Understanding the Impact of Deviant Peer Association on Risk for Stimulant/Amphetamine Use: The Moderating Role of Exposure to Violence

Abstract

Prior research has indicated that deviant peer association is associated with increased risk for stimulant/amphetamine use. However, there remain gaps in our understanding of the context under which this relationship occurs. This study examined two forms of exposure to violence (direct victimization, witnessed violence) as moderators of the relationship between deviant peer association and stimulant/amphetamine use risk. All 11 waves of the Pathways to Desistance longitudinal data were used in analyses. Mixed effects logistic regression models were used to estimate relationships of interest while accounting for repeated measures nested within individual participants across time. Findings indicated that greater deviant peer association was associated with greater odds of stimulant/amphetamine use. Experiencing direct victimization significantly moderated this relationship, but in the opposite of the hypothesized direction, with individuals reporting direct victimization also reporting less reactivity to deviant peer association as it pertained to stimulant/amphetamine use. Witnessed violence did not moderate this relationship.

Keywords

stimulants amphetamines deviant peer association exposure to violence

Introduction

Stimulant/amphetamine (SA) use presents a public health issue that has grown in relevance in the present-day United States (Admon et al., 2019; Winkelman et al., 2018). SA present a class of drugs characterized by stimulating euphoric effects that may provide energy boosts lasting for long periods of time and include drugs like methamphetamine and Adderall. However, use of these drugs is associated with a range of deleterious health outcomes, including poor cardiovascular health and overdose death; among others (Bazmi, Mousavi, Giahchin, Mokhtari, & Behnoush, 2017; Bramness & Rognli, 2016; Crouse et al., 2018; Fischbach, 2017; Moratalla et al., 2017). While prevalence of use among United States adolescents varies depending on the specific SA type, past-year estimates of use generally range from about 0.5%–8% to (Goodhines, Taylor, Zaso, Antshel, & Park, 2020; Palamar, Han, & Keyes, 2020; Yockey, King, & Vidourek, 2020). Further, both fatal and non-fatal reports of methamphetamine use among adolescents in the United States have increased in the past decade, reaching a prevalence of about .4 persons per 100,000. That said, rates have spiked for older populations, all the way to about 1.75 persons per 100,000 for adults aged 20–29 (Chen et al., 2021). The health impact of these drugs makes identification of risk factors for use and misuse a paramount concern for public health professionals and researchers if harm is to be mitigated. Two risk factors for SA use that have been identified by prior research are deviant peer association and exposure to violence (Mburu et al., 2019; Turner et al., 2018; Wojciechowski, 2021; Yangyuen, Kanato, Mahaweerawat, Mahaweerawat, & Somdee, 2020). Despite research indicating the relevance of these social factors for predicting use, there remain noteworthy gaps in our understanding of these relationships. First, there is a dearth of research which has focused on examining distinct forms of exposure to violence as predictive of SA use. Further, research which has examined the interactional effects between deviant peer association and exposure to violence for predicting SA use is lacking also. It seems possible that the relationship between exposure to violence is moderated by one’s level of exposure to deviant peers and that such a relationship may be contingent on the specific type of exposure to violence experienced. The present study sought to address these gaps in the literature by delineating two distinct forms of exposure to violence as predictors of SA use: witnessed violence and direct victimization. This study then tested to determine whether or not deviant peer association moderated the impact of both or either form of exposure to violence on SA use risk among a sample of justice-involved youth (JIY).¹

