(0.056)
Note . ACEs = adverse childhood experiences; SES = socioeconomic status.
two-tailed significance tests.
Table 3 shows results for the second analytical aim, which was to determine whether and how ACEs moderate the association between peer influences and substance use. Looking at the first four columns of Table 3 , ACEs did not moderate the associations between either of the peer influence variables and number of days drank or used drugs in the past month. ACEs also did not moderate the association between unstructured socializing and number of cigarettes smoked in the past month. However, ACEs did moderate the association between peer substance use and number of cigarettes smoked in the past month. For ease of interpretation, Figure 2 shows the predicted number of cigarettes as a function of the number of ACEs and level of peer substance use. At average and low levels of peer substance use (defined as the mean level of peer substance use and one standard deviation below the mean, respectively), the predicted number of cigarettes smoked did not differ as a function of ACEs history. At high levels of peer substance use (one standard deviation above the mean), the predicted number of cigarettes smoked in the past month increased tenfold from 3.18 cigarettes at zero ACEs to 31.4 cigarettes at the maximum number of seven ACEs. These results partially supported the hypothesis that ACEs magnify youth’s vulnerability to substance use promoting peer influences—specifically, youth with a greater number of ACEs smoked more when they were around substance-using peers than youth without a history of ACEs.
Predicted number of cigarettes smoked in past month, by number of adverse childhood experiences and level of peer substance use.
Moderation of Peer Influences on Substance Use by ACEs ( N = 1,912).
Negative Binomial Regression Coefficients | ||||||
---|---|---|---|---|---|---|
Days drank in past month | Days used drugs in past month | Cigarettes smoked in past month | ||||
Variables | (1) | (2) | (3) | (4) | (5) | (6) |
ACEs | 0.062 (0.063) | −0.002 (0.067) | 0.053 (0.159) | −0.080 −0.097 | 0.033 (0.107) | 0.079 (0.101) |
Peer substance use | 1.390 (0.201) | 1.054 (0.149) | 2.032 (0.405) | 1.801 (0.229) | 1.094 (0.514) | 2.222 (0.211) |
Unstructured socializing | 0.809 (0.099) | 0.818 (0.219) | 1.160 (0.169) | 0.623 (0.303) | 1.321 (0.183) | 0.889 (0.485) |
Peer substance use × ACEs | −0.109 (0.063) | −0.075 (0.587) | 0.374 (0.155) | |||
Unstructured socializing × ACEs | −0.001 (0.061) | 0.186 −0.105 | 0.125 (0.146) | |||
1,912 | 1,912 | 1,912 | 1,912 | 1,912 | 1,912 |
Note . Models control for child gender, child race/ethnicity, child age, primary caregiver’s age, household socioeconomic status, and family size. ACEs = adverse childhood experiences.
The third aim was to examine how neighborhood collective efficacy shapes the associations between ACEs, peer influences, and substance use. The three-way interactions between ACEs, collective efficacy, and each of the peer influences were not significant for drug use, thus only the results for number of cigarettes and number of days drank are shown. The results for drug use are available from the author upon request.
Table 4 displays the three-way interactions between ACEs, the two peer influence variables, and neighborhood collective efficacy for number of cigarettes smoked and number of days drank in the past month. For ease of interpretation, Figure 3 shows how the associations (expressed as incidence rate ratios) between the two peer influence variables and number of cigarettes smoked changed based on the number of ACEs and level of neighborhood collective efficacy. In Figure 3 , “low” and “high” refer to one standard deviation below and above, respectively, the mean neighborhood collective efficacy score. In low collective efficacy neighborhoods, the association between peer substance use and smoking varied considerably by a youth’s number of ACEs. For youth with no ACEs in these neighborhoods, a one point increase in peer substance use was associated with an average expected increase of 1.3 cigarettes smoked in the past month. For an adolescent with five ACEs in the same neighborhood, a one point increase in peer substance use was associated with an increase of 717 cigarettes smoked in the past month (i.e., a little more than a pack a day). This moderation of peer substance use by ACEs was also present in high collective efficacy neighborhoods, but to a smaller extent. The average expected increase of cigarettes smoked in the past month associated with a one point increase in peer substance use ranged from 5.6 cigarettes for youth with no ACEs to 30.7 cigarettes for youth with five ACEs. The moderation of unstructured socializing by ACEs on smoking did not differ by level of neighborhood collective efficacy.
Associations between peer influences and number of cigarettes smoked in past month by number of ACEs and neighborhood CE.
Note . ACEs = adverse childhood experiences; CE = collective efficacy.
Moderation of Peer Influences on Smoking and Drinking by ACEs and Neighborhood Collective Efficacy ( N = 1,912).
