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. Author manuscript; available in PMC: 2021 Mar 23.
Published in final edited form as: J Child Adolesc Subst Abuse. 2020 Oct 15;29(1):46–57. doi: 10.1080/1067828x.2020.1827471

A preliminary examination of substance use risk among metropolitan and non-metropolitan girls involved in the juvenile justice system

Michele Staton a,b, Megan F Dickson a,b, Martha Tillson a,c, Carl Leukefeld a,b, J Matthew Webster a,b, Hannah K Knudsen a,b, Carrie B Oser a,c
PMCID: PMC7986582  NIHMSID: NIHMS1648295  PMID: 33762805

Abstract

Research is limited on geographic differences in substance use risk factors among juvenile justice-involved girls. This secondary data analysis from one state juvenile justice system, collected as part of the NIH/NIDA funded JJTRIALS cooperative agreement, assessed criminogenic needs at intake for 160 girls from metropolitan and non-metropolitan counties. Although girls from different geographic areas did not differ significantly on key variables of interest, including substance use risk and related criminality variables, findings suggest that substance use risk is related to criminal history, substance-related offenses, and relationship problems among justice-involved girls. Implications include gender-specific juvenile justice programming and research.

Keywords: girls, substance use, criminogenic needs, non-metropolitan

Introduction

While juvenile arrests have largely declined over the past thirty years, the percentage of juvenile arrests involving adolescent girls has increased (Puzzanchera & Ehrmann, 2018). As of 2014, girls represented 28% of delinquency cases handled by juvenile courts (Hockenberry & Puzzanchera, 2017). National data show that girls represent a small proportion of youth in residential placement (15%; Sedlak & Bruce, 2017), and they are more likely to receive diversion or probation than boys (Puzzanchera & Ehrmann, 2018). However, girls with juvenile justice system involvement represent a uniquely vulnerable group with significant health, behavioral health, and service needs. Girls in the juvenile justice system disproportionately experience mental health disorders (Welch-Brewer & Roberts-Lewis, 2011), abuse and victimization (Lennings, Kenny, Howard, Arcuri, & Mackdacy, 2007), school and family problems (including ineffective parental control; Rodney & Mupier, 2004), risky romantic relationships (Garcia & Lane, 2012), and sexual risk behaviors (Guthrie, Hoey, Ravoira, & Kintner, 2002; Teplin, Mericle, McClelland, & Abram, 2003).

Substance use among youth offenders also varies by gender (Leve, Chamberlain, & Kim, 2015). Although system-involved boys and girls report similar patterns of alcohol and marijuana use, girls have been shown to report earlier age of initiation, and are more likely to report current use for drugs such as cocaine, crack, and stimulants (Guthrie et al., 2002; Neff & Waite, 2007; Rodney & Mupier, 2004), with increasing rates of prescription pills and heroin in recent years (McCuish, 2017). Latent class analyses also suggest that girls involved in the juvenile justice system may be more likely than boys to fit a high-risk profile, with a versatile substance use pattern (e.g., high-risk use beyond alcohol, marijuana, and hallucinogens; McCuish, 2017). This risk profile is particularly problematic since adult studies have shown that women have a faster trajectory from substance use initiation to a substance use disorder than men (Westermeyer & Boedicker, 2000), suggesting that girls’ earlier age of onset may be a significant marker for future problematic use. Substance use disorder (SUD) diagnosis is often associated with higher prevalence of mental health issues among girl offenders (Lennings et al., 2007), and may also be related to psychosocial functioning, including memory, confused thinking, and aggression (Welch-Brewer & Roberts-Lewis, 2011).

Gender differences in the relationship between drug use and crime have also been documented. Similar to the national data for women offenders (e.g., Bronson, Stroop, Zimmer, & Berzofsky, 2017), girls in residential placement are more likely than boys to report being under the influence of drugs or alcohol at the time of their most recent offense (48% vs. 43%; Sedlak & Bruce, 2017). While some studies suggest that girls’ substance use may not be directly associated with legal problems, it may be related to other criminogenic need factors. Criminogentic needs have been defined as the dynamic individual level factors (e.g., family and peer relationships, financial status, substance misuse, mental health) that increase an individual’s risk for engaging in criminal activities (Andrews & Bonta, 1995; Hollin & Palmer, 2006). For example, substance use has been associated with other criminogenic needs such as poor school performance and decreased social skills for both male and female youthful offenders (van der Put et al., 2014). Among system-involved girls specifically, substance use has been highly correlated with negative peer relationships (Tarter, Kirisci, Mezzich, & Patton, 2011). Girls are also more likely to report family needs, including lack of discipline, poor maternal relationships, inadequate monitoring or parental control, and homelessness (Thompson & McGrath, 2012). Because problem relationships with friends and family are also factors which might influence substance use, it is important to understand the associations of these factors with drug use and crime among girls.