Deviant peer association has been identified as a robust risk factor predicting SA use (Dhein, Schmelmer, Guenther, & Salameh, 2018; Wojciechowski, 2021; Yangyuen et al., 2020). One of the most prominent theoretical frameworks providing the mechanisms underpinning this relationship is Akers’ (1973) social learning theory. This theory posits that exposure to individuals who endorse antisocial values and motivations can lead to contagion effects through a socialization process. The theory predicts that exposure to more definitions that are favorable towards antisocial behavior should result in an individual coming to endorse those definitions themselves and thus increasing odds that they will themselves engage in antisocial behavior. Beyond this, mechanisms of reinforcement and punishment experienced through social interaction may further impact risk for engagement in antisocial behavior. Reinforcement refers to any action or potential action taken that increases risk of engagement in a given behavior, like peers offering praise and encouragement when an individual engages in substance use. Punishment is just the opposite, wherein individuals may take action to try to discourage engagement in given behaviors. Deviant peers should then be more likely to offer reinforcement than punishment when an individual engages in antisocial behavior, thus, increasing the odds that they will do so again in the future. It is through these mechanisms that association with peers who endorse antisocial values should increase risk for engagement in behavior like SA use. As noted above, prior research has indicated that association with deviant peers has been identified as a risk factor for SA use (Akbari et al., 2021; Darvishzadeh, Mirzaee, Jahani, & Sharifi, 2019; Dhein et al., 2018). For example, Wojciechowski (2021) found that increased association with deviant peers predicted increased odds of SA use among a sample of justice-involved youth (JIY) at follow-up. Further, this study found that this effect was moderated by age, as the impact of deviant peer association on SA use risk generally declined in a linear manner as participants aged. However, while there has been this limited research on the role that peers play in predicting SA use, there remain important gaps in our understanding of the contexts in which this relationship is observed.

One relevant area where there remains limited understanding of the relationship between deviant peer association and SA use is the role of exposure to violence. Exposure to violence here refers to instances where individuals have either directly experienced being the victim of a violent act or being the witness to another person being victimized in such a way. While exposure to violence has been identified as a risk factor predicting SA use (Turner et al., 2018; Xavier Hall, Newcomb, Dyar, & Mustanski, 2021; Wojciechowski, 2019), research on the role that exposure to violence may play for moderating the relationship between deviant peer association and SA use remains limited. In a general sense, it may be expected that individuals who have experienced exposure to violence might demonstrate increased response to deviant peers that may manifest in greater risk for SA use. Prior research has indicated that experiencing exposure to violence may result in cognitive dysfunction, with diminished impulse control as one problematic outcome (Cole et al., 2016; Telzer, Miernicki, & Rudolph, 2018; Walters & Espelage, 2018). It may be that individuals who have experienced exposure to violence demonstrate cognitive issues like these and thus become more susceptible to the influence of deviant peers. For example, an individual with diminished impulse control may lack the capacity to stop and consider the consequences of engagement in SA use when peers attempt to pressure them to engage in such behaviors. In this way, individuals who have experienced exposure to violence may be particularly susceptible to peer pressure in this way and this may manifest in increased risk for SA use. However, this is not the only consideration that must be made when examining exposure to violence as a key moderator of this relationship of interest.

Further compounding the broader issue discussed above is the fact that exposure to violence as a concept is not a monolith. Rather, there are various forms of exposure to violence that an individual may experience and these different forms may have differential impact on outcomes of interest (Johnsona et al. 2002; Jones et al., 2010; Litrownik, Newton, Hunter, English, & Everson, 2003). For example, Wojciechowski (2020) examined the impact of exposure to violence on violent offending as moderated by pre-existing post-traumatic stress disorder and found that only witnessed violence significantly interacted with disorder status to exacerbate effects on violent offending. It may be that different forms of exposure to violence also exert different moderating effects on the relationship between deviant peer association and SA use risk. Two distinct forms of exposure to violence are proposed to be examined in this manner: direct victimization and witnessed violence. This specific delineation has been identified as relevant to the issue of different forms of exposure to violence exerting differential influence on outcomes of interest also (Johnsona et al., 2002; Shields et al., 2010; Wojciechowski, 2020). In terms of the proposed moderation effect, it may be that direct victimization exerts a stronger effect on cognition than does witnessed violence. While this is supposition here, it would seem logical that directly experiencing a violence exposure may result in greater harm than vicariously witnessing violence occur to someone else. If this is the case, then a stronger moderation effect might be observed for direct victimization in the relationship between deviant peer association and SA use. In this way, while experiencing either form of exposure to violence may exacerbate the effects of deviant peer association as predicted, it may be that experiencing direct victimization exacerbates this impact of peer influence to a greater extent. Alternatively, it may be that only direct victimization exerts a strong enough effect on cognition to play a moderating role in this regard at all. Examination of these exposures to violence as moderators is necessary to determine their differential relevance for understanding the relationship between deviant peer association and SA use.