Negative Binomial Regression Coefficients | ||||||
---|---|---|---|---|---|---|
Cigarettes smoked in past month | Days drank in past month | |||||
Variables | (1) | (2) | (3) | (1) | (2) | (3) |
Adverse childhood experiences | 0.083 (0.139) | −0.232 (0.218) | −0.073 (0.147) | −0.004 0.051 | 0.012 (0.065) | −0.015 (0.062) |
Peer substance use | 3.206 (0.341) | 1.013 (0.665) | 2.818 (0.620) | 1.092 (0.150) | 1.335 (0.208) | 1.158 (0.138) |
Unstructured socializing | 1.481 (0.259) | 1.470 (0.245) | 0.359 (0.744) | 0.798 (0.102) | 0.818 (0.105) | 0.902 (0.224) |
Collective efficacy | 0.284 (0.440) | 0.292 (0.425) | 0.107 (0.476) | 0.075 (0.143) | 0.024 (0.240) | 0.057 (0.186) |
Collective efficacy × ACEs | 0.175 (0.124) | 0.069 (0.052) | 0.069 (0.035) | −0.005 (0.042) | ||
Peer substance use × ACEs | 0.787 (0.210) | −0.053 (0.061) | ||||
Peer substance use × collective efficacy | 0.738 (0.493) | −0.136 (0.128) | ||||
Peer substance use × collective efficacy × ACEs | −0.465 (0.150) | −0.047 (0.048) | ||||
Unstructured socializing × ACEs | 0.334 (0.275) | −0.020 (0.062) | ||||
Unstructured socializing × collective efficacy | 0.232 (0.445) | −0.400 (0.145) | ||||
Unstructured socializing × collective efficacy × ACEs | −0.041 (0.198) | 0.149 (0.053) |
Note . Models control for child gender, child race/ethnicity, child age, primary caregiver’s age, household socioeconomic status, and family size at the individual level; and concentrated disadvantage, immigrant concentration, residential stability, and violent crime at the neighborhood level. ACEs = adverse childhood experiences.
Turning to number of days drank in Figure 4 , the moderation of peer substance use by ACEs did not differ by level of neighborhood collective efficacy. However, ACEs did moderate the effect of unstructured socializing on number of days drank differently based on the level of collective efficacy in the youth’s neighborhood. In low collective efficacy neighborhoods, ACEs moderated the effect of unstructured socializing in an opposite direction than in previous models—an increase in ACEs attenuated the association between unstructured socializing and number of days drank. However, the pattern was reversed in high collective efficacy neighborhoods such that ACEs intensified the association between unstructured socializing and drinking. A one point increase in unstructured socializing was associated with an average expected increase of 1.7 days drank in the past month for youth with no ACEs. For youth with five ACEs, this expected increase per one point in unstructured socializing rose to 3.1 days drank in the past month. These results provided partial support to both hypotheses regarding the interactive effects between ACEs, neighborhood collective efficacy, and peer influences, depending on the specific type of peer influence and substance use outcome examined.
Associations between peer influences and number of days drank in past month by number of ACEs and neighborhood CE.
ACEs consistently predict worse mental and physical health outcomes throughout the life course, in part through the development of risky health behaviors. In particular, youth with adverse experiences are more likely to ever drink, smoke, and use drugs ( Dube et al. 2006 ), use these substances in greater quantities ( Anda et al. 1999 ) and to initiate use at earlier ages ( Dube et al. 2003 ). Peer influences, particularly the increased availability and perceived rewards of substance use within certain peer groups, also consistently predict adolescents’ own substance use. Given these findings, more research needs to consider how peers shape substance use behaviors for youth affected by ACEs in particular. This study fills in these gaps in the literature by considering the extent to which peer influences explain the association between ACEs and substance use as well as how ACEs moderate the established relationship between peer influences and substance use. Community contexts also shape opportunities for substance use and the extent to which peer groups can facilitate substance use. Thus, this study also considers how peer influences operate differentially for youth depending on both their history of ACEs and neighborhood context.
As hypothesized, peer substance use and unstructured socializing mediated the association between ACEs and all three substance use outcomes examined. Furthermore, ACEs strengthened the association between peer substance use and number of cigarettes smoked in the past month. Lastly, two three-way interactions between neighborhood collective efficacy, peer influences, and ACEs revealed that the strength of the link between peer influences and substance use differed not only by a history of ACEs but also by community context and the type of substance use examined. Specifically, the strengthening effect of ACEs on the association between peer substance use and smoking was more pronounced in low collective efficacy neighborhoods. The opposite pattern emerged for drinking and unstructured socializing—ACEs attenuated the association between unstructured socializing and drinking in low collective efficacy neighborhoods but strengthened it in high collective efficacy neighborhoods. These results point to three main themes.