Substance use also increases the risk of recidivism for youthful offenders when adjusting for demographic and offense characteristics, particularly for girls with co-occurring mental health issues (McReynolds, Schwalbe, & Wasserman, 2010). In fact, substance use has been shown to have a stronger relationship to female offender recidivism when compared to other risk factors than for male offenders (Andrews et al., 2012). Understanding the factors associated with substance use among juvenile justice system-involved girls is important considering longitudinal and other data indicating that throughout the life course, involvement with substance use and crime is highly correlated, particularly for women (e.g., Estrada & Nilsson, 2012).

A gendered pathways perspective offers a useful lens for understanding the unique nature of the relationship between substance use and crime for girls. This framework posits that women’s pathways into and out of criminal justice systems and substance use are shaped by life chances, histories, and experiences that are unique to them, and which affect them uniquely, as women (Chesney-Lind & Pasko, 2013; Salisbury & Van Voorhis, 2009; Wesely & Dewey, 2018). An example of this among system-involved girls might include presenting with extensive histories of abuse and victimization (Lennings et al., 2007), but compared to boys, are more likely to respond to that victimization by running away and engaging in survival or coping strategies such as transactional sex, shoplifting, or substance use (Chesney-Lind & Pasko, 2013).

Pathway frameworks also have important theoretical overlap with social ecological models, which provide a comprehensive, holistic analysis of context across multiple ecological levels (Golden & Earp, 2012; McLeroy, Bibeau, & Steckler, 1988) and have been used previously in studies examining risk factors for youth substance use (Connell, Gilreath, Aklin, & Brex, 2010; Williams, Barnes, Leoni, & Hunt, 2012). Much research in the area of youth substance use risk has focused on factors at the individual/intrapersonal level (such as mental health, cognitive function, or victimization history; Lennings et al., 2007; Welch-Brewer & Roberts-Lewis, 2011), interpersonal/relational level (including peer or family relationships; Tarter et al., 2011; Thompson & McGrath, 2012), or occasionally institutional level (e.g., school engagement; Rodney & Mupier, 2004), but a need remains for research to acknowledge factors at the community level, which may take into consideration geographical contexts.

Beyond other factors at the community ecological level, geographic contexts may also impact substance use, particularly among youth with criminal justice involvement. Although some research has indicated that urban youth may be at greater risk of illicit drug use (Warren, Smalley, & Barefoot, 2017), other studies have observed higher rates of particular substances (i.e. methamphetamine) among rural youth, as well as higher rates of driving while under the influence of drugs or alcohol (Lambert, Gale, & Hartley, 2008). Alternatively, nationally representative data show little to no difference in illicit drug use between metropolitan and non-metropolitan areas, with the exception of marijuana (higher in urban areas; Miech et al., 2018). Despite these inconsistent results, research has demonstrated the unique influence of rural contexts in shaping adolescent substance use experiences (Pettigrew, Miller-Day, Krieger, & Hecht, 2012; Rhew, Hawkins, & Oesterle, 2011). Rural areas have also experienced an increase in the availability of a variety of illicit drugs (Dombrowski, Crawford, Khan, & Tyler, 2016), particularly prescription opioids (Keyes, Cerda, Brady, Havens, & Galea, 2014), which may impact likelihood of substance use among adolescents already at risk due to other social and contextual factors (Park, Melander, & Sanchez, 2016). Furthermore, juvenile justice system-involved youth in rural communities may be less likely to be screened or assessed for substance use (Marks et al., 2019) or referred to treatment (Pullman & Heflinger, 2009), exacerbating disparities in access to, or utilization of, needed services. For system-involved girls, who already demonstrate heightened physical and mental health needs, understanding the role of geographic and community contexts in relation to substance use has important implications for clinical practice in juvenile justice settings.