Beyond the key relationships under examination here, it is also important to note other potential factors that may confound said relationships. For example, gender differences in SA use have been identified by prior research, but with research indicating mixed evidence regarding whether men or women use at higher prevalence (Dluzen & Liu, 2008; Palamar et al., 2020), though gender gaps may be decreasing also (Bach et al., 2020). Racial disparities have also been identified, with SA use higher among Whites than racial minority groups, thought Hispanic and other ethnicities may also be a relevant consideration here (Maloney, Beer, Tie, & Dasgupta, 2020; Teter, McCabe, LaGrange, Cranford, & Boyd, 2006). Social class is also a relevant consideration in understanding SA use risk, as past research has indicated that use is elevated at the lower rungs of the social class ladder (King, Vidourek, & Yockey, 2019; Peltzer & Pengpid, 2018). Finally, other risk factors have also been identified as important here; as low impulse control and experiencing depression are also risk factors for SA use (Lopez-Patton et al., 2016; Wojciechowski, 2021).

Prior research has indicated that deviant peer association and exposure to violence are risk factors predicting SA use (Mburu et al., 2019; Turner et al., 2018; Wojciechowski, 2021; Yangyuen et al., 2020). While these relationships have been well established at this point, there remain important gaps in our understanding of these relationships. There has yet to be any research which has examined interactive effects of these two risk factors for predicting SA use, specifically whether experiencing exposure to violence may exacerbate the effects of deviant peer association on risk for SA use. Further, whether or not the specific form of exposure to violence matters for this moderation remains understudied also. This is a major omission considering that direct victimization and witnessed violence have been found to influence outcomes of interest differently (Johnsona et al., 2002; Shields et al., 2010; Wojciechowski, 2020). The present study sought to address these gaps in the literature by testing the following hypotheses:

Hypothesis 1

Greater deviant peer association will be associated with greater odds of SA use at follow-up.

Hypothesis 2

Experiencing direct victimization and/or witnessing violence will be associated with greater odds of SA use at follow-up.

Hypothesis 3

The relationship between deviant peer association and SA use will be significantly stronger for participants who experienced either form of exposure to violence during the previous observation period.

Methods

Data

The present study utilized data from all 11 waves of the Pathways to Desistance study. This dataset is comprised of the responses of 1354 JIY who were recently adjudication for a serious offense just prior to baseline measurements. These data then were longitudinal and followed participants over the course of the 7 years after this adjudication from adolescence to emerging adulthood. Serious offenses which qualified participants for inclusion consisted of all felony offenses and misdemeanor weapons charges and sexual assault charges. The observation periods for each of the 11 data points for participants were spaced 6 months apart for the first 36 months of the study and 12 months apart for the remainder of their time in the study. Participants were recruited from 2000–2003 from study sites located in Maricopa County, Arizona and Philadelphia, Pennsylvania; with the entire of the study period lasting from 2000–2010. Of all qualified JIY approached regarding their interest in the study, 20% declined the opportunity to take part. Attrition reached its peak at the final period of data collection, with 16.2% of the original sample no longer providing any data for the study. A cap on the number of male drug offenders at baseline was also applied to the study at 15% of the total sample. This was done to ensure baseline heterogeneity on these characteristics.

All of the data included in analyses were collected via participant self-report. Interviews were conducted in locations that were convenient for participants (e.g., libraries, participants’ homes, criminal justice facilities). Participants were provided with laptop computers by the research team to use during interview sessions to manually input responses to verbal prompts from interviewers. This was done in order to maximize confidentiality in reporting.

Measures

Stimulant/Amphetamine Use

The main dependent variable examined in this study was SA use during each observation period. This was operationalized as a binary variable which delineated participants who reported any SA use during a given observation period from those who did not (0 = No; 1 = Yes).

Deviant Peer Association

One of the key independent variables examined in this study was deviant peer association assessed at every wave. This construct was measured using a scale adapted from the Rochester Youth Development Study (Thornberry, Lizotte, Krohn, Farnworth, & Jang, 1994). This was assessed using seven ordinal items which asked participants to indicate the general number of peers who influenced them to engage in different antisocial behaviors. A mean score was then computed from these seven individual items so that all participants had a single deviant peer association score at each wave. A one-observation period lag was applied to this variable so as to establish temporal ordering between this independent variable and the dependent variable in analyses.²

Exposure to Violence

The other set of independent variables examined in analyses measured exposure to violence. These constructs were measured at each wave using the Exposure to Violence Inventory (Selner-Ohagan, Kindlon, Buka, Raudenbush, & Earls, 1998). This instrument assessed the presence/absence of exposure to two distinct types of violence: direct victimization and witnessed violence. Two binary variables were then included in analyses which delineated participants who reported experiencing either form of exposure to violence during a given observation period from those who reported no such exposures during that observation period (0 = No; 1 = Yes). A one-observation period lag was applied to this variable so temporal order was established between these independent variables and the dependent variable of interest.