First, youth with a history of ACEs were more likely to spend time in peer settings that are associated with increased substance use; specifically, they are more likely to engage in unstructured socializing and have friends who drink, smoke, and use drugs. A major methodological concern in the literature on peer effects on adolescent substance use is the distinction between peer selection and peer influence. In other words, do youth who already drink or smoke seek out peers who engage in similar behaviors (i.e., selection) or do substance-using peers encourage their friends to partake (i.e., influence)? This study cannot precisely disentangle whether youth with a greater number of ACEs simply gravitate toward friends who have similar substance use habits or if their friends’ influence exerts a causal effect. Both mechanisms likely shape adolescents’ risk of substance use ( Haynie 2001 ; Kandel 1978 ; Krohn et al. 1996 ; Matsueda and Anderson 1998 ; Thornberry 1987 ); a better understanding of both how youth with ACEs select peer groups and how these peers affect them can aid in prevention and outreach efforts. Another limitation of estimating peer effects is that respondents may overestimate how much their friends actually use substances. However, this study supports prior research (e.g., Haynie and Osgood 2005 ) indicating that both unstructured socializing and peer substance are independently associated with youth’s own substance use, particularly for youth with a history of ACEs. New methodological advances such as the use of intensive longitudinal modeling (e.g., Weerman, Wilcox, and Sullivan 2018 ) can help to better elucidate the proximate processes linking peer settings and influences to youth’s substance use.
Second, a history of ACEs was associated with greater vulnerability to peer substance use regarding number of cigarettes smoked. This result corresponds to Julie S. Olson and Robert Crosnoe’s (2018) finding that the association between peer drinking and youth’s own drinking was stronger for youth who had binge-drinking parent(s) compared with youth whose parents who had not recently binge drank. Going beyond parental substance use, this study considers multiple types of family experiences that may set the stage for adolescent substance use. The intensification may apply to cigarette smoking in particular because cigarettes are easier to procure (especially among older adolescents who can buy them legally) compared with alcohol or illicit drugs. Smoking may be an attractive and easily available tool for emotional regulation among youth with ACEs who spend time with peers who also smoke.
One limitation of these data is that the ACE index was derived from primary caregiver reports of their own behaviors and household experiences; thus, this measurement likely represents an underreport of ACEs if primary caregivers are reluctant to disclose abusive behavior. In this case, estimates reported in this study are conservative. Another limitation is that this measure of ACEs only captures whether or not youth ever experienced each type of adversity, rather than chronicity and/or severity. Nevertheless, this measure of ACEs improves over many past studies by capturing more temporally proximate experiences, rather than relying on adults’ retrospective reports of their childhoods. Next steps for this line of research include more sophisticated measurement of ACEs, such as explicit consideration of the severity and chronicity of maltreatment, as well as the cumulative and interactive effects of multiple types of trauma. Other research also expands the definition of ACEs to consider exposure to violence in the community, foster care involvement, and other adverse experiences at school and in the neighborhood (e.g., Cronholm et al. 2015 ; Fagan et al. 2014 ).
Third, the extent to which peer influences matter for youth’s substance use depended not only on their history of ACEs but also their neighborhood context. Not only do neighborhood characteristics moderate parental influences on adolescent substance use ( Zimmerman and Farrell 2017 ), but this study suggests that they also shape the influence of peers on adolescent substance use, and in nuanced ways depending on an adolescent’s ACEs history and type of substance. Collective efficacy limited the interactive effect between peer substance use and ACEs on smoking. In other words, youth with a high number of ACEs and substance-using peers smoked less in neighborhoods with high collective efficacy compared with their peers with the same number of ACEs and same level of peer substance use but in low collective efficacy neighborhoods. This finding aligns with the hypothesis that collective efficacy can have a protective effect for youth in vulnerable situations and is consistent with Abigail A. Fagan and colleagues’ (2014) finding that violence-exposed youth are less likely to turn to substance use in high collective efficacy neighborhoods compared with their peers in lower collective efficacy neighborhoods. This could be because adults in high collective efficacy neighborhoods are more likely to monitor adolescents and intervene to prevent negative health behaviors like smoking.
However, this pattern did not hold true in the models for drinking. Collective efficacy intensified the interactive effect between ACEs and unstructured socializing on number of days drank in the past month. The magnification of the interaction between ACEs and unstructured socializing on drinking in the context of high neighborhood collective efficacy observed in this study stands in contrast to David Maimon and Christopher R. Browning’s (2010) findings, in which greater collective efficacy attenuated the association between unstructured socializing and violent behavior. Consistent with routine activity and negotiated coexistence theory ( Browning 2009 ), these findings reflect the idea that the deviant behavior may flourish in what may typically be thought of as prosocial environments ( Osgood et al. 1996 ). Specifically, the greater trust among adults and youth may actually facilitate adolescent drinking.