Present study

Although previous research has examined the unique correlates of substance use among juvenile justice system-involved girls in general (Andrews & Bonta, 1995; Hollin & Palmer, 2006; Tarter et al., 2011; van der Put et al., 2014), to date, there are no studies focused on possible unique vulnerabilities among girls living in rural or non-metropolitan areas. This research is critical in light of the opioid epidemic, particularly in rural Appalachia – an area of the country hardest hit by use of illicit prescription opioids and heroin (e.g., Keyes et al., 2014; Staton-Tindall, Harp, Winston, Webster, & Pangburn, 2015). A number of studies with adult populations have suggested an increased likelihood of prescription opioid misuse among rural residents (e.g., Shannon, Perkins, & Neal, 2014; Young, Havens, & Leukefeld, 2012), while Monnat & Rigg (2016) also reported that adolescents from rural areas are 35% more likely to engage in prescription opioid misuse than urban adolescents. During a time when opioid misuse is rampant in communities across rural America, it is possible that family and peer networks which are typically protective for adolescent drug use now actually serve as increased criminogenic risk factors. Therefore, the present study aims to: 1) examine differences in factors associated with substance use among metropolitan and non-metropolitan girls involved in the juvenile justice system; 2) examine correlates of substance use among girls including juvenile justice involvement; and 3) to examine criminogenic needs as independent correlates of substance use among system-involved girls.

Methods

Data & Procedures

Data for the current study were collected as part the Juvenile Justice-Translational Research on Interventions for Adolescents in the Legal System (JJ-TRIALS) Project. This National Institute on Drug Abuse (NIDA)-funded research cooperative incorporates implementation science with the goal of improving substance use treatment outcomes for juvenile justice system-involved youth (Knight et al., 2016). Due to variations in measurement and operationalization of key variables of interest across cooperative sites, only data collected from the six Kentucky DJJ sites were utilized in this analysis. The six sites were regional DJJ offices which serve between 4 and 11 counties with youth primarily on community supervision. Data were extracted from a statewide electronic records system used by Kentucky DJJ agencies.

The sample for the current study included girls (N=178) who were referred to Kentucky DJJ between October 1, 2014 and December 31, 2017 and upon referral, completed the state’s recidivism risk assessment and criminogenic needs questionnaire with a DJJ case manager. Due to missing data, including county of residence and criminal charges, 18 youth were removed from the sample for a final sample size of 160 Kentucky justice system-involved girls involved in the Kentucky DJJ system. All procedures were approved by the university Institutional Review Board.

Measures

Demographics

Demographic information was recorded for each of the girls referred to the Kentucky DJJ and included information such as birthdate, race, and county of residence. County of residence was coded into categories based on the 2013 Rural-Urban Continuum Codes (commonly referred to as Beale Codes), which classify counties based on population size and proximity to metropolitan areas (United States Department of Agriculture Economic Research Service [USDA ERS], 2016). This coding has been previously used in other studies utilizing criminal justice system-involved populations to understand potential geographic differences in substance use risk (Staton-Tindall et al., 2015; Webster, Dickson, Staton-Tindall, & Leukefeld, 2015). Due to the majority of the sample being from metropolitan areas, classifications for this analysis included more densely populated counties defined as metropolitan counties (1=metro counties of 1 million people or more; 2=metro counties with 250,000 – 1 million people; and 3=metro counties with fewer than 250,000), and less densely populated counties defined as non-metropolitan counties (populations ranging from 20,000 or more as code 4 to less than 2,500 as code 9). Beale codes consider not only population size, but also adjacent location of a county to a metropolitan area. For the current study, age at the time of DJJ intake was calculated using birthdate and intake date.

Substance use and criminogenic needs

Information regarding youths’ substance use risk and other criminogenic needs was collected at intake by DJJ staff using an instrument developed by the Kentucky Department of Juvenile Justice. Grounded in the literature on criminogenic needs and modeled from evidence-based assessments such as the Youth Level of Service/Case Management Inventory (Hoge, Andrews, & Leschied, 2002), the instrument assessed youths’ risks and needs related to employment/education, substance use, criminal thinking/attitudes, relationships, and personality (e.g., impulsivity). Each area of need was assessed through a series of questions and were scored as a sum of items with a ‘yes’ response (or ‘no’ response for reverse-scored items). As described in the following, possible range of scores varied based on assessment area with higher scores indicating greater need in that assessment domain.