Control Variables

Several additional variables were included in analyses in order to mitigate risk of omitted variable bias. The first of these control variables was gender, as prior research has indicated that SA use risk is delineated by gender (Dluzen & Liu, 2008). Gender was assessed at baseline as a binary variable which established male and female gender categories (0 = Male; 1 = Female).

Race was also included as a control variable, as prior research has indicated racial disparities in risk for SA use (Maloney et al., 2019). A four-category nominal variable was used to measure race at baseline and separated participants into the following categories: Black, Hispanic, White, and Other Race. A series of four dummy variables were coded which delineated participants in a given race category from all other participants (e.g., 1 = Black; 0 = All other participants). The dummy variable corresponding to White participants was omitted from analyses in order to provide a reference group to interpret coefficient effects in relation to.

Socioeconomic status (SES) was also included as a control variable because past research has indicated that risk for SA is stratified by social class (King et al., 2019). SES was measured at baseline using Hollingshead’s (1957) two-factor index of social position. This instrument assessed SES as a weighed score comprised of participants’ parents’ occupational prestige and educational attainment scores. If both parents were available to provide data, then a mean score was computed from the individual scores so that every participant had a single SES score at baseline.

Another control variable included in analyses was depression, as prior research has indicated that depression is a risk factor associated with SA use (Lopez-Patton et al., 2016). The Brief Symptom Inventory was used to assess depression at each wave (Derogatis & Melisaratos, 1983). This instrument utilized a series of ordinal items to assess the degree to which participants reported being bothered by depressive symptoms during the prior week (ex: Feeling no interest in things). A mean score was then computed from the individual item scores so that every participant had a single depression score at each wave. A one-observation period lag was assigned to this variable.

Impulse control was also controlled for in analyses, as past research has identified low levels of impulse control as a risk factor for SA use (Wojciechowski, 2021). Impulse control was measured at each wave using the Weinberger Adjustment Inventory (Weinberger, Feldman, Ford, & Chastain, 1987). This instrument utilized a series of ordinal items which asked participants to indicate the degree to which they agreed or disagreed that statements were reflective of their own levels of impulse control (ex: I say the first thing that comes into my mind without thinking enough about it). Seven of the eight individual items were then reverse coded so that higher scores corresponded to higher levels of impulse control. A mean score was then computed from the individual items so that every participant had a single impulse control score at each wave. A one-observation period lag was applied to this variable.

Age was also controlled for in analyses because research has indicated that risk for SA use may be age-graded in nature (Chan et al., 2019). Age was assessed at each wave in single-digit year intervals.

It was also necessary to control for the amount of time out of each observation period that participants spent in secured facilities without community access (e.g., jail, prison, psychiatric facilities). This was because spending time in such facilities may have impacted access to SA, thus, impacting risk for use. This was operationalized as a proportion variable at each wave ranging from 0–1, with higher scores reflecting a greater proportion of that observation period spent in a secured facility (e.g., .37 = 37% of observation period spent in a secured facility).

The final control variable included in analyses was observation period length, as longer observation periods entailed greater exposure time which would result in more time for SA use to occur. While observation period length was generally the same across all participants during a given wave, there was some variance in the exact number of days for individual participants. Further, observation period length shifted from 6 months to 12 months during the study period. This variable then was a count of the exact number of days in each observation period for each participant.

Analytic Strategy

This study utilized a series of mixed effects logistic regression models to assess the direct effect of deviant peer association on SA use risk and the moderating effects that exposure to violence had on this relationship at each wave. Mixed effects modeling was chosen because of the method’s capacity to account for the repeated measures that were nested within individual participants across the multiple waves. Repeated measures nested within individual participants were accounted for by modeling random intercepts at the individual-participant level. All other variables were then modeled as fixed effects. Logistic regression was used because the dependent variable in this study was binary in nature. Coefficients are described in the form of odds ratios (OR). Model 1 assessed the direct effects of the exposure to violence and deviant peer association variables on odds of SA use. Model 2 then included interaction terms which assessed the moderating effects of deviant peer association on the relationship between direct victimization and witnessed violence variables and SA use. Missing data were managed via full-information maximum likelihood estimation. Stata/MP 16.1 was utilized to conduct all analyses.

