:13–21. doi: 10.1016/j.addbeh.2015.08.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Laub JH, Vaillant GE. Delinquency and mortality: A 50-year follow-up study of 1,000 delinquent and nondelinquent boys. The American Journal Of Psychiatry. 2000;157(1):96–102. doi: 10.1176/ajp.157.1.96. [DOI] [PubMed] [Google Scholar]
  16. Morris RE, Harrison EA, Knox GW, Tromanhauser E, Marquis DK, Watts LL. Health risk behavioral survey from 39 juvenile correctional facilities in the United States. Journal Of Adolescent Health. 1995;17(6):334–344. doi: 10.1016/1054-139X(95)00098-D. [DOI] [PubMed] [Google Scholar]
  17. Myers MG, Doran NM, Brown SA. Is cigarette smoking related to alcohol use during the 8 years following treatment for adolescent alcohol and other drug abuse? Alcohol And Alcoholism. 2007;42(3):226–233. doi: 10.1093/alcalc/agm025. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Simons RL, Simons LG, Burt CH, Drummund H, Stewart E, Brody GH, Cutrona C. Supportive parenting moderates the effect of discrimination upon anger, hostile view of relationships, and violence among African American boys. Journal Of Health And Social Behavior. 2006;47(4):373–389. doi: 10.1177/002214650604700405. [DOI] [PubMed] [Google Scholar]
  19. Smokowski PR, Guo S, Rose R, Evans CR, Cotter KL, Bacallao M. Multilevel risk factors and developmental assets for internalizing symptoms and self-esteem in disadvantaged adolescents: modeling longitudinal trajectories from the Rural Adaptation Project. Development And Psychopathology. 2014;26(4 Pt 2):1495–1513. doi: 10.1017/S0954579414001163. [DOI] [PubMed] [Google Scholar]
  20. Solberg VH, Carlstom AH, Howard KS, Jones JE. Classifying At-Risk High School Youth: The Influence of Exposure to Community Violence and Protective Factors on Academic and Health Outcomes. Career Development Quarterly. 2007;55(4):313–327. [Google Scholar]
  21. Thornberry TP, Huizinga D, Loeber R. The causes and correlates studies: findings and policy implcations. Juvenile Justice. 2004;9(1):3–19. [Google Scholar]
  22. Tobler AL, Madonado-Molina MM, Staras SAS, O’Mara RJ, Livingston MD, Komro KA. Perceived racial/ethnic discrimination, problem behaviors, and mental health among minority urban youth. Ethnicity and Health. 2013;18(4):337–349. doi: 10.1080/13557858.2012.730609. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Vaux A. Social support: Theory, research, and intervention. New York, NY. England: Praeger Publishers; 1988. [Google Scholar]
  24. Wiehe SE, Aalsma MC, Liu GC, Fortenberry JD. Gender differences in the association between perceived discrimination and adolescent smoking. American Journal of Public Health. 2010;100(3):510–516. doi: 10.2105/AJPH.2009.169771. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Williams DR, Mohammed SA. Discrimination and racial disparities in health: Evidence and needed research. Journal Of Behavioral Medicine. 2009;32(1):20–47. doi: 10.1007/s10865-008-9185-0. [DOI] [PMC free article] [PubMed] [Google Scholar]

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