Substance use.

This subscale measured participants’ substance use through 11 questions about their substance use history (e.g., past or current alcohol or drug use or problems with alcohol or drugs), if they had ever participated in substance use treatment, and about family and friends’ substance use. Further, participants were asked to report whether their substance use had ever caused problems with jobs/school, relationships, or the law. Possible scores could range from 0 to 11 (α=.87).

Employment/Education.

As part of this scale, youth were primarily asked to report on their school experiences, including 7 questions about past suspensions or expulsions and about their relationships with teachers and other students. Participants were also asked about their employment. The scores could range from 0 to 7 (α=.72).

Criminal Thinking/Attitudes.

The Criminal Thinking/Attitudes subscale focused on participants’ pro-criminal attitudes and included 7 questions such as “Have you lied to get out of trouble?” Possible scores ranged from 0 to 7 (α=.67).

Relationships.

This subscale focused on participants’ friends and family. Participants were asked about their friends’ and family’s involvement with the law and any pro-social attitudes among their close ties. Participants also reported on their degree of involvement with any law-breaking friends or family. Consisting of 9 questions, subscale scores ranged from 0 to 9 (α=.85).

Personality.

The Personality subscale consists of 11 questions that assess participants’ needs related to personality characteristics. Specifically, youth were asked questions about their impulsivity (e.g., “Do you often do things without thinking?”) and risk-taking (e.g., “Do you try new things because ‘you only live once’?”). Scores could range from a low of 0 to a high of 11 (α=.90).

Criminal history

Criminal history information was also included in the secondary data received from the DJJ electronic records system. Specifically, information regarding girls’ past and present criminal charges was recorded, including how many times they had been adjudicated (i.e., found to be responsible) on a public offense. Girls with one or more prior formal adjudications were identified as having a past adjudication (yes/no). Using state Uniform Crime Reporting Codes, specific criminal charges (including past and present) were coded into six dichotomous (yes/no) variables based on whether or not the youth reported having ever been charged with violent, property, substance-related, weapon, probation/parole violation, and status offenses. In addition, having ever been charged with a felony (yes/no) was included as a potential correlate.

Analytic Plan

For the first study aim, participants were placed into groups based on their county of residence (1=metropolitan; 0=non-metropolitan). The two groups were compared using a series of chi-square and t-tests. The second study aim was accomplished by examining bivariate correlations among substance use, other criminogenic needs, and criminal history, using the Benjamini-Hochberg (1995) procedure to control the false discovery rate. For the final aim, a linear regression model was used to regress substance use scores on demographic characteristics, other criminogenic needs, and criminal history correlates previously identified as statistically significant at the bivariate level. SPSS v.24 was used for study analyses (IBM Corp., 2017).

Results

Sample Descriptive Information

Distribution of the sample by Beale codes for metropolitan counties (58.1%) included 1=53 (33.1%); 2=35 (21.9%) 3=5 (3.1%). The distribution of the sample by Beale codes for non-metropolitan counties (41.9%) included 4=11 (6.9%); 5=16 (10.0%), 6=34 (21.3%); 7=7 (1.3%); 8=4 (2.5%); 9=0 (0%). As shown in Table 1, there were no significant differences in demographics, criminogenic needs, or criminal history between girls in metropolitan and those in non-metropolitan counties. The average girl was about 15 years old and more than three-quarters of girls (75.6%) were white.

TABLE 1.