Results

Preliminary analyses examined the bivariate relationships between key variables. A T-test examining mean deviant peer association scores was estimated for participants who reported SA use versus those who reported no SA use during a given observation period pooled across all waves. Results from this T-test indicated that participants who reported SA use during a given observation period reported significantly greater deviant peer association scores (SA = Yes: 1.413; SA = No: .746). Chi² tests were estimated to examine bivariate relationships between each exposure to violence variable and SA use also. For both witnessed violence and direct victimization, significant Chi² test statistics were observed and this indicated that participants reporting SA use had higher likelihoods of experiencing both forms of exposure to violence (Direct victimization Chi² = 329.884; Witnessed violence Chi² = 101.795).

Table 1 provides descriptive statistics for all variables that were included in the analyses. The sample was comprised mainly of male adolescents (Male = 86.4%; Female = 13.6%). Black participants comprised the modal racial group in the sample (41.4%), followed by Hispanic participants (33.5%), then White participants (20.2%), and finally Other Race participants (4.8%). The average age at baseline was 16.044 years old. Table 2 provides Model 1 results, whereas Table 3 provides Model 2 results.

Table 1.

Descriptive Statistics.

	Mean	Standard Deviation	Minimum	Maximum
Stimulant/Amphetamine use (Pooled)
No = 94.3%
Yes = 5.7%
Direct victimization (Pooled)
No = 81.6%
Yes = 18.4%
Witnessed violence (Pooled)
No = 52.7%
Yes = 47.3%
Deviant peer association (Pooled)	.783	.822	0	4
Gender
Male = 86.4%
Female = 13.6%
Race
Black = 41.4%
Hispanic = 33.5%
White = 20.2%
Other Race = 4.8%
Socioeconomic status at baseline	51.409	12.299	11	77
Depression (Pooled)	.435	.642	0	4
Impulse control (Pooled)	3.197	.969	1	5
Age
(Baseline)	16.044	1.143	14	19
(Pooled follow-up)	19.215	2.353	14	26
Days in observation period (pooled)	248.399	93.832	61	428
Proportion of observation period spent in secured facility (pooled)	.329	.316	0	1

^aPooled measures are population-average statistics derived from pooling data from all waves of the study together.

Table 2.

Mixed Effects Logistic Regression Modeling of Direct Effects of Deviant Peer Association and Exposure to Violence on Odds of Stimulant/Amphetamine Use in Odds Ratios Net of All Control Covariates (OR): Model 1.

	OR	p-Value	95% Confidence Interval
Deviant peer association (Lagged)	1.295	.001	1.106	1.515
Direct victimization (0 = No; 1 = Yes) (Lagged)	1.302	.067	.981	1.728
Witnessed violence (0 = No; 1 = Yes) (Lagged)	1.098	.520	.826	1.458
Gender (0 = Male; 1 = Female)	2.023	.006	1.219	3.358
Race (reference = White)
Black	.034	p < .001	.017	.067
Hispanic	.635	.635	.400	1.009
Other race	.470	.470	.202	1.096
Socioeconomic status at baseline	.989	.186	.972	1.005
Depression (Lagged)	1.376	p < .001	1.158	1.636
Impulse control (Lagged)	.705	p < .001	.598	.832
Age	.891	.012	.815	.974
Days in observation period	1.003	.013	1.001	1.005
Proportion of observation period spent in secured facility	.592	.004	.416	.844
Constant	.260	.146	.042	1.595
Constant variance at ID level	3.565

Table 3.

Mixed Effects Logistic Regression Modeling of Interactions Between Deviant Peer Association and Exposure to Violence on Odds of Stimulant/Amphetamine Use in Odds Ratios Net of All Control Covariates (OR): Model 2.