These findings raise the question as to why collective efficacy operated so differently for the two different substance use outcomes. The effect of collective efficacy on youth’s substance use may depend on local norms among adults ( Ahern et al. 2009 ) whose intervention can either facilitate or prevent adolescent substance use. Most parents, regardless of socioeconomic status, likely do not approve of adolescent smoking. However, many parents may approve of youth drinking in moderation, particularly in wealthier communities ( Snedker, Herting, and Walton 2009 ). One study found that collective efficacy was associated with more drinking for older adolescents (ages 16–19), but less drinking for young adolescents (under age 16) ( Jackson et al. 2016 ). Adults may see it as appropriate for older adolescents to drink and may facilitate this drinking. Furthermore, collective efficacy may reflect the trust among adults in the community that facilitates opportunities for vulnerable youth to engage with substance-using peers in unstructured, unsupervised settings. A previous study demonstrates that youth’s own perception of safety is also positively associated with binge-drinking initiation ( Tucker et al. 2013 ). The combination of high collective efficacy and high unstructured socializing which predicts more drinking among youth with ACEs may also reflect youth’s own perceptions of neighborhood safety. As the authors of the study note, youth’s perception of safety likely refers to safety from being punished or stopped by adults, rather than from neighborhood crime or violence. In high collective efficacy neighborhoods, adults are willing to intervene to stop adolescents from engaging in unhealthy or unsafe behavior—but they might not perceive drinking as such.
Another limitation of this study is that unstructured socializing and substance use may not necessarily occur within the bounds of an adolescent’s neighborhood. Collective efficacy may be confounded with some other characteristic of a youth’s school or home environment; however, the analysis controls for several neighborhood-level covariates such as crime, concentrated disadvantage, and residential instability to minimize this possibility. In addition, collective efficacy was measured three years prior to youth’s substance use and peer settings and the data do not capture the characteristics of a different neighborhood if youth moved between waves. However, prior research indicates that the social environment of a neighborhood generally does not change drastically in a short period of time, nor do families usually move into significantly different types of neighborhoods ( Sampson 2011 ). Future research using new methodological tools such as ecological momentary assessment (e.g., Roberts et al. 2019 ) and theoretical concepts such as activity spaces and ecological networks (e.g., Browning, Soller, and Jackson 2015 ) can clarify how place matters for youth’s substance use.
It is important to note that the PHDCN sample is not nationally representative and is disproportionately made up of non-white adolescents living in urban areas. Although this group may experience more ACEs than the general population, ACEs are common across all socioeconomic groups ( Merrick et al. 2018 ). Black and Hispanic adolescents are less likely to smoke cigarettes, use illicit drugs, and drink heavily compared with white adolescents (although these differences have narrowed in recent years; Johnston et al. 2017 ). Furthermore, the association between economic deprivation and substance use is weaker for Black and Hispanic youth ( Bachman et al. 2011 ). Thus, these results may be an underestimate of the associations between ACEs, peer influences, neighborhood context, and substance use. A major advantage of using the PHDCN is its rich data on neighborhood contexts that is not available with nationally representative data sets like Monitoring the Future. Nevertheless, future national data collection efforts regarding adolescent substance use may benefit from questions regarding youth’s neighborhood context. More research should also pay attention to adolescents in rural areas, particularly those most affected by the opioid epidemic.
Linking insights from criminology, sociology, and developmental psychology, this study demonstrates the relevance of examining family and peer contexts as crucial influences on adolescent health behaviors that may persist into adulthood. It also offers a start to the examination of the neighborhood contexts of substance use trajectories for youth affected by ACEs. In sum, peer settings explained a large portion of the association between ACEs and adolescent substance use; additionally, youth with a history of ACEs were more vulnerable to problematic peer influences with regard to cigarette smoking. Last, collective efficacy further modified the interactive effect of ACEs with peer influences, but in different ways depending on the type of substance use examined. These results invite further investigation into the proximate school, peer, and community factors influencing the substance use behaviors of youth affected by ACEs.
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by grant P2CHD042849, Population Research Center, and grant T32HD007081, Training Program in Population Studies, awarded to the Population Research Center at The University of Texas at Austin by the Eunice Kennedy Shriver National Institute of Child Health and Human Development. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Haley Stritzel received her PhD in Sociology from the University of Texas at Austin and is a current post-doctoral scholar at the University of North Carolina at Chapel Hill. She studies the family and community contexts of youth well-being, with a particular emphasis on children involved with the child welfare system. Her current research focuses on foster children affected by parental substance use and kinship caregivers.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
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