Descriptive Comparisons based on County of Residence

Metropolitan
(n=93)
%/Mean (SD)		 Non-metropolitan
(n=67)
%/Mean (SD)		 Total
(N=160)
%/Mean (SD)		 t-test/x2
Demographics
 Age (years) 15.2 (1.5) 15.5 (1.2) 15.3 (1.4) −1.16
 Caucasian/White 73.1% 79.1% 75.6% 0.76
Criminogenic Needs
 Employment/Education (possible range 0–7) 2.2 (1.9) 2.0 (1.9) 2.1 (1.9) 0.91
 Substance Abuse (possible range 0–11) 1.7 (2.4) 2.3 (2.8) 1.9 (2.6) −1.40
 Criminal Thinking (possible range 0–7) 1.6 (1.6) 1.6 (1.7) 1.6 (1.7) −0.20
 Relationships (possible range 0–9) 2.7 (2.5) 2.9 (2.8) 2.8 (2.6) −0.31
 Personality (possible range 0–11) 4.0 (3.6) 4.1 (3.9) 4.0 (3.7) −0.16
Criminal History (past charges)
 Past Adjudication 21.5% 26.9% 23.8% 0.62
 Ever a property crime 26.9% 21.2% 24.4% 0.67
 Ever a violent crime 48.4% 53.0% 50.0% 0.33
 Ever a substance-related crime 16.1% 28.8% 21.3% 3.68
 Ever a weapons crime 3.2% 3.0% 3.1% 0.01
 Ever a probation or parole violation 8.6% 18.2% 12.5% 3.22
 Ever a status crime 6.5% 10.6% 8.1% 0.89
 Ever charged with a felony 6.5% 13.6% 9.4% 2.33
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Note: There were no significant differences between metropolitan and non-metropolitan girls.

These girls experienced a variety of criminogenic needs, with 70.0% reporting scores greater than 0 across all five of the criminogenic needs scales. Specifically, the highest scores were for personality indicators like impulsivity (average score of 4.0; median=3.5; possible range from 0 – 11). Other average scores included 2.8 for relationship needs (median=3.0; possible range 0 – 9), 2.1 for criminal thinking (median=1.0; possible range from 0 – 7), 2.1 for needs associated with education/employment (median=2.0; possible range from 0 −7), and 1.9 for substance use (median=1.0; possible range 0 – 11). About one-quarter (23.8%) had been adjudicated, half (50.0%) had committed a violent crime, but only 9.4% had ever been charged with a felony offense.

Substance Use Correlations

Girls who reported substance use primarily used alcohol (58.3%) and marijuana (70.2%), and substance use patterns did not differ by county of residence. As shown in Table 2, there were no correlations between girls’ score on the substance use scale and demographic characteristics, including residing in a non-metropolitan county. However, girls’ scores on the substance use scale were positively correlated with having at least one past adjudication (r=.292, p<.001), a substance-related charge (r=.248, p<.01), and a probation violation (r=.286, p<.001). There was also a significant positive relationship between the substance use scale and the scores on each of the other score indicators – employment/education (r=.420, p<.001), criminal attitudes (r=.325, p<.001), relationships (r=.704, p<.001), and personality indicators (r=.536, p<.001).. For additional correlations, refer to Table 2.

Table 2:

Correlations of Demographics, Criminogenic Needs Scales, and Criminal History Variables (N=160)

Variables 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1. Age at intake -
2. White −.074 -
3. Non-metro (home county) .089 .069 -
4. Past adjudication .067 .112 .062 -
5. Substance Use Scale .118 .103 .110 .292*** -
6. Employment/Education Scale −.108 .027 −.072 .188 .420*** -
7. Criminal Attitudes Scale .071 .032 .016 .017 .325*** .564*** -
8. Relationships Scale .018 .062 .024 .148 .704*** .611*** .518*** -
9. Personality Scale −.028 .073 .013 .155 .536*** .681*** .591*** .728*** -
10. Property charge(s) .055 −.015 −.065 .195 .052 −`.007 −.098 .067 −.007 -
11. Violent charge(s) −.055 −.040 .046 .114 −.143 .040 −.086 −.064 .022 −.164 -
12. Substance-related charge(s) .075 .048 .152 .211 .248** −.127 .036 .099 .019 −.012 −.095 -
13. Weapons charge(s) −.093 −.149 −.006 −.016 −.079 .144 .066 .082 .017 −.019 −.037 −.006 -
14. Probation/Parole Violation(s) .047 −.048 .142 .321*** .286*** .025 −.055 .217** .057 .136 −.002 .218** .040 -
15. Status charge(s) −.020 −.043 .075 .210 −.011 .042 .060 .041 .064 .150 −.025 .012 −.054 −.044 -
16. Felony charge(s) .063 .034 .121 −.130 −.159 −.057 −.063 −.113 −.177 .016 −.153 −.168 .065 −.122 −.096 -
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Unique Correlates of Substance Use