	OR	p-Value	95% Confidence Interval
Deviant peer association (Lagged)	1.536	.001	1.187	1.987
Direct victimization (0 = No; 1 = Yes) (Lagged)	2.058	.004	1.267	3.343
Witnessed violence (0 = No; 1 = Yes) (Lagged)	1.119	.614	.724	1.730
Deviant peer association X direct victimization	.715	.031	.527	.970
Deviant peer association X witnessed violence	.952	.771	.683	1.327
Gender (0 = Male; 1 = Female)	2.056	.005	1.238	3.414
Race (reference = White)	.034	p < .001	.017	.067
Black Hispanic	.638	.057	.402	1.014
Other race	.472	.083	.203	1.102
Socioeconomic status at baseline	.989	.189	.973	1.006
Depression (Lagged)	1.386	p < .001	1.166	1.647
Impulse control (Lagged)	.709	p < .001	.600	.837
Age	.886	.008	.810	.969
Days in observation period	1.003	.011	1.001	1.005
Proportion of observation period spent in secured facility	.600	.005	.421	.855
Constant	.237	.121	.038	1.462
Constant variance at ID level	3.559

Model 1 results indicated that greater deviant peer association predicted increased odds of reporting SA use at follow-up (OR=1.295). Neither form of exposure to violence was a significant predictor of SA use in this model. Being female, lower impulse control, greater depression, being younger, longer observation periods, and spending less time in secured facilities also all predicted increased odds of SA use in this model.

Model 2 results indicated that greater deviant peer association continued to be associated with increased odds of SA use at follow-up (OR = 1.536). Experiencing direct victimization was also associated with increased odds of SA use in this model (OR = 2.058). The direct effect of witnessed violence remained nonsignificant in this model. The interaction between direct victimization and deviant peer association was significant and negative in this model (OR = .715), indicating that the impact of deviant peer association scores on odds of SA use was significantly lower for participants who reported experiencing direct victimization compared to those who did not during a given observation period. The interaction between witnessed violence and deviant peer association was nonsignificant in this model. Being female, greater depression, lower impulse control, longer observation periods, being younger, and spending less time in secured facilities were also all associated with increased odds of SA use in this model.

Additional analyses examined the marginal effects of deviant peer association on odds of SA use delineated by direct victimization status, net of all control covariates using the margins and marginsplot functions in Stata. Figure 1 provides visual depiction of these marginal effects.³ While there were no significant differences between participants who reported experiencing direct victimization during a given observation period from those who did not across the continuum of the deviant peer association scale, there was a great deal of variation in the patterns of effects of deviant peer association within the two victimization groups. Participants who reported experiencing direct victimization during a given observation period reported almost no variation in SA use based on differences in deviant peer association scores, demonstrating a relatively flat slope. There are not any within-group significant differences in SA use across this continuum. Alternatively, participants who did not report experiencing direct victimization demonstrated a great deal more variation in SA use risk based on deviant peer association scores, demonstrating large increases in SA use risk as deviant peer association scores increased. While significant differences in SA use risk between the two groups did not appear, there were significant differences in SA use risk within the non-victimized group at the lower and higher ends of the deviant peer association continuum. This provides indication that the significant moderation effect observed in Model 2 was mainly due to the lack of reactivity to deviant peer association among the victimized participants and a large degree of reactivity to deviant peer association among those participants who did not report being victimized.⁴

Discussion

The findings from this study provided a novel view of the relevance of deviant peers and exposure to violence for predicting SA use among JIY. Deviant peer association was found to predict increased odds of SA use. Additionally, this relationship was found to be moderated by experiences of direct victimization, with the impact of deviant peer association on SA use found to be significantly weaker among JIY who reported experiencing direct victimization during the prior observation period. No such interaction was observed for witnessed violence. There are a number of relevant implications of these findings for public health professionals and researchers focused on reducing health impacts of SA use.