Factors which were significant at the bivariate level were included in the multivariate model to understand the unique correlates of substance use, in addition to non-metropolitan residence. As shown in Table 3, the model explained more than half of the variance in substance use (R2=.561, adjusted R2=.538 F(8,150)=24.0, p<.001. Girls with a prior adjudication (p=0.026) and a history of a substance use charge (p=0.020) had significantly higher substance use scores. Also, there was a positive relationship between relationship need scores (p<0.001) and substance use scores. Similar to the bivariate analyses, residing in a non-metropolitan county was also not related to substance use in the multivariate model. Given the sample size (N=160) and the final number of predictors (8), a power analysis determined that the current model had a power greater than 0.99 with a medium expected effect size. This finding is supported by Green’s (1991) rule-of-thumb, which suggests the current model would require a minimum of 114 subjects for a medium-sized effect.

TABLE 3.

Regression Identifying Correlates of Substance Use (N=160)

B SE B β
Non-metro (home county) 0.31 0.29 0.06
Past adjudication history* 0.82 0.36 0.13
Employment/Education Scale Score 0.02 0.12 0.01
Attitudes Scale Score −0.09 0.11 −0.06
Relationships Scale Score*** 0.60 0.08 0.61
Personality Scale Score 0.06 0.06 0.09
Charged with a substance-related offense (current/past)* 0.87 0.37 0.14
Probation/parole violation 0.50 0.48 0.06
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*

p ≤ .05;

***

p ≤ .001

Note: Model fit: R=.749; R2=.561; adjusted R2=.538

Discussion

Research on girls in the juvenile justice system suggests that risk for engaging in criminal behavior is highly related to substance use (Rodney & Mupier, 2004; Guthrie et al., 2002; Leve et al., 2015; Teplin et al., 2003), as well as abuse and victimization (Lennings et al., 2007), problems with school and family (Rodney & Mupier, 2004), risky relationships (Garcia & Lane, 2012), and high-risk sexual behaviors (Guthrie et al., 2002; Teplin et al., 2003). These studies are largely grounded in the individual, interpersonal, or institutional levels of a social ecological framework (Golden & Earp, 2012; McLeroy et al., 1988). While findings from this study are consistent with some of these findings, this is the first to look at these factors among juvenile justice system-involved girls in different geographic areas, a potentially relevant community-level factor.

The first study aim examined differences in substance use and related factors among metropolitan and non-metropolitan girls involved in the juvenile justice system. More than half (58%) of the girls in this study lived in metropolitan areas. At the bivariate level, there were no significant differences between girls in metropolitan vs. non-metropolitan counties on critical study variables such as criminogenic needs (including substance use) or criminal history. This finding was surprising given other studies highlighting important differences between rural and urban youth related to substance use (Donath et al., 2011; Havens, Young, & Havens, 2011; Lambert et al., 2008), delinquency (Harden et al., 2009; Lambert et al., 2008), and other criminogenic needs (Byun, Meece, & Irvin, 2012; Chuang, Ennett, Bauman, & Foshee, 2009). It is possible that the small sample size (N=160) in this study masked some of the detectable differences at the bivariate level, which should be further examined in a larger sample. The small sample size also required collapsing the categories of county residence, as defined by the Beale codes, into “non-metro” rather than a broader range of rural and suburban. This may also have masked some of the anticipated variation in geographic areas on the key variables of interest.

However, these findings are similar to adult samples of women offenders which noted no differences in health or behavioral health problems among those living in rural or urban communities (Staton-Tindall et al., 2007). Although there were no differences in health or behavioral health needs among one sample of adult women offenders, Staton-Tindall and colleagues (2007) did identify a significant difference in service utilization with individuals in rural areas having significantly less access to health and behavioral health care. While not available in the current dataset, service utilization is an important area in understanding possible differences in criminogenic needs including substance use. This is a critical area for future research in understanding success of system-involved girls.

Despite no significant differences between metropolitan and non-metropolitan girls at the bivariate level, non-metropolitan residence was included as a control in the remaining analyses focusing on the relationship between substance use and related factors to address the second and third research questions. At the bivariate level, substance use scores were positively related to education/employment, criminal attitudes, problematic relationships, and personality issues (e.g., impulsivity and anger). In addition, substance use scores were positively correlated with criminal activities including having a history of substance-related charges and probation violations. Of these, the only variables that remained significant in the multivariate model included having a past adjudication, relationship risks, and having a substance-related charge.

While the county of residence variable was not a significant correlate, this finding suggests system-involved girls with a higher substance use score also have more involvement with the juvenile justice system. Recognizing that formal substance use services are more limited in non-metropolitan areas, and that engaging in treatment in the community would likely be ideal, the juvenile justice system may provide an opportune setting for substance abuse interventions for girls, particularly those whose risk for drug use and crime increases with external pressures from family, peers, and partners. This finding suggests a shift in thinking of a girl’s problem behavior as an opportunity for treatment rather than a need for criminalization (Brown, Chesney-Lind, & Stein, 2007). This would also call for gender-specific conceptualizations of criminogenic needs for female youth offenders, consistent with gendered pathways frameworks (Chesney-Lind & Pasko, 2013; Salisbury & Van Voorhis, 2009; Wesely & Dewey, 2018), which should include a history of abuse and victimization (Moreland, et al., 2018), and other early childhood traumatic events (Basto-Pereira, et al., 2016). These early traumatic events are uniquely related to initiation of drug use and criminal activities (Tossone, Wheeler, Butcher, & Kretschmar, 2018) among girls, but they are often left out of assessments focused on criminogenic needs (Chesney-Lind, n.d.), including the assessment tool used in the current study. This is a study limitation and an important area for future research.

A history of abuse and victimization may be driving some of the significant findings related to substance use and problematic relationships. In the current study, “problematic relationships” were assessed using a scale focused on friend and family involvement with the law and the participants’ degree of involvement with any law-breaking friends or family. The literature on adult women and substance use is fairly consistent in noting that women’s initiation of substance use and maintenance of high risk substance use behaviors are closely tied to their relationships with intimate partners (Covington, 1998; Staton-Tindall et al., 2007; Staton et al., 2017). The measure of problematic relationships in this study primarily included peers and family, which are more commonly reported as problematic among juvenile girls (Kapetanovic, Skoog, Bohlin, & Gerdner, 2018; Tarter et al., 2011), especially those in rural areas (Cotter & Smokowski, 2017). The rural culture is typically characterized by tightly knit kinship networks (Jones, 2010), which may be protective in some ways but also may be higher risk in relational situations involving violence and/or abuse. The influence of family and peers is an important area of future research for system-involved girls, and research should continue to incorporate such interpersonal factors in context of other social ecological levels to better understand the salience of peer and family relationships to girls’ behaviors. Considering the importance of relationships in women’s substance use across the lifespan, it is also critical to target relationship-focused interventions to girls who enter the juvenile justice system.

This study has limitations. First, the current study included secondary data from a statewide dataset and, thus, analyses were limited to only the measures collected across all participating DJJ agencies. Further, assessment measures were collected and entered into the state system by juvenile justice case managers as part of intake into the juvenile justice system, so all data in this analysis are based on self-report at the time of intake. In addition, while grounded in evidence-based assessment tools, measures were developed by the state Department of Juvenile Justice, and data should be interpreted as preliminary. The small sample size and cross-sectional data also limited more complex analyses to understand potential mediators and moderators of the relationship between substance use and crime among juvenile girls, as well as a more targeted analysis focused on characteristics of girls living in very rural areas of the state. Finally, analysis only focused on girls who received a screening in six sites in the juvenile justice system in one southern state with a fairly homogenous demographic composition, which limits generalizability.

Despite these limitations, this is the first examination of the substance use and related needs among metropolitan and non-metropolitan girls involved in the juvenile justice system. Metropolitan and non-metropolitan girls did not differ significantly on key variables of interest including substance use and related criminal indicators. While substance use and related risks may not differ by geographic area in this study, access to needed health and behavioral health care in rural areas may be more challenging and utilization of care may be differentially related to juvenile justice outcomes. Findings did suggest that substance use is related to criminal history, substance-related offenses, and relationship problems amonggirls involved in the juvenile justice system. Future research should also examine the complexities of relationship factors and trauma in substance use and the commission of crime as a possible preventative strategy for problem behaviors that can continue into adulthood.

Acknowledgements:

This study was funded under the JJ-TRIALS cooperative agreement, funded at the National Institute on Drug Abuse (NIDA) by the National Institutes of Health (NIH) under cooperative agreement (U01DA036158). The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the NIDA, NIH, or cooperative participating universities or JJ systems.

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