Greater deviant peer association was found to be associated with elevated risk of SA use at follow-up. This finding was consistent with prior research on the topic (Dhein et al., 2018; Wojciechowski, 2021; Yangyuen et al., 2020), thus providing additional evidence of the robust relationship between deviant peers and SA use. This provides evidence of the importance of reducing peer effects among JIY as a mechanism to mitigate the public health impacts of SA use. This indicates the potential relevance of designing and implementing programming focused on reducing affiliation with peers involved in antisocial behavior as a means of reducing SA use. Consistent with Akers’ (1973) social learning theory, this may involve a socialization process wherein antisocial values and motives are being transmitted from such peers and this results in increased risk for SA use and/or deviant peers opening up channels for obtaining SA. To reduce these mechanisms of behavioral transmission, programming may focus on prosocial modeling as a means of providing socialization that facilitates more normative behavior and values. Mentoring programs involving providing such models using older peer models who provide such socialization may have some utility for reducing risk for engagement in antisocial behavior (Caldarella, Adams, Valentine, & Young, 2009; Keating, Tomishima, Foster, & Alessandri, 2002). Providing programming options like this for JIY may similarly have utility for reducing risk for SA use and should potentially be considered for implementation among indicated populations. Future research should seek to evaluate the effectiveness of such programming for impacting SA use and also identifying potential limitations and obstacles for implementing this type of programming within a juvenile justice context.

While the direct effect of deviant peers was found to be relevant in this regard, this is not the only potential risk factor for SA use that was considered in this study. Exposure to violence was examined as a moderator of this relationship of interest and was delineated into two separate forms: witnessed violence and direct victimization. The direct effects of exposure to violence variables were generally not significant which was inconsistent with prior studies on the topic (Turner et al., 2018; Xavier Hall et al., 2021; Wojciechowski, 2019). This may have been due to the delineation of these specific forms of exposure to violence in the present study, as this impacted sample size for those responding that they experienced either form and this may have impacted them otherwise being averaged together. However, the present study was the first to examine the moderating effect of exposure to violence on the relationship between deviant peer association and SA use. Direct victimization was found to significantly moderate this relationship, demonstrating that the impact of deviant peer association on SA use was more salient for participants who reported not experiencing direct victimization during a given observation period. This finding was contrary to expectations which predicted that being victimized would exacerbate the effects of deviant peer association in this regard. This may be because youth who have been victimized have reached a ceiling of risk pertaining to SA use, thus, making any risk imparted by deviant peer association superfluous. This also provides indication that the provision of treatment focused on reducing the impact of deviant peer association on SA use may be best targeted toward youth who have not experienced victimization. In this way, it may be more fruitful to focus on the trauma-related symptoms of JIY who have been victimized in order to reduce risk for SA use in this population and focusing more on peer relationships for those JIY who have not experienced such traumatic exposures. This finding also highlights the need to determine whether this form of moderation exists for other outcomes as well, as this may help to better target resources and treatment toward JIY who will be most responsive. This, however, remains speculative and in need of further study.

While this study provided a nuanced examination of the context under which affiliation with antisocial peers influence risk for SA use, there remain a number of important limitations. First, the sample utilized here was comprised solely of JIY. Because of the elevated risk for substance use and risk factor exposure among this population (Baglivio et al., 2014; McClelland, Teplin, & Abram, 2004; Underwood & Washington, 2016), the generalizability of these findings may be limited. This issue may be further compounded by the purposive sampling strategy that was used to identify the original sample. This indicates the need to determine the robustness of these findings using novel general population data collected via a probability sampling strategy. Doing so would provide better indication of how well these findings conform to processes observed among a broader swath of adolescents and young adults. Another sampling-related issue is the cap that was applied to the number of male drug offenders included in the sample. Given that SA use would specifically be affected by this cap, it may be that this study present an underestimate of the prevalence of SA use in the sample and thus may bias results to some extent. Future research should seek to re-examine these relationships using data not limited by this cap on the proportion of male drug offenders. Another limitation of the present study pertains to coarseness of the exposure to violence variables utilized in analyses. While delineation between witnessed violence and direct victimization as predictors/moderators in this study was novel, there are many more ways in which these exposure to violence variables may be broken down to examine how different forms impact relationships of interest. For example, it may be that there is a difference in the impact of sexual victimization versus physical victimization on SA use that the relatively coarse measure of direct victimization used here did not identify. This indicates the need for continued study of these processes using data which has the capacity to delineate more specific forms of exposure to violence in order to better understand whether there exist differences in effects based on the more specific details of experiencing such exposures.

Footnotes

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.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Thomas Wojciechowski

Notes

Author Biography

Thomas Wojciechowski is an assistant professor with the Michigan State University School of Criminal Justice. HIs research focuses mainly on substance use, mental health, and human development